Evidence First

Trump May Deserve Impeachment. But What Would Another Attempt Accomplish?

Evidence First — Wed, 14 Oct 2026 14:46:57 GMT

Donald Trump is heading into the November midterm elections with the kind of approval ratings that would leave most presidents in serious political trouble. Just 30 percent of Americans approve of his performance, according to an October survey by the Pew Research Center. Nearly seven in 10 disapprove, and even among those who voted for him in 2024, almost a third now disapprove of the job he is doing. Yet Trump has survived two impeachments, criminal prosecutions and an election defeat, only to return to the White House.

Now Democrats may have another opportunity to confront him. If they win control of Congress in November, they could launch investigations into his administration and potentially impeach him for a third time. There are serious reasons to consider it. Federal courts have repeatedly found that the administration exceeded its authority or violated constitutional protections, while other actions have raised questions about whether Trump has used presidential powers to punish opponents or reward allies. But another impeachment presents a problem that is easy to overlook: Even if Congress has good reasons to pursue it, what would the effort actually accomplish?

Impeachment is Congress’s way of formally accusing a president of serious wrongdoing. A simple majority in the House can approve the charges, but removing the president requires a two-thirds vote in the Senate. Those are very different hurdles, particularly when the president retains the loyalty of his own party. And in Trump’s case, the distinction raises a larger question about the difference between establishing wrongdoing and imposing meaningful consequences.

The concerns about Trump’s conduct go well beyond disagreements over his policies. Some involve a more fundamental question about the presidency: Can a president use the powers of his office against people who criticize or oppose him? In August, Reuters examined 93 cases involving challenges to the administration under the First Amendment, which protects freedom of speech, religion and the press. In 75 of those cases, federal judges had ruled against the government, often in disputes involving journalists, universities, law firms and protesters.

Not every ruling was final. Some were preliminary, and appeals courts had blocked or overturned decisions in 15 cases. The administration also prevailed in others. But even allowing for those qualifications, the findings reveal substantial judicial concern about the administration’s treatment of its critics. One particularly revealing example involved Perkins Coie, a law firm that had represented Trump’s political opponents. In 2025, Trump issued an executive order restricting the firm’s access to government facilities and affecting its ability to work with federal agencies. A federal judge struck down the order as unconstitutional, explaining that the government could not use its authority to punish people or businesses simply because the president disliked them.

A similar dispute arose in October 2026, when a federal judge ruled that Trump’s decision to exclude journalists from CNN, MS NOW and Politico from the White House was likely unconstitutional. Trump had publicly criticized their coverage, and the judge found that the restrictions appeared to punish the organizations for their viewpoints. The ruling was preliminary, but the underlying concern was familiar: Presidential authority was being used in ways that appeared intended to penalize criticism rather than serve a legitimate government purpose.

Other controversies have raised different questions about the limits of Trump’s power. In February, the Supreme Court ruled 6 to 3 that he lacked the legal authority he claimed to impose sweeping emergency tariffs. Reuters also reported in October that Trump had granted clemency to at least 85 public officials or political figures accused or convicted of corruption-related crimes, most of them Republicans or Trump supporters. Meanwhile, his military actions against Iran have raised questions about whether he exceeded his constitutional authority by conducting sustained hostilities without congressional authorization.

These are not equally strong cases for impeachment. Presidents have broad pardon powers, and favoritism does not necessarily establish corruption. Similarly, losing a case in court does not automatically mean a president committed an impeachable offense. But impeachment does not require a criminal conviction. It can apply to serious abuses of presidential power, particularly when a president uses public authority for improper purposes. In September, Democratic Representative Steve Cohen introduced 26 proposed articles of impeachment covering allegations ranging from political retaliation to financial conflicts of interest. Those allegations would need to be examined individually, but the documented conduct provides substantial grounds for congressional scrutiny.

Whether Americans would support taking that next step is another question, although the polling may surprise those who assume the public is exhausted by efforts to hold Trump accountable. An April survey by Strength in Numbers and Verasight found that 55 percent of American adults supported the House voting to impeach him, while 37 percent opposed it. A second survey in June found that 53 percent believed Congress already had grounds to impeach Trump, including some voters who supported him in 2024. Another survey, commissioned by an organization advocating impeachment, also found majority support. The questions and populations differed, and the advocacy-sponsored research warrants additional caution. Nevertheless, the results suggest that another impeachment would not necessarily begin as an unpopular effort opposed by most Americans.

Public support, however, cannot substitute for Senate votes. Even if Democrats win both chambers, they are unlikely to control anything close to the 67 seats required for conviction if all senators participate. Removing Trump would mean persuading a substantial number of Republicans to turn against their own president. After the January 6, 2021, attack on the Capitol, seven Republican senators joined Democrats in voting to convict him. The final tally was 57 to 43, ten votes short of what was needed. Trump was acquitted, and the Republican Party ultimately nominated him for president again.

A 2026 study examining 44 impeachment cases across different countries helps explain the difficulty. Researchers found that getting enough lawmakers to accuse a president of wrongdoing and getting enough lawmakers to remove him are fundamentally different challenges. Even serious allegations may not be enough when the president retains substantial support within his own party. That is especially relevant to Trump, whose influence over Republican politics remains considerable. Democrats could present a compelling case, win an impeachment vote in the House and still watch the Senate refuse to remove him.

The political consequences of such an outcome are harder to predict. Trump has become remarkably effective at portraying investigations and prosecutions against him as political persecution, and another impeachment would give him an opportunity to make that argument again. He could accuse Democrats of trying to overturn the voters’ decision, rally his supporters and present a Senate acquittal as proof that he had been unfairly targeted. But there is a difference between what Trump might say and what voters would believe.

There is surprisingly little evidence that his previous impeachments made him more popular. During his first impeachment, his approval ratings remained relatively stable. Gallup recorded a temporary increase around his February 2020 acquittal, but the improvement faded. Researchers studying that impeachment also found that providing people with additional facts about the proceedings did little to change their opinions. Political loyalties had a much stronger influence on how they interpreted the information. That helps explain why impeachment may be a particularly difficult political tool against Trump, but it does not establish that another attempt would benefit him.

A third impeachment might uncover serious misconduct, deepen public disapproval or force Republican lawmakers to defend actions many voters consider unacceptable. It might also energize Trump’s supporters and shift attention away from his unpopular policies. The research offers no reliable way to predict which effect would be greater, particularly when so many Americans already hold strong opinions about him.

There is also an unusual complication if impeachment somehow succeeds. Vice President JD Vance would become president, potentially entering the 2028 election as the incumbent rather than as the vice president of an unpopular administration. That might give him an opportunity to establish his own political identity and appeal to voters who have grown tired of Trump. Or he might inherit responsibility for the administration’s failures. Either way, removing Trump would not necessarily leave Democrats better positioned for the next presidential election.

These uncertainties help explain why congressional investigations may offer Democrats a different approach. With control of Congress, they could examine possible corruption, financial conflicts and the use of federal authority against political opponents. They could demand documents, question administration officials and scrutinize how taxpayer money is being spent. Unlike impeachment, such oversight would not depend on securing a two-thirds Senate majority, although enforcing congressional demands can present its own difficulties.

There is evidence that investigations can influence public opinion. A 2014 study published in The Journal of Politics examined more than 3,500 congressional investigative hearings over five decades. The researchers found that increased investigations were associated with declining presidential approval. An accompanying experiment suggested that accusations made through congressional investigations could carry more weight with voters than similar accusations made elsewhere. Those findings indicate that congressional scrutiny can have consequences beyond the hearing room, though they cannot tell us exactly how voters would respond to investigations of Trump today.

There is another consideration. Trump is already deeply unpopular, and Americans have other priorities. In Pew’s October survey, 84 percent of registered voters said the economy was very important to their congressional vote. Just 36 percent said the same about investigations into Trump. Among people supporting Democratic congressional candidates, however, 63 percent considered those investigations very important, compared with only 7 percent of Republican supporters.

The findings reveal a genuine tension. Many Americans are unhappy with Trump’s performance and primarily concerned about their finances, while a large share of Democratic voters also wants his conduct examined. Extensive investigations could uncover wrongdoing and strengthen accountability, but they could also consume political attention without producing meaningful new findings. Much would depend on what Congress discovered, whether voters considered the findings important and whether lawmakers could use the information to constrain future abuses.

Congress has other tools as well. It can scrutinize government spending, challenge executive actions through legislation and use its oversight powers to expose misconduct. These approaches have limits, especially when the president can veto legislation, but they do not require removing him from office. They also allow Congress to focus on specific abuses rather than committing itself to a prolonged impeachment battle whose outcome in the Senate is largely foreseeable.

None of this eliminates the constitutional case for impeachment. If the evidence establishes serious presidential misconduct, Congress has a responsibility to consider an appropriate response, even when doing so carries political costs. A Senate unwilling to convict does not make an abuse of power acceptable, and an unsuccessful impeachment can still establish an important public record. But the decision should be grounded in the seriousness of the conduct and what Congress can realistically accomplish, rather than the assumption that impeachment itself will weaken Trump.

The evidence points to a distinction that is easily lost in the political debate. There are substantial grounds for investigating Trump’s exercise of presidential power, meaningful public support for impeachment and serious legal questions about actions his administration has taken. What is much less certain is whether a third impeachment would remove him, change his behavior or improve either party’s political position.

Trump has already survived two impeachments. A third could expose further wrongdoing and reaffirm constitutional limits. It could also end with another acquittal, leaving him in office and his political support largely unchanged. Democrats may soon have the authority to pursue either path, but the real challenge is larger than deciding whether to impeach him again.

It is determining how Congress can impose meaningful consequences for abuses of presidential power when removing the president may be politically impossible.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Trump’s Approval Rating and Voter Priorities

Claim or topic:

Trump’s approval rating fell to 30 percent in October 2026. The economy remained a higher priority for voters overall than investigations into his administration.

Source:

Pew Research Center, October 8, 2026

Source type:

Primary public-opinion research.

What it supports:

Pew found that 30 percent of Americans approved of Trump’s performance, while 68 percent disapproved. Among registered voters, 84 percent considered the economy very important to their congressional vote, compared with 36 percent who said the same about investigations into Trump.

Important caveat:

The survey measures opinions at a particular point in time. It does not establish how voters would respond to impeachment or investigations in 2027.

2. First Amendment Rulings and Retaliation Against Critics

Claim or topic:

Federal judges repeatedly ruled against Trump’s administration in cases involving freedom of speech, religion and the press, including actions affecting political opponents and critical news organizations.

Source:

Reuters, August 4, 2026

Reuters, October 14, 2026

Source type:

Original investigative journalism based on court records; legal reporting.

What it supports:

Reuters identified 75 adverse lower-court rulings among 93 First Amendment cases it examined. Its reporting includes the executive order targeting Perkins Coie and judicial findings concerning retaliation against critics.

The October reporting addresses a preliminary ruling protecting White House access for journalists from CNN, MS NOW and Politico.

Important caveat:

Not all 75 rulings were final. Some were preliminary, and appeals courts stayed or overturned decisions in 15 cases. The October press-access ruling was also preliminary. These judicial findings do not automatically establish impeachable offenses.

3. The Supreme Court’s Tariff Decision

Claim or topic:

The Supreme Court ruled that Trump lacked authority under the International Emergency Economic Powers Act to impose his sweeping emergency tariffs.

Source:

U.S. Supreme Court, Learning Resources, Inc. v. Trump, February 20, 2026

Source type:

Primary Supreme Court ruling.

What it supports:

In a 6-to-3 decision, the Court held that the statute did not authorize the president to impose tariffs.

Important caveat:

The ruling establishes that the tariffs lacked authorization under the statute Trump invoked. It does not establish that imposing them constituted an impeachable offense.

4. Trump’s Clemency Decisions

Claim or topic:

Trump granted clemency to numerous public officials and political figures accused or convicted of corruption-related offenses, with Republicans and Trump supporters accounting for most recipients.

Source:

Reuters, October 13, 2026

Source type:

Original investigative journalism.

What it supports:

Reuters identified at least 85 public officials or political figures accused or convicted of corruption-related crimes who received clemency. Most were Republicans or Trump supporters.

Important caveat:

The pattern does not establish that individual clemency decisions were unlawful, corrupt or exchanged for personal benefits. The president possesses broad constitutional pardon powers.

5. The Proposed Articles of Impeachment

Claim or topic:

Representative Steve Cohen introduced 26 proposed articles of impeachment against Trump in September 2026.

Source:

Congressman Steve Cohen, September 24, 2026

Source type:

Primary congressional statement and proposed impeachment charges.

What it supports:

Cohen’s office published 26 proposed articles alleging abuses of presidential authority, including political retaliation, misuse of the pardon power and financial conflicts.

Important caveat:

The proposed articles establish what Cohen alleged, not that every allegation has been proven or independently adjudicated.

Source needed: The article also refers to disputes over whether Trump’s sustained military actions against Iran exceeded his constitutional authority. The existing article does not identify a specific underlying legal opinion, congressional record or other source sufficient to assess that claim independently.

6. Public Support for Impeachment

Claim or topic:

Multiple surveys conducted in 2026 found majority support for impeaching Trump or agreement that grounds for impeachment existed.

Source:

Strength In Numbers/Verasight, April 22, 2026

Strength In Numbers/Verasight, June 25, 2026

Lake Research Partners, April 2, 2026

Source type:

Primary public-opinion research.

What it supports:

The April Strength In Numbers/Verasight survey found 55 percent support for a House impeachment vote. Its June survey found that 53 percent believed grounds for impeachment existed.

The Lake Research Partners survey, commissioned by Free Speech For People, found 52 percent support among likely voters.

Important caveat:

The surveys used different questions and populations. The two Strength In Numbers surveys share a polling partnership, while the Lake Research Partners survey was commissioned by an impeachment advocacy organization.

Support for impeachment does not necessarily mean support for conviction and removal.

7. The Difficulty of Removing Trump

Claim or topic:

Impeaching a president and removing that president are separate constitutional and political challenges.

Source:

U.S. Senate, February 13, 2021, Official Roll Call

Farag, Montini and Schemm, European Political Science Review, June 2026

Source type:

Primary government record and peer-reviewed academic research.

What it supports:

The Senate record confirms that Trump’s second impeachment ended with 57 senators voting guilty and 43 voting not guilty, short of the required two-thirds majority.

The academic study examined 44 impeachment cases and analyzed the different political conditions involved in approving impeachment and securing removal.

Important caveat:

The 2026 study uses cross-national comparisons and cannot predict how individual U.S. senators would vote in a future trial. The constitutional voting threshold is established, but the outcome of a hypothetical third impeachment is not.

8. The Political Effects of Trump’s Previous Impeachment

Claim or topic:

Trump’s first impeachment did not produce a sustained increase in his approval rating, and research found that providing additional factual information had limited influence on people’s attitudes.

Source:

Gallup, March 17, 2020

Bose and Burnett, PS: Political Science & Politics

Source type:

Primary public-opinion research and peer-reviewed academic research.

What it supports:

Gallup documented a temporary rise in Trump’s approval around his February 2020 acquittal, followed by a return to 44 percent in March.

Bose and Burnett examined the effects of providing respondents with factual information about Trump’s first impeachment and the role of partisanship in their responses.

Important caveat:

The Gallup figures do not establish that impeachment caused the approval changes. Other major events were occurring simultaneously. The academic study’s information intervention does not replicate the effects of an entire impeachment proceeding.

Neither source establishes whether a third impeachment would help or hurt Trump politically.

9. Congressional Investigations and Presidential Approval

Claim or topic:

Congressional investigations can influence public opinion and reduce presidential approval.

Source:

Kriner and Schickler, The Journal of Politics, 2014

Source type:

Peer-reviewed academic research combining historical data analysis and a survey experiment.

What it supports:

The researchers examined more than 3,500 congressional investigative hearings and over 50 years of presidential approval data. They found that increased investigative activity was associated with declining presidential approval.

Their experiment also found that allegations made through congressional investigations could carry greater influence than equivalent allegations made outside Congress.

Important caveat:

The historical analysis covered 1953 through 2006, before Trump’s presidency. Its findings cannot establish whether investigations in 2027 would have the same effects or whether they would be more effective than impeachment.

How to read this evidence

This article is the author’s analysis. The sources above are provided so readers can see where the factual claims come from and judge the evidence for themselves. Some sources support direct facts, while others provide context, estimates, or background evidence.

Production transparency

Evidence First uses artificial intelligence extensively for research, analysis, drafting, and editing. AI may generate substantial portions of the written article. Human editorial judgment determines the questions investigated, evaluates the evidence and competing explanations, reviews important factual claims and sources, determines what conclusions the evidence supports, and approves the article for publication. AI-generated statements are not treated as evidence; conclusions must be supported by the cited sources.

Corrections and updates

If a factual error is identified, this post will be corrected in the web version with a dated note explaining the change. Because email versions cannot be edited after sending, the web version should be treated as the current version.

Subscribe now

Christopher Columbus Changed the World. But Should We Still Celebrate Him?

Evidence First — Mon, 12 Oct 2026 16:26:57 GMT

Christopher Columbus was born in what is now Italy, sailed across the Atlantic on behalf of Spain and died centuries before the United States existed. Yet every October, Americans argue over whether he deserves a national holiday.

For some, Columbus represents courage, exploration and one of the most important achievements in world history. For others, he represents slavery, violence and the suffering of Indigenous people.

The disagreement has become increasingly political. Statues have been removed, communities have replaced Columbus Day with Indigenous Peoples’ Day, and politicians have taken sides.

But how did an explorer working for Spain become an American hero and a symbol of Italian-American pride? And why has someone celebrated for generations become so controversial?

The answers begin with what happened in 1492, but the story extends far beyond Columbus’s lifetime.

The Voyage That Changed the World

In 1492, Columbus set sail from Spain with three ships, hoping to find a faster route to Asia. European traders wanted better access to valuable goods such as spices and silk, and Columbus believed he could reach Asia by sailing west across the Atlantic.

On October 12, he reached an island in the Bahamas.

He thought he had arrived near Asia. Instead, he had reached the Caribbean, part of a world that most Europeans knew little about.

For generations, American schoolchildren learned that Columbus discovered America. But that isn’t accurate.

Millions of Indigenous people were already living throughout North and South America. They had developed cultures, cities, farming systems and trade networks thousands of years before Columbus arrived.

He wasn’t even the first European to reach North America. Archaeological evidence shows that Norse explorers were present in what is now Newfoundland, Canada, in the year 1021, nearly 500 years earlier.

So why is Columbus considered such an important historical figure?

Because his voyages helped establish something earlier European expeditions had not: a lasting connection between Europe and the Americas.

In March 1493, Columbus returned to Spain with news of the lands he had encountered. His letters describing what he had found were printed and circulated throughout Europe.

At the time, information could only cross the ocean aboard ships. Messages took weeks or months to arrive, depending on the journey.

Columbus’s return encouraged Spain to organize another expedition. Just six months later, he sailed west again, this time with 17 ships and roughly 1,200 to 1,500 people.

The purpose was no longer simply exploration. Spain wanted to establish settlements, acquire resources and claim territory.

The early attempts were difficult. One settlement, La Isabela, struggled with hunger, illness, conflict and disappointing efforts to find gold. It was abandoned within a few years.

But the voyages continued. Ships carried people, supplies, letters and instructions between Spain and its growing colonies. New settlements followed, establishing connections that would endure.

Europeans and Indigenous people also had to find ways to communicate. Initially, they relied on gestures, exchanges of objects and local guides. Over time, some people learned one another’s languages and served as interpreters.

What followed transformed societies on both sides of the Atlantic.

Potatoes, tomatoes and corn spread from the Americas to Europe and eventually across the world. Horses, cattle and other animals traveled in the opposite direction. Trade expanded, European empires grew, and millions of people eventually crossed the Atlantic, some voluntarily and others through slavery.

Historians call this the Columbian Exchange.

Columbus could not have anticipated everything that followed. But his voyages helped set in motion one of the most important transformations in human history.

The first people to experience this new connection between continents were the Indigenous communities of the Caribbean. And for them, Columbus’s arrival brought consequences far different from the opportunities Europeans saw.

The Human Cost of Columbus’s Voyages

When Columbus arrived in the Caribbean, he encountered communities that had lived there for generations.

He saw opportunities for trade and wealth. But he also saw people who could be controlled and forced to work.

We know this partly because Columbus wrote about it himself.

In a letter following his first voyage, he told Spanish officials that, with additional support, he could provide gold, valuable goods and enslaved people.

This wasn’t an accusation invented centuries later by his critics. It was something Columbus openly proposed.

During subsequent voyages, Indigenous people were captured and transported to Spain for enslavement.

On Hispaniola, the island now shared by Haiti and the Dominican Republic, Columbus and other Spanish authorities imposed harsh demands on Indigenous communities. People were required to provide gold or labor, and resistance was sometimes met with violence.

Not everyone in Spain approved.

Queen Isabella objected to some of the Indigenous captives sent to Spain, and Spanish authorities debated whether the people Columbus encountered could legally be enslaved.

Those objections did not end the exploitation, but they show that the treatment of Indigenous people was controversial even during Columbus’s lifetime.

His administration eventually became so troubled that the Spanish monarchy sent an investigator, Francisco de Bobadilla, to examine what was happening.

In 1500, Columbus was removed as governor and returned to Spain in chains.

The investigation included accusations of severe mistreatment and brutal punishment. Some witnesses had their own grievances against Columbus, and the proceedings reflected disputes among Spanish colonists as well as concerns about his government.

The monarchy later released Columbus, but he never regained his position as governor.

Not every accusation against him can be accepted without qualification. But his participation in Indigenous enslavement and his use of force to establish colonial rule are well documented.

The consequences of European colonization extended far beyond his time in office.

Over the following generations, enormous numbers of Indigenous people died. Disease played a major role, as Europeans brought infections to populations that had little previous exposure to them. Epidemics, including smallpox, devastated entire communities.

But disease wasn’t the only cause.

War, forced labor, hunger and displacement also contributed. These hardships often reinforced one another. People exhausted by forced labor or weakened by food shortages were less able to withstand illness.

Research by historical demographer Massimo Livi-Bacci helps explain why this distinction matters. On Hispaniola, Indigenous populations had already suffered devastating losses before the island’s first recorded smallpox epidemic in 1518.

Historians still disagree about precisely how many people died, partly because estimates of the population before European arrival remain uncertain.

There is also the question of how much responsibility belongs to Columbus personally.

He did not cause every death that followed European arrival. Much of the devastation occurred long after his lifetime, under governments and leaders he never controlled.

Nor does the available evidence clearly establish that his overriding goal was to physically destroy Indigenous peoples. His documented ambitions included obtaining wealth, securing labor, spreading Christianity and expanding Spanish control.

Whether particular actions during European colonization constituted genocide remains a separate historical and legal question, depending partly on evidence of an intention to destroy a group, in whole or in part.

Those uncertainties do not erase what is known about Columbus’s own conduct. He participated in enslavement and helped establish a colonial system that relied on force and exploitation.

Yet the story Americans would eventually tell about Columbus focused far more on his voyages than on his treatment of Indigenous people. In the generations after American independence, he became a symbol of exploration, ambition and national achievement.

How Columbus Became an American Hero

Columbus was admired in America long before Columbus Day became a federal holiday, and long before large numbers of Italian immigrants arrived.

After gaining independence from Britain, Americans were searching for historical figures and symbols that could represent their new national identity.

Columbus became one of them.

He was portrayed as a bold explorer whose voyages had helped open the way for the country that eventually emerged. His name appeared in monuments, patriotic celebrations and place names.

In 1792, Americans commemorated the 300th anniversary of his arrival, including a major celebration organized by the Tammany Society in New York.

But his importance to Italian Americans developed later, for very different reasons.

Columbus was born in 1451 in Genoa, a city in what is now northern Italy. At the time, Italy was not a unified country. Genoa was an independent republic, and modern Italy would not emerge as a unified nation until the 1800s.

Although Columbus was Genoese, his voyages were financed by Spain, under King Ferdinand and Queen Isabella.

His connection to Italian-American identity developed centuries after his death.

During the late 1800s and early 1900s, millions of Italians immigrated to the United States. Many faced discrimination and hostility. They were often treated as outsiders, and prejudice sometimes turned violent.

In 1891, following a controversial murder investigation in New Orleans, a mob stormed a jail and killed 11 men of Italian descent. Some had already been acquitted, while others had not yet been tried.

The killings triggered a diplomatic crisis between the United States and Italy and exposed the hostility many Italian immigrants faced.

The following year marked the 400th anniversary of Columbus’s arrival in the Americas.

President Benjamin Harrison called for a nationwide commemoration, encouraging schools and communities to celebrate Columbus and American history.

The anniversary had been planned for years, and Harrison’s official proclamation emphasized patriotism, education and national progress. It would be inaccurate to say the New Orleans killings were the sole reason for the celebration.

Still, the recognition of a Genoese explorer carried particular meaning at a time when Italian immigrants were struggling for acceptance.

For Italian Americans, Columbus represented a man associated with their ancestral homeland whose accomplishments were already celebrated throughout the country.

At a time when many Americans looked down on Italian immigrants, he offered a widely recognized figure they could take pride in.

Italian-American organizations increasingly embraced Columbus Day. Parades and celebrations became opportunities to honor their heritage and contributions to American society.

Other Catholic immigrant communities also adopted Columbus as a symbol. The Knights of Columbus, for example, were founded in 1882 by Michael McGivney, an American priest whose parents were Irish immigrants.

National recognition of Columbus Day developed gradually. In 1934, Congress called for an annual presidential proclamation recognizing October 12. In 1968, Congress passed legislation making Columbus Day a federal legal holiday, a change that took effect in 1971.

By then, the holiday had come to mean much more to many Italian Americans than an explorer’s arrival in the Caribbean. It represented generations of immigrants who had struggled for acceptance and helped shape American life.

For Italian Americans, Columbus became a symbol of belonging in a country that had not always welcomed them. But the meaning of a national holiday also depends on the history of the person being honored. And for Indigenous Americans, that history looks very different.

A Holiday With Two Very Different Meanings

For many Native Americans, Columbus’s arrival marks the beginning of European colonization, the loss of Indigenous lands and centuries of suffering.

From that perspective, a holiday celebrating Columbus can feel like honoring someone who helped set those events in motion while overlooking the people who paid the price.

This is why many communities now recognize Indigenous Peoples’ Day.

The holiday celebrates the people who lived in the Americas long before Columbus arrived, their cultures and contributions, and the Indigenous communities that continue to exist today.

The movement began decades ago but gained greater national attention in 2021, when President Joe Biden became the first president to formally recognize Indigenous Peoples’ Day through a presidential proclamation.

Columbus Day remained a federal holiday, while states and communities continued making their own decisions about how to observe the occasion.

For many Italian Americans, Columbus Day represents recognition and belonging after generations of discrimination. For many Indigenous Americans, it celebrates a man whose actions helped begin a long period of dispossession and suffering.

Both perspectives are rooted in genuine historical experiences. The difficulty is that the same individual has become a symbol of pride for one community and suffering for another.

The debate, then, isn’t really about whether Columbus changed history. That much is clear. It’s about what a national holiday is supposed to celebrate.

Columbus demonstrated remarkable determination and navigational ability. His voyages helped transform the world. He was also directly involved in enslaving Indigenous people and establishing colonial rule through force and exploitation.

The evidence supporting both conclusions is strong. Other questions, including his personal responsibility for later deaths and the precise intentions behind particular actions, require greater care.

But historical importance and public admiration are not necessarily the same thing.

We study historical figures because they changed the world. We establish holidays and build monuments because we believe certain people or events deserve public honor.

Whether Columbus belongs in that second category is ultimately a judgment about what we believe our public celebrations should represent.

There are also ways to recognize Italian-American and Indigenous history without making Columbus the center of the occasion. Both communities have long histories and contributions that deserve recognition on their own merits.

The evidence cannot tell us which holiday to celebrate. But it can help us understand the man at the center of the disagreement, including both what he accomplished and the harm he caused.

Columbus changed the world. That much is beyond serious dispute. Whether changing the world makes someone worthy of celebration is a different question.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

Claim or topic:

Indigenous populations suffered devastating losses following European colonization. Disease played a major role, but forced labor, warfare, hunger, and social disruption also contributed.

Source:

Massimo Livi-Bacci, The Depopulation of Hispanic America After the Conquest, Population and Development Review (2006)

Source type:

Peer-reviewed academic demographic and historical analysis.

What it supports:

The research examines the causes of Indigenous population decline and challenges explanations that attribute the catastrophe entirely to imported diseases.

It emphasizes the combined effects of disease, labor exploitation, warfare, displacement, and disruption of Indigenous societies.

Important caveat:

Historical population estimates remain uncertain, as do the relative contributions of different causes across regions and periods. This research does not establish an exact death toll attributable to Columbus personally or determine whether particular actions constituted genocide. Source needed for a separate legal or historical assessment of genocidal intent.

5. How Columbus Became an American Hero

Claim or topic:

Columbus became a symbol of American national identity long before large numbers of Italian immigrants arrived in the United States.

Source:

Smithsonian National Museum of American History, The Monument That Created Columbus (2020)

Source type:

Expert museum historical analysis.

What it supports:

The Smithsonian documents how Americans celebrated Columbus as a symbol of exploration, liberty, and national ambition after independence.

It describes the Tammany Society’s 1792 celebration and the development of a heroic image that emphasized his achievements while overlooking aspects of Spanish colonization.

Important caveat:

The source explains how Columbus’s public image developed. It does not establish that all Americans shared the same understanding of him or that his later association with Italian-American identity had a single cause.

Production transparency

Corrections and updates

Subscribe now

Three Years After Oct. 7, Did Israel Win the War?

Evidence First — Wed, 07 Oct 2026 11:57:17 GMT

Three years ago today, Hamas-led fighters crossed from Gaza into southern Israel, killing about 1,200 people and taking 251 hostages. Hamas, an Islamist political and armed movement, had governed Gaza since 2007.

Israel responded with a large-scale war. Its government said it intended to return the hostages, dismantle Hamas’s military and governing capabilities, and prevent Gaza from again being used to launch an attack like the one on Oct. 7, 2023.

Three years later, much of the immediate record is clear. Hamas is militarily weaker than it was before the war. Much of its senior leadership was killed, its forces suffered heavy losses, and U.S. intelligence now describes the organization as degraded. All 251 hostages were eventually either returned alive or had their remains returned.

Yet Hamas survived. It remains armed, and the same U.S. intelligence assessment says it has used the cease-fire to restore some capabilities and reassert control in parts of Gaza. The United Nations reported in September that Hamas and other armed groups had not disarmed, while the Palestinian governing and international security arrangements envisioned for postwar Gaza were still not fully operating there.

That combination, a badly weakened Hamas that nonetheless remains intact, is the key to understanding both what Israel achieved and what it did not.

The wrong question may be whether Hamas still exists. The more useful one is what Hamas can still do.

Hamas is not simply a collection of fighters and weapons. It is also a political movement with an ideology, a bureaucracy, and roots in Palestinian society. Military force can kill leaders, destroy weapons, and disrupt command networks. Those actions can reduce an organization’s ability to fight without necessarily causing the organization itself to disappear.

Research conducted before the current war helps explain why. Political scientist Jenna Jordan examined years of Israeli efforts to kill or capture Hamas leaders and found that the group’s organizational structure, ideology, and social base made it unusually resilient to leadership losses. Her broader research found that large, established, religious, and separatist militant groups were especially likely to survive the deaths of senior leaders.

That research does not show that Hamas can never be dismantled. The war that began in 2023 was far larger than the leadership-targeting campaigns covered by those studies. But it does show why simple organizational survival is an incomplete measure of military success.

Capability is more revealing.

Can Hamas field a large, organized fighting force? Can it rebuild major tunnel networks and command centers? Can it acquire or manufacture large quantities of weapons? Can it control territory in ways that allow it to train, organize, and plan? Most importantly, can it again prepare and carry out a cross-border attack on anything approaching the scale of Oct. 7?

By those measures, Hamas is substantially less capable than it was three years ago. U.S. intelligence also says, however, that it has begun restoring some of what it lost.

That is a more precise description of Israel’s principal military achievement. It did not make Hamas disappear. It significantly reduced its ability to fight.

The hostage objective was more concrete. According to a 2026 United Nations report, 168 of the 251 people abducted on Oct. 7 were eventually returned alive. The last living hostage returned in October 2025, and the final remains were returned in January 2026.

With the hostages returned and Hamas badly weakened, the war’s most difficult question is no longer about what happened during the fighting. It is about what happens after it.

The cease-fire that took effect in October 2025 ended the period of large-scale hostilities, though not all violence. Israeli strikes and military operations have continued. Hamas and other armed groups remain armed. And the system intended to govern Gaza after the war has not yet fully taken hold.

On paper, the proposed postwar arrangement is straightforward. Hamas would give up its weapons and cease operating as an independent military force. A new Palestinian administration would govern Gaza. Palestinian police would handle day-to-day security, supported by outside monitoring and an international stabilization force. Israeli troops would withdraw in stages as those arrangements became operational.

In practice, the most important elements remain incomplete.

That is the central strategic problem because destroying military capabilities and preventing their return are two different tasks. Israel has already shown that it can kill commanders, destroy weapons, collapse tunnels, and disrupt military planning. What remains unproven is whether Hamas, or another armed organization, can be prevented from rebuilding once the heaviest military pressure recedes.

The fact that Hamas is both degraded and already restoring some capabilities illustrates the problem.

There is no historical formula for solving it. Gaza differs in important ways from the civil wars and insurgencies that make up much of the research on post-conflict security. But those cases offer some clues.

United Nations programs dealing with armed groups generally do not stop at collecting weapons. They also seek to break up fighting units and help former combatants move into civilian life. Other research on post-conflict settlements has found that functioning government, credible policing, and outside monitoring can make renewed violence less likely, particularly when former enemies do not trust one another to comply with an agreement.

The lesson is not that Gaza can simply copy another country’s model. It is that military gains tend to be more durable when an armed group loses not only weapons but also the ability to rebuild them under the protection of weak or absent institutions.

That is why the current Gaza plan includes more than Hamas disarmament. It also calls for Palestinian police, a replacement governing authority, outside monitoring, and an international stabilization force. None of those measures is guaranteed to work. But all are aimed at the same unresolved problem: preventing the capabilities Israel destroyed from being recreated.

As of September, that system remained largely unfinished. Hamas had not disarmed, the replacement Palestinian administration had not fully assumed power, and the international security force had not deployed.

That unfinished transition also changes how the question of renewed large-scale fighting should be judged.

Another major offensive could destroy additional Hamas fighters, weapons, and infrastructure. Israel’s previous operations provide strong evidence that it is capable of doing so. The harder question is whether the additional destruction would make Israel meaningfully safer for longer, or whether Hamas would eventually rebuild again.

Ending military pressure altogether presents the opposite risk. Hamas remains armed, and U.S. intelligence says it has already recovered some capabilities during the cease-fire.

That makes “continue the war” or “end the war” too crude a choice. The more useful test is whether any proposed strategy, renewed military action, negotiated disarmament, international security arrangements, or some combination, can keep Hamas from rebuilding the military capabilities necessary for another major attack.

And that test cannot be separated from cost.

Israel has already spent heavily. The Bank of Israel estimates the fiscal cost of the war at roughly 350 billion shekels from 2023 through 2026, or about $114 billion at early-October exchange rates. It estimates another 177 billion shekels, roughly $58 billion, in lost economic output through the end of 2025. Government debt rose from 60.5 percent of gross domestic product before the war to 68.5 percent by the end of 2025.

The military cost has also been substantial. As of late September, the Israel Defense Forces listed 970 military deaths since Oct. 7, including 472 since the start of the Gaza ground operation, along with thousands wounded. The larger total includes deaths on Oct. 7 itself and in other theaters, so it should not be read as the death toll from the Gaza campaign alone.

The human and physical costs in Gaza have been far greater. By late September, Gaza health authorities were reporting more than 74,000 Palestinian deaths. United Nations agencies use those figures while identifying Gaza’s Ministry of Health as the source. The total does not by itself establish precisely how many of the dead were civilians and how many were fighters.

A joint assessment by the World Bank, United Nations, and European Union estimated roughly $35 billion in physical damage and more than $71 billion in recovery and reconstruction needs.

Those numbers matter not only as a record of the past. They change the calculation from here.

Israel is no longer making decisions against the Hamas that existed on Oct. 6, 2023. It is making them against an organization already weakened by three years of war, after the hostages have been returned and after Gaza has suffered enormous destruction.

That means the relevant measure of any new military action is its marginal benefit: what additional threat it removes, how long that gain is likely to last, and what additional human, economic, and political costs are required to achieve it.

An operation that removes a serious remaining capability and makes its return difficult could have lasting security value. An operation that destroys capabilities Hamas later rebuilds would produce a real but temporary gain. A diplomatic arrangement that allows Hamas to rearm rapidly would fail the same test.

The method matters. But the durability of the result matters more.

Three years after Oct. 7, the basic record is no longer especially mysterious. Israel substantially reduced Hamas’s military capabilities. The hostages were returned. Hamas nevertheless remains armed, has begun restoring some of its strength, and has not yet been replaced by a fully functioning governing and security system in Gaza.

The most meaningful test of the war is therefore not whether Hamas still exists. It is whether Gaza can again produce an armed organization with the manpower, weapons, command structure, and freedom of action necessary to carry out an attack remotely comparable to Oct. 7.

For now, the evidence indicates that Hamas is substantially less capable of doing that than it was three years ago.

The unresolved question is whether it will stay that way.

That answer will emerge not from declarations of victory or defeat, but from observable facts: whether Hamas continues rebuilding or continues weakening, whether weapons and military infrastructure return, whether another Palestinian authority can govern and police Gaza, and whether Israel can eventually reduce its military presence without facing another large-scale threat from the territory.

If those conditions take hold, there will be evidence that Israel converted battlefield gains into durable security.

If Hamas repeatedly rebuilds and Israel repeatedly has to destroy the same capabilities, there will be evidence that the war changed the balance of power without solving the underlying security problem.

Three years after Oct. 7, Israel has already shown what military force can destroy.

What remains to be shown is whether what was destroyed can be kept from returning.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Israel’s stated war aims

Claim or topic:

Israel said its objectives included returning the hostages, dismantling Hamas’s military and governing capabilities, and preventing Gaza from again posing a comparable threat.

Source:

Government of Israel

Source type:

Primary document.

What it supports:

The source documents the Israeli government’s stated objectives during the war.

Important caveat:

This establishes what Israeli leaders publicly said their objectives were. It does not establish whether those objectives were achieved or whether other unstated objectives existed.

2. Hamas’s military condition three years later

Claim or topic:

Hamas is substantially weaker than before the war, but it remains active and has begun restoring some capabilities and control in parts of Gaza.

Source:

U.S. Office of the Director of National Intelligence, 2026 Annual Threat Assessment

Source type:

Government assessment.

What it supports:

The assessment describes Hamas as degraded while also reporting that it used the cease-fire period to restore some capabilities and control.

Important caveat:

The assessment does not provide a complete public accounting of Hamas’s current manpower, weapons, tunnels, or ability to carry out another attack on the scale of Oct. 7. Claims about its exact remaining capacity therefore remain uncertain.

3. The hostages

Claim or topic:

All 251 people taken hostage on Oct. 7 were ultimately either returned alive or had their remains returned; 168 were returned alive.

Source:

United Nations report on implementation of Security Council Resolution 2803

Source type:

Primary document.

What it supports:

The U.N. report provides the hostage totals and dates for the return of the last living hostage and the final remains.

Important caveat:

The article uses this source to establish the outcome, not to determine how much military pressure, negotiation, diplomacy, or other factors caused the releases.

4. Hamas disarmament and Gaza’s unfinished postwar system

Claim or topic:

Hamas and other armed groups had not disarmed as of September 2026, and the planned replacement governing and international security arrangements were not yet fully operating in Gaza.

Source:

United Nations, September 2026 Gaza report

U.N. Security Council Resolution 2803

Source type:

Primary documents.

What it supports:

The September report describes the status of Hamas disarmament, Palestinian governing arrangements, and the proposed international stabilization force. Resolution 2803 describes the planned postwar structure, including disarmament, Palestinian policing, international involvement, and phased Israeli withdrawal.

Important caveat:

The resolution describes an intended framework, not an accomplished outcome. The article distinguishes between the plan on paper and the degree to which it had actually been implemented.

5. Why Hamas’s continued existence is not the same as military failure

Claim or topic:

Research predating the war found that Hamas and similar established militant organizations can survive severe leadership losses, making simple organizational survival an incomplete measure of military effectiveness.

Source:

Jenna Jordan, Oxford Academic

Source type:

Academic research.

What it supports:

Jordan’s research examines leadership targeting and organizational resilience, including Hamas, and finds that characteristics such as organizational structure, ideology, age, and social support can make militant groups more resistant to collapse after leaders are killed or captured.

Important caveat:

This research does not establish that Hamas could never be dismantled through a broader military and political campaign. Leadership targeting is only one part of the much larger war conducted since 2023.

Production transparency

Corrections and updates

Subscribe now

The AI Tipping Point Bill Gates Is Worried About

Evidence First — Sat, 03 Oct 2026 15:17:05 GMT

Bill Gates has become considerably more worried about artificial intelligence. That shift is notable because Gates is not an instinctive technological pessimist. He built one of the most important technology companies in history and still believes AI could improve medicine, education, scientific research and living standards on an extraordinary scale. Yet in a recent essay and an interview with Ezra Klein, Gates described a technology that he believes is approaching, and in some cases has already crossed, thresholds for which governments and societies are badly prepared.

The easiest way to understand his argument is this: Intelligence has always been expensive because it required a trained human being. If you wanted medical expertise, you needed a doctor. If you wanted sophisticated software, you needed a programmer. If you wanted advanced scientific knowledge, you needed a scientist. Gates thinks AI could make many forms of intelligence cheap, abundant and widely available.

That could be extraordinarily good.

It could also be extraordinarily dangerous.

For the moment, humans still have an advantage. AI makes mistakes. It needs supervision. It often performs parts of a job rather than the entire job. Gates acknowledges that AI has not yet produced the mass unemployment he thinks could eventually follow. His argument is that this balance depends on machines remaining less capable or less reliable than people in important ways. Once that changes, he believes the economics can change very quickly.

Consider customer service. A company can use AI to handle routine questions today, but if the system still makes enough mistakes, people must remain behind it. Now imagine a system that answers more accurately than the average employee, knows every company policy, speaks every major language, works continuously and costs a fraction as much as a human worker. At some point, the fact that a person used to perform the job may stop carrying much economic weight.

The same thought experiment becomes more uncomfortable in professions we tend to regard as distinctly human. Imagine calling an AI nurse at three in the morning. It has your complete medical history. It remembers every previous conversation. It has access to more medical knowledge than any individual clinician could retain. It does not hurry because another patient is waiting and does not become exhausted at the end of a shift. If such a system eventually became demonstrably better than the average human at particular kinds of care, Gates doubts that people would indefinitely insist on a human simply because the alternative was a machine.

This is one of his more provocative claims. People often assume there will always be jobs protected by a preference for human beings: a nurse caring for us, a therapist listening to us, a driver taking us home, a teacher instructing our children. Gates’s response is that this preference may itself depend on quality. We may prefer the human while the human offers the better experience. What happens when the machine becomes substantially better, always available and dramatically cheaper?

That is still a prediction, not an established fact. People may continue to value human interaction even when AI performs better on measurable dimensions. But Gates sees early examples, including autonomous driving and AI-mediated services, as evidence that preferences can change once machine performance becomes good enough.

The economic pressure would not necessarily depend on anyone wanting to replace workers. Gates uses insurance claims as an example. Imagine one insurer employing thousands of people to review claims while another can perform essentially the same work more accurately with AI and a much smaller staff. The second company may be able to charge less. The first company does not need to dislike human workers to automate their jobs. Competition may do the work for it.

This is why Gates believes AI could differ from earlier technological revolutions. Tractors displaced agricultural workers, but people moved into factories. Factory automation displaced some industrial workers, but services expanded. Computers eliminated clerical work while creating demand for programmers, managers and designers. Human intelligence was still something machines could not replace.

Gates is asking what happens when that is no longer true.

An AI system does not sleep. It can process more information than a human could read in a lifetime. It can operate in many languages, be copied almost instantly and potentially perform enormous numbers of similar tasks at once. If AI eventually becomes capable of completing whole cognitive jobs rather than merely assisting with pieces of them, Gates argues, there may be no obvious higher category of work for displaced people to move into.

This is also why he is skeptical of one of the traditional arguments against technological unemployment. Economists often point out that when technology makes something cheaper, people consume more of it. If AI makes programmers three times as productive, businesses may simply build much more software and continue employing programmers.

Gates accepts that logic while humans remain necessary. But if AI can eventually complete the entire task on its own, increased demand does not necessarily produce increased human employment. In his interview with Klein, he put the distinction bluntly: the extra spending goes into the “token budget,” not the “human salary budget.” In plain English, the company spends more on computing power instead of hiring more people.

That prediction remains uncertain. We do not yet have an economy in which AI can reliably perform most human cognitive jobs from beginning to end. New forms of work may appear. Demand may expand in ways Gates underestimates. People may value human relationships and human judgment more than markets alone would suggest. Gates himself has proposed preserving certain categories of work as “Human Reserved,” including parts of caregiving, education and medicine, even if machines eventually become cheaper or more capable.

But that proposal reveals how far his argument goes. If society eventually needs to reserve certain jobs for people because the market no longer requires a person, that would represent something fundamentally different from previous automation. Human intelligence would not have lost its moral or social value. It would have lost some of its economic scarcity.

The same idea, intelligence becoming cheaper and more widely available, also explains why Gates is worried about security.

Cybersecurity is the clearest case. A sophisticated cyberattack once required sophisticated people. Expertise acted as a barrier. The British government’s AI Security Institute has documented rapid improvements in the most advanced AI systems on cyber tasks, including work that only recently would have required highly experienced specialists. AI-assisted malicious cyber activity is already occurring, and the systems are becoming capable of completing longer and more complicated technical tasks without continuous human help.

That does not mean AI can autonomously shut down a country’s power grid today. But it changes the economics of attack. Smaller groups can potentially accomplish more. Less-skilled attackers can borrow capabilities they do not personally possess. A task that once required a team of experienced hackers may eventually require fewer people.

Cyber is therefore the part of Gates’s warning that requires the least imagination. The dangerous capability is not hypothetical. It is already appearing, even if the most catastrophic scenarios remain uncertain.

Biology is more complicated.

Gates has argued that AI has crossed an important biological threshold and can now significantly increase the danger posed by malicious actors. There is evidence behind the concern. The most advanced AI systems increasingly perform at or above expert level on some biology and chemistry tests. They can help plan experiments and can sometimes help scientists figure out why an experiment failed. Those capabilities suggest that some of the knowledge barriers that historically limited sophisticated biological research are weakening.

But Gates is more confident here than the evidence currently allows.

A model being very good at biology is not the same thing as a terrorist being able to create a catastrophic pathogen. Biological work still requires materials, equipment, practical skill and the ability to make experiments work in the physical world. The most comprehensive current assessments continue to describe substantial uncertainty about how much AI actually increases real-world biological-weapons capability.

The risk becomes more plausible, however, once the imagined attacker is not an amateur.

A malicious actor could already be a trained scientist. That person might already work in a laboratory, have legitimate access to equipment and materials and possess years of experience. In that case, AI does not need to turn a novice into a world-class biologist. It only needs to fill gaps in an expert’s knowledge.

That is a much more credible threat model. It is also why Gates’s concern should not be dismissed simply because the strongest version of his claim runs ahead of the evidence. AI is clearly lowering some barriers to advanced biological work. What remains uncertain is whether enough of those barriers have fallen to make catastrophic biological misuse substantially more achievable today.

This difference between cyber and biology matters. Gates often discusses them together, but they are not equally mature threats. Cyber is already an observed misuse problem. Bio is a serious emerging risk inferred partly from rapidly improving capabilities.

The distinction also helps explain Gates’s most dramatic comparison. In his interview with Klein, he said AI could make nuclear weapons “look like nothing.”

Taken literally, that is difficult to defend. Nuclear weapons can destroy cities within minutes, and a large-scale nuclear war could kill billions of people. Gates’s broader argument, however, appears to be less about the destructive force of a single weapon than about how easily dangerous capabilities can spread.

Nuclear weapons are extraordinarily difficult to build. The necessary materials are scarce, the infrastructure is conspicuous and governments have spent decades creating treaties, inspections, export controls and intelligence systems designed to limit their spread. AI has very different characteristics. Once a powerful model exists, its capabilities can in principle be shared far more widely and used again at very little additional cost.

AI is also general-purpose. A nuclear weapon performs one devastating function. A sufficiently capable AI system might strengthen cyberattacks, biological research, surveillance, fraud, autonomous weapons and other forms of coercion at the same time. The useful and dangerous applications are often inseparable. A system capable of finding software vulnerabilities can help defend a network or attack it. A model capable of sophisticated biological reasoning can help develop medicines or potentially assist harmful research.

That helps explain why Gates sees AI as unusually difficult to contain. The world eventually built elaborate institutions around nuclear technology. Nothing comparable yet exists around AI, and the technology is developing largely in private companies rather than inside government weapons programs.

Still, Gates is not primarily warning about a future superintelligent AI escaping from human control. That puts him at some distance from one of the most familiar arguments in AI safety.

Some researchers are especially worried about what is called recursive self-improvement. The basic idea is simple: AI systems become capable enough to help design better AI systems, which then help design even more capable ones. If that process accelerated dramatically, AI might improve faster than humans could understand or control it.

Related to that is the “control problem”: the possibility that AI eventually becomes so capable and autonomous that humans can no longer reliably direct it, constrain it or shut it down.

Klein raised this possibility directly. Gates did not dismiss it. He called the control problem serious and acknowledged that sufficiently advanced systems could eventually act against human interests.

But he drew a clear distinction.

“The control problem is a serious problem,” Gates said, “but the imminent risk is not RSI.”

That sentence captures much of his worldview. Gates does not appear to think artificial superintelligence, meaning AI that greatly exceeds humans across most intellectual abilities, is harmless or irrelevant. He thinks it is a more distant concern and requires more uncertain steps.

His nearer-term scenario is simpler.

The AI becomes powerful, and a person uses it badly.

The people already exist. The computer networks already exist. The laboratories already exist. So do governments, corporations, criminal organizations and political movements with their own goals. Gates’s argument is that AI does not need intentions of its own to amplify theirs.

That is why he can sound more alarmed than many technologists while remaining less preoccupied with superintelligence than some AI-safety researchers. He is not saying humanity will never face a control problem. He is saying that severe AI-driven problems may arrive while humans remain completely in control of the machines.

His proposed response follows from that diagnosis. Gates is not calling for an end to AI development. He wants stronger government oversight, monitoring of powerful models, safeguards that users cannot simply remove and controlled access to capabilities that could be dangerous if universally available. His skepticism toward industry self-regulation comes from the belief that the costs of misuse would be borne by people far beyond the companies developing the systems.

He is also skeptical of framing the entire issue as a race in which the United States must defeat China. Gates has questioned what it would mean for either country to “win” if dangerous capabilities eventually spread. A biological outbreak would not respect national borders. A cyberattack on globally interconnected systems could have consequences well beyond the country where it began. Competition between the United States and China, in his view, does not eliminate their shared interest in preventing certain catastrophic uses.

There are good reasons not to accept all of Gates’s conclusions. AI progress could slow. Reliability may prove harder than expected. Defensive cyber systems may improve alongside offensive ones. Biological safeguards may become much stronger. Economies may generate new forms of work that Gates cannot foresee. Consumers may continue to value human beings even when machines outperform them on measurable dimensions.

Most importantly, greater capability does not tell us exactly what the consequences will be. If AI becomes better, cheaper and more reliable than humans at performing an entire job, significant job displacement becomes a very reasonable expectation, though we still cannot know how much new demand or new work might offset it. In biology, increasingly sophisticated AI makes dangerous misuse more plausible, but it does not by itself overcome the physical barriers involved in successfully creating and deploying a biological weapon. And in cybersecurity, more capable AI strengthens attackers while also giving defenders more powerful tools. The capabilities matter enormously. What remains uncertain is how markets, institutions, defenses and human behavior respond to them.

This is where the evidence both supports and limits Gates’s case.

He is strongest when describing the direction in which AI capabilities are moving. Those changes are measurable. AI systems are becoming better at software, scientific reasoning, cyber operations and increasingly long autonomous tasks. Human expertise is becoming easier to reproduce in machine form.

He becomes less certain when he predicts exactly what happens next.

On cyber, his concern is strongly grounded in developments already underway. On biology, the concern is real but his claim that the decisive threshold has already been crossed is stronger than the evidence currently supports. On jobs, the mechanism is plausible but the final economic outcome remains deeply uncertain. On superintelligence and loss of control, Gates sees a serious long-term problem, just not the one he thinks will arrive first.

His nuclear comparison is best understood in the same way. It is less a literal claim about explosive power than an argument about how widely dangerous capability can spread. Nuclear technology concentrated extraordinary destructive power in a small number of hands. AI could make powerful forms of intelligence cheap, replicable and widely available.

That possibility cuts both ways.

Cheap intelligence could accelerate scientific discovery, expand access to medicine and education, strengthen cybersecurity and allow people everywhere to accomplish things that once required enormous amounts of money, training or institutional access. Gates still believes deeply in that future.

But the same abundance could change what a hacker can do, what a malicious scientist can do and what employers still need people to do.

For most of history, if you wanted intelligence, you needed a person.

Gates’s warning is about what happens when you no longer do.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Gates’s argument about human intelligence, jobs and AI

Claim or topic:

Bill Gates argues that AI could reduce the economic scarcity of human intelligence as systems become more capable, reliable and inexpensive. He has discussed the possibility of significant job displacement, the importance of crossing a reliability threshold, the shift from a “human salary budget” toward a “token budget,” and preserving some roles as “Human Reserved.”

Source:

Bill Gates, “The turbulent AI era is here. The choices we make now are critical.”

Bill Gates interview on The Ezra Klein Show, transcript

Source type:

Primary essay and transcript of primary interview.

What it supports:

These sources support Gates’s stated concern that increasingly capable AI could perform work now done by humans, his emphasis on reliability as an important threshold, his discussion of computing spending replacing some human labor spending, and his proposal to preserve some activities for humans even if machines become capable of doing them.

Important caveat:

These are Gates’s arguments and predictions about future economic effects. They establish what Gates believes, not that widespread permanent job loss will necessarily occur.

2. Rapid improvement in AI cybersecurity capabilities

Claim or topic:

Advanced AI systems have improved substantially at cybersecurity tasks, including increasingly difficult tasks that previously required significant human expertise.

Source:

UK AI Security Institute, Frontier AI Trends Report

Source type:

Government research and technical evaluation.

What it supports:

The report documents substantial improvements in AI performance on cybersecurity evaluations, including success on increasingly difficult tasks and longer sequences of work completed with less human assistance. It supports the article’s claim that the cyber capability trend is already measurable rather than purely hypothetical.

Important caveat:

Performance on controlled cybersecurity evaluations does not establish that AI can autonomously carry out the most catastrophic real-world attacks. Defensive AI capabilities are also improving.

3. AI capabilities in biology

Claim or topic:

The most advanced AI systems are becoming increasingly capable at biological reasoning, experimental planning and laboratory troubleshooting.

Source:

UK AI Security Institute, Frontier AI Trends Report

Source type:

Government research and technical evaluation.

What it supports:

The report supports the article’s description of advanced models performing at or above expert level on some biology-related evaluations, producing useful experimental guidance and performing strongly on laboratory troubleshooting tasks.

Important caveat:

These evaluations measure components of biological expertise. They do not demonstrate that an AI user can successfully create or deploy a catastrophic biological weapon.

4. Uncertainty about real-world AI-enabled biological weapons risk

Claim or topic:

Although AI is lowering some knowledge barriers in biology, it remains uncertain how much this increases the practical ability of malicious actors to create biological weapons.

Source:

International AI Safety Report 2026

Source type:

Expert organization and international scientific assessment.

What it supports:

The report supports the distinction made in the article between improving biological capabilities and successful real-world misuse. It discusses continuing barriers including materials, equipment, experimental execution and other practical constraints, while also treating biological misuse as a serious emerging risk.

Important caveat:

The report does not establish that Gates’s proposed biological “threshold” has definitively been crossed. It emphasizes substantial uncertainty about the translation from benchmark performance to real-world biological-weapons capability.

5. Gates on recursive self-improvement and the control problem

Claim or topic:

Gates considers loss of control over highly advanced AI a serious concern, but he places greater emphasis on human misuse in the nearer term and said that “the imminent risk is not RSI.”

Source:

Bill Gates interview on The Ezra Klein Show, transcript

Source type:

Transcript of primary interview.

What it supports:

The interview directly supports the article’s description of Gates distinguishing between the longer-term AI control problem and more immediate risks involving humans using powerful AI systems for harmful purposes.

Important caveat:

Gates does not dismiss artificial superintelligence or loss of control. The article therefore presents his position as a difference in priority and timing, not as a rejection of those risks.

6. Gates’s comparison between AI and nuclear weapons

Claim or topic:

Gates has used unusually strong language comparing AI risk with nuclear weapons, including saying that AI could make nuclear weapons “look like nothing.”

Source:

Bill Gates interview on The Ezra Klein Show, transcript

Source type:

Transcript of primary interview.

What it supports:

The source supports the article’s account of Gates making the nuclear comparison and presenting AI as an unusually broad and difficult-to-govern source of risk.

Important caveat:

The article’s interpretation that the comparison is largely about accessibility, proliferation and difficulty of containment draws on Gates’s broader argument. The interview does not establish that a single AI-enabled attack would literally be more destructive than a nuclear weapon.

7. Gates on safeguards, government oversight and China

Claim or topic:

Gates argues for stronger safeguards around powerful AI systems, greater government involvement and some degree of international cooperation, including between the United States and China, on risks that cross national borders.

Source:

Bill Gates, “The turbulent AI era is here. The choices we make now are critical.”

Bill Gates interview on The Ezra Klein Show, transcript

Source type:

Primary essay and transcript of primary interview.

What it supports:

These sources support Gates’s calls for stronger oversight, restrictions or safeguards around particularly dangerous capabilities, and his argument that some AI risks cannot be understood solely as a competition in which one country “wins.”

Important caveat:

These are Gates’s policy judgments. The sources establish his position, not that the safeguards he proposes will be sufficient or that meaningful international cooperation will be achievable.

How to read this evidence

Production transparency

Corrections and updates

Subscribe now

Why Some Things Go Viral

Evidence First — Tue, 29 Sep 2026 13:55:46 GMT

By the time most people see a viral post, it can feel as if its success was obvious from the start. The joke seems perfectly timed. The video feels instantly shareable. The argument appears to have found exactly the right audience. Within hours, a clip, phrase, image or idea can move far beyond its original circle and start showing up in group chats, feeds, news stories and everyday conversations.

But virality is rarely just about making something good. It usually comes from a mix of psychology, technology, timing and luck.

At its simplest, something goes viral when people do more than look at it. They pass it along. They quote it, remix it, argue with it, imitate it or send it to someone with the unspoken message: you need to see this. That act of sharing is the engine of virality. Platforms can speed it up, but people still provide the spark.

The most shareable material usually creates a strong emotional reaction. It might make people laugh, gasp, feel angry, feel seen, feel inspired or feel surprised. Mild approval is usually not enough. A post that makes someone think “that’s nice” may get a like. A post that makes someone laugh out loud or immediately want another person’s reaction is much more likely to be forwarded.

One famous example is “the dress,” a 2015 photograph of a striped dress that spread across the internet because people could not agree on its colors. Some saw it as blue and black. Others saw it as white and gold. The photo itself was ordinary, but the disagreement was immediate and personal. People showed it to friends, family members and co-workers because they wanted to know what someone else saw. Its power came from how easy it was to join in. Everyone could look, decide and pull someone else into the debate.

Emotion alone, though, does not explain why some things spread and others disappear. Sharing is also a way people present themselves. People send content because it helps them say something about who they are or how they see the world. A person might share something because it makes them look funny, informed, compassionate, skeptical, stylish, outraged or ahead of the curve. Viral content often gives people a small but immediate social reward. To share it is to take part.

That is why relatability is so powerful. A joke about workplace exhaustion, a video about dating fatigue, a meme about family group chats or a clip about the misery of modern travel can feel less like content than recognition. “This is me,” “this is us” or “someone finally said it” are some of the strongest reasons people share. Virality often begins when a person sees not just an idea, but a reflection of their own life.

The idea also has to travel easily. The most contagious posts tend to be simple to understand and quick to explain. A complicated argument can go viral, but it usually needs a clear hook: a memorable phrase, a striking image, a sharp contrast or a feeling people understand right away. Online, friction kills momentum. If a viewer has to work too hard to understand why something matters, the chain of sharing usually breaks.

Novelty matters too. The internet rewards things that interrupt the pattern: the unexpected joke, the surprising fact, the strange image, the useful tip, the beautiful moment, the outrageous claim. People like the feeling of discovery. When they share something new, the message is not only “look at this.” It is also “I found this.”

Then there is the machinery of distribution. In an earlier media world, editors, producers and publishers decided what reached a mass audience. Now attention is shaped by a mix of human behavior and algorithmic recommendation. A video that people watch to the end, replay, comment on, save or share may be shown to more people. If that next group responds in the same way, the platform may push it even further.

This feedback loop can make popularity look sudden. In reality, many platforms are constantly testing content with different groups of people. When early engagement is unusually strong, the audience can grow very quickly. What looks like a spontaneous explosion may actually be the result of many small signals building on one another.

But even a strong post needs the right environment. Timing can turn ordinary content into a phenomenon. A joke lands differently during a major news event. A song lyric can take on new meaning after a celebrity breakup. A practical tip spreads faster during a crisis. A meme format catches fire because it gives people a way to talk about the mood of the week.

Context does not just surround viral content. It helps create it. The same post that fails on Monday may take off on Friday because the public conversation has changed. Virality depends not only on what is said, but on what people are already ready to hear.

This is one reason formulas for virality are unreliable. Marketers, creators and political strategists often try to copy the surface features of viral posts: the caption style, the editing rhythm, the outrage, the joke structure, the thumbnail. Sometimes it works. Often it does not. The visible traits of a viral post are only part of the story. The hidden variables, like who saw it first, what else was happening, how a platform ranked it and what mood the audience was in, are much harder to reproduce.

Luck also plays a bigger role than people like to admit. A post may reach one highly connected account. A niche community may adopt it. A critic may attack it and accidentally spread it further. A platform may test it with an unusually receptive audience. A phrase may become useful to people in ways the original creator never expected.

Some viral campaigns work because they give people a role to play. The Ice Bucket Challenge, a 2014 social media campaign for ALS, also known as amyotrophic lateral sclerosis or Lou Gehrig’s disease, asked people to dump a bucket of ice water over themselves, post the video online and challenge others to do the same or donate. It was not just a message people could watch. It was an action they could perform. It turned support for a cause into something public, repeatable and easy to pass along. Each participant became both audience and broadcaster.

The most durable viral objects often work this way. They invite people to imitate, argue, adapt or add their own version. A dance challenge, meme template, catchphrase or controversial claim spreads not only because people consume it, but because they can do something with it. The audience becomes part of the distribution system.

That participatory quality helps explain why virality can be exciting and dangerous at the same time. A viral post can launch a career, raise money, expose wrongdoing or bring attention to an overlooked issue. It can also distort facts, reward outrage, flatten nuance and subject ordinary people to overwhelming scrutiny. Scale changes meaning. A joke meant for friends can become a public statement. A half-formed thought can become evidence in a trial of public opinion.

Misinformation often travels through the same channels as harmless entertainment. A false claim that provokes fear, anger or moral urgency can spread because it feels important to share before it has been checked. In those cases, the same qualities that make content contagious, including emotion, simplicity, identity and speed, can also make it misleading.

In the end, virality is not one magic quality inside a piece of content. It is a relationship between the content, the audience, the platform and the moment. The content has to give people a reason to care. The audience has to have a reason to share. The platform has to have a reason to amplify it. The moment has to be ready for it.

That is why going viral is easier to explain afterward than to engineer in advance. The internet can measure attention with incredible precision, but it cannot fully predict human impulse. People share because they feel something, because they want to belong, because they want to be seen, or because they want to warn, delight, persuade or provoke someone else.

A viral post is not simply something many people saw. It is something many people decided, for their own reasons, to carry forward.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Emotion and virality

Claim or topic:

The article says viral content often produces strong emotional reactions, and that high-arousal emotions such as awe, anger, anxiety, surprise or amusement can make people more likely to share.

Source:

Jonah Berger and Katherine L. Milkman, “What Makes Online Content Viral?”

Source type:

Academic research.

What it supports:

The study found that online content was more likely to be widely shared when it evoked high-arousal emotions, including awe, anger and anxiety. It also found that practical usefulness, interest and surprise were associated with sharing.

Important caveat:

The study analyzed New York Times content and controlled experiments, so it supports the general relationship between emotional arousal and sharing, not a universal formula for every platform, format or audience.

2. Social reasons people share

Claim or topic:

The article says people share content partly because it helps them express identity, maintain relationships, support causes or show others what they care about.

Source:

ANA summary of The New York Times Customer Insight Group’s “The Psychology of Sharing”

Source type:

Analysis / survey summary.

What it supports:

The source summarizes research by The New York Times Customer Insight and Advertising Groups, including ethnographic work and a survey of 2,500 medium-to-heavy online sharers. It describes motivations such as defining oneself to others, nourishing relationships and supporting causes or brands.

Important caveat:

This was marketing-oriented research, not a peer-reviewed academic study. It is useful for context about stated sharing motivations, but it should not be treated as definitive evidence about all users or all platforms.

3. “The dress” viral example

Claim or topic:

The article describes “the dress” as a 2015 viral photo that spread because viewers disagreed over whether the dress appeared blue and black or white and gold.

Source:

Time, “How #TheDress Went Viral”

Source type:

Reputable journalism.

What it supports:

The source documents the 2015 viral phenomenon, the disagreement over the dress’s perceived colors and the rapid spread of the debate online.

Important caveat:

This source supports the basic history and spread of the example. The article’s explanation of why the example was compelling, because it was easy to participate in and argue about, is interpretive.

4. Algorithmic recommendation and platform amplification

Claim or topic:

The article says platforms can amplify content based on user behavior such as watching, sharing, following, commenting or otherwise engaging with posts.

Source:

Wired, “TikTok Finally Explains How the ‘For You’ Algorithm Works”

Source type:

Reputable journalism based on platform explanation.

What it supports:

The source reports TikTok’s explanation that its recommendation system uses signals including user interactions and video information, and that stronger signals can include actions such as watching a video to completion, sharing it or following a creator.

Important caveat:

This source is specific to TikTok and to the information TikTok publicly described. Recommendation systems vary across platforms and change over time, so it should not be read as a complete explanation of all social media algorithms.

5. Social networks and information spread

Claim or topic:

The article says virality depends not only on content quality, but also on network placement, early exposure and whether content reaches people or communities that can spread it further.

Source:

Kristina Lerman and Rumi Ghosh, “Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks”

Source type:

Academic research.

What it supports:

The study analyzes how news spread through Digg and Twitter and finds that social network structure plays an important role in information diffusion.

Important caveat:

The study concerns older social platforms and news-sharing dynamics. It supports the general role of network structure in spread, but it does not directly measure today’s TikTok, Instagram, YouTube Shorts or Substack ecosystems.

6. The Ice Bucket Challenge

Claim or topic:

The article describes the Ice Bucket Challenge as a 2014 social media campaign for ALS in which people dumped ice water over themselves, posted videos and challenged others to participate or donate.

Source:

The ALS Association, “The ALS Ice Bucket Challenge”

Source type:

Expert organization / campaign participant organization.

What it supports:

The source supports the article’s description of the Ice Bucket Challenge as a major ALS awareness and fundraising campaign. It also states that the campaign raised $115 million for The ALS Association.

Important caveat:

The ALS Association is not an independent outside observer; it was one of the central organizations benefiting from the campaign. It is strong for the campaign’s own fundraising figures and mission description, but less independent for broader claims about cultural impact.

7. Mechanics of the Ice Bucket Challenge

Claim or topic:

The article says the Ice Bucket Challenge worked partly because it gave people a simple, repeatable public action to perform and pass along.

Source:

Paul Song, “The Ice Bucket Challenge: The public sector should get ready to promptly promote the sustained development of a system of medical research and care”

Source type:

Academic commentary / analysis.

What it supports:

The source describes the basic challenge format: donating money or dousing oneself with ice water, filming it and passing the challenge on to others. This supports the article’s description of the campaign as participatory and repeatable.

Important caveat:

This is commentary, not a controlled study of why the challenge spread. It supports the mechanics of the campaign more directly than the causal explanation for its virality.

8. Misinformation and viral spread

Claim or topic:

The article says misinformation can travel through the same sharing channels as harmless entertainment, and that emotionally compelling false claims can spread quickly.

Source:

MIT News summary of Vosoughi, Roy and Aral, “The spread of true and false news online”

Source type:

Academic research summary.

What it supports:

The source summarizes a large study of Twitter sharing patterns that found false news spread farther, faster and more broadly than true news in the dataset analyzed.

Important caveat:

The study focused on Twitter data from 2006 to 2017. It does not prove that every false claim spreads faster than every true claim, nor does it automatically generalize to every platform or later period.

How to read this evidence

Production transparency

Corrections and updates

Subscribe now

What Is a Book Worth When AI Can Explain It?

Evidence First — Mon, 28 Sep 2026 14:19:26 GMT

Imagine a book on your nightstand about why people make bad decisions. You bought it hoping to understand your own habits a little better. You have made it through the introduction. Now imagine asking an AI assistant to explain the book’s central argument, give you three examples that apply to your life and identify its weakest claims. Within minutes, you have something that feels useful. You ask a follow-up question. The explanation gets clearer. The book is still on the nightstand. But the reason to finish it is less obvious.

That is a serious challenge to the value of some books. It deserves a better answer than an automatic defense of reading or a confident prediction that artificial intelligence will replace it. Books do different jobs. They explain, investigate, entertain, persuade and preserve experience. AI’s ability to perform one of those jobs does not tell us whether it can replace the others. But neither should our affection for books prevent us from acknowledging when a faster alternative serves a reader well.

The most vulnerable book, in this sense, may be one whose main contribution is packaging familiar information. Consider a manager whose weekly meetings keep running long without producing decisions. She could buy a book on effective meetings. Or she could describe the problem to an AI assistant, ask for a few approaches and work with it to draft an agenda for Tuesday. When a suggestion seems impractical, she can explain why and ask for another. If her goal is a better meeting next week, that exchange may provide much of what she wanted from the book. The relevant comparison is what each approach helps her accomplish, not how many pages she reads.

A book must anticipate where an audience might struggle. An AI assistant can respond to the particular point at which one person gets stuck. Ask for a simpler explanation, a different example or an objection, and the conversation can change direction. For a book built around a few useful ideas and many repetitive illustrations, that creates a plausible substitute. This is an argument about usefulness, however, not evidence that AI has already reduced such books’ sales or prices. Establishing that would require separate evidence about buying behavior and its causes.

There is also a complication: Getting an explanation and acquiring an understanding are not always the same achievement. Return to the reader with the book about bad decisions. Suppose the AI explains the tendency to favor information that supports what we already believe. The idea seems obvious. Yet the next morning, the reader dismisses criticism of a favorite business proposal and eagerly forwards the one comment that supports it. Being able to recognize a principle in an explanation is different from noticing it at work in yourself.

In a 2025 study published in PNAS, researchers tested AI assistance in high-school mathematics. Students with access to a relatively unrestricted GPT interface performed better while receiving help, but performed worse than the comparison group when that help was taken away. An alternative designed with tutoring safeguards largely mitigated the problem. The finding does not establish that reading books is better than using AI. It suggests something narrower and more useful: A tool that helps people complete a task can fail to build their ability to do it independently.

The evidence also cuts against a blanket dismissal of AI as a shortcut. In a separate randomized study, published in Scientific Reports in 2025, 194 college physics students learned through a purpose-built AI tutor or an active-learning class. The researchers found better immediate learning outcomes, in less time, with the AI tutor. That study did not compare a chatbot with a book, and it did not establish that the advantage would persist over years. Together, the studies suggest that the design of the interaction matters. AI can guide someone through thinking, or make it easier to avoid doing the thinking themselves.

Books offer no automatic protection against the second problem. A reader can finish 300 pages, underline several passages and remember very little. Time spent is not proof of learning. Difficulty is not always depth. Still, a well-constructed book can offer something that a sequence of questions may miss: an intellectual route chosen by someone who knows the territory.

Imagine asking why an empire collapsed. A summary of a history book might give you the author’s answer: Its institutions became too weak to withstand a crisis. That is an intelligible conclusion. But how did the author distinguish institutional weakness from military defeat, economic trouble or decisions made by particular leaders? In the full book, you might encounter records that contradict one another, explanations that initially look persuasive and evidence that changes their meaning. Those chapters give you a chance to examine the author’s judgment. They may also leave you less certain than the summary did, for good reason.

A summary can report the conclusion. It may not preserve enough of the reasoning for a reader to judge whether the conclusion was earned. AI can also organize a sustained course of inquiry. The distinction is not an absolute limit of the technology. It is a difference between ways of using it. Asking for the takeaway from a difficult argument is a different activity from working through that argument, whether on a page or in a conversation.

Original reporting presents another distinction. A historian who uncovers neglected correspondence or a journalist who persuades people to describe events they have never discussed publicly is producing material that did not previously exist in that form. Summarizing the resulting book can make its findings more accessible. It does not perform the work that made those findings available. Indeed, a reader could find the summary sufficient while still depending on the original book’s existence. Personal convenience and the value of producing knowledge are separate questions.

With fiction and memoir, the limits of the summary become easier to see. Imagine a novel about two lifelong friends. An AI summary tells you that one eventually betrays the other. You know the central event. But in the novel, you might spend a hundred pages watching them protect each other, laughing at their private jokes and overlooking the small resentments that later become consequential. When the betrayal comes, you remember the promises that preceded it. The summary gives you the event. The novel gives the event its weight. For a reader who came for that experience, the time spent getting there is part of the value.

That does not make literature immune to competition from AI-generated entertainment. Nor does it prove that human authorship will always command a premium. Those are predictions about readers’ preferences. The more modest point is that replacing a story’s information does not automatically replace the reasons someone enjoys reading it.

AI might also make a demanding book more approachable. Our reader could pick up the book about decisions, finish a chapter and ask the assistant to challenge their understanding: What would count as evidence against that business proposal? What criticism have they dismissed too quickly? Used this way, the technology becomes a companion to reading. Whether that improves understanding depends on the quality of the assistance and what the reader does with it.

The sensible response is to become more deliberate about what we want from a book. Sometimes we need a fact. Sometimes we need a framework. Sometimes we want to follow a particular mind through a problem, or inhabit a story that cannot be reduced to its lesson. AI makes the first two easier to obtain without reading an entire volume. It also gives authors a stronger reason to offer something beyond information that can be conveniently rearranged.

The book on the nightstand may deserve to remain unfinished. Or the conversation about it may reveal why it deserves closer attention. The useful question is what disappears when we skip it. If the answer is mostly repetition, little may be lost. If the answer is the evidence, the uncertainty, the voice or the experience that makes the conclusion matter, getting the point may be only the beginning.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. AI assistance and independent learning

Claim or topic:

Students using a relatively unrestricted GPT interface performed better with assistance but worse than the comparison group after assistance was removed. A version with tutoring safeguards largely mitigated that problem.

Source:

Bastani and colleagues, “Generative AI without guardrails can harm learning: Evidence from high school mathematics,” PNAS (2025).

Source type:

Academic research; randomized controlled study.

What it supports:

In the mathematics setting studied, better performance while using AI did not necessarily translate into better independent performance. The design of the assistance mattered.

Important caveat:

The study did not compare AI use with reading books. It does not establish that AI generally harms learning or that books produce better understanding.

2. Learning with a purpose-built AI tutor

Claim or topic:

In a randomized study involving 194 college physics students, a purpose-built AI tutor produced better immediate learning outcomes, in less time, than an active-learning class.

Source:

Kestin and colleagues, “AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting,” Scientific Reports (2025).

Source type:

Academic research; randomized controlled study.

What it supports:

A deliberately designed AI tutor can support effective learning in a specific educational setting.

Important caveat:

The comparison involved a particular tutor and classroom setting, not ordinary chatbot use versus reading a book. It did not establish that the learning advantage would persist over years.

3. AI as a substitute for some practical reading

Claim or topic:

An interactive AI exchange may provide much of what a reader wants from a book that packages familiar information, such as advice on running meetings.

Source:

Source needed for a direct empirical comparison of AI assistance with full-book reading for these purposes.

Source type:

Author’s analysis; direct supporting evidence not cited.

What it supports:

The article presents this as a plausible substitution, illustrated through a hypothetical manager. Neither cited study directly tests that example or measures the value of business or self-help books.

Important caveat:

The article does not establish that AI has reduced book sales or prices. Its argument concerns possible usefulness to individual readers, not a demonstrated market effect.

4. What summaries may leave out

Claim or topic:

A summary may omit reasoning needed to evaluate an author’s conclusion or the narrative experience that gives a fictional event emotional weight. Summarizing original reporting also differs from producing it.

Source:

The article’s analysis and hypothetical examples. No external source is cited for these comparisons.

Source type:

Interpretation and illustrative reasoning.

What it supports:

The history-book and friendship examples explain distinctions between receiving a conclusion, examining its evidence and experiencing a story. They are illustrations, not research findings.

Important caveat:

The article does not empirically establish how much readers lose through summaries or whether full-book reading produces better outcomes. Source needed for any measured claim about those effects. The cited educational studies do not answer those questions.

How to read this evidence

Production transparency

Corrections and updates

Subscribe now

Jewish Success, American Power and the Myth of Collective Control

Evidence First — Fri, 25 Sep 2026 14:31:36 GMT

The argument often starts with a list.

A senator, a movie producer, a university president, a financier. The names are assembled, their Jewish backgrounds highlighted, and a question follows: How can a group that makes up only a small percentage of the country appear so frequently in prominent positions?

There is a factual observation buried inside that question. Jewish Americans constitute about 2.4 percent of the adult population, according to the Pew Research Center, yet they are represented at higher rates in at least some influential institutions. In the 119th Congress, for example, 32 members identify as Jewish, about 6 percent of Congress. Nine percent of senators are Jewish.

The history of Hollywood offers another conspicuous example. The Academy Museum of Motion Pictures describes the studio system as having been established largely by Jewish immigrant filmmakers and entrepreneurs. Their companies helped turn Los Angeles into the center of the American movie industry.

But from there, discussions often move too quickly. A measurable difference in representation becomes a sweeping assertion about “power.” Then representation is treated as evidence of “control.” Finally, the actions of individual politicians, executives, or artists are attributed to Jews collectively.

That final step is the crucial one. Antisemitic theories of Jewish power have historically depended not simply on the idea that individual Jews are successful or influential, but on the claim that Jews act collectively, coordinating behind the scenes to advance shared interests or control institutions. The demographic evidence examined here cannot establish such coordination. To demonstrate collective control would require evidence of common objectives, communication, and coordinated action, not simply a list of people who share a background.

Each step introduces a new claim. Each requires new evidence.

The available evidence supports a complicated history of disproportionate achievement in certain fields. It does not support the idea of a unified Jewish project to direct American institutions.

A Number That Does Not Exist

One difficulty is that there is no comprehensive count of Jewish Americans in “positions of power.”

The phrase itself has no agreed definition. Does it include elected officials, judges, and cabinet secretaries? Corporate chief executives and board members? Billionaires, newspaper editors, university trustees, television producers, physicians, and law firm partners? How should their influence be measured and compared?

The federal government cannot supply the answer. The Equal Employment Opportunity Commission requires many large employers to report workers by job category, sex, race, and ethnicity, but not by religion. The decennial census also does not ask Americans to identify their religion. As a result, federal data do not provide the kind of religious workforce tables that would allow researchers to calculate the Jewish share of corporate leadership across the economy.

Jewish identity is also unusually difficult to classify from the outside. It can refer to religion, ancestry, ethnicity, culture, or some combination of them. Pew counts people who identify Judaism as their religion, as well as people with no religion who nevertheless identify as Jewish and have a Jewish parent or were raised Jewish. But Jewish ancestry alone is not enough. Someone may have a Jewish parent or grandparents and still fall outside Pew’s estimate if they do not identify as Jewish themselves.

Those definitional difficulties do not mean the overall population estimate is arbitrary. A separate research project at Brandeis University, the American Jewish Population Project, has produced a broadly similar estimate of the size of the American Jewish population using a different methodology. The project combines data from many national surveys rather than relying on a single Jewish population survey. The fact that different methods produce similar overall estimates provides some reassurance about the approximate size of the population, although the projects do not define or measure every category in exactly the same way.

What those estimates cannot do, however, is turn surnames or internet biographies into a reliable census of Jewish influence. Lists assembled from surnames, grandparents, or internet biographies therefore cannot provide a reliable census. They may count secular people as representatives of a religion they do not practice, omit converts or people with less recognizable names, and change the definition of Jewishness from one person to another.

Even apparently precise statistics can create a different problem: confusing the numerator and denominator. Pew found, for example, that 11 percent of employed Jewish respondents worked in a broad category covering arts, entertainment, recreation, travel, accommodations, and food service. That does not mean Jews constitute 11 percent of the entertainment industry. It means 11 percent of the Jewish workers surveyed were employed somewhere inside that very broad collection of industries.

The distinction is elementary, but it is frequently lost when demographic claims circulate online.

The Education Pipeline

Where disproportionate representation does exist, education provides the clearest immediate explanation.

In Pew’s 2023 to 2024 Religious Landscape Study, 65 percent of Jewish adults had at least a bachelor’s degree, compared with 35 percent of American adults overall. Jewish Americans and Hindus were the country’s two most highly educated major religious populations by that measure.

That difference matters because many jobs associated with influence are drawn from a narrow educational pipeline. Law, medicine, finance, academic research, senior government service, and corporate management usually require a university degree, and often an advanced one. A population with nearly twice the national college graduation rate will enter the pool of candidates for such positions at a disproportionately high rate, even before hiring preferences or professional networks are considered.

Education, however, is an immediate mechanism rather than a complete historical explanation. It raises another question: Why did educational and professional attainment become so pronounced?

Part of the answer lies in migration and geography.

Roughly two million Jews arrived in the United States from Europe during the age of mass migration in the late 19th and early 20th centuries, many fleeing persecution and seeking economic opportunity. Economic historians using census records found that Jewish immigrants were concentrated in urban, semi-skilled occupations rather than agricultural labor. Many lived in overcrowded and impoverished enclaves, particularly in New York, but their occupations often supplied skills that could be transferred to commerce, manufacturing, and professional life in a rapidly urbanizing economy.

The distinction is important. These immigrants did not generally arrive as a wealthy ruling class. Many worked in garment shops, small stores, and crowded tenements. But their location in cities and their concentration in occupations connected to commerce placed them differently from immigrant populations whose economic lives were centered on farming or unskilled rural labor.

Later generations inherited not merely money, since many families had little, but also information, expectations, occupational knowledge, and proximity to expanding institutions.

Exclusion and the Building of New Institutions

Jewish advancement also occurred alongside discrimination, not in its absence. Anti-Jewish prejudice in America long predates the university quotas and professional barriers of the 20th century. By the early 1900s, some of that exclusion had become embedded in elite educational and professional institutions.

Universities imposed formal or informal barriers. Elite social clubs and old professional networks frequently excluded Jews. A study of Harvard students in the 1920s and 1930s found that students with distinctively Jewish surnames made up 6.6 percent of the student body but just 0.2 percent of the membership in selective final clubs.

Those clubs were not merely social diversions. Membership was associated with much higher later earnings and a substantially greater likelihood of entering finance. The findings illustrate how elite networks could shape careers independently of academic achievement, and how Jewish students could be admitted to a prestigious university while remaining outside some of its most consequential social circles.

Exclusion closed doors and imposed real costs. In some cases, it also encouraged Jewish entrepreneurs and professionals to build businesses, cultural institutions, and networks outside older establishments.

Hollywood is the most famous case. Many of the industry’s founders were Jewish immigrants or the children of immigrants. They entered a young and disreputable business that was not yet guarded by the same established elites that dominated older industries. The studios they created eventually became among the country’s most influential cultural institutions.

There is an irony in that history. People partly shut out of existing centers of prestige helped build new ones. Later generations encountered the outcome, a visible concentration of Jewish names in entertainment, without necessarily seeing the migration, exclusion, entrepreneurship, and decades of institutional development that preceded it.

The Familiarity Effect

Another part of the explanation may be more ordinary.

People tend to know, trust, and refer people who resemble those already in their social worlds. Social scientists call this “homophily,” a term meaning that relationships often form among people with similar backgrounds, experiences, or identities.

A 2025 field study of job referrals, for example, found that men and women were much more likely to refer candidates of their own gender. The researchers concluded that the pattern was driven largely by the gender composition of close friendship networks. The participants did not need to belong to a coordinated campaign. They referred people they knew, and the people they knew tended to resemble them.

Similar dynamics can operate through families, neighborhoods, colleges, religious communities, professional associations, and former workplaces. A manager may call a trusted former colleague. An employee may recommend a friend from school. A producer may hire a writer with whom she has previously worked. A senior professional may mentor a younger person whose background feels familiar.

Such choices can reproduce a group’s representation over time, even when no one explicitly intends to exclude outsiders.

It is therefore reasonable to suspect that Jewish social and professional networks have sometimes helped Jewish candidates, just as alumni networks, family connections, church communities, ethnic associations, and old boys’ clubs have helped members of many other groups.

But a plausible general mechanism is not the same as a demonstrated group-specific explanation. There is no comprehensive evidence showing how frequently Jewish decision-makers favor Jewish applicants or how much such preferences contribute to Jewish representation in business, entertainment, or government.

The relevant affinity may not even be religion. People who appear to be choosing “people like themselves” may actually be selecting for the same university, neighborhood, class background, profession, political outlook, or style of communication. These characteristics frequently overlap, making their individual effects difficult to separate.

The careful conclusion is that in-group networking probably occurs sometimes, among Jews as among other populations. Its scale is unknown. Nothing about the mechanism requires a coordinated ethnic objective.

Representation Is Not Control

The difference between representation and control is the central evidentiary question.

A population can be disproportionately represented without acting collectively. A university may have many Jewish professors who disagree about politics, religion, university policy, and Israel. A movie studio may employ Jewish executives who compete against one another. Jewish members of Congress may vote differently because of party, ideology, constituency, or personal judgment.

American Jews are themselves politically and religiously diverse. Pew found that the population overall leaned strongly Democratic, while Orthodox Jews were a notable exception and leaned heavily Republican. Reform, Conservative, Orthodox, and unaffiliated Jews also expressed sharply different attitudes on religious and political questions.

When we asked Leonard Saxe, a Brandeis social psychologist who studies American Jewish life, about this piece, he pointed to a dark irony in the idea of seamless Jewish coordination. Jewish culture, he noted, has an old joke: “2 Jews, 10 opinions.”

Anyone familiar with Jewish communal life will recognize the point. Coordinating millions of Jews around a single hidden agenda would require overcoming an impressive amount of disagreement first. Humor aside, the underlying point is measurable: American Jews differ substantially in religious practice, politics, ideology, and attitudes toward Israel. Shared identity is not evidence of shared intent.

To establish collective control, evidence would have to show more than identity or presence. It would need to identify who possesses actual decision-making authority, demonstrate communication or coordination, establish shared objectives, and connect that coordination to institutional decisions.

A roster of names does none of those things.

The same reasoning would apply to any group. A large number of Catholics on the Supreme Court would not, by itself, prove that Catholics secretly coordinate judicial decisions. A high percentage of Protestants in Congress would not establish unified Protestant control of the government.

Indeed, Jewish Americans are not the only religious group overrepresented in Congress. In the 119th Congress, Protestants make up about 55 percent of members, compared with 40 percent of American adults. Christians overall constitute 87 percent of Congress but approximately 62 percent of the adult population. Meanwhile, religiously unaffiliated Americans make up more than a quarter of the population but less than 1 percent of Congress.

Political institutions rarely mirror the population perfectly. Education, age, geography, wealth, social networks, party recruitment, and voters’ preferences all shape who reaches office.

Why the Leap Matters

Claims of collective Jewish control are not merely inaccurate descriptions of demographics. They belong to a longstanding form of conspiracy thinking: the idea that visible events are secretly directed by a powerful and concealed group.

Psychological research has found that conspiracy theories about social groups are associated with prejudice and discrimination. A major review of the field concluded that antisemitic conspiracy beliefs predict broader hostility toward Jews and discriminatory intentions. Experimental research has also found that exposure to intergroup conspiracy theories can increase prejudice.

It is also important not to mistake antisemitic conspiracy beliefs for the dominant American attitude toward Jews. Pew has repeatedly found Jews to be among the most positively viewed religious groups in the United States. In a 2023 survey, Jews were the only religious group to receive net-positive ratings from every other religious group with enough respondents to measure. Antisemitism can therefore coexist with broadly favorable attitudes toward Jews, which is another reason to distinguish specific conspiracy beliefs from general public sentiment.

The danger lies partly in the theory’s flexibility. Successful Jewish individuals are counted as evidence of control. Jews who disagree with one another are treated as camouflage. A lack of direct evidence becomes proof that the supposed conspiracy is secretive. The claim becomes impossible to disprove because every possible observation is absorbed into it.

This does not mean that institutions should be shielded from scrutiny. Concentrated ownership, nepotism, lobbying, campaign spending, conflicts of interest, and exclusionary hiring practices are legitimate subjects of investigation.

But those questions are best pursued through evidence about conduct and institutional structure.

Who owns the voting shares? Who made the hiring decision? Were similarly qualified applicants treated differently? Which lobby spent what amount on which issue? What authority did an executive actually exercise? Was there documented communication or coordination?

These are answerable questions. “Which ethnic group do these people belong to?” is usually not an adequate substitute.

A More Honest Way to Talk About Power

A productive discussion can acknowledge several things at once.

Jewish Americans are disproportionately represented in some prominent institutions. High educational attainment, urban migration, occupational history, entrepreneurship, adaptation to exclusion, and intergenerational networks all offer evidence-based parts of the explanation. Familiarity and in-group referrals may sometimes reinforce existing patterns, though their specific contribution has not been measured adequately.

None of this proves that representation is entirely meritocratic. No institution operates free from personal connections, inherited advantages, or bias. Nor does it prove that Jewish professionals never favor relatives, friends, or members of their communities. Members of every population are capable of favoritism.

But individual favoritism is not collective control. A recurring social tendency is not an ethnic plot. A demographic pattern does not reveal the intentions of every person counted inside it.

Research on correcting misinformation suggests that rebuttals are more effective when they do more than label a claim false. They should explain what is wrong and provide a better account of the evidence.

Here, the better account is not mysterious.

A small immigrant population became heavily urban, invested strongly in education, entered particular professions, built institutions when older ones excluded it, and transmitted professional knowledge and networks across generations. Those advantages sometimes compounded, as advantages often do. The result was disproportionate representation in some highly visible fields, alongside considerable diversity, disagreement, and ordinary life among millions of Jewish Americans who hold no unusual power at all.

That history is complicated. A conspiracy is simpler.

But the simpler story is not the truer one.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Jewish population size and representation in Congress

Claim or topic:

Jewish Americans make up about 2.4 percent of U.S. adults and are represented at a higher rate in Congress, including roughly 6 percent of the 119th Congress.

Source:

Pew Research Center, “The Size of the U.S. Jewish Population”

Brandeis University, American Jewish Population Project, “U.S. Jewish Population Estimates 2020”

Pew Research Center, “Faith on the Hill: The Religious Composition of the 119th Congress”

Source type:

Expert organizations; demographic survey research, statistical population estimation, and analysis of congressional membership.

What it supports:

Pew estimated that Jewish adults made up about 2.4 percent of the U.S. adult population in 2020. Pew identified 32 Jewish members of the 119th Congress, about 6 percent of Congress, including nine senators. Brandeis University’s American Jewish Population Project produced a broadly similar estimate of the total American Jewish population using a different methodology based on combining data from many surveys.

Important caveat:

Pew and the Brandeis project use different methodologies and do not define or estimate every component of the Jewish population in exactly the same way. Their similar overall estimates provide useful corroboration, but they should not be treated as identical measurements.

2. Jewish identity and the limits of federal demographic data

Claim or topic:

Jewish identity cannot be determined reliably from ancestry or surnames alone, and federal data do not provide a comprehensive count of Jewish Americans across occupations or leadership positions.

Source:

Pew Research Center, “Jewish Americans in 2020”

U.S. Equal Employment Opportunity Commission, EEO Data Collections

U.S. Census Bureau, FAQs for Faith-Based Communities

Source type:

Expert-organization survey research and government data.

What it supports:

Pew distinguishes between people who identify Judaism as their religion and people with no religion who identify as Jewish and meet additional background criteria. Jewish ancestry alone does not automatically place someone in Pew’s Jewish population. Federal employment reporting includes categories such as race, ethnicity, sex, and job category, but not religion, and the decennial census does not collect religious affiliation.

Important caveat:

These sources explain why a comprehensive national religion-based workforce count is unavailable. They do not establish that no private, academic, or industry-specific estimates exist for particular occupations or institutions.

3. Educational attainment

Claim or topic:

Jewish Americans have unusually high levels of educational attainment, which is relevant to their representation in professions requiring university or postgraduate credentials.

Source:

Pew Research Center, “Which U.S. Religious Groups Are Most Highly Educated?”

Source type:

Expert organization; national survey analysis.

What it supports:

Pew’s 2023 to 2024 Religious Landscape Study found that 65 percent of Jewish adults had at least a bachelor’s degree, compared with 35 percent of U.S. adults overall.

Important caveat:

The education difference does not by itself establish why Jewish educational attainment is high or how much education explains Jewish representation in any particular profession or institution.

4. Immigration, occupations, and economic mobility

Claim or topic:

Jewish immigrants during the era of mass migration were heavily urban and concentrated in skilled and semi-skilled occupations, factors relevant to subsequent economic mobility.

Source:

National Bureau of Economic Research, research on Jewish economic mobility in the United States

Source type:

Academic research; economic history using historical census data.

What it supports:

The research supports the article’s discussion of the occupational position, urban concentration, and economic advancement of Jewish immigrants and their descendants.

Important caveat:

The research does not by itself establish every historical claim in this section or provide a single causal explanation for later Jewish achievement. Source needed: The article’s specific statement that roughly two million Jews arrived in the United States from Europe during the late 19th and early 20th centuries should have a source that directly establishes that immigration estimate.

5. Anti-Jewish discrimination and exclusion from elite institutions

Claim or topic:

Anti-Jewish prejudice in America predates 20th-century university discrimination, and Jewish students were later sharply underrepresented in some elite social networks despite attending prestigious universities.

Source:

Pamela S. Nadell, Antisemitism, an American Tradition

The Quarterly Journal of Economics, study of Harvard social networks and later outcomes

Source type:

Historical scholarship and peer-reviewed academic research.

What it supports:

Nadell’s historical work traces anti-Jewish prejudice and discrimination in America back to the colonial period. The Harvard study found that students with distinctively Jewish surnames were substantially underrepresented in selective final clubs during the period examined and that club membership was associated with later economic and professional advantages.

Important caveat:

The Harvard evidence concerns one university and one historical period. It illustrates how exclusionary elite networks could operate, but it does not establish that such exclusion alone explains subsequent Jewish professional success.

6. Jewish involvement in the development of Hollywood

Claim or topic:

Jewish immigrants and their children played an unusually important role in building the early Hollywood studio system.

Source:

Academy Museum of Motion Pictures, “Hollywoodland”

Source type:

Expert organization; historical synthesis.

What it supports:

The Academy Museum describes the early Hollywood studio system as having been established largely by Jewish immigrant filmmakers and entrepreneurs.

Important caveat:

Historical prominence in the founding of the studio system does not establish the current proportion of Jewish executives or creators in entertainment, nor does it demonstrate coordinated decision-making among Jewish individuals.

7. Social networks, referrals, and homophily

Claim or topic:

Social networks and similarity-based referrals can reproduce demographic patterns without centralized planning or explicit coordination.

Source:

Labour Economics, research on gender homophily in job referrals

Source type:

Academic research; field study.

What it supports:

The study found that men and women were more likely to refer candidates of their own gender and linked much of that pattern to the composition of close friendship networks. It supports the article’s discussion of homophily as a general social mechanism.

Important caveat:

The study examined gender, not Jewish hiring or Jewish professional networks. It supports the plausibility of the general mechanism, not the magnitude of its contribution to Jewish representation in any particular field.

8. Conspiracy beliefs, prejudice, and broader attitudes toward Jews

Claim or topic:

Conspiracy beliefs about social groups are associated with prejudice and discriminatory attitudes, while Americans overall can simultaneously hold broadly favorable views of Jews.

Source:

Annual Review of Psychology, review of conspiracy theories and their consequences

Pew Research Center, “Americans Feel More Positive Than Negative About Jews, Mainline Protestants, Catholics”

Source type:

Peer-reviewed academic review and expert-organization survey research.

What it supports:

The psychology review supports the article’s discussion of relationships between conspiracy beliefs, prejudice, and discrimination. Pew’s 2023 survey found broadly positive attitudes toward Jews and found that Jews received net-positive ratings from every other religious group with enough respondents to analyze.

Important caveat:

The evidence does not establish conspiracy thinking as the sole cause of antisemitism. Likewise, favorable average attitudes toward Jews do not mean antisemitism is absent or insignificant. The findings measure different aspects of beliefs and public attitudes.

How to read this evidence

Production transparency

Corrections and updates

Subscribe now

Can Scientists Really Reverse Aging?

Evidence First — Thu, 24 Sep 2026 15:50:00 GMT

For decades, reversing human aging belonged mostly to science fiction. In June, researchers treated the first participant in a clinical trial of ER-100, an experimental gene therapy intended to rejuvenate damaged cells in the eye. The therapy activates three genes associated with cellular reprogramming, and its developers hope those genes can restore more youthful behavior in cells of the optic nerve without stripping the cells of their identities.

Some coverage presented the trial as the beginning of human age reversal. The study itself is far narrower. It is an early-stage trial involving adults with open-angle glaucoma or non-arteritic anterior ischemic optic neuropathy, two conditions that damage the optic nerve. Its main purpose is to evaluate the safety and tolerability of a single dose. It is not designed to make participants younger throughout their bodies, extend their lives or prove that human aging can be reversed.

Both descriptions contain part of the truth. The trial represents an important step for a field moving from experiments in cells and animals toward cautious testing in people. It also shows how easily a legitimate scientific milestone can be transformed into a much larger public claim. That gap, between altering some biology associated with aging and rejuvenating an entire person, is where much of the confusion begins.

What Does It Mean to Reverse Aging?

To most people, reversing aging suggests that an older person becomes physically younger. Muscles grow stronger, arteries become more flexible, memory sharpens, damaged organs repair themselves, and the risks of cancer, dementia, heart disease and frailty decline together.

In scientific reports, however, the phrase may mean something much more limited. A group of cells may begin producing proteins in a pattern more commonly seen in young cells. A mouse may recover more effectively from an injury. One human tissue may regain part of a function it had lost. A blood sample may receive a younger score from a computer model.

Each result could be scientifically important. None, on its own, shows that the aging of an entire person has been reversed.

Aging is not controlled by a single switch. It involves overlapping changes in DNA stability, gene regulation, energy production, immune function, inflammation, stem cells and communication among tissues. Scientists increasingly believe that some of these processes can be altered. Animal studies have shown that certain diets, drugs and genetic interventions can extend life or preserve function, helping establish geroscience, a field that asks whether targeting common mechanisms of aging could delay several diseases at once.

Human research is at a much earlier stage. Clinical trials have tested compounds and strategies aimed at particular pathways, diseases or biological measurements, but no intervention has been shown to reverse aging throughout the human body or extend the lifespan of healthy people.

Researchers have learned to manipulate parts of aging biology. They have not shown that they can rejuvenate an entire human being. Yet as findings move from laboratories into press releases, headlines and social media, that distinction often disappears.

How a Finding Becomes a Cure

Longevity hype does not require anyone to fabricate a result. Exaggeration can emerge gradually as information travels. A scientific paper may report that an intervention partially restored youthful gene activity in aged mouse tissue. A university news release may call the work a rejuvenation breakthrough. A company may say its technology could one day reverse diseases of aging. A headline may then declare that scientists are reversing aging.

By the time the finding reaches social media, the mouse, the tissue and the word “partially” may have disappeared.

The incentives are not difficult to understand. Researchers compete for funding, universities seek attention, biotechnology companies need investors, news organizations need readers, and clinics and supplement sellers need customers. Those pressures do not mean that the underlying science is wrong. They do mean that the boldest interpretation may travel farther than the most defensible one.

When a report says that scientists have “reversed aging,” several questions matter. Was the experiment conducted in isolated cells, animals or people? Did a laboratory measurement change, or did patients become healthier? Was there a control group? How many people participated? How long were they followed? Was the study testing safety or effectiveness? Has another research team reproduced the result?

The answers often transform an apparent medical revolution into something narrower, though still scientifically valuable. Few tools have contributed more to the confusion than biological-age tests, which can turn complex changes in the body into a deceptively simple number.

When a Biological Clock Runs Backward

Chronological age is simply the number of years since birth. Biological age is an estimate of how old the body appears according to selected measurements.

Some tests analyze blood chemistry, proteins, medical images or physical performance. Others examine DNA methylation, chemical tags that help regulate gene activity. Because methylation patterns change partly predictably over time, researchers can use algorithms known as epigenetic clocks to estimate age or the apparent pace of aging.

These clocks can be useful research tools, and some are associated with the later risk of disease or death across large populations. But they are not literal measurements of how many years a person has gained or lost. Different clocks examine different biological patterns and can give different answers for the same person. A treatment could change the variables used by one clock without improving strength, memory, organ function or survival.

For a biological-age measurement to replace a meaningful health outcome in a clinical trial, researchers would need to show that changing the measurement reliably predicts a change in outcomes patients care about. Current aging biomarkers have not yet met that standard.

The CALERIE study illustrates the problem. Researchers assigned 220 healthy adults without obesity either to reduce their calorie intake or to continue eating normally for two years. Calorie restriction improved several cardiovascular and metabolic risk factors.

When scientists later examined participants’ DNA methylation, one measure, called DunedinPACE, suggested a small slowing in the pace of aging. Other biological-age clocks did not change significantly. The researchers described the effect sizes as small.

It was reasonable to conclude that calorie restriction affected one measured feature of aging biology. It was not reasonable to conclude that the participants had become younger. The clocks disagreed, and the study was too short to determine whether anyone would live longer.

Yet a finding like that can readily become a headline declaring that a diet “reversed biological age.” The number sounds precise. What it proves is far less certain.

That measurement problem follows every experimental anti-aging treatment. Scientists must determine not only whether an intervention changes a molecular signal, but whether it leaves people healthier.

Three Ways Scientists Are Trying to Alter Aging

Several experimental approaches have attracted serious attention. Each is based on plausible biology. Each has produced encouraging findings in animals or early human studies. None has demonstrated whole-body human rejuvenation.

One of the most ambitious is partial epigenetic reprogramming, the approach behind ER-100. Nearly every cell in the body contains essentially the same DNA, but a liver cell and a nerve cell behave differently because they use different parts of that genetic instruction book. As cells age, some of the molecular controls governing gene activity change. Scientists discovered that a group of proteins known as Yamanaka factors can reset many of those patterns.

Applied fully, the process can turn a mature cell into something resembling an embryonic stem cell. That discovery transformed biology, but it also revealed the danger. A nerve cell that forgets it is a nerve cell has not been usefully rejuvenated. It has lost the identity it needs to function. Cells pushed too far may also grow abnormally or form tumors.

Partial reprogramming attempts to stop the process midway, far enough to restore more youthful activity but not far enough to erase the cell’s specialized role. Experiments in laboratory-grown cells and animals have produced younger molecular patterns and improvements in selected tissues. Researchers still face major questions about dosage, delivery, durability, loss of cell identity and safety. Reviews of the field describe partial reprogramming as promising while emphasizing that a younger reading on an epigenetic clock does not by itself establish organism-wide rejuvenation.

The eye is a logical place to begin because treatment can be delivered locally and the organ can be examined directly. If researchers eventually restore vision in people with optic-nerve damage, that would be a major medical advance. The immediate question, however, is whether targeted reprogramming can be delivered safely to one human tissue.

Other approaches are less radical. Rapamycin, for example, suppresses a cellular system called mTOR, which helps regulate growth, nutrient use and metabolism. The drug has repeatedly extended lifespan in mice, including in studies in which treatment began relatively late in life. That unusually strong animal evidence has led some people to take rapamycin off-label in hopes of slowing their own aging.

Human evidence remains much thinner. The PEARL trial followed healthy adults ages 50 to 85 who received intermittent low-dose rapamycin or a placebo for 48 weeks. Researchers reported that the regimens were relatively safe during the study period and found improvements in lean tissue mass and pain among some women.

The trial did not show that rapamycin slowed aging generally, prevented major age-related diseases or extended life.

A review of the clinical evidence similarly concluded that human studies have not established rapamycin or related drugs as proven treatments for delaying aging in healthy older adults. Existing studies tend to be small, use different doses and schedules, and focus on biomarkers or short-term physiological outcomes.

The evidence supplies a strong reason for larger trials. It does not give patients a reason to assume that lifespan gains in mice will transfer to people.

Senolytics take a different approach, targeting cells that may already be contributing to tissue damage. Some damaged cells enter a state called senescence. They stop dividing but remain alive and may release inflammatory substances that damage nearby tissue. Because they linger without functioning normally, they are sometimes described as “zombie cells.”

Removing certain senescent cells has improved several age-related conditions in mice. Small human studies have tested drugs intended to kill them selectively. An early pilot study tested the combination of dasatinib and quercetin in people with idiopathic pulmonary fibrosis, a serious lung disease. Researchers reported changes in some measures of physical function, but the trial was small, open-label and lacked a placebo group, making it impossible to determine whether the treatment caused the apparent improvements.

Another small study found evidence that the same drug combination reduced senescent-cell markers in people with diabetic kidney disease. That suggested biological activity, not general rejuvenation or longer life.

The zombie-cell metaphor also oversimplifies the biology. Senescent cells are not uniformly harmful. In some circumstances they can contribute to wound healing or restrain damaged cells that might otherwise divide uncontrollably. A successful treatment may need to remove harmful senescent cells while preserving useful ones.

Each of these approaches offers a plausible way to influence part of the aging process. The harder question is how far such advances could realistically extend, from repairing one tissue to rejuvenating an entire body.

How Far Could the Science Go?

Organ-specific repair is the most realistic near-term possibility. A treatment aimed at the eye, skin, muscle or immune system can be delivered to a relatively defined target. Researchers can measure whether that tissue improves and monitor it for harm.

Over the next several years, early clinical trials may show whether reprogramming, senolytics and other experimental treatments can safely restore some function in particular organs or diseases. Many trials will fail. Others may produce only modest improvements. But success in one tissue would still matter, even if the rest of the body continued to age normally.

The first useful rejuvenation therapies may look less like a universal fountain of youth and more like a new branch of regenerative medicine.

Expanding a local treatment into a whole-body therapy would be a very different problem. The boldest version of rejuvenation is easy to imagine: a treatment makes a 70-year-old function, across much of the body, like a healthy 50-year-old. No human intervention has shown that.

There is no single biological-age gauge inside the body. The heart, brain, muscles, kidneys and immune system can age at different rates. A person may have unusually healthy arteries but advanced arthritis, or strong muscles but early cognitive decline.

To demonstrate 10 or 20 years of whole-body rejuvenation, researchers would need more than a younger score on one test. They would have to show lasting improvements across several organ systems, such as stronger muscles, healthier blood vessels, more resilient immunity, better tissue repair and, eventually, lower rates of multiple major diseases.

Those effects would need to be reproduced by independent investigators and achieved without unacceptable increases in cancer, immune problems or other serious harms. Treating the entire body would also create an enormous delivery challenge. The appropriate dose for a liver cell might be dangerous for a neuron. A treatment that stimulates repair in one organ might promote abnormal growth in another.

A cautious forecast is possible, but it must be recognized as a forecast. During the next several years, the most informative results are likely to come from small studies testing safety, delivery and effects in individual tissues. Over the following 10 to 20 years, it is conceivable that medicine could develop treatments that restore lost function in selected organs or modestly slow deterioration across more than one body system, provided the early approaches prove safe and effective.

There is no comparable evidence-based timeline for making the entire body 10 or 20 years younger. That achievement would require breakthroughs in delivery, measurement, coordinated organ repair and long-term safety that have not yet been demonstrated.

Predictions that whole-body rejuvenation will arrive by a particular year are therefore expressions of judgment, optimism or commercial ambition. They are not conclusions that can be calculated from existing clinical evidence.

Even if researchers eventually learned to make several organ systems younger, that would not mean aging had been stopped. A one-time treatment might wear off, leaving the underlying processes to resume.

Stopping aging indefinitely is a much larger claim. It would require medicine to detect, prevent or repeatedly repair many forms of damage throughout the body for as long as treatment continued. It would also require balancing processes that can be both helpful and harmful. Cellular growth supports tissue repair but can promote cancer. Inflammation helps fight infection but can damage organs when it becomes chronic. Senescence may restrain dangerous cells but also contribute to age-related disease.

The brain presents an additional difficulty. Any repeated repair process would need to preserve memories, learned skills and personal identity.

No human experiment has shown that this can be done. No trial has established even a temporary, safe halt to overall biological aging. Stopping aging would also not mean immortality. A person who no longer experienced age-related deterioration would remain vulnerable to infection, injury and other causes of death.

Demographic evidence suggests that radical life extension is not already emerging from ordinary improvements in medicine. A 2024 analysis of the longest-lived populations found that increases in life expectancy had slowed and concluded that radical extension was unlikely this century unless the biological processes of aging could be markedly slowed.

Such population data cannot rule out a future breakthrough. They describe what has happened under existing conditions, not what an undiscovered technology might accomplish. But they illustrate the scale of the challenge. Preventing one age-related disease often allows another condition to become the limiting factor. Radical life extension would require reducing vulnerability across many diseases at once.

There is no credible scientific timeline for stopping aging indefinitely. That does not prove that it is impossible. It means the necessary steps have not been demonstrated, the technical path remains unclear and the available evidence does not support assigning a date.

The farther the forecast extends, from tissue repair to whole-body rejuvenation to indefinite control of aging, the thinner the evidence becomes.

Meanwhile, the interventions most clearly shown to preserve health in later life are far less futuristic.

What Already Extends Healthy Life

There is an irony at the center of the anti-aging debate. The approaches supported by the strongest human evidence are rarely marketed as age reversal.

Regular physical activity can help older adults maintain physical function, mobility and independence. Avoiding smoking, treating high blood pressure and cholesterol, receiving recommended vaccinations and managing metabolic disease can prevent or delay conditions that shorten healthy life.

These measures do not make anyone chronologically younger. They do something more concrete: reduce the likelihood of becoming sick, disabled or dependent earlier than necessary. That is the current foundation of extending healthspan, the portion of life spent in reasonably good health.

Experimental longevity medicine will eventually have to improve on that foundation, or add meaningfully to it.

The likely future may be less cinematic than the headlines suggest. Instead of one cure, medicine may develop a collection of treatments: one that repairs an injured optic nerve, another that preserves immune function, another that removes a harmful class of senescent cells, and perhaps combinations that delay several diseases at once.

Such advances could add healthy years to millions of lives without making anyone wholly young again. That distinction can sound disappointing only because the promise has become so enormous.

By ordinary medical standards, preserving sight, strength, memory and independence would be a revolution of its own.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. ER-100 and the first human trial

Claim or topic:

ER-100 has entered an early-stage human trial involving people with open-angle glaucoma or non-arteritic anterior ischemic optic neuropathy. The study is primarily designed to evaluate the safety and tolerability of a single dose, not to demonstrate whole-body age reversal or lifespan extension.

Source:

ClinicalTrials.gov: ER-100 clinical trial

Source type:

Primary document.

What it supports:

The trial registry supports the article’s description of the study population, intervention and early-stage safety focus. It also shows the much narrower scope of the trial compared with claims about reversing aging throughout the human body.

Important caveat:

An early-stage clinical trial establishes that a treatment is being tested in humans. It does not establish that the treatment is safe, effective, rejuvenating or capable of extending life.

2. Aging biology and the state of human anti-aging research

Claim or topic:

Scientists can alter biological processes associated with aging, particularly in laboratory animals, but human research is much less developed and has not established whole-body rejuvenation or lifespan extension in healthy people.

Source:

Academic review on geroscience

Academic review of human anti-aging interventions

Source type:

Academic research and analysis.

What it supports:

These sources provide background for the article’s description of geroscience and the effort to target biological processes associated with multiple age-related diseases. They also support the distinction between extensive experimental work on aging biology and the much more limited state of clinical evidence in humans.

Important caveat:

These are reviews of a developing research field. They provide context and synthesis, but they do not constitute evidence that any specific intervention has reversed whole-body human aging.

3. Biological-age tests and their limitations

Claim or topic:

Biological-age and epigenetic-clock measurements can be useful research tools, but a change in such a measurement does not by itself prove that a person has become biologically younger in a clinically meaningful sense.

Source:

Academic research on biomarkers of aging

Source type:

Academic research and analysis.

What it supports:

The source supports the article’s discussion of the challenges involved in validating biomarkers of aging and the distinction between a measurable biological signal and an outcome that reliably predicts improved health, function or survival.

Important caveat:

The article uses this evidence to explain a measurement problem. It does not imply that epigenetic clocks have no scientific value. Their usefulness depends on the particular clock, population and purpose for which they are being used.

4. Calorie restriction and the CALERIE biological-aging analysis

Claim or topic:

In the CALERIE trial, calorie restriction produced a small change in one measure of the pace of aging, DunedinPACE, while other biological-age measures did not show significant changes. The study did not establish that participants had become generally younger or would live longer.

Source:

CALERIE DNA-methylation analysis

Source type:

Academic research.

What it supports:

The study directly supports the article’s description of differing results across biological-aging measures and the characterization of the observed effect on DunedinPACE as small.

Important caveat:

The study examined biological-aging measurements over two years. It was not designed to determine whether calorie restriction extends human lifespan, and a change in one aging measure should not be interpreted as proof of whole-body rejuvenation.

Claim or topic:

Recent demographic evidence suggests that gains in life expectancy have slowed in the world’s longest-lived populations and that radical life extension is unlikely under existing patterns of medical progress unless biological aging itself can be substantially altered.

Source:

Nature Aging analysis of life-expectancy trends

Source type:

Academic research and analysis.

What it supports:

The study supports the article’s discussion of slowing gains in life expectancy among long-lived populations and the scale of the change that would be required for radical life extension.

Important caveat:

Demographic trends cannot determine what an undiscovered future technology might accomplish. The source does not prove that whole-body rejuvenation or radical life extension is impossible, nor does it provide a timeline for such a breakthrough.

How to read this evidence

Production transparency

Corrections and updates

Subscribe now

The Dangerous AI Shift Most People Still Don’t Understand

Evidence First — Sun, 20 Sep 2026 16:44:34 GMT

Imagine you wake up to your phone buzzing on the nightstand. Not once. Again and again.

By the time you pick it up, there are twenty missed calls, a string of texts, and an inbox full of messages marked urgent. For a few seconds, you think someone must be hurt. Then you open the first email and realize the emergency is coming from your own office.

A longtime customer is furious. She wants to know why she was sent a file with someone else’s private information in it. A staff member is panicking because confidential notes are sitting in a shared folder. Someone outside the company has replied to an email thread they never should have received.

You run a small business, maybe a dental office, a contractor, a nonprofit, or a local accounting firm. You know most of your customers by name. You know which ones need extra patience, which ones always call instead of email, which ones trusted you with things they would not want passed around. Your inbox is always full, your records are messy, and your staff is stretched thin. So a few weeks ago, when a new AI assistant promised to save you hours, it seemed like exactly the kind of help you needed.

The demo looked harmless enough. Useful, even. It could organize files, clean up client records, draft emails, and prepare reports. You gave it access to your email, calendar, shared drive, billing system, and customer list because that was the whole point. It could not help unless it could see things and do things.

The night before, you typed one simple instruction: “Clean up these records and send the updated file to the team.”

While you slept, the AI went to work. It found duplicate names, merged accounts, moved folders, prepared a spreadsheet, and sent the email. It moved quickly. It did not get tired. It did not pause because a file name looked odd, or because a folder contained something sensitive, or because a recipient list included someone who should not have been there.

In the narrowest sense, it followed the instruction. What it did not understand was the judgment behind the instruction. It knew how to complete the task, but not what should never be exposed.

Now you are staring at a spreadsheet full of names, addresses, payment details, private notes, maybe even medical or legal information, depending on the business. Some of it went to the wrong people. Some of it is now sitting in places it should never have been.

There was no villain here. No hacker in a dark room. No dramatic breach. You invited the system in because it promised to help. Now you have to call customers one by one and explain that their information went somewhere it should not have gone. You have to notify people who trusted you, perhaps call a lawyer, perhaps report the breach, and then keep answering the same question in different forms: How did this happen?

Nobody meant to cause harm. That is what makes it so unsettling. The system was simply powerful enough to act and not reliable enough to trust.

That is the AI shift many people still do not understand.

For years, most people have thought of artificial intelligence as something that answers questions. You type something into a box, and it gives you words back. Sometimes those words are useful. Sometimes they are wrong. Either way, the system mostly waits for you.

But AI is moving from answering to acting. We are starting to give systems goals, tools, permissions, and access. Instead of asking, “What should I write?” we will increasingly ask, “Can you handle this?” And once we do, the system may not just suggest the next step. It may take it.

Early versions of this already exist. Today’s AI systems can browse websites, write and run code, search documents, summarize records, draft messages, call software tools, and work through multi-step tasks when connected to the right apps and permissions. They still need setup. They still need oversight. They still make mistakes. But the direction is clear: AI is being connected to tools that let it do things, not just say things.

That may sound like a technical difference, but it is the difference between a map suggesting a route and a car turning the wheel. It is the difference between a calculator showing you a number and a banking app moving your money. It is the difference between advice and action. A bad answer can mislead you. A bad action can change something before you even know it happened.

This is what people mean by AI agents. An agent is not just a chatbot with better manners. It is a system that can be given an objective, break that objective into steps, use tools, check results, adjust course, and keep going with less direct human supervision.

Ask a chatbot to write an email to a customer, and it may give you a draft. Ask an agent to handle customer complaints while you are away, and the job changes. The system may read messages, decide which ones matter, draft responses, issue refunds, update account notes, escalate some cases, ignore others, and move on before a manager has reviewed the first decision.

The same shift matters in more technical settings. A company might ask an agent to find and fix security weaknesses, not just explain a bug. A lab might ask one to run an experiment from beginning to end, not just summarize papers. In those cases, the system is no longer merely describing work. It is scanning code, changing permissions, ordering materials, operating instruments through software, analyzing results, and adjusting the next step.

The promise is real. These systems could help small businesses survive paperwork, help doctors find research, help teachers prepare lessons, help scientists move faster, and help people with disabilities navigate systems that are otherwise exhausting. But usefulness is not the same as safety. The very thing that makes an AI agent valuable, its ability to do things, is what makes it risky.

Speed makes the risk harder to manage. A human employee makes one decision, then another, and usually someone has a chance to notice when something looks wrong. An AI agent can move at machine speed. It can read thousands of files, send hundreds of messages, test code, change settings, create accounts, or move information before anyone has had time to understand what it is doing. By the time the warning light appears, the action may already be over.

The issue is not that every AI agent will fail. The issue is that we are beginning to connect unreliable systems to places where failure matters: email accounts, databases, company software, cloud systems, public platforms, private records, financial tools, and eventually far more sensitive systems. If this is dangerous in an inbox or a company database, it becomes far more dangerous where mistakes do not just embarrass people or expose data, but alter the physical world.

Imagine an AI system used to detect threats, respond to cyberattacks, monitor power grids, operate laboratory tools, move money through financial networks, or recommend military action. It does not need to hate anyone to cause a disaster. It only needs to misread a signal, treat uncertainty as confidence, act faster than humans can review, or find a shortcut no one expected. A human mistake may unfold in minutes or hours. A machine-speed mistake could unfold in seconds.

No one can say exactly what the first truly large-scale AI failure would look like. It might be a cyberattack, a financial shock, a weapons incident, a breakdown in critical infrastructure, a flood of synthetic evidence during a crisis, or something no one has imagined yet. But if increasingly capable systems are given more access and autonomy without strong safeguards, it is reasonable to believe something very serious will eventually go wrong.

That uncertainty is not comforting. It is the point. We do not know which door will fail first, so we should stop handing out keys before we have locks.

Recent reporting suggests that some major AI leaders share this concern: frontier AI development may be moving faster than safety testing and governance. Anthropic CEO Dario Amodei has called for “pacing” the most advanced systems; OpenAI CEO Sam Altman has said he agrees; Elon Musk has said Amodei is right. Those warnings are not proof of catastrophe. But they are a sign that even people close to the technology see the gap: AI systems are gaining power faster than public rules are catching up.

The public does not need to understand every technical detail to demand limits on what these systems are allowed to do. The principle should be simple: the more an AI system can do, the more it should have to prove before we let it act.

That does not mean regulating every chatbot like a nuclear reactor. A tool that helps someone plan dinner does not need the same oversight as a system that can write code, access private records, move money, use lab equipment, operate inside a company network, or influence high-stakes decisions. But once an AI system can act in the real world, the rules should get stronger.

Powerful AI agents should be tested by outsiders before companies put them into the world, not just by the companies trying to sell them. Systems that can browse the web, write code, send messages, use software tools, handle private data, or operate with limited supervision should be tested for what they can do, how they fail, whether they can be manipulated, and what happens when they are given vague or risky goals.

They should also have real limits on what they can do on their own. Drafting a message is one thing; sending it is another. Suggesting a payment is one thing; moving the money is another. A system should not be able to delete files, change databases, place orders, access sensitive records, or trigger real-world actions unless a human has clearly approved that power. The default should not be “do everything.” The default should be “ask before doing anything that could cause harm.”

And when harm happens, responsibility should not disappear into the machine. If a company gives AI real-world power, it should carry real-world responsibility. When an AI system leaks private data, causes financial harm, acts outside its limits, or creates a serious safety risk, the answer cannot be: the algorithm did it.

Production transparency

Corrections and updates

Subscribe now

Who Gets the Stage?

Evidence First — Thu, 17 Sep 2026 15:50:02 GMT

It would be easy to reduce the controversy surrounding Ed Sheeran and Macklemore to a simple story. One version says Macklemore was punished for speaking out for Palestinians, making his removal an example of wealthy venue owners and powerful entertainment interests using their leverage to silence political speech. Another says Macklemore used someone else’s stadium tour to deliver a highly charged and one-sided political message, against the backdrop of an earlier controversy involving imagery widely criticized as antisemitic, leaving venues and promoters to decide what they were willing to host. Both versions contain elements of truth. Both also leave important things out.

The real story is more complicated, and more revealing. The controversy exposes a distinction that arguments about free speech often miss: being free to say something is not the same as controlling access to the places where it can be heard. Macklemore controlled his words, but the stages on which he could deliver them depended on a network of artists, promoters, venue owners and operators, each with different forms of leverage. Understanding what happened therefore requires following that power rather than simply deciding who was right about Israel and Palestine.

Macklemore, the American rapper best known for songs like “Thrift Shop” and “Same Love,” was scheduled to open eight of the remaining 10 U.S. dates on Ed Sheeran’s stadium tour. Sheeran is one of the biggest pop stars in the world and regularly performs in front of tens of thousands of people. At MetLife Stadium in New Jersey, Macklemore used that audience to make an explicitly political statement.

He told the crowd that one reason he wanted to join the tour was to stand in large American stadiums and say “Free Palestine.” He spoke about Gaza and the occupied West Bank, praised pro-Palestinian student activists, accused Israel of apartheid and genocide, and performed “Hind’s Hall,” a song inspired by the campus protest movement that spread across American universities in 2024. This was not an offhand remark between songs. Macklemore was deliberately using access to a massive commercial audience to make a political argument.

What he actually said is important because some of the controversy has turned on whether advocacy for Palestinians crossed into hostility toward Jewish people. The published remarks from MetLife do not show Macklemore attacking Jewish people as a group. He directly told Jewish listeners that criticism of Israel was not criticism of them and described his message in terms of peace, dignity, respect, and equality. Nor is it accurate to say that Macklemore has never condemned Hamas’s October 7 attacks. In October 2023, he described the bombings, kidnappings, and killings of Israelis by Hamas as horrific and called the violence an “abomination,” while arguing that those attacks did not justify collective punishment of Palestinians. His most prominent advocacy since then has focused heavily on Palestinian suffering and Israeli conduct, but that is different from having excused or denied the violence against Israelis.

Macklemore’s critics, however, are not judging the MetLife performance in isolation. In 2014, he appeared onstage in a disguise that included a dark wig, beard, and exaggerated prosthetic nose. The costume was widely criticized for resembling an antisemitic caricature. More than a decade later, that incident has become part of the argument over how his current political activism should be understood.

Robert Kraft, the owner of the New England Patriots, whose Kraft Group owns Gillette Stadium in Massachusetts, made that broader record central to his objection. Kraft said Macklemore would not be permitted to perform at Gillette, citing his recent conduct, material displayed during the MetLife performances, and what Kraft described as a history of antisemitic rhetoric and imagery. He also accused Macklemore of presenting the conflict selectively and giving insufficient attention to Hamas’s responsibility for suffering among Israelis and Palestinians. Kraft stressed that his objection was not to advocacy for Palestinians itself. He acknowledged Palestinian suffering and argued that supporting Palestinians, opposing Hamas, and opposing antisemitism should not be treated as incompatible positions.

That is a more substantial argument than saying Macklemore was barred simply because he uttered the words “Free Palestine.” But it also does not settle the dispute. The MetLife remarks themselves contain an explicit attempt to distinguish criticism of Israel from hostility toward Jews, and Macklemore’s earlier condemnation of Hamas complicates the suggestion that he has simply ignored its violence. Kraft’s objection is therefore partly about the broader context he believes should be attached to Macklemore’s speech: the 2014 controversy, the imagery used during the performance, the accusations directed at Israel, and the relative emphasis Macklemore places on different parts of the conflict. The disagreement is not only over what Macklemore said. It is over what his words and record should mean when a venue decides whether to give him a stage.

But the objection to Macklemore was not confined to Gillette Stadium. Messina Touring Group, the promoter responsible for the U.S. leg of Sheeran’s tour, said multiple upcoming venues would not allow concerts to proceed with Macklemore on the lineup. Messina then removed him from the remaining dates. The company is not a small independent promoter. Messina Touring Group operates as part of the broader AEG Presents network, one of the major businesses in global live entertainment, illustrating how stadium tours depend on overlapping relationships among artists, promoters, venue operators, and owners.

This is where Sheeran’s role becomes more difficult to describe. He says Macklemore’s removal was the promoter’s decision, not his. He also says he spent days speaking with venues and others in an effort to find a solution, but that the venues and promoter ultimately would not change their position. Macklemore has likewise described difficult conversations with Sheeran rather than claiming that Sheeran personally initiated the effort to remove him.

It would nevertheless be misleading to portray the headliner of a stadium tour as powerless. Music-industry professionals interviewed by the Associated Press said major artists of Sheeran’s stature typically exercise substantial control over their tours, including creative decisions and support acts, while promoters generally handle much of the financing and logistics. Venues hold a different form of leverage because they can refuse to host a show. The result is a system in which influence and formal authority do not necessarily sit with the same actor.

We do not have the contracts or private communications necessary to know whether Sheeran could have forced unwilling venues to accept Macklemore, relocated shows, threatened cancellation, or pursued some other workable alternative. What the public record does establish is that venues threatened not to host shows with Macklemore on the bill, Messina removed him, and Sheeran participated in efforts to resolve the dispute before continuing the tour without him. It is therefore too simple to describe Macklemore’s removal as a decision made by Sheeran alone. It is equally difficult to characterize Sheeran as having no meaningful role. The evidence shows influence; it does not establish ultimate control.

Sheeran has also resisted being placed neatly on either side of the underlying political conflict. He has said he is appalled by the suffering caused by the Israel-Palestine conflict and has personal views that he chooses not to make part of his public platform. He says his concerts are intended to offer unity, joy, and escapism rather than function as political forums, while also saying he respects Macklemore’s determination to speak publicly about what he believes. Critics see that posture as an inadequate form of neutrality. But criticism of Sheeran’s approach is not evidence that he supports the Israeli government or opposes Palestinian rights.

There is another fact that complicates simple assumptions about both Sheeran and Kraft. Kraft says Sheeran committed $2 million to Palestinian humanitarian aid and personally asked Kraft to match that amount. Kraft agreed, according to his account. The conversation occurred before Macklemore publicly announced that he would donate roughly $1 million in net tour earnings to six organizations assisting Palestinians and challenged Kraft to match his contribution. The humanitarian commitments do not resolve the dispute over Macklemore, and they do not tell us what either Sheeran or Kraft believes about every aspect of the conflict. But they are relevant when evaluating claims that their positions can be explained simply by indifference toward Palestinian suffering.

Other musicians made a different calculation. Finneas, Lukas Graham, Aaron Rowe, and Beoga withdrew from upcoming tour appearances or their involvement with the tour after Macklemore’s removal. In explaining their decisions, they cited solidarity with Macklemore, opposition to silencing political expression, and concerns about the influence of money and institutional power. Their departures turned what had begun as a conflict between one performer, a promoter, and several venues into a broader argument within the music industry over who gets to set the boundaries of political expression.

The phrase “free speech” has consequently appeared throughout the controversy, but it can obscure as much as it clarifies. The First Amendment generally restricts government action, not the choices of private businesses. A privately controlled stadium ordinarily does not have a constitutional obligation to provide an artist with a stage. A promoter can decline to book a performer, just as a newspaper can decline an op-ed or a bookstore can choose what it sells. Such decisions may be described as censorship in the broader cultural sense without necessarily amounting to unconstitutional censorship. The analysis could be different if a government entity itself participated in excluding a performer because of his political viewpoint, but the public record has not established that here.

The legal question is also different from the question of responsibility. Macklemore chose to make political advocacy part of his performance. Kraft chose to bar him from Gillette. Other venues or their operators objected to his continued participation. Messina responded by removing him. Sheeran participated in efforts to resolve the dispute and then continued the tour without him. Other musicians responded by leaving. The controversy is better understood as a chain of decisions than as a single act by a single person.

Important parts of that chain remain hidden. Messina has not publicly identified every venue that objected or provided a venue-by-venue account of what each demanded. Macklemore has alleged that Kraft helped rally other stadium owners against him, but Kraft’s role in coordinating other venues has not been independently established in the public record. We also do not know what contractual alternatives Sheeran possessed, what those alternatives would have cost, or whether any could realistically have preserved both Macklemore’s place on the tour and the scheduled stadium dates. Those unknowns limit how confidently responsibility can be assigned.

They do not, however, prevent the controversy from revealing something larger. Speech in America is shaped not only by government rules but by access to institutions. Employers, universities, publishers, technology companies, sponsors, promoters, donors, and venue operators can determine who receives an audience, who receives amplification, and under what conditions. In the stadium concert business, those decisions are distributed across artists with enormous commercial value, global promotion companies, private venue operators, wealthy owners, and sometimes public authorities. No single participant necessarily controls the entire system, but each may possess enough leverage to change what ultimately reaches the stage.

None of that means an artist has an automatic right to someone else’s platform. Kraft and other private venue operators have their own rights of association and substantial authority over what they are willing to host. Macklemore also knew that turning a stadium performance into political advocacy could carry professional consequences. Sheeran was entitled to decide that cancelling an enormous tour would impose costs on fans, workers, and other musicians that he was unwilling to accept. The departing artists were equally entitled to conclude that remaining on the tour conflicted with their own principles. The difficult question is not whether these people were free to make choices. They were. It is how much power each choice carried.

The more revealing story, then, is about what happens when the freedom to speak meets the power to provide access. Someone owns or operates the building. Someone promotes the show. Someone signs the contracts. Someone can refuse to host it. Someone can decide that the financial or reputational cost has become too high. The person holding the microphone may control what he says into it, but not whether another microphone will be waiting at the next stadium.

Macklemore controlled what he said from the stage. He did not control whether there would be another one.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. What Macklemore said at MetLife Stadium

Claim or topic:

Macklemore used his MetLife Stadium appearance to call for a “Free Palestine,” praise pro-Palestinian student activists, criticize Israel’s conduct, and explicitly tell Jewish listeners that his criticism of Israel was not criticism of them.

Source:

Boston Globe: transcript of Macklemore’s MetLife remarks

Source type:

Reputable journalism based on a recorded public performance.

What it supports:

The Globe published a transcript of the relevant remarks, allowing readers to evaluate Macklemore’s actual words rather than relying on descriptions of them.

Important caveat:

The transcript establishes what Macklemore said during this portion of the performance. It does not by itself resolve broader arguments about the imagery used during the show or Macklemore’s record outside this performance.

2. Macklemore’s earlier condemnation of Hamas

Claim or topic:

Macklemore had publicly condemned Hamas’s October 7 attacks before the 2026 controversy, so claims that he never condemned the attacks omit relevant evidence.

Source:

Pitchfork: Macklemore’s 2023 statements on Hamas, Israel and Gaza

Source type:

Reputable journalism reporting Macklemore’s public statements.

What it supports:

Pitchfork reported that Macklemore’s October 2023 statement condemned the Hamas attacks while also sharply criticizing Israel’s subsequent actions in Gaza.

Important caveat:

Condemning Hamas in 2023 does not determine whether later speeches were balanced, persuasive or appropriate. It establishes the narrower factual point that he had publicly condemned the attacks.

3. The 2014 costume controversy

Claim or topic:

In 2014, Macklemore performed wearing a dark wig, fake beard and large prosthetic nose, prompting widespread criticism that the costume resembled an antisemitic Jewish caricature.

Source:

The Guardian: Macklemore costume controversy

Source type:

Reputable journalism.

What it supports:

Contemporary reporting documents the costume, the criticism it generated and the dispute over how it should be interpreted.

Important caveat:

The appearance of the costume and the criticism are documented. The incident itself does not establish Macklemore’s internal intent, and the article does not claim that it does.

Claim or topic:

Private decisions about who may perform at a venue are not automatically First Amendment violations. Government involvement in viewpoint-based exclusion could present a different constitutional question.

Source:

GBH: First Amendment questions surrounding Macklemore’s removal

Source type:

Reputable journalism citing a free-speech legal expert.

What it supports:

The source explains the distinction between restrictions imposed by private actors and restrictions attributable to government entities, which is the limited constitutional distinction made in the article.

Important caveat:

The public record has not established that a government entity participated in excluding Macklemore because of his political viewpoint. The article therefore does not claim that his removal violated the First Amendment.

How to read this evidence

Corrections and updates

Subscribe now

Is AGI Already Here?

Evidence First — Wed, 16 Sep 2026 15:56:23 GMT

For decades, artificial intelligence was impressive in ways that were easy to contain. A chess program could beat a grandmaster, but it could not write a legal memo. A spam filter could clean your inbox, but it could not tutor your child. A translation engine could move a sentence from French to English, but it could not help design a business strategy.

That world is gone.

The most advanced AI systems today can write, code, summarize research, analyze images, generate plans, tutor students, draft contracts, debug software and use digital tools. They are not reliable enough to trust blindly. They still hallucinate, misunderstand instructions and sometimes fail at tasks a person would find obvious. But they have crossed an important threshold. They no longer feel like single-purpose tools. They feel, at least some of the time, like general-purpose assistants.

That is why the debate over artificial general intelligence, or AGI, has become so slippery. We keep asking when AGI will arrive, as if there will be a press conference, a benchmark score or a cinematic moment when the machine wakes up. But the more likely possibility is stranger and harder to govern. AGI may arrive gradually, unevenly and ambiguously. We may spend years arguing over whether it is already here.

The question, then, is not simply whether we have AGI. It is what an AI system would have to do before we were willing to treat it as generally intelligent.

In plain English, AGI means AI that can handle many kinds of intellectual tasks rather than one specialized job. But that simple definition hides a major disagreement. Some people use AGI to mean a system that can perform a wide range of cognitive tasks as well as a typical human. Others mean a system that can learn new tasks without being rebuilt. Others mean something more demanding: a highly autonomous system that can plan, act, verify its work and adapt across the messy conditions of the real world. Those definitions lead to very different conclusions. They also explain why two reasonable people can look at the same AI system and disagree about whether AGI has already arrived.

Under a loose definition, there is a serious argument that AGI, or at least an early version of it, is already here. Today’s frontier AI systems are not merely better autocomplete. They can shift from poetry to programming, from biology to business strategy, from interpreting a chart to drafting a contract. They can answer questions, use tools, generate code, explain concepts, analyze images and help solve problems in domains they were not individually designed for.

Older AI systems were like specialized machines in a factory, each built for one job. Today’s AI looks more like a general-purpose cognitive engine. It may be inconsistent, but it is broadly useful. If “general intelligence” means the ability to operate across many intellectual domains, then the claim that we have no AGI begins to sound too neat.

That is the strongest case for saying AGI is already here: perhaps the thing people were waiting for did not arrive as a conscious machine, a robot coworker or a perfectly rational mind. Perhaps it arrived first as an unreliable but astonishingly capable assistant embedded in ordinary software. Perhaps it looks less like science fiction and more like a text box.

But the skeptical case is just as important. Breadth is not dependability. Fluency is not judgment. A system that can answer questions across many subjects is not the same as a system that can safely manage an open-ended project with real consequences.

Current AI can still be dazzling in one moment and wrong in the next. It can invent sources, miss context, fail to notice contradictions and produce confident answers that require careful checking. It often performs best when a human has framed the problem, supplied the context, broken the task into steps and reviewed the result. That is not nothing. But it is also not the same as independent general intelligence.

A useful analogy is the talented intern. Modern AI can draft the memo, prepare the spreadsheet, explain the concept and sketch the plan. It can be fast, knowledgeable and surprisingly creative. But it still needs supervision. It may misunderstand the assignment. It may overlook a critical fact. It may not know when to stop and ask for help.

AGI, in the stronger sense, would be more like a dependable senior operator. You would not merely ask it to write an email. You might ask it to launch a product, manage a legal discovery process, run a research program, teach a student algebra over six months or redesign a company’s supply chain. It would understand the goal, gather information, use tools, coordinate with people, notice when something was going wrong and change course.

That difference, from completing prompts to pursuing goals, is the real line. And it explains why the AGI debate is really a debate about trust.

This is why a single benchmark, demo or announcement will not settle the debate. A model might ace an exam, win a programming contest or solve a hard math problem and still fail at ordinary real-world work. Real life is not a test sheet. Goals are vague. Information is incomplete. Deadlines shift. People disagree. Mistakes compound. The environment pushes back.

Nor should we put too much weight on whether a company announces that it has achieved AGI. Such an announcement would tell us something about corporate confidence, public messaging and legal incentives. It would not, by itself, prove that a system had crossed a meaningful scientific threshold. A company might have reasons to use the term aggressively. It might also have reasons to avoid it, even if its systems become much more capable.

The better test would be practical: Can an AI system reliably complete a wide range of unfamiliar, economically useful, intellectually demanding tasks with minimal human supervision?

That question contains the words that matter.

Wide range, because AGI should not be limited to one domain. Unfamiliar, because memorized patterns and benchmark training are not the same as flexible intelligence. Economically useful, because real-world competence matters more than parlor tricks.

Intellectually demanding, because the standard should involve reasoning, planning and adaptation, not merely routine automation. Minimal human supervision, because the difference between a tool and an autonomous problem-solver is whether humans must constantly steer and check it.

Reliability may be the most underrated part of the definition. Occasional brilliance is not enough. A bridge engineer, surgeon, accountant or pilot cannot be excellent 80 percent of the time and dangerously wrong the rest. The more consequential the task, the more intelligence must be paired with judgment, verification and accountability.

This is where current systems still fall short. They can do many things, but they are not yet broadly dependable. They can assist with coding, but complex software projects still need human architecture, testing and responsibility. They can summarize research, but their conclusions require verification. They can draft legal language, but lawyers remain accountable for accuracy and strategy. They can tutor, but they do not replace the full role of a teacher who understands motivation, family context and emotional development.

And yet it would be a mistake to dismiss them as mere tools. The gap between today’s AI and robust AGI may be large, but it is no longer abstract. We can now see the outline of the thing we are debating: systems that are already useful enough to reshape work, but not reliable enough to deserve full responsibility.

That is why the label matters. Not as a trophy for AI labs, but as a warning light for everyone else. If we tell ourselves AGI is far away until a machine becomes flawless, conscious or humanoid, we may fail to adapt to systems already powerful enough to reorganize work and institutions. If we declare AGI here too early, we may exaggerate capabilities and trust systems before they deserve it.

The right posture is neither panic nor complacency. It is disciplined attention, because once machines can be trusted with goals, the consequences move quickly from the laboratory to the workplace, the classroom, the hospital and the state.

Work would change first. The jobs most exposed would likely be those built around language, analysis, software and coordination: programming, law, finance, consulting, marketing, customer support, design, operations, research and management. Some workers would become dramatically more productive. Others would find that important parts of their jobs can be done cheaply by software. New roles would emerge around directing, auditing and integrating AI systems, but transitions are rarely painless.

The economic question would not simply be whether AGI creates wealth. It almost certainly would, if it works. The harder question is who captures that wealth. The owners of models, chips, data centers, distribution platforms and AI-dependent businesses could gain extraordinary power. Workers and consumers could benefit too, but that would depend on competition, policy, access and bargaining power.

Science could accelerate. A general AI researcher could read across fields, generate hypotheses, design experiments, analyze data and help coordinate labs. Drug discovery, materials science, climate modeling and engineering could all benefit. A world with more scientific intelligence could be a world with faster cures, cleaner energy and better tools for solving collective problems.

But intelligence is not the same as wisdom. The same system that helps discover a medicine might help design a toxin. The same system that finds software vulnerabilities might help criminals exploit them. The same system that helps citizens navigate government services might help governments monitor citizens. Generality is powerful because it travels across domains. That is also what makes it dangerous.

Trust would become harder. More capable AI could generate convincing text, images, audio, video, fake identities, fake evidence and personalized persuasion at scale. The danger is not only that people will believe false things. It is that people may stop believing true things. When every image can be disputed and every recording can be synthetic, societies need better ways to prove authenticity.

Education would face a reckoning. The best version is extraordinary: every student gets a patient tutor that adapts to their pace and explains ideas in whatever way works. The worse version is a system in which students outsource the struggle that produces learning, teachers become software monitors and wealthy families get better AI support than everyone else. As with most technologies, the outcome will depend on design, access and institutional choices.

The same pattern would reach ordinary life: not AI answering isolated questions, but AI taking over small chains of responsibility. Much of modern life consists of navigating systems: forms, insurance rules, tax questions, medical paperwork, travel changes, subscriptions, passwords and customer service portals. AGI could become the interface to all of that. Instead of learning every bureaucracy, a person might ask an AI agent to handle it.

That could be liberating. It could also create dependence. If one assistant manages your schedule, purchases, messages, finances and paperwork, then whoever controls that assistant may gain enormous influence over your choices.

By then, the question would no longer be whether the system sounds intelligent. It would be whether we had quietly made it responsible for parts of our lives.

This is why the AGI threshold cannot be left only to technologists or marketers. It is not just a question about model performance. It is a question about delegation, accountability and power.

A useful public test would be this: Would you trust the system with a consequential goal if you could not check every step?

Not a prompt. Not a party trick. Not a polished demo. A goal.

Would you trust it to manage a lawsuit? Run a clinical trial? Teach a child? Operate a company department? Secure a hospital network? Negotiate a contract? Handle a government benefits case?

Corrections and updates

Subscribe now

The Case Against Mamdani That Holds Up

Evidence First — Sun, 13 Sep 2026 21:18:56 GMT

Zohran Mamdani’s critics weaken their case when they reach for the most explosive version of it.

Calling him a communist antisemite may satisfy his harshest critics, but the evidence does not establish that charge. The more accurate concern is also the more useful one. Mamdani is an anti-capitalist democratic socialist whose economic instincts run against the market foundations of successful mixed economies, and whose Israel politics appear one-sided in ways that raise legitimate questions about judgment, moral consistency, and coalition.

The argument does not depend on guessing what Mamdani privately believes. It turns on public judgment. Mamdani sees real failures in the status quo. The question is whether his answers are disciplined by evidence, alert to tradeoffs, and morally consistent, or narrowed by ideology.

Mamdani’s appeal is easy to understand. He speaks to a city where daily life feels punishingly expensive. Rent is crushing. Child care is unaffordable. Buses are slow and costly. Groceries feel like another monthly bill that keeps moving out of reach. For many New Yorkers, politics has sounded abstract for too long. Mamdani offers something concrete: free buses, universal child care, rent freezes, city-supported grocery stores, and higher taxes on the wealthy and large corporations.

To supporters, this sounds practical, even obvious. If people cannot afford the basics, government should step in. That impulse is not wrong. A serious mixed economy requires government to act where markets fail, where basic services are underprovided, and where ordinary people are exposed to risks they cannot reasonably manage alone.

The most successful economies tend to combine private enterprise, competition, and growth with public investment, social insurance, regulation, and redistribution. Markets create wealth. Government keeps markets from becoming cruel, unstable, monopolistic, or indifferent to human need.

Mamdani’s critique of capitalism is not imaginary. It is central to his politics. His appeal comes from the idea that New York’s private markets have failed ordinary people. That is where the disagreement begins. Most Democrats seek to reform capitalism. DSA seeks to replace it. Mamdani is not simply proposing a larger safety net inside capitalism; he is aligned with a movement that describes capitalism not merely as flawed or unequal but as a system that “must” be replaced with democratic socialism. Its program calls for public ownership of the largest corporations and essential industries.

That distinction matters because the policy argument is more complicated than the label. Communism is a radical branch of socialism, but not all socialism is communism. Some of Mamdani’s more controversial ideas have a mixed-economy logic. A public grocery option in underserved neighborhoods is not the same thing as nationalizing the food industry. A rent freeze is not the same thing as abolishing landlords. Higher taxes on millionaires are not the same thing as confiscating wealth. The real question is not whether these ideas are communist. They are not. It is whether they move New York toward the kind of mixed economy that works, or toward a politics that distrusts markets too much to use them well.

That is where the Nordic comparison, often invoked by American progressives, becomes so important and so often misunderstood. Countries such as Denmark, Sweden, and Norway have large welfare states, strong unions, and high taxes. They also rely heavily on private firms, competitive markets, and global trade. They discipline capitalism. They do not generally replace ordinary production and retail with public ownership.

Steven Pinker’s formulation in Enlightenment Now is closer to the lesson of successful rich democracies: “The empirical picture at present suggests that people flourish most in liberal democracies with a mixture of civic norms, guaranteed rights, market freedom, social spending, and judicious regulation.” That is not an argument for government-free capitalism. It is a reminder that the evidence from modern prosperity vindicates mixed economies in which markets remain central and government regulates, taxes, insures, and corrects them.

That is the standard by which Mamdani’s agenda should be judged. His policy instincts often mirror a suspicion of markets that goes beyond ordinary regulation. His central promise is not simply to regulate markets better or redistribute more of their gains. It is to intervene directly in areas where markets are producing unacceptable outcomes, sometimes as provider, sometimes as price-setter, and sometimes as subsidized competitor.

Some of Mamdani’s proposals can fit inside a reasonable mixed economy. Universal child care is not radical by international standards. It can help parents work, ease family pressure, and benefit children. Public transit subsidies are common. A city may reasonably treat buses more like libraries or parks than like ordinary consumer products.

Other proposals are more troubling because they reveal the risk in Mamdani’s approach: a habit of treating prices as enemies rather than information, and private providers as obstacles rather than institutions that also create value.

Rent freezes are the clearest example. They sound humane because, in the short run, they often are. A family in a rent-stabilized apartment does not experience a rent freeze as ideology. It experiences it as relief. It may mean staying near a child’s school, a job, a doctor, or a support network.

Yet the best evidence on rent control shows the tradeoff that slogans usually avoid. A major American Economic Review study of San Francisco found that rent control helped covered tenants stay in their homes, but also led landlords to reduce rental housing supply by about 15 percent, likely increasing rents elsewhere. The researchers also found that landlords reduced supply by selling to owner-occupants or redeveloping buildings.

This does not mean every tenant protection is bad. It means rent freezes are not an affordability strategy by themselves. They protect some current renters while potentially worsening the shortage for everyone else. In a city with too few homes, freezing prices without rapidly expanding supply can amount to rationing scarcity while refusing to confront its cause.

The grocery proposal raises a similar concern. City-supported grocery stores are presented as a response to high food prices and gaps in access. That goal is legitimate. Food access is a real public concern, and government already plays a major role through school meals, food stamps, emergency food programs, and support for underserved neighborhoods.

The problem comes when government uses subsidy to compete directly with ordinary businesses and then treats lower prices as proof that public provision is better. Recent reporting says private grocers have sued over New York’s city-backed grocery initiative, arguing that subsidized stores offering steep discounts would amount to unfair competition against private retailers operating on thin margins. Business Insider reported that the challenged plan involves city-backed stores by 2029, discounts on essential goods, and public support such as rent or property-tax advantages.

A private grocer has to cover rent, labor, theft, spoilage, insurance, taxes, and energy costs. If a city-backed store receives special public support and undercuts prices, some shoppers may benefit in the short term. But the policy may also weaken the private stores most residents still depend on. That is not proof the plan will fail. It is proof that the concern is serious.

This is the economic case against Mamdani at its strongest. He is not outside democracy, and he is not proposing Soviet communism. But his politics are clearly anti-capitalist in orientation. The best evidence favors capitalism disciplined by strong public institutions. Mamdani’s political identity and some of his signature policies lean toward replacing or suppressing market mechanisms rather than improving them.

That is the central policy objection. And it leads naturally to the broader question that runs through Mamdani’s politics: when his framework identifies a real injustice, does it also narrow what he is willing to see?

The same question follows Mamdani from economics into foreign policy. Does his framework clarify the problem, or narrow what he is willing to see? Palestinian suffering is real. Israel is a state, and states can be criticized harshly. But the question is whether Mamdani’s politics allow him to see the whole moral field clearly.

The evidence again requires precision. Mamdani is not proven to be an antisemite. He has condemned antisemitism. Reporting has noted that he has condemned Hamas while supporting Palestinian rights and rejecting many mainstream pro-Israel formulations.

To be fair, that record has to be separated from the harshest accusation. Mamdani’s record does not support the claim that he hates Jews, supports Hamas, or wants Jewish New Yorkers to feel unsafe. He has condemned Hamas’s Oct. 7 attack as a horrific war crime, called for the hostages’ return, spoken about antisemitism as a real crisis, and pledged to protect Jewish New Yorkers. As mayor, he also kept the city’s Office to Combat Antisemitism in place and appointed Phylisa Wisdom, a Jewish communal advocate who had led the New York Jewish Agenda, to direct it.

The harder question is not whether those statements count. They do. It is whether they are central enough to his politics to answer the concern about imbalance.

Mamdani has become one of the most prominent sharply anti-Israel and anti-Zionist figures in American politics. He supports the Boycott, Divestment, and Sanctions movement and has aligned himself with Palestinian-rights activism. For many Jewish New Yorkers, especially after Hamas’s Oct. 7, 2023 attack and the war in Gaza, this is not an abstract foreign-policy disagreement. It is about whether their fear is being heard, minimized, or treated as a political inconvenience.

The problem is not criticism of Israel. The problem is imbalance. Mamdani’s public emphasis appears to fall overwhelmingly on Palestinian suffering and Israeli wrongdoing. There is far less visible attention to Hamas’s murder of Israeli civilians, hostage-taking, authoritarianism, or the ways Palestinian civilians are also endangered by armed groups operating from civilian areas.

That asymmetry matters. A politician can be formally against antisemitism and still participate in a politics that makes many Jews feel that Jewish vulnerability is recognized only when it fits the movement’s preferred narrative.

Still, asymmetry is not the same thing as hatred. The evidence does not establish that Mamdani personally hates Jews, supports attacks on Jews, or celebrates Oct. 7. The better-supported conclusion is narrower and more defensible. He is strongly anti-Zionist, harshly critical of Israel, and too politically comfortable around movements where the line between Palestinian advocacy and antisemitic or violence-excusing rhetoric is not always clearly policed.

The issue is not whether Mamdani is responsible for every word spoken by allies or relatives. He is not. The issue is whether his standard of moral judgment changes when the subject shifts from Israel to people closer to his own politics.

That concern becomes more concrete in coalition politics. Endorsements matter. Repeated patterns matter. Mamdani’s endorsement of Darializa Avila Chevalier is one example. Chevalier reportedly attended a controversial pro-Palestinian rally on Oct. 8, 2023, one day after Hamas’s attack on Israel. City & State reported that she later defended her attendance, while Jewish Telegraphic Agency reported that she doubled down on having gone. Mamdani did not attend that rally, and endorsement is not the same as adopting everything an endorsee has said or done. But voters are entitled to consider such choices as evidence of the political world he is helping empower. They suggest a tolerance for certain risks, especially within a pro-Palestinian left whose boundaries around Jewish trauma and Hamas violence have sometimes been badly drawn.

The same question becomes more delicate with Rama Duwaji, Mamdani’s wife. She is a separate person, not an extension of his candidacy, and her statements and online activity should not be treated as Mamdani’s own record. Still, she is part of the personal and political world around him, and his response to scrutiny of that world is relevant to the question of judgment.

The scrutiny has not been vague. AP summarized prior reporting that Duwaji had shared posts praising female Palestinian militants involved in plane hijackings and bombings in the 1960s and early 1970s, shared a 2015 post saying Tel Aviv “shouldn’t exist,” liked an Instagram post that appeared to cheer Hamas’s Oct. 7 attack, and illustrated an essay by an author who had described Oct. 7 as “spectacular” and used dehumanizing language about Jewish Israelis.

Mamdani emphasized Duwaji’s integrity, said she should be considered separately from his political career, and said she had no prior knowledge of the author’s controversial statements when commissioned for the illustration. He called that author’s rhetoric “patently unacceptable” and “reprehensible.” Those facts do not prove Mamdani’s beliefs. They matter because they show how he responds when troubling rhetoric comes from inside his own orbit.

His father, the scholar Mahmood Mamdani, raises a different question. He is not evidence of Zohran’s intent. He is evidence of context. A parent’s worldview can plausibly shape the intellectual world in which a politician develops. Mahmood Mamdani has long been critical of Zionism and Israeli statehood as structured around Jewish national sovereignty. Critics cited by Times of Israel say he underplays Jewish historical vulnerability and antisemitism, minimizes Palestinian political agency and internal divisions, and treats Zionism too much through a colonial frame.

There is also direct evidence that these themes are not incidental in Zohran Mamdani’s political story. Mahmood himself has described Zohran’s campaign as built around two issues: affordability and “a critique of the state of Israel,” praising his son’s “refusal to budge” or “soften his critique of the state of Israel.” Al Jazeera has also reported Mahmood saying that “social justice” and “the rights of Palestinians” were “near and dear” to Zohran.

Together, that context makes it more plausible that Palestine is not merely one issue among many for Mamdani, but one of the formative commitments of his political life. His Muslim identity may be part of that formation, as may his father’s postcolonial scholarship, his wife’s Syrian-American background and public artistic focus, and the political world in which he came of age. None of that proves hostility toward Jews, and none of it should be reduced to religion alone. But it helps explain why Israel and Palestine may occupy such a central place in his moral imagination, and why his public emphasis on the conflict can appear so lopsided.

That context matters, but it has limits. Anti-Zionism can overlap with antisemitism, and sometimes does, but it is not automatically identical to hatred of Jews. Family influence can help explain a politician’s formation, but it does not establish intent.

The contrast is what matters. Mamdani speaks about Israel with unusual force for a New York mayoral politician. He is far more guarded when the controversy involves people in his own political or personal orbit. That contrast does not prove bigotry or bad faith. It does, however, strengthen the concern that his moral scrutiny is uneven, severe toward Israel, more forgiving toward allies and intimates whose words or actions many Jewish leaders, commentators, and voters have described as alarming.

Socialism is the broad family. It includes many views that want economic life to be more collectively, publicly or democratically controlled than in ordinary capitalism. Social democracy is the mildest version in practice. It accepts capitalism and markets, but uses taxes, unions, regulation and welfare programs to make capitalism fairer and less harsh. Democratic socialism goes further. It usually wants democratic politics to move more economic power away from private owners and toward workers, communities, public institutions or democratic control. Communism sits at the radical end of the tradition. In classic Marxist theory, communism means a classless society without private ownership of the means of production. In 20th-century practice, communist regimes usually meant one-party rule, central planning, state ownership of major industry and severe limits on political freedom.

So the line into communism is not always perfectly clean, because political movements use these words differently. But a practical line can be drawn: a socialist politics becomes communist when it seeks a revolutionary or totalizing break with capitalism, abolishing private ownership of major productive property, replacing markets with central planning, and concentrating political power in a party or state claiming to act for the working class. Mamdani is not shown by the evidence to cross that line. The concern is that his politics sit well to the left of ordinary social democracy, in a democratic-socialist tradition that treats capitalism not merely as a system to reform, but as one to move beyond.

It also helps to be clear about the line on antisemitism. Criticizing Israel, even harshly, is not antisemitism by itself. Israel is a state, and states can be judged for their actions. But anti-Israel politics can cross into antisemitism, or come uncomfortably close to it, when it excuses violence against Israeli civilians, treats Jewish fear as manipulation, uses old anti-Jewish tropes, denies Jews rights it grants to others, or applies standards to Israel that it does not apply elsewhere. Mamdani is not shown by the evidence to be in the clearest category of antisemitism: hostility to Jews because they are Jews. The concern is more specific. His politics sit in a zone where anti-Zionism, coalition tolerance and moral imbalance make many Jewish voters reasonably uneasy.

That leaves the real question, not what label can be pinned on Mamdani, but what kind of judgment he brings to office.

Production transparency

Corrections and updates

Subscribe now

Why We Built Evidence First

Evidence First — Sun, 13 Sep 2026 20:47:54 GMT

For a long time, we treated information as if more of it would naturally bring us closer to the truth.

If everyone could publish, search, share, and argue, surely the best ideas would rise. Surely bad claims would be corrected. Surely truth would win in the open marketplace of information.

That is not what happened.

More information does not automatically produce more truth. It often produces more noise. Truth is only a small part of all the information moving through the world, and it is usually the hardest part to produce.

A truthful account takes work. It requires evidence, context, checking, comparison, analysis, and a willingness to say when something is uncertain. Bad information has an advantage. It is cheaper to make. It can be simpler than reality. It can be more emotional, more entertaining, more flattering, and more certain than the evidence allows.

That is why bad claims often travel faster than careful ones. They ask less of us.

Evidence First was built around a simple response to that problem: we are not short on information. We are short on ways to judge it.

Evidence First is a place for careful, accessible, evidence-based analysis. The idea is simple: put the evidence before the conclusion.

That matters because many misleading claims do not look like obvious lies. They often contain real facts, real quotes, real charts, real studies, or real events. The problem is what happens next.

Facts get stripped of context. Correlation gets treated as causation. One weak study becomes “science says.” A possibility becomes a certainty. A real number gets used with the wrong baseline. A true quote gets used to imply something it does not prove.

That is why evidence-based analysis has to do more than label things true or false. A chart can be real and still misleading. A quote can be accurate and still be missing context. A study can exist and still be too weak to support the claim being made.

Different claims also require different kinds of evidence. A medical treatment claim, a legal allegation, a policy prediction, a viral screenshot, and a historical claim should not all be judged the same way. The first question is not just “Is this true?” It is “What kind of claim is this, and what evidence would actually support it?”

So we ask: What exactly is being claimed? What evidence would support it? What evidence would weaken it? Are there better explanations? How strong is the evidence? What is still uncertain? And how confident should we be, given the evidence available?

That is the Evidence First approach.

We break arguments down into their core claims. We separate facts from opinion, evidence from interpretation, and certainty from speculation. We look for what is supported, what is overstated, what is misleading, and what has not been established.

That also means we are not here to perform political balance for its own sake. We are here to be evidentially fair.

Sometimes the evidence will support a claim made by the left. Sometimes it will support a claim made by the right. Sometimes it will undermine both. And sometimes one side of an argument will simply have stronger evidence than the other.

Our job is not to make every debate look even. Our job is to understand what is true, what is likely, what is uncertain, and what has not been established.

The principle is simple. The work is harder.

It means slowing down when everyone else is rushing to react. It means asking whether a number has the right denominator. It means checking whether a study actually says what people claim it says. It means separating what happened from why it happened. It means being willing to say “we do not know yet” when the evidence is not strong enough.

We know evidence-based analysis has a reputation problem. Too often, it sounds cold, academic, or unreadable. It can feel like a lecture. It can bury the point under qualifications. It can be so careful that it becomes useless.

We think that is a mistake.

Evidence-based thinking should be clear, useful, and accessible. It should respect uncertainty without hiding from conclusions. It should be strong enough to challenge bad claims and humble enough to change when better evidence appears.

Truth does not need to be dull. Science does not need to be inaccessible. Careful thinking does not need to be weak.

In a noisy world, clarity is compelling.

That is the standard we are trying to build around: evidence before identity, ideology, loyalty, outrage, and convenience. People deserve more than fact-check labels and partisan counterclaims. Uncertainty should not be treated as weakness. Sometimes it is the most honest part of the answer. And changing your mind in response to better evidence is not a failure. It is the whole point.

You will see that standard in the pieces we publish: analysis of viral posts, public statements, news stories, health and science claims, political arguments, social trends, policy debates, and questions where the answer depends on evidence rather than vibes.

Some pieces will be short. Some will require a deeper breakdown. But the standard will stay the same: evidence first.

Send us the claim, the screenshot, the article, the quote, the chart, or the question you want checked. We will do what too much of the internet refuses to do: slow down, separate the claim from the evidence, and say clearly what the evidence supports.

Evidence first. Conclusions second.

Subscribe now

What Is a Life Well Spent?

Evidence First — Thu, 10 Sep 2026 16:18:44 GMT

There is a particular unease that often arrives not in crisis, but in ordinary moments. You are waiting for coffee to brew. You are scrolling in bed, half-aware that 40 minutes have passed. You are answering emails you will not remember next week. Then a thought breaks through: This is my life. It is passing while I am doing this.

The feeling is not exactly panic. It is closer to grief in advance, a sense that time is moving faster than attention can catch. Childhood summers once seemed endless. Now whole seasons disappear between bills, errands, meetings, notifications and sleep. The question that follows is ancient, but it feels newly urgent in an age of constant distraction: What should a person spend a life on?

No study can answer that completely. Science cannot tell you whom to love, what calling to pursue or which memories will matter most when you are old. Human lives are too varied for that. But research can help separate better bets from worse ones. It can show which investments tend to deepen well-being and which tend to disappoint. It can reveal, with appropriate humility, what people usually need not merely to survive, but to feel that their lives have been meaningfully lived.

The evidence points less toward a formula than toward a pattern. A life well spent is usually not built around constant pleasure, relentless achievement or private comfort. It is built around close relationships, a body and mind cared for well enough to participate in life, meaningful contribution and experiences that command our presence. Just as important, it requires resisting the temptations that make life busier without making it deeper.

The clearest finding may also be the least surprising: relationships matter enormously.

One of the best-known bodies of evidence on the subject comes from the Harvard Study of Adult Development, which has followed participants and their families for decades. Its recurring conclusion is simple enough to sound like folk wisdom: good relationships are strongly tied to happiness and health. Not fame. Not wealth. Not unbroken professional ascent. Relationships.

That finding does not mean extroverts win at life, or that everyone needs a packed social calendar. The point is not constant sociability. It is dependable connection. People tend to do better when they have others they can rely on, speak honestly with, care for and be cared for by. A weekly dinner with an old friend, a marriage repaired through attention, a sibling called regularly, a neighborhood where people notice one another: these are not sentimental luxuries. They are part of the architecture of a good life.

This matters because modern life often treats relationships as whatever remains after the “important” obligations are done. Work gets the best hours. Screens fill the gaps. Loved ones get the leftovers: tired attention, delayed replies, distracted evenings. Yet the evidence suggests this order is often backward. If close relationships are among the strongest predictors of long-term well-being, then protecting time for them is not indulgent. It is rational.

Health belongs in the same category. It is easy to discuss health in the language of optimization: steps, macros, sleep scores, body-fat percentages, supplements, longevity hacks. But the deeper reason to care for the body is more basic. Health is what allows a person to keep entering the life they value.

The evidence for physical activity is broad and unusually consistent. Regular movement is associated with lower risk of premature death, cardiovascular disease, diabetes and some cancers. It is also linked to better sleep, mood, cognitive function and daily capacity. But a meaningful life does not require becoming a fitness obsessive. The humane version of the evidence is gentler: move enough, sleep enough and care for your body enough that your future self is not unnecessarily robbed of participation.

A walk with a friend, a bike ride to work, dancing in the kitchen, lifting weights, gardening, hiking, playing with children: these are not merely ways to avoid disease. They are ways of staying available to life. A body is not just an object to improve. It is the instrument through which every conversation, embrace, meal, journey and act of service is experienced.

The same is true of mental health. Chronic stress, loneliness and exhaustion narrow a life. They make the world smaller. They make people less patient, less generous, less curious and less able to enjoy what they already have. Rest is therefore not the enemy of a meaningful life. It is one of its conditions. A person who is always depleted may be technically alive, but much of life becomes inaccessible.

Then there is the question of meaning. Pleasure matters. Comfort matters. But research on well-being has repeatedly suggested that people tend to need something more than feeling good. They need to feel that their lives count for something.

This is where contribution enters. Studies of prosocial behavior, a term researchers use for actions intended to benefit others, generally find a positive relationship between helping and well-being. The evidence is not a commandment. It does not prove that every person must volunteer in a formal organization, become a parent, enter a helping profession or devote themselves to a grand cause. But the broader pattern is persuasive: human beings often flourish when they are useful to someone or something beyond themselves.

That usefulness can be quiet. It might look like caring for an aging parent, mentoring a younger colleague, raising children, doing honest work, showing up for a friend in grief, building something that serves a community or simply being the person others can count on. In a culture that often equates meaning with scale, it is worth remembering that significance is not the same as visibility. Some of the most meaningful lives are not impressive from a distance. They are deeply felt up close.

Experiences also appear to matter more than we often assume. Research comparing experiential and material purchases has generally found that people derive more lasting satisfaction from experiences than from possessions, especially when those experiences are shared or become part of a person’s identity. A useful object can improve life, of course. A safe car, a comfortable home, a good pair of shoes: these things matter. But the evidence gives us reason to be skeptical of the endless pursuit of upgrades whose main purpose is status.

The vacation remembered for years, the concert with a friend, the ordinary Sunday meal that becomes a family ritual, the class that opens a new world, the afternoon spent outdoors: these often become part of the story people tell about who they are. Possessions can fade into the background. Experiences, especially shared ones, can become memory, identity and connection.

This may explain why time can feel so strangely lost when it is consumed by passive entertainment. There is nothing wrong with rest, television, games or scrolling in moderation. A good life need not be stern. But many people recognize the difference between leisure that restores and distraction that merely anesthetizes. One leaves you more yourself. The other leaves you wondering where the evening went.

That distinction brings us to attention, perhaps the least visible but most important currency of all. We often speak as if time is spent only by the hour: eight hours working, one hour commuting, two hours with family. But attention determines whether we actually inhabit those hours. A parent can be physically present and mentally absent. A couple can sit together while each disappears into a phone. A person can walk through a beautiful place and barely see it.

The research on social media and well-being is complicated. It does not support the simplistic claim that all digital life is harmful. Online connection can be real connection. Communities formed through screens can sustain people who might otherwise feel alone. But evidence increasingly suggests that heavy, passive or comparison-driven use can be associated with lower well-being, particularly among young people. The mechanism is not mysterious. A tool that constantly redirects attention toward comparison, outrage, performance and novelty can make ordinary life feel insufficient.

The problem is not technology alone. It is the broader human temptation to be absent from one’s own life. We can be absent through ambition, worry, fantasy, resentment, entertainment or fear. We can trade our attention for things we do not even value. And because attention feels weightless moment by moment, we may not notice the cost until months or years have passed.

This is why the common advice to “live every day like it is your last” is both powerful and flawed. Taken literally, it can become frantic or irresponsible. Most people should still save money, answer emails, do laundry, attend appointments and plan for a future they may well get to live. The better lesson is not that every day should be extraordinary. It is that ordinary days are the substance of a life.

A life is not mostly made of mountaintop revelations. It is made of Tuesdays. It is made of how you speak to the person making breakfast beside you. It is made of whether you go outside when the weather is good. It is made of whether you call back, repair the rupture, notice your child asking for attention, do the work with care and let the small pleasures register.

The evidence also warns against a common substitute for meaning: status. Ambition can be healthy. Work can provide mastery, purpose, income, friendship and identity. There is nothing noble about avoidable financial stress, and there is nothing shallow about wanting to use one’s talents well. But when status becomes the organizing principle of life, the returns are unreliable.

The problem with status is that it is comparative. There is always someone richer, more admired, more productive, more beautiful, more free. Status also tends to move the goalpost. What once seemed like success quickly becomes normal, and a new level appears necessary. A person can spend decades climbing toward a feeling that keeps receding.

The evidence does not say achievement is meaningless. It says achievement is a poor substitute for love, health, contribution and presence. A career triumph is real. But if it costs every dinner, every friendship, every quiet morning, every unmeasured joy, the bargain may be worse than it looked at the time.

There are important qualifications. Well-being research often deals in averages, and averages do not dictate individual lives. Some people find deep meaning in solitude. Some flourish in demanding careers. Some experience family not as refuge but as pain. Some forms of service become exploitative when they erase the self. Poverty, discrimination, illness, grief and unsafe environments can constrain choices in ways that lifestyle advice often ignores. The evidence should not be used to scold people for failing to thrive under burdens they did not choose.

Nor should it be reduced to a moral checklist. The point is not to optimize every hour, convert friendship into a longevity strategy or turn meaning into another achievement project. The point is to ask what kinds of time tend to repay human beings at the deepest level.

That question becomes especially important because many of the worst uses of time do not feel bad immediately. They feel easy. Comparison feels stimulating. Overwork feels responsible. Avoidance feels relieving. Scrolling feels harmless. Buying feels rewarding. Saying yes to everything feels virtuous. But a life can be drained by activities that are pleasant, respectable or convenient in the short run.

The better uses of time are often modest and sometimes effortful. Relationships require patience. Health requires maintenance. Contribution requires inconvenience. Presence requires resisting distraction. Meaning often asks something of us before it gives something back.

Still, the picture that emerges from the evidence is hopeful. It suggests that a good life is not hidden behind rare genius, perfect discipline or dramatic transformation. Much of it is available in ordinary choices repeated over time: to be close rather than merely connected, useful rather than merely busy, active rather than numb, present rather than entertained, rested rather than constantly depleted, guided by values rather than appearances.

No study can tell a person exactly what to do with a Saturday afternoon. But the evidence can help sharpen the question. Not “How do I fit more into my life?” but “What makes my life more worth inhabiting?” Not “How do I become impressive?” but “What would I be grateful to have given myself to?” Not “How do I avoid wasting time forever?” but “What deserves my attention now?”

Time will pass either way. That is the hard fact underneath the question. The days do not wait until we have clarified our values. They move through whatever is already on the calendar, whatever habit is already installed, whatever screen is already open, whatever fear is already making the decision.

The good news is that a life does not have to be rebuilt all at once to become more honest. Attention can return. A friendship can be revived. A body can be walked around the block. A conversation can be entered fully. A person can do one useful thing for someone else. A meal can be tasted. A sunset can be noticed. A phone can be put down.

The evidence does not promise that these things will make life painless. They will not. But it strongly suggests that they are among the things least likely to be regretted. In the end, the best-supported answer to how we should spend our short time on earth may also be the most human one: with people we love, in bodies we care for, doing things that matter, while we are still here to notice.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Relationships and long-term well-being

Claim or topic:

Close relationships are strongly associated with happiness, health, and long-term well-being.

Source:

Harvard Gazette, “Things money can’t buy, like happiness and better health”

Source type:

Reputable journalism summarizing long-running academic research.

What it supports:

This source summarizes findings from the Harvard Study of Adult Development, a decades-long study often cited for the conclusion that good relationships are strongly linked with happiness and health.

Important caveat:

The article is a public-facing summary, not the full underlying dataset or a systematic review. The Harvard Study is influential, but no single cohort study can fully answer what makes every individual life meaningful.

2. Social connection and happiness

Claim or topic:

Social connection and perceived social support are associated with higher life satisfaction and well-being.

Source:

World Happiness Report 2025, “Connecting with Others: How Social Connections Improve the Happiness of Young Adults”

Source type:

Expert organization / research report.

What it supports:

This source supports the article’s claim that social connection is not merely sentimental, but is consistently linked with well-being in population-level research.

Important caveat:

Much of this evidence is observational. It can show strong associations, but it does not always prove that social connection alone causes higher well-being in every case.

3. Physical activity, health, and daily functioning

Claim or topic:

Regular physical activity is associated with lower risk of major diseases and premature death, and with better sleep, mood, cognitive function, and daily capacity.

Source:

Physical Activity Guidelines for Americans

Source type:

Government evidence-based guidance.

What it supports:

This source supports the article’s claim that movement is not just an “optimization” project. It is one of the better-supported ways people protect long-term health and preserve their ability to participate in life.

Important caveat:

The article uses this evidence at a broad level. It does not offer individualized medical advice, and physical activity needs vary by age, disability, health status, and circumstance.

4. Helping others and well-being

Claim or topic:

Prosocial behavior, meaning actions intended to benefit others, is generally associated with higher well-being.

Source:

American Psychological Association summary of research on prosocial behavior and well-being

Source type:

Academic research.

What it supports:

This source supports the article’s claim that contribution, usefulness, and helping others are often linked with well-being.

Important caveat:

The evidence does not prove that every form of helping improves every person’s life. Context matters. Helping can become draining or exploitative when it erases the helper’s own needs.

5. Experiences versus possessions

Claim or topic:

Experiential purchases often produce more lasting satisfaction than material purchases, especially when experiences are shared or become part of identity.

Source:

PNAS / PMC article on experiential and material consumption

Source type:

Academic research.

What it supports:

This source supports the article’s claim that experiences often repay people differently from possessions, particularly through memory, identity, and social connection.

Important caveat:

The evidence does not mean possessions never matter. Useful material goods can improve life substantially, especially when they increase safety, comfort, mobility, or financial stability.

6. Social media, attention, and well-being

Claim or topic:

Heavy, passive, or comparison-driven social media use may be associated with lower well-being, especially among young people.

Source:

World Happiness Report 2026, “International Evidence on Happiness and Social Media”

Source type:

Expert organization / research report.

What it supports:

This source supports the article’s cautious claim that digital life is not automatically harmful, but some forms of social media use may be linked with lower well-being.

Important caveat:

The evidence is complicated. Online connection can be valuable, and the relationship between social media and well-being varies by person, platform, pattern of use, and social context.

7. “Top regrets of the dying” and limits of anecdotal evidence

Claim or topic:

Popular claims about the “top regrets of the dying” should be treated cautiously because they are not based on a systematic scientific study.

Source:

Absolutely Maybe / PLOS Blogs, “Why do scientists cite the ‘Top 5 Regrets of the Dying’?”

Source type:

Analysis / science commentary.

What it supports:

This source supports the article’s caveat that hospice-regret claims can be emotionally resonant but should not be treated as rigorous population-level evidence.

Important caveat:

The source critiques the evidentiary basis of a popular claim. It does not show that the themes are false, only that they should not be overstated as settled scientific findings.

8. Individual differences and structural limits

Claim or topic:

Well-being research often deals in averages, and averages do not dictate individual lives. Poverty, illness, discrimination, grief, unsafe environments, and family pain can constrain people’s choices and affect health and well-being.

Source:

World Health Organization, “Social determinants of health”

Source type:

Expert organization.

What it supports:

This source explains that health and well-being are shaped not only by individual choices, but also by the conditions in which people are born, grow, live, work and age, as well as access to power, money and resources.

Important caveat:

This source supports the structural-limits caveat at a broad level. It does not directly address every example in the article, such as grief or family pain, so the claim should remain framed as a caution rather than a precise causal finding.

How to read this evidence

Corrections and updates

Subscribe now

Climate Science Aged Better Than the Debate Around It

Evidence First — Fri, 04 Sep 2026 00:05:09 GMT

In October 2018, a major climate report acquired a much simpler public meaning: We had 12 years left.

The underlying report, issued by the Intergovernmental Panel on Climate Change, did not say the world would end in 2030. It examined what would be required to limit global warming to 1.5 degrees Celsius above preindustrial levels and concluded that doing so would require global carbon dioxide emissions to fall sharply by 2030 and reach roughly net zero around the middle of the century. That was an urgent finding, but it was not a prediction that catastrophe would begin on a fixed date.

For years, climate change was often presented through two competing stories. One said that the warnings were exaggerated, the models were unreliable and the science was too uncertain to justify much concern. The other suggested that humanity was approaching a hard deadline after which disaster would become unavoidable. With decades of climate projections now behind us, both stories can be tested against what actually happened.

The result is more interesting than either side suggested. The central science held up better than many critics claimed, but the consequences were also less like the sudden apocalypse that some public rhetoric implied. What the science actually described was a growing risk problem, one in which additional warming increases the likelihood and cost of serious harm. That story was harder to turn into a slogan, but it also aged better.

What actually held up

The first question is whether the basic science was reliable. On that point, the record is strong. Greenhouse gases trap heat in the atmosphere. Human activity, especially the burning of fossil fuels, has sharply increased their concentration. The planet has warmed. Sea level has risen. Extreme heat has intensified. Over time, the evidence increasingly established human activity as the dominant cause of recent global warming.

The World Meteorological Organization reported that 2025 was about 1.43 degrees Celsius warmer than the average from 1850 to 1900, a period commonly used as a baseline for preindustrial conditions. The years from 2015 through 2025 were the 11 warmest years in the observational record. A single unusually warm year does not mean the world has permanently crossed a particular long-term threshold. Natural variation still pushes individual years up and down, but the long-term warming trend is clear.

Older climate models also performed better than many people remember. A 2020 study in Geophysical Research Letters examined climate models published between 1970 and 2007 and compared their projections with subsequent temperature observations. Most performed reasonably well at projecting global temperature change. When researchers accounted for differences between the greenhouse gas emissions the models assumed and the emissions that actually occurred, their performance improved further. The study found no systematic tendency among those models to overestimate warming.

That does not mean climate models predict everything well. Global average temperature is easier to project than rainfall, storms or agricultural conditions in a particular place decades into the future. Some old forecasts were too warm. Others were too cool. But the core relationship between greenhouse gases and global temperature held up much better than the public memory of failed climate predictions often suggests.

Getting the temperature trend broadly right was important. But temperature was never the real end point of the warning. The question was what a warmer climate would do to the systems people depend on.

What warming actually looks like

If climate change was serious, some public rhetoric implied, then it had to look like a civilization-ending catastrophe. The science never required that conclusion.

For most people, climate change will not arrive as one giant event labeled “global warming.” It is more likely to appear as a growing burden on systems people already depend on: power grids, water supplies, roads, farms, insurance markets, public health systems and coastal infrastructure. Climate change also does not cause every heat wave, drought, flood or wildfire. Weather extremes have always occurred, and local conditions still matter. What warming can do is change the probability or severity of some events.

Extreme heat is one example. A hotter day may sound like an inconvenience. Enough additional extreme heat becomes a public health and infrastructure problem. Outdoor work becomes more dangerous. Electricity demand rises as more people need cooling. Hospitals, schools and workplaces have to prepare for conditions that occur more often.

Food systems face a similar kind of pressure. Climate change does not mean crops fail everywhere or that the world suddenly runs out of food. Some regions may benefit for a time from longer growing seasons or other changes. But farming depends on temperature, rainfall and water availability. Severe heat and drought can reduce harvests, livestock productivity and farm labor.

The more concerning situations are the ones in which several stresses overlap. A major growing region suffers drought while another has a poor harvest. Food prices rise. A country that depends heavily on imports has less ability to absorb the shock. At the same time, heat may reduce worker productivity and increase electricity demand. Climate risks can compound in other ways, too. Drought can reduce hydropower just as more people need air conditioning. Crops can suffer while wildfire smoke adds another health burden. Flooding can damage infrastructure at the same time insurance becomes more expensive.

Researchers call these compound and cascading risks. No single event needs to overwhelm a country for the combination to become severe. Imagine a coastal city that is already hot in summer. Over time, it faces more frequent heat waves, heavier demand on its power system, more expensive cooling, greater flood risk during storms and rising insurance costs in low-lying neighborhoods. None of those problems is catastrophic on its own. Together, they force households, businesses and local governments to spend more just to maintain roughly the same level of safety and reliability.

This is the realistic climate threat. It is less cinematic than the rhetoric surrounding it, but it is not trivial. The harm comes from many pressures accumulating across places and decades. The science did not need to predict extinction to describe a serious problem.

Why a few degrees matter

The temperature numbers used in climate discussions can sound strangely small. The difference between 1.5 and 2 degrees Celsius may seem trivial in ordinary conversation, but global temperature is not like the thermostat in a living room. It is an indicator of how much the entire climate system has shifted.

Coral reefs provide one of the clearest examples. The IPCC has assessed that warm-water coral reefs are projected to decline by a further 70 to 90 percent at 1.5 degrees Celsius of global warming, with losses greater than 99 percent at 2 degrees. Those estimates carry uncertainty, and individual reefs will not respond identically. But they show how half a degree in the global average can translate into a very large difference in risk for an ecosystem.

Species extinction is another irreversible concern. The IPCC has estimated that among the terrestrial species it assessed, roughly 3 to 14 percent could face a very high risk of extinction at 1.5 degrees Celsius of warming, with the range increasing at higher temperatures. That does not mean all of those species will disappear. It means the probability of extinction becomes very high for a growing share of the species assessed. Species confined to a mountaintop, island or narrow climatic range are especially vulnerable because they can eventually run out of suitable habitat.

Sea-level rise shows the same logic over a much longer timescale. The immediate concern is not that major coastal cities suddenly disappear beneath the ocean. It is that higher seas make flooding more frequent, storm surges more damaging and coastal protection more expensive. Sea-level rise also does not stop in 2100. Oceans and ice sheets respond slowly, so sea levels can continue rising for centuries even after temperatures stabilize. Some places will defend themselves for a long time with seawalls, pumps and other infrastructure. Others may eventually find retreat more practical than permanent defense.

The United Nations Environment Programme estimated in 2025 that policies then in place would put the world on a course of roughly 2.8 degrees Celsius of warming this century. Full implementation of countries’ existing climate pledges would lower that projection to roughly 2.3 to 2.5 degrees. Those are projections, not destiny. Policies change. Technologies improve. Energy systems evolve.

Nor is 2.8 degrees a scientific code for “the world ends here.” Its significance is that many risks increase as warming rises. Heat extremes intensify. Ecosystems face greater losses. Some forms of adaptation become harder. The chances of some irreversible changes increase.

This is why the science speaks in gradients rather than deadlines. A world at 1.7 degrees is not “safe” while a world at 2 degrees is “doomed.” But the risks are not identical either. The same is true of 2.5 and 3 degrees. There is no single cliff. There is a worsening slope.

The existence of that slope also means the future is not predetermined. How much people suffer depends not only on temperature, but also on how well societies respond. Flood barriers, better drainage, water management, cooling, stronger buildings, heat-warning systems and more resilient infrastructure can all reduce harm. A wealthy society that invests heavily in adaptation can experience the same physical hazard very differently from a poor society that lacks those protections.

But adaptation has limits. Protecting a city from occasional flooding is one problem. Protecting it from steadily rising seas for generations is another. Adjusting farming practices to modest changes in weather is easier than adapting indefinitely to increasing heat or water stress. Saving an ecosystem becomes impossible once the species that make it up are gone.

Each additional increment of warming can therefore make adaptation harder and more expensive. Adaptation can reduce harm. Emissions cuts can limit how much additional warming occurs. Neither makes all risk disappear.

Mainstream climate assessments also do not establish human extinction as the likely result of warming expected this century. Claims that climate change will probably eliminate humanity go beyond the central scientific evidence. But “not human extinction” is an extremely low standard for deciding whether a problem matters.

We do not judge a disease by asking whether it will wipe out the species. A recession can cause enormous hardship without permanently destroying the economy. A war does not need to end civilization to devastate millions of lives. Climate change should be judged by the same ordinary standard. How much harm is likely? Who bears the costs? Which losses cannot be reversed? How much can be prevented? How much worse does the problem become as warming rises?

For humanity as a whole, the most realistic danger is not extinction. It is a hotter, more expensive, more disruptive and less biologically rich world, with some of the greatest burdens falling on people and places least able to absorb them.

That conclusion sat awkwardly between the two stories that dominated the debate. Climate change was far more serious than “the science is too uncertain to worry about,” but more measured than “we are approaching a fixed date after which the world is doomed.” That was not a political compromise. It was where the evidence pointed.

How the debate went wrong in both directions

The problem was that this kind of conclusion is difficult to turn into a slogan.

For years, American journalism often made climate science look more divided than it was. A 2004 study of coverage in The New York Times, The Washington Post, the Los Angeles Times and The Wall Street Journal found that the journalistic habit of giving opposing sides similar weight often created a misleading impression about the state of the science.

The problem became known as “balance as bias.” When the great majority of relevant research points in one direction, giving a small number of dissenters equal prominence can make uncertainty appear much larger than it really is. A later study published in Nature Communications in 2019 found a related pattern. Researchers compared the media visibility of hundreds of climate scientists with hundreds of prominent climate contrarians across roughly 100,000 media articles. The contrarians received disproportionately high attention relative to their scientific standing.

For years, one prominent distortion made the science look less settled than it was. At other times, another made it sound more absolute than it was. As scientific confidence increased and public concern grew, complicated findings were sometimes compressed into slogans that sounded more certain and more catastrophic than the assessments themselves.

The 2018 IPCC report is a useful example. Its actual message was that limiting warming to 1.5 degrees required rapid emissions reductions. Public discussion often transformed that finding into a hard deadline after which catastrophe would become unavoidable.

The urgency was real. The cliff was not.

A deadline is easier to communicate than a probability distribution. “Twelve years left” is more memorable than saying that each additional increment of warming raises risk. But the second statement is much closer to the science. Once the countdown became part of public memory, every missed milestone could also be interpreted as evidence that climate science itself had failed.

The science was simplified in opposite directions: sometimes into excessive uncertainty, and at other times into excessive certainty. Strip away both simplifications, and the record becomes clearer.

What aged well

Scientists did not get everything right. Individual scientists have made poor predictions. Models have limitations. Estimates change as new evidence arrives. Local climate impacts are much harder to predict precisely than the basic global temperature response to greenhouse gases.

But the central framework held up. The planet warmed substantially. Human influence emerged clearly as the dominant cause. Older models generally captured the broad temperature response to rising greenhouse gases. Sea level rose. Extreme heat intensified. Evidence accumulated that many risks increase as warming rises.

What aged poorly were the binary stories built around that science. The skeptical version said the science was too uncertain and the predictions too unreliable to take seriously. The apocalyptic version suggested that the science pointed to a single deadline, a hard threshold or a likely end to civilization. Neither version aged especially well.

The science described something less dramatic and more durable: a range of outcomes. A world that warms by 1.7 degrees is generally preferable to one that warms by 2.5. A world at 2.5 degrees is generally preferable to one at 3. There is no point at which preventing additional warming suddenly stops mattering.

The public debate wanted a verdict: hoax or catastrophe, safe or doomed, deadline met or deadline missed. The science kept describing a risk curve. More warming meant more danger. Less warming meant less danger. Adaptation could reduce some harms. Some losses would still be irreversible. Uncertainty remained, but uncertainty did not erase the direction of the evidence.

That was always a harder story to tell. It was also the one that aged best. The science was less apocalyptic than some of the rhetoric surrounding it suggested. It was more consequential than its loudest critics allowed.

And it aged better than the debate around it.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Human-caused warming and rising climate risk

Claim or topic:

Human activity is the dominant cause of recent global warming, and climate risks generally increase as warming rises.

Source:

IPCC Sixth Assessment Report, Synthesis Report

Source type:

Expert organization / scientific assessment.

What it supports:

The IPCC concludes that human activities have unequivocally caused global warming and that risks to people and ecosystems increase with additional warming.

Important caveat:

The IPCC synthesizes a large scientific literature rather than reporting a single experiment. Confidence varies by specific impact, region and level of warming.

2. Recent global temperatures

Claim or topic:

Recent years have been the warmest in the observational record, with 2025 about 1.43 degrees Celsius above the 1850 to 1900 average.

Source:

World Meteorological Organization, State of the Global Climate 2025

Source type:

Expert organization / observational climate data.

What it supports:

The WMO provides the global temperature estimates used in the article and documents the long-term warming trend.

Important caveat:

A single unusually warm year is not the same as permanently crossing a long-term warming threshold. Year-to-year natural variability still matters.

3. How well older climate models performed

Claim or topic:

Older climate models generally did a reasonably good job projecting subsequent global temperature change.

Source:

Hausfather et al., “Evaluating the Performance of Past Climate Model Projections,” Geophysical Research Letters

Source type:

Academic research.

What it supports:

The study compared climate projections published from 1970 to 2007 with what later occurred. It found that most models performed reasonably well, especially after accounting for differences between assumed and actual greenhouse gas forcing.

Important caveat:

This finding applies most directly to global temperature projections. Climate models are less precise for many local and regional outcomes.

4. Risks at 1.5, 2 and higher degrees of warming

Claim or topic:

Relatively small differences in global average warming can produce large differences in risk, including for coral reefs, species and other ecosystems.

Source:

IPCC Special Report on Global Warming of 1.5°C and IPCC Sixth Assessment, Working Group II

Source type:

Expert organization / scientific assessment.

What it supports:

The assessments support the article’s discussion of sharply increasing risks to warm-water coral reefs, rising extinction risk for some species, and broader increases in ecological and human vulnerability as warming rises.

Important caveat:

These are risk estimates, not guarantees that a specific percentage of species or ecosystems will disappear. Outcomes vary by region, species and adaptation capacity.

5. Current-policy warming projections

Claim or topic:

Under policies in place in 2025, the world was projected to warm by roughly 2.8 degrees Celsius this century, with lower warming if national pledges are fully implemented.

Source:

United Nations Environment Programme, Emissions Gap Report 2025

Source type:

Expert organization / estimate and analysis.

What it supports:

The UNEP report provides the projected warming ranges used to explain why the difference between roughly 2, 2.5 and 3 degrees matters.

Important caveat:

These are scenario-based projections, not fixed predictions. Future policies, technology, energy use and emissions can change the outcome.

6. Media “balance” and climate science

Claim or topic:

Some earlier U.S. climate coverage made the science appear more evenly divided than the scientific literature justified.

Source:

Boykoff and Boykoff, “Balance as Bias,” Global Environmental Change

Source type:

Academic research.

What it supports:

The study examined major U.S. newspaper coverage from 1988 to 2002 and found that journalistic norms of balance often gave disproportionate weight to views questioning human-caused climate change.

Important caveat:

The study covers a specific group of newspapers and an earlier period. It should not be generalized to all climate journalism or all media eras.

7. Media visibility of climate contrarians

Claim or topic:

Climate contrarians received more media attention than their scientific standing alone would predict.

Source:

Petersen, Vincent and Westerling, Nature Communications

Source type:

Academic research.

What it supports:

The study compared hundreds of prominent climate scientists and contrarians across a large media sample and found disproportionate visibility for contrarian voices relative to their scientific authority.

Important caveat:

Media visibility is not the same as influence on public opinion, and the imbalance varied across different types of media.

8. The 2018 “12 years” framing

Claim or topic:

The IPCC’s 2018 emissions pathway was widely simplified in public discussion into a fixed 12-year climate deadline.

Source:

IPCC Special Report on Global Warming of 1.5°C

Source type:

Primary scientific assessment.

What it supports:

The IPCC report supports the underlying claim that limiting warming to 1.5 degrees required steep emissions reductions by 2030 and roughly net-zero carbon dioxide emissions around midcentury.

Important caveat:

The report did not say catastrophe would begin in 2030 or that climate action would become pointless after that date. A separate media source would be useful if the published article wants to document a specific “12 years left” headline or quotation.

How to read this evidence

Corrections and updates

Subscribe now

The Psychology of Donald Trump

Evidence First — Tue, 01 Sep 2026 23:36:00 GMT

Donald Trump is easiest to misunderstand when he is treated as a riddle with a hidden answer. Is he acting? Is he sincere? Is the outrage real? Is the charm fake? Is the public persona a performance masking a more ordinary private man, or is the private charm merely another performance?

The evidence points to a less tidy answer. Trump is not best understood as two separate people, the raging rally figure and the affable private host. He is better understood as one unusually consistent personality system operating in different theaters.

In public, he tries to win the crowd. In private, he tries to win the person in front of him. The methods change. The goal does not.

That goal is control: of attention, of the story, of the emotional temperature, of who is seen as strong and who is seen as weak. Around Trump, politics often becomes interpersonal drama. Allies are loyal or disloyal. Opponents are not merely wrong but pathetic, corrupt, stupid or treacherous. Defeat is not just defeat. It is humiliation. Victory is not simply success. It is vindication.

None of this is a clinical diagnosis. That needs to be said plainly. The American Psychiatric Association’s Goldwater Rule cautions psychiatrists against diagnosing public figures they have not personally examined and who have not consented to evaluation. Diagnosis requires more than speeches, interviews, memoirs, courtroom documents and televised behavior. It requires clinical access, context, history and evidence of impairment across a person’s life.

But public evidence can still tell us something. It can reveal patterns. Trump’s patterns are unusually visible because he has spent decades performing himself: in tabloids, on television, in business branding, in campaign rallies, in courtrooms, in the Oval Office and on social media.

The pattern begins with status.

Trump’s language has long sorted the world into winners and losers, strength and weakness, dominance and disgrace. He does not merely disagree with rivals. He diminishes them. He does not merely praise himself. He inflates himself. Everything tends toward rank. Who is up? Who is down? Who is laughing? Who has been made to look small?

This is not just the impression of critics. In a peer-reviewed study that asked Trump supporters and Hillary Clinton supporters to rate Trump’s personality, the two groups sharply disagreed about whether his traits were good or bad. But they overlapped on some basic observations: he was widely seen as highly assertive and low in modesty. Supporters often interpreted those traits as confidence, strength and success. Opponents saw hostility, arrogance and lack of warmth.

That distinction matters. Trump’s psychology cannot be separated from the audience that completes it. The same gesture can read as bullying to one group and courage to another. The same insult can sound cruel to one listener and cathartic to another. The same factual exaggeration can appear disqualifying to critics and irrelevant, even thrilling, to supporters who think the larger system is already dishonest.

Trump’s political gift has been to make his own emotional needs feel like collective needs. His grievances become his supporters’ grievances. His enemies become their enemies. His humiliation becomes their humiliation. His revenge becomes their vindication.

That helps explain why conflict is not incidental to Trump’s public life. It is central to it. Many politicians try to survive controversy. Trump often uses controversy as fuel. A shocking statement forces attention. Attention confirms significance. Criticism proves persecution. Persecution strengthens loyalty. The cycle is not accidental. It is the machine.

His relationship with the press captures the paradox. In public, Trump attacks “the media” as corrupt, fake and hostile. Yet reporters have repeatedly described a man who is intensely engaged with the media as a practical instrument of power. The Associated Press reported in 2025 that some journalists could call Trump’s personal cellphone and reach him directly. In one account, he began by berating a reporter for past coverage, then continued talking, answered questions and was described as gracious.

This is not necessarily a contradiction. It is more like a split-screen strategy. “The media” as an institution can be an enemy to attack before a crowd. An individual reporter, on the phone, can be a person to charm, pressure, flatter, scold or use. The public attack and the private access both serve the same purpose: Trump remains the central figure through whom the story must pass.

The private Trump, by many accounts, can be funny, direct, gossipy and oddly accessible. He does not always operate through formal channels. He calls people. He watches television. He reacts. He asks who is saying what. He draws outsiders, friends, donors, aides and media figures into a loose orbit of conversation and influence. Axios’s reporting on his first-term schedules described long stretches of unstructured “Executive Time,” a phrase that critics mocked but that also pointed to something real about Trump’s style: he prefers porous boundaries, informal access and immediate feedback to orderly institutional process.

At Mar-a-Lago, according to reporting from his first term, that style found its natural habitat. The club setting allowed proximity, performance and governance to blur. A conversation could be social, political, transactional and theatrical all at once. Trump could be host, celebrity, candidate, president, client and master of ceremonies in the same afternoon.

That world helps explain the importance of loyalty. Every president wants dependable advisers. Trump’s demand has often seemed more personal. In the Mueller report, investigators found substantial evidence corroborating James Comey’s account that Trump asked him for loyalty at a private dinner, including Comey’s contemporaneous memo and recollections from FBI officials. The question was not merely whether Comey would enforce policy. It was whether he would be “loyal.”

That word opens a door into Trump’s political psychology. Loyalty, in his world, often appears less like commitment to law, party or ideology than allegiance to him personally. Those who criticize him after serving him are not simply dissenters. They become traitors, weaklings, liars or failures. Former allies are recast as people who were never impressive in the first place. The past is rewritten to protect the hierarchy: Trump above, critic below.

This is one reason Trump’s orbit can feel courtlike. Favor matters. Access matters. Praise matters. Public defense matters. The leader’s regard is valuable, and his contempt can be politically dangerous. People around him learn to manage not just policy preferences but mood, grievance and status.

The most consequential example came after the 2020 election.

Trump lost. Courts rejected his claims. State officials, federal agencies and his own attorney general did not find evidence of widespread fraud sufficient to change the outcome. Reuters later summarized the repeated failure of the stolen-election claim to survive institutional scrutiny. Yet Trump did not concede in any ordinary sense. He insisted the election had been stolen and made that claim central to his post-election identity.

Seen psychologically, the stolen-election narrative did several things at once. It turned loss into victimization. It protected Trump’s identity as a winner. It gave supporters a reason to remain emotionally mobilized. It transformed a political defeat into a moral injury. It kept Trump at the center of the national drama even after he had been voted out of office.

The House January 6 committee concluded that Trump pressured Vice President Mike Pence to take actions Pence had been told he lacked authority to take. That episode was not an isolated rupture from the rest of Trump’s behavior. It was an extreme version of familiar themes: loyalty over institutional restraint, personal victory over procedural limits, pressure applied to a subordinate and refusal to accept humiliation as final.

This is where discussions of Trump often go wrong. Some critics treat him as purely irrational. But too much of what he does has been useful to call it random. He captures attention better than almost any modern American politician. He compresses complicated policy disputes into vivid emotional stories. He makes supporters feel not merely represented but avenged. He uses attacks against him as evidence that he is threatening the right people.

At the same time, some admirers and analysts treat him as purely strategic, as if every outburst were a chess move. That explanation is also too neat. The consistency of his reactions, especially to insult, criticism, defeat and disloyalty, suggests more than tactics. It suggests temperament. Trump often appears not just to use grievance but to live inside it.

The best explanation is that strategy and temperament reinforce each other. Trump seems drawn by disposition toward dominance, admiration, conflict and grievance. American media rewards conflict. Partisan politics rewards loyalty. Social media rewards emotional intensity. Supporters reward defiance. Over time, the traits and the incentives have fused.

This is why the everyday language of narcissism comes up so often around Trump. Used clinically, narcissistic personality disorder has specific criteria and cannot responsibly be diagnosed from afar. Used descriptively, however, “narcissistic traits” points to observable tendencies: grandiosity, admiration-seeking, sensitivity to humiliation, low modesty, retaliatory anger and difficulty acknowledging defeat or fault.

The distinction is crucial. To say Trump shows narcissistic traits is not the same as declaring him mentally ill. It is to say that a particular pattern predicts much of what we see.

It predicts the obsession with crowd size. It predicts the superlatives: the best, the greatest, the strongest, the most successful. It predicts the nicknames and belittling of opponents. It predicts the difficulty admitting error. It predicts the treatment of criticism as attack and attack as betrayal. It predicts the hunger for praise from people whose institutions he publicly condemns. It predicts the way a political loss can be narrated as theft rather than accepted as defeat.

It even predicts the charm.

That is the part many people miss. A dominance-oriented person need not be grim or cold in every interaction. Charm can be a form of dominance too, especially when it pulls another person into one’s frame. Trump’s private warmth, when it appears, does not necessarily contradict his public aggression. Both can be ways of winning. A compliment can control a room as surely as an insult. A phone call can be as strategic as a rally line.

This is why the question “Which Trump is real?” may be the wrong question. The rally Trump is real. The charming Trump is real. The aggrieved Trump is real. The transactional Trump is real. The performer is real too. For some public figures, performance conceals the self. For Trump, performance may be one of the primary ways the self exists.

The danger is reducing everything to personality. Trump did not create American distrust, polarization, resentment or institutional decay by himself. His supporters have many reasons for supporting him: immigration, trade, courts, religion, culture, economics, party identity, anger at elites, fear of social change. A psychological account of Trump is not a full account of Trumpism.

Corrections and updates

Subscribe now

Are Electric Cars Really Cleaner?

Evidence First — Thu, 27 Aug 2026 14:29:43 GMT

For many Americans, the electric vehicle has become more than a car. It is a symbol.

To supporters, it represents a cleaner future: no gas stations, no tailpipe smoke, no engine rumble, no carbon dioxide pouring out the back. To critics, it is a taxpayer-subsidized illusion, a car that merely hides its pollution somewhere else: in a coal plant, a lithium mine or a battery factory overseas.

Both stories contain a piece of the truth. Neither tells the whole story.

The best evidence points to a conclusion that is less satisfying for a political slogan but more useful for anyone trying to understand the issue. Electric vehicles are not zero-impact. They are not a cure-all for the environmental costs of driving. But compared with similar gasoline vehicles, they usually produce substantially less climate pollution over their full lives, even after accounting for battery manufacturing and the electricity used to charge them.

That does not make every electric vehicle equally clean. A small electric hatchback charged in a state with a relatively clean power grid is not the same as a 7,000-pound electric pickup charged in a region still heavily dependent on fossil fuels. Nor does it mean that replacing every gasoline car with an electric one would solve traffic, sprawl, road deaths, tire pollution or the environmental damage of mining.

But it does mean that one of the most common claims in the public debate, that electric cars are no cleaner than gas cars once you “count everything,” is not supported by mainstream evidence.

The important question is what, exactly, counts as “everything.”

For a gasoline car, the obvious pollution comes out of the tailpipe. Every gallon of gasoline burned releases carbon dioxide, the main heat-trapping gas driving climate change. But the full picture also includes drilling for oil, transporting it, refining it into gasoline and moving it to stations.

For an electric vehicle, the picture is different. There is no tailpipe. But there are emissions from building the car, manufacturing the battery, mining and processing minerals, generating electricity and eventually recycling or disposing of the battery. A fair comparison has to count all of that.

This is called a life-cycle analysis. In plain English, it means looking at a vehicle from production to retirement rather than judging it only by what happens while it is being driven.

When researchers do that, electric vehicles generally come out ahead.

The International Energy Agency, which tracks global energy systems, has found that a medium-size battery-electric car sold in 2023 produces roughly half the life-cycle greenhouse-gas emissions of a comparable gasoline car over about 15 years of driving. The exact number depends on where the car is used and how electricity is generated. But even if the power grid did not become cleaner over the vehicle’s lifetime, the agency found that battery-electric vehicles would still have a sizable emissions advantage.

The United States government’s own modeling points in the same direction. The Department of Energy and Argonne National Laboratory, using the widely cited GREET life-cycle model, have estimated that a representative 2025 electric vehicle produces about 46 percent fewer life-cycle greenhouse-gas emissions than a comparable gasoline vehicle using the average U.S. electricity mix. The Environmental Protection Agency similarly concludes that electric vehicles typically have a smaller carbon footprint than gasoline vehicles, even after accounting for battery production and charging.

Why does the electric car do so well, even when some of its electricity comes from fossil fuels?

The answer is efficiency.

A gasoline engine is an impressive technology, but it is also a wasteful one. Much of the energy in gasoline is lost as heat. Only a fraction becomes motion. The E.P.A. has estimated that conventional gasoline cars convert only about 16 to 25 percent of the energy in gasoline into movement at the wheels. Battery-electric vehicles, by contrast, convert a much larger share of stored energy into motion. The agency puts that figure around 87 to 91 percent from battery to wheels.

That efficiency advantage is enormous. It means that even when electricity generation produces emissions, the electric drivetrain often uses energy so much more efficiently that total emissions are lower.

This is the part of the story often missing from the claim that electric cars “just move pollution from the tailpipe to the power plant.” There is a grain of truth there. Some pollution does shift from the road to the electricity system. But it does not shift one-for-one. Power plants can be more efficient than millions of small engines, and electricity grids can be cleaned up over time by adding wind, solar, nuclear, hydro or other lower-carbon sources. A gasoline car bought today will keep burning gasoline for as long as it is driven.

Still, the critics are right about one important thing: Electric cars start with an environmental debt.

Manufacturing an electric vehicle, especially its battery, usually creates more emissions than manufacturing a comparable gasoline vehicle. Batteries require energy-intensive production and minerals such as lithium, nickel, cobalt, manganese and graphite. Mining and processing those materials can damage ecosystems, consume water and create toxic waste. In some places, mineral supply chains have also raised serious labor and human rights concerns.

That is not a footnote. It is central to the environmental accounting.

But a gasoline vehicle also has a supply chain. It depends on oil extraction, refining, shipping, pipelines, tanker trucks and combustion. The key difference is that battery-related emissions are mostly front-loaded, while gasoline emissions continue mile after mile. Over time, the gasoline car’s operating emissions usually overtake the electric car’s higher manufacturing emissions. The more the vehicle is driven, and the cleaner the electricity used to charge it, the clearer the electric vehicle’s climate advantage becomes.

A simple example helps. Imagine two similar cars leaving the factory, one gasoline and one electric. The electric car may begin with a larger carbon footprint because of its battery. But once both are on the road, the gasoline car emits carbon dioxide every time its engine runs. The electric car’s emissions depend on the electricity used to charge it. On a cleaner grid, the electric car pays back its manufacturing disadvantage relatively quickly. On a dirtier grid, the payback takes longer. But in most cases studied, the electric car eventually pulls ahead.

That does not mean the grid is irrelevant. Far from it.

An electric car in a region where electricity comes largely from coal will have a smaller climate advantage than one charged in a region with abundant renewables, nuclear power or hydropower. The same car can have different life-cycle emissions depending on where it is plugged in. This is why broad claims about electric vehicles can be misleading. “EVs are clean” and “EVs are dirty” are both too blunt. The better question is: compared with what, charged where, driven how far, and built how?

Vehicle size matters too. One of the uncomfortable truths of the electric-vehicle boom is that automakers have brought the same appetite for size and weight into the electric era. A huge electric SUV or pickup needs a bigger battery, more minerals and more energy than a smaller electric car. It may still emit less over its life than a comparable gasoline truck, but it will not be as clean as a smaller, lighter EV.

This is where political and media narratives tend to flatten the issue.

Pro-EV messaging often leans heavily on the phrase “zero-emission vehicle.” In regulatory language, that usually means zero tailpipe emissions. But to an ordinary person, it can sound like zero emissions, period. That is not accurate. Electric vehicles have no exhaust pipe, which is a major benefit, but they are not free of environmental costs.

Anti-EV messaging makes the opposite move. It points to real costs, including mining, battery production and fossil-fuel electricity, and then leaps to the much stronger claim that electric vehicles are no better than gasoline cars. That leap is where the argument usually breaks down. The caveats are real. The conclusion often is not.

Air pollution adds another layer.

Because electric vehicles have no tailpipe, they eliminate exhaust emissions where they are driven. That matters for people living near highways, ports, warehouses and busy roads, where vehicle pollution contributes to asthma, heart disease and other health risks. Replacing gasoline and diesel vehicles with electric ones can reduce local pollutants such as nitrogen oxides and tailpipe particles.

But no car is pollution-free. Vehicles also create non-exhaust particulate pollution from tires, brakes and road dust. Electric vehicles often use regenerative braking, which can reduce brake wear by using the motor to slow the car and recover energy. But they can also be heavier because of their batteries, and heavier vehicles can wear tires more quickly.

The Organization for Economic Cooperation and Development has estimated that electric vehicles generally emit somewhat less PM10, a category of larger inhalable particles, from non-exhaust sources than internal-combustion vehicles. But for the smaller PM2.5 particles, which can penetrate deep into the lungs, very heavy electric vehicles may emit more than comparable gasoline vehicles because of weight-related tire and road wear.

This is another reason the cleanest electric car is not simply the one with the largest battery or longest range. Smaller, lighter vehicles are better not only for climate emissions but also for road safety, tire pollution and material demand.

Battery recycling is often presented as a solution to the mineral problem. It can help, but it is not a magic wand.

In the long run, recycling could reduce the need for new mining by recovering valuable materials from old batteries. The International Energy Agency has projected that effective recycling could eventually cut demand for newly mined lithium, nickel and cobalt substantially, especially as the first large generations of EV batteries reach end of life. But the recycling system is still developing, and for now the rapid growth of electric vehicles means new mining remains necessary.

The strongest environmental case for electric vehicles, then, is not that they are harmless. It is that they are less harmful than the dominant alternative, particularly for climate change.

That distinction matters.

Climate change is driven by cumulative emissions. Carbon dioxide stays in the atmosphere for a long time. The transportation sector is one of the major sources of greenhouse gases. Passenger vehicles are a large part of that. If a technology can cut the lifetime emissions of cars by a large fraction, it matters, even if it does not eliminate emissions entirely.

But it also matters how the transition is carried out.

An electric future built around ever-larger vehicles, long commutes, sprawling development and disposable consumer habits will carry large environmental costs. An electric future built around smaller vehicles, cleaner grids, better transit, safer streets, battery recycling and fewer unnecessary car trips would deliver much larger benefits.

Claim or topic:

Battery minerals can create environmental and social concerns, including mining impacts, water use, toxic waste, displacement, and labor or human-rights risks.

Source:

International Energy Agency, The Role of Critical Minerals in Clean Energy Transitions

Amnesty International, DRC: Cobalt and copper mining for batteries leading to human rights abuses

U.S. Department of Energy, 2023 Critical Materials Assessment

Source type:

Expert organization analysis; human-rights investigation; government assessment.

What it supports:

The IEA identifies lithium, nickel, cobalt, manganese, and graphite as important battery materials and discusses supply-chain, environmental, and social risks tied to critical-mineral production. Amnesty documents alleged forced evictions and other human-rights abuses linked to industrial cobalt and copper mining in the Democratic Republic of Congo. The DOE assessment provides U.S. government context on critical materials used in batteries and clean-energy technologies.

Important caveat:

These sources do not show that EVs are environmentally worse than gasoline vehicles overall. They support the narrower point that battery supply chains have real environmental and social risks that should be counted, managed, and reduced.

How to read this evidence

Corrections and updates

Subscribe now

The Gun Law That Survives the Evidence

Evidence First — Wed, 26 Aug 2026 13:51:44 GMT

Start with a country that does not yet exist.

Its founders are gathered around a table, writing the first public-safety laws of a new republic. There is no inherited constitutional fight over firearms, no warehouse of private weapons already scattered across millions of homes, no decades-old slogans carved into politics. There is only a blank page and a practical problem.

Who, if anyone, should be allowed to have a gun?

The first answer is the cleanest: no one. If guns make violence more deadly, then the safest country must be one without civilian firearms. Ban them all, and the problem shrinks before it begins.

The second answer has its own appeal: let responsible people defend themselves. Police cannot be everywhere. Attackers may arrive before the state does. In a dangerous world, perhaps an armed public is not the problem but the backup plan.

Those two answers dominate much of the public argument over guns. One imagines safety through disarmament. The other imagines safety through deterrence. Both contain a truth. Neither survives the evidence intact.

The question matters because guns change the outcome of moments that might otherwise end differently. Most gun owners will never shoot anyone. Many will never draw a weapon. But public policy is not written for the average Tuesday afternoon. It is written for the night when a domestic argument turns violent, the hour when a teenager finds a parent’s pistol, the minute when a suicidal person reaches for the most lethal object nearby, the encounter when anger becomes a killing before anyone has time to reconsider.

A major systematic review and meta-analysis published in Annals of Internal Medicine found that access to firearms in the home was associated with substantially higher odds of suicide and homicide victimization. The evidence was observational, which means it cannot prove causation with the certainty of a randomized trial. But randomized trials assigning households guns would be unethical and impossible. For this question, the best available evidence is necessarily imperfect, and still deeply relevant.

Suicide is the clearest example of why access matters. Many suicide attempts are impulsive. The method available in the moment can determine whether a person dies or survives long enough for the crisis to pass. The World Health Organization identifies limiting access to lethal means as one of its evidence-based suicide-prevention strategies. Depending on the country, that can mean restricting pesticides, placing barriers at jumping sites or reducing access to firearms. The common idea is not mysterious: when the deadliest method is harder to reach, some people live.

If guns make crises more lethal, the cleanest answer seems obvious: remove the guns. That is the moral force behind a total civilian ban.

A country starting from scratch would have one great advantage over countries already saturated with weapons: it could prevent a private arsenal from forming in the first place. There would be no need to persuade millions of existing owners to surrender guns, no need to buy back weapons purchased over decades, no need to trace firearms that had long ago disappeared into private storage. In theory, the new state could say that civilian gun ownership is simply not part of its public order.

That case should be taken seriously. The fewer guns in homes, cars, bars, schools and domestic disputes, the fewer chances there are for firearms to turn fear, despair or rage into death. A peaceful country with strong border controls, little rural need for firearms and high public trust might rationally choose something close to a civilian ban.

But the moment a total ban becomes an actual law, the clean line starts to blur.

Does it ban a shotgun locked in a rural armory for pest control? A rifle used by a licensed biathlete? A museum weapon rendered inoperable? A tranquilizer firearm used by wildlife officials? A community defense unit in a border town where police may not arrive for half an hour? The closer one gets to real governance, the more exceptions appear. The question becomes whether to pretend they do not exist, or to regulate them so tightly that they do not become a path to general gun ownership.

Then there is the harder problem: a ban is only as strong as the state behind it. If the government cannot control imports, theft, corruption, illegal manufacturing and trafficking, a paper ban may mostly bind the compliant while leaving illegal actors armed. That does not mean permissive laws are safer. It means that the real object of policy is not just possession, but control: who can acquire a weapon, how it is traced, where it is stored, when it is removed and how illegal supply is interrupted.

And that weakness in the total-ban argument leads directly to the opposite temptation. If the state cannot always keep weapons out, and cannot always arrive in time, perhaps citizens should not be asked to wait unarmed.

The intuitive appeal is easy to understand. A shopkeeper facing a robber, a parent protecting a child, a citizen caught in an attack: these examples carry emotional force because they describe moments when waiting for help may not be enough. Defensive gun use does happen. A serious policy argument should not deny that.

But vivid examples are not the same as population-level evidence. A gun carried in public can stop an attack. It can also escalate an argument, be stolen, be misread by police, intensify domestic intimidation or turn a fistfight into a shooting. The national policy question is not whether a gun can ever be used defensively. It is whether broad public carrying makes society safer overall.

The evidence does not give much comfort to the “arm everyone” theory. RAND’s review of gun-policy research reports supportive evidence that more permissive concealed-carry laws are associated with increases in total homicides, firearm homicides and violent crime. Gun-law studies are difficult because jurisdictions often change multiple policies at once, enforcement varies and cultural conditions differ, so no single estimate should be treated as the final word. But the pattern is still important for lawmakers: when carrying guns in public becomes common, the aggregate risks appear to outweigh the hoped-for deterrent benefits.

Once the promise of deterrence weakens, the policy question changes. It is no longer how many people can be armed, but which risks can be identified before a gun is misused.

That is where targeted restrictions matter. RAND finds evidence that child-access prevention laws reduce firearm harms among young people, that waiting periods reduce firearm suicides and total homicides, and that domestic-violence firearm prohibitions reduce intimate-partner homicides. These policies share an insight: gun risk is not evenly distributed. It spikes around youth access, acute crisis, domestic abuse and moments of anger or despair. Good policy makes guns harder to reach precisely where the risk is most predictable.

Background checks and permit-to-purchase laws fit the same pattern. Reviews of firearm homicide research have found that stronger background-check and permit-to-purchase systems are associated with reductions in firearm homicide. That does not mean every individual study is perfect or that all policies work equally well. But it reinforces a central point: before the state asks only which weapons are allowed, it should ask who is being licensed, why, with what training and under what conditions permission can be revoked.

The value of that layered approach becomes clearer when a country actually tries to move from widespread access toward tighter control. Australia offers one important example. After the 1996 Port Arthur massacre, Australia adopted the National Firearms Agreement, which banned several types of firearms, tightened licensing and registration, and included a large buyback. RAND’s assessment notes that firearm suicides and homicides fell after the reforms, while also emphasizing an important qualification: some declines had begun before the law, so the exact causal effect cannot be measured with perfect certainty. The Australian experience does not prove every part of a model policy, but it is consistent with a practical lesson: a country can move strongly toward lower civilian gun availability without needing a metaphysical rule that every narrow civilian use must be criminal.

But any universal theory has to survive the hardest case: not a peaceful country, but one under real external threat.

Consider an Israel-like situation: a small state facing armed groups nearby and the possibility of cross-border attacks. On Oct. 7, 2023, Hamas’s military wing and other Palestinian armed groups attacked southern Israel. Human Rights Watch concluded that the attacks included war crimes and crimes against humanity against civilians. In some communities, local defenders and police tried to resist before larger forces arrived. In that setting, the first minutes are not theoretical. They are the difference between waiting helplessly and buying time.

This changes the analysis, but not in the way gun maximalists often suggest. A country under external threat may need armed local defense capacity. Border villages, rural communities, schools, farms or transport hubs may require trained responders who can act before the army or police arrive. But that is not the same as giving everyone a private weapon.

The better analogy is not the armed individual as a free agent. It is a volunteer fire brigade, reserve medical unit or auxiliary defense team: selected, trained, equipped, supervised and accountable. The weapons are not symbols of personal sovereignty. They are tools assigned for a public function. The members are vetted. Their authority is defined. Their storage is inspected. Their permission can be removed after misconduct, domestic-violence concerns, mental-health disqualification or failure to train.

The same gun that may help defend a border town can still be misused in a home, a political confrontation or a mistaken encounter. That is why external threat strengthens the case for organized defense capacity, not for abandoning screening, storage rules and accountability.

This distinction matters because fear can easily become a pipeline to uncontrolled armament. After a major attack, people understandably want immediate protection. Governments feel pressure to distribute weapons quickly. But speed can weaken safeguards, the very rules that separate public defense from vigilantism.

If even the hardest security case does not justify arming everyone, and the quietest peacetime case does not always require banning every last gun, the answer lies in control rather than absolutism.

The better system is one in which civilian access is rare, justified, traceable, securely stored and easy to revoke when risk rises. In many countries, especially those without acute security threats, that would mean something close to a near-ban: no general right to own a firearm, no ordinary handgun ownership, no routine public carry and no military-style weapons in civilian hands. But it would leave room for narrow exceptions, such as a farmer, a sport shooter, a wildlife officer or a trained border-town response team, under rules strict enough that permission can be withdrawn as easily as it is granted.

This is not a soft position. It would likely ban ordinary civilian possession of handguns, automatic weapons, military-style semi-automatic rifles and high-capacity magazines. It would make public carry rare. It would control ammunition. It would require every legal weapon to be marked, registered and traceable. That approach fits the logic of the United Nations Firearms Protocol, which emphasizes marking, recordkeeping, tracing and controls against illicit manufacturing and trafficking.

It is also not a single universal statute. Countries differ too much for that. A peaceful island state with strong border control may rationally choose something close to a civilian ban. A rural agricultural country may permit licensed long guns for pest control while banning handguns and public carry. A post-conflict country with many illegal weapons may need amnesties, buybacks, tracing and anti-trafficking enforcement before paper restrictions become meaningful. A country facing cross-border raids may need trained local defense teams.

But the universal principles are clearer than the politics around them.

No one should receive a firearm by default. Permission should require a reason. The reason should be verified. The person should be screened and trained. The weapon should be registered and traceable. The gun should be stored securely. Public carry should be rare. High-risk weapons should be banned or confined to tightly controlled state-supervised uses. Domestic violence, violent crime, credible threats and acute self-harm risk should trigger suspension or removal, with due process. Security exceptions should be institutional, not improvised through private fear.

The blank-page thought experiment matters because most countries do not write gun law on a blank page. They inherit weapons, institutions, fears, legal traditions and political compromises. Among wealthy democracies, nowhere is that clearer than in the United States.

Modern Second Amendment doctrine begins from a premise almost opposite to the one suggested by an evidence-first approach: that ordinary citizens have an individual constitutional right to possess firearms for lawful purposes, especially self-defense. In District of Columbia v. Heller, the Supreme Court held that the Second Amendment protects such an individual right and struck down Washington, D.C.’s handgun ban. Later doctrine has generally required gun regulations to be justified by constitutional text and historical tradition, not simply by a legislature’s judgment that a law would improve public safety.

An evidence-first system would begin somewhere else. It would treat access to firearms not as a default entitlement, but as a conditional public license. The analogy would not be speech, worship or voting. It would be closer to driving, prescribing narcotics, flying an aircraft or handling hazardous materials: lawful for qualified people under regulated conditions, but not something everyone may do simply because they want to. In that model, licensing is not a burden on a right; it is the condition that makes access lawful.

Corrections and updates

Subscribe now

When A.I. Helps You Write, Whose Idea Is It?

Evidence First — Sat, 22 Aug 2026 12:14:25 GMT

Imagine that you have spent several days thinking through an argument. You know what you believe, why you believe it and what examples support your view. Perhaps you even have a rough outline. What you do not have is a polished piece of writing.

So you open an artificial intelligence system and ask it to organize your argument, make it clearer, remove repetition and improve the transitions without adding new ideas. A few seconds later, the prose is better. The structure is tighter. A clumsy sentence has become an elegant one. What had been a pile of notes now reads like an essay.

Then someone tells you that you did not really write it. The A.I. did.

Did it?

The question matters because generative A.I. is becoming part of ordinary intellectual work. Students use it to revise essays. Office workers use it to prepare reports and emails. Researchers use it to summarize material or test arguments. Writers use it to brainstorm, restructure and edit. Yet much of the debate still treats A.I. use as a binary matter. Either a person wrote something or a machine did.

That distinction is too simple. There is a difference between using A.I. to express a thought and using A.I. to supply the thought itself. The difficulty is that generative A.I. can do both, sometimes in the same conversation.

To see where the difference lies, it helps to begin with tools whose role is easier to understand.

A Tool Can Do the Work Without Owning the Idea

Consider a calculator. Suppose an analyst wants to know whether a company’s revenue increased faster than inflation. The analyst chooses the relevant years, identifies the numbers, selects the calculation and interprets the result. The calculator performs the arithmetic.

No one seriously thinks the calculator deserves intellectual credit for the conclusion. The reason is not that the machine did little. It may have performed thousands of operations. The reason is that the machine did not decide what problem mattered, which inputs belonged in it or what the answer meant.

Spell-check makes the point even more starkly. It may alter dozens of words in a manuscript without becoming the source of the author’s argument. Statistical software occupies a more complicated position. A program can fit elaborate models and perform calculations that would be unrealistic to do by hand, yet the central intellectual choices may still belong to the researcher. The researcher decides what question to ask, which variables to include, which model is appropriate and how the result should be interpreted.

The amount of labor a tool performs is not the same thing as the amount of intellectual authorship it deserves. The harder test comes when assistance reaches beyond calculation or correction and begins shaping the language in which an idea is expressed.

Editors Have Always Complicated Authorship

An author sends an editor a draft with a clear argument but poor organization. The editor moves paragraphs, cuts repetition, sharpens the opening and replaces awkward language with more precise prose. The published version is substantially better.

Does the editor now own the argument?

Usually, no.

Professional authorship conventions already recognize this distinction. In medical and scientific publishing, standards used by the International Committee of Medical Journal Editors distinguish substantive intellectual contributions, such as developing a study’s conception or interpreting its findings, from writing assistance, technical editing, language editing and proofreading.

A similar distinction appears in the CRediT taxonomy used to describe contributions to academic research. It treats conceptualization separately from drafting, review and editing. These practices reject a simple assumption. The person who produces the final sentence is not necessarily the person who originated the thought inside it.

A speechwriter can improve a politician’s language without inventing the politician’s beliefs. A book editor can transform a manuscript without becoming the source of its thesis. A communications employee can turn a chief executive’s notes into a polished letter while leaving the underlying judgment intact.

So far, A.I. looks familiar. The difficulty begins when the tool can generate the choices themselves.

Where the Calculator Analogy Breaks

Suppose someone begins with a belief that social media has made political discussion worse. One use of A.I. would be to provide three reasons for that belief and ask the system to organize them. That looks much like editing.

But another use would be to ask for the strongest arguments supporting the claim.

Now the system may suggest mechanisms the user had not considered. It might mention algorithms that reward outrage, social incentives that favor extreme statements, the collapse of different audiences into a single online space, or the rapid spread of emotionally charged falsehoods. The user may find those arguments persuasive and adopt them.

At that point, something important has changed. The conclusion may still have originated with the person, but some of the reasons supporting it did not.

Research on human and A.I. collaboration suggests that this is more than a hypothetical concern. In one controlled study of creative writing, participants who received ideas from generative A.I. produced stories judged more creative on average, but those stories also became more similar to one another. The system was not merely correcting grammar. It was influencing what people produced.

Other experiments have explored whether language models can generate research proposals that human evaluators regard as novel. Such studies have important limitations. Novelty is difficult to measure, and an apparently original idea may be impractical or wrong. Still, they reinforce the broader point that generative systems can contribute candidate ideas, not merely language.

A calculator can answer a question chosen by a person. A generative system can also suggest the question, the argument and the conclusion. Once a tool can contribute substantive reasoning rather than merely execute instructions, the better comparison is no longer a calculator. It is another mind helping to write.

The Ghostwriter Is a Better Analogy

Consider two politicians preparing speeches.

The first gives a speechwriter a detailed position. The politician opposes a tax proposal, believes it would burden small businesses, thinks it fails to solve the underlying problem and wants to propose a narrower alternative. The politician also provides a personal example and asks the writer to address the strongest objection.

The speechwriter turns those instructions into a polished speech. Most people would still regard the political position as belonging to the politician. The speechwriter deserves substantial credit for expression, structure and rhetoric, but the judgment and argument were supplied beforehand.

Now imagine the second politician asks the ghostwriter to figure out what the position should be and write the speech. The ghostwriter chooses the position, develops the reasons, selects the evidence and writes the speech. The politician reads it and agrees.

The politician may now sincerely believe the argument, but agreement is not the same as origination.

Generative A.I. can play either role. It can turn a developed human position into better prose, or it can supply much of the intellectual content and leave the human mainly to approve, reject or modify what has been proposed.

That seems to offer a simple test. Ask who supplied the ideas.

But that test assumes ideas exist fully formed before writing begins. Often they do not.

Writing Can Change What We Think

Writing is not simply the act of recording finished thoughts. People often discover what they believe by trying to explain it. A paragraph that will not come together may reveal a contradiction. An analogy may expose a connection the writer had not seen. Rearranging an argument can change which conclusion seems strongest.

Research on writing has long treated planning, drafting and revision as interacting mental processes rather than a simple sequence in which thought is completed first and merely transcribed afterward.

That means A.I. can influence thought even when the user initially intends only to improve expression.

Suppose someone gives an A.I. system five points and asks it to arrange them. The system notices that two imply a sixth point and includes it in a transition. The writer reads the sentence and realizes that the connection is correct and important, even though it had not previously been noticed.

Who owns the insight?

There may be no perfectly objective answer, but blurry boundaries do not make the distinctions meaningless. Human beings have always developed ideas through interaction with other minds. Teachers ask questions that change students’ views. Editors expose weaknesses that force authors to rethink arguments. Colleagues notice implications that someone else has missed.

Outside influence does not automatically erase authorship. But it raises a harder question. If an idea first enters the conversation from somewhere else, can it later become genuinely yours? Answering that requires separating having a belief from originating it.

An Idea Can Become Yours Without Starting With You

Almost none of us develops a worldview in isolation. We inherit concepts from books, teachers, parents, colleagues and culture. Someone may first encounter an argument in a classroom, spend years testing it, reject parts of it and eventually adopt a modified version as a deeply considered belief.

That belief is not necessarily inauthentic. But there is still a difference between saying that something is genuinely what you think and saying that you came up with the idea independently. A person can truthfully make the first claim while the second is false.

The same is true with A.I.

If a chatbot presents an argument the user has never encountered, and the user scrutinizes it, tests it against evidence, modifies it and eventually adopts it, the resulting view may genuinely become that person’s considered judgment. But sincere adoption does not change where the argument originated.

That distinction matters especially in education, scholarship and any setting in which someone is claiming credit for independent intellectual work.

By this point, the original question has split apart. Asking whose work something is really means asking who supplied the ideas, who shaped the expression and who stands behind the result.

There Is More Than One Kind of Authorship

One question concerns intellectual authorship. Who supplied the thesis, interpretation, reasoning or judgment?

Another concerns compositional authorship. Who supplied the wording, structure, examples and rhetorical presentation?

A third concerns responsibility. Who understands the argument, endorses it and is prepared to defend or correct it?

Those roles can be divided. A researcher can conceive an experiment while a statistician performs part of the analysis. An executive can determine the substance of a letter while a communications team drafts it. An author can develop an argument while an editor improves its presentation.

The United States Copyright Office has made a related distinction in its treatment of works involving artificial intelligence. Copyright law is not a complete theory of intellectual authenticity, but the office has distinguished between using A.I. as an assisting tool and allowing a system to determine expressive elements itself.

The broader principle is useful. Machine assistance does not automatically eliminate human authorship. What matters is what the human actually contributed and controlled.

Once those contributions are separated, the practical question becomes easier. What intellectual work did the human bring to the process before the system intervened?

A Better Test Than Asking Whether A.I. Was Used

Several questions help distinguish one kind of A.I. assistance from another.

Before using A.I., could the person explain the main conclusion and the principal reasons supporting it? Would the central insight probably have existed if the A.I. had never suggested it? Was the system deciding mainly how to express something, or was it also deciding what should be said? And can the person explain and defend every substantive step in the final argument without depending on the machine’s wording?

These questions separate uses that otherwise look identical.

Asking A.I. to fix grammar involves almost no transfer of intellectual authorship. Asking it to make an existing argument concise and persuasive without adding new ideas leaves the intellectual content largely with the human, even if the system supplies much of the final language.

Asking A.I. to organize notes gives the system some control over presentation and perhaps over emphasis. Asking it to generate the strongest objections to a position introduces outside intellectual content, even though the original thesis remains the user’s. Asking for the best arguments supporting an existing belief leaves the conclusion human but may make the supporting reasoning substantially A.I. generated.

Asking A.I. what one should think about a subject and then asking it to write the essay delegates much of both the thinking and the expression.

Calling all of those activities simply A.I. use hides the distinction that matters most. And it brings us back to the person at the beginning.

The Intellectual Credit Should Follow the Contribution

They came to the machine with a conclusion already formed, reasons already considered and a point of view already their own. The A.I. did not tell them what to believe. It helped them say what they already believed more clearly.

That assistance is real. The final wording may not be entirely theirs, and in some academic or professional settings the use of A.I. may need to be disclosed. But neither fact settles the more important question of where the intellectual work came from.

The question becomes harder when the machine supplies more than language. If it proposes the central argument, discovers the decisive connection, generates the reasons that make the conclusion persuasive or produces an interpretation the user had not reached, then some of the thinking has come from outside the person.

Much A.I. use will fall between those extremes. A machine may sharpen an argument and reveal something the writer had not noticed. A suggestion may begin as external input and later become a deeply considered human judgment. There will not always be a precise point at which assistance becomes intellectual contribution.

But the absence of a perfect boundary does not make every case equally ambiguous. What matters is not simply how much work the helper performed, but what kind of work it was.

If the judgment, reasoning and point of view were substantially yours before the machine helped articulate them, clearer language does not make the thought less authentically yours. If the machine supplied the judgment or reasoning on which the work depends, then intellectual credit should reflect that contribution too.

The question worth asking is not whether A.I. touched the words.

It is what intellectual work the human contributed, and what intellectual work the machine supplied.

The intellectual credit should follow the intellectual contribution.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Authorship and writing assistance

Claim or topic:

Professional publishing standards distinguish substantive intellectual contributions from writing assistance, editing and proofreading.

Source:

International Committee of Medical Journal Editors

Source type:

Expert organization.

What it supports:

ICMJE authorship standards treat conception, interpretation and accountability as central to authorship. Writing assistance, technical editing, language editing and proofreading alone do not qualify someone for authorship.

Important caveat:

These are biomedical publishing standards, not a universal philosophical definition of authorship.

2. Separating ideas from drafting and editing

Claim or topic:

Research contribution standards can treat conceptualization, drafting and editing as different kinds of work.

Source:

CRediT Contributor Roles Taxonomy

Source type:

Primary document.

What it supports:

The CRediT taxonomy separately identifies contributions such as conceptualization, writing an original draft, and writing through review and editing. This supports the article’s distinction between originating an idea and helping express it.

Important caveat:

CRediT is a framework for describing research contributions. It does not determine philosophical ownership or settle every authorship dispute.

3. Generative A.I. can influence creative output

Claim or topic:

Generative A.I. can affect what people produce, not merely the grammar or presentation of existing material.

Source:

Science Advances study on generative A.I. and creative writing

Source type:

Academic research.

What it supports:

In a controlled creative-writing experiment, access to generative A.I. ideas increased judged creativity for individual stories while also making the resulting stories more similar to one another.

Important caveat:

The experiment involved a particular creative-writing task. It does not show that all A.I.-assisted writing works the same way or determine who deserves authorship credit.

4. A.I. can generate candidate research ideas

Claim or topic:

Language models can contribute candidate ideas that human evaluators may regard as novel, rather than serving only as editing tools.

Source:

Can LLMs Generate Novel Research Ideas?

Source type:

Academic research.

What it supports:

The study tested research proposals generated by language models with more than 100 NLP researchers and found that some machine-generated proposals were judged highly novel.

Important caveat:

This was a preprint, and judged novelty is not the same as scientific validity, feasibility or successful discovery. Evaluating novelty itself is also difficult.

5. Human control and A.I.-assisted copyright

Claim or topic:

Using A.I. as an assisting tool does not automatically eliminate human authorship for copyright purposes.

Source:

U.S. Copyright Office, Copyright and Artificial Intelligence

Source type:

Government analysis.

What it supports:

The Copyright Office distinguishes between human use of A.I. as an assisting tool and situations in which expressive material is generated by the system without sufficient human authorship.

Important caveat:

Copyright law answers a legal question about protectable authorship. It does not by itself determine whether an idea is intellectually or personally authentic.

6. Planning matters in A.I.-assisted writing

Claim or topic:

Research on human and A.I. writing distinguishes assistance with planning from assistance with drafting and revision, making the stage at which A.I. enters the process relevant to intellectual contribution.

Source:

Systematic review of human-A.I. writing research

Source type:

Academic research.

What it supports:

The review examines A.I. assistance across different stages of writing, including planning, formulation and revision. It supports the article’s point that help with developing what to say is meaningfully different from help expressing an already developed position.

Important caveat:

The review was released as a preprint, and the studies it surveys vary substantially in design, population and quality.

7. Generative A.I. in everyday intellectual work

Claim or topic:

Students, knowledge workers, researchers and professional writers use generative A.I. for activities including summarizing, drafting, editing, brainstorming, organizing material and other forms of writing assistance.

Source:

HEPI Student Generative AI Survey 2025

Microsoft Research, Shifting Work Patterns with Generative AI

Nature, How ChatGPT has changed scientists’ lives

Authors Guild survey of writers and generative A.I.

Source type:

Survey research, field research, reputable journalism and expert-organization survey.

What it supports:

The HEPI survey found widespread generative A.I. use among surveyed university students for tasks including explaining concepts, summarizing material and suggesting research ideas. Microsoft researchers examined how thousands of knowledge workers used generative A.I. integrated into workplace tools. Nature reported researchers using ChatGPT for tasks including polishing academic writing, reviewing literature and coding. An Authors Guild survey found writers using generative A.I. for activities including grammar review, brainstorming and organizing drafts.

Important caveat:

These sources examine different populations, tasks and time periods, so they should not be combined into a single estimate of how common A.I. use is across society. The Authors Guild is an advocacy organization, and Microsoft develops commercial A.I. products, which are relevant considerations when interpreting their findings. Together, the sources support the narrower claim that these forms of A.I. assistance are already being used across education, knowledge work, scientific research and professional writing.

How to read this evidence

Corrections and updates

Subscribe now

The Surprisingly Simple Science of Better Small Talk

Evidence First — Tue, 18 Aug 2026 18:42:49 GMT

Small talk has a branding problem.

The phrase itself suggests something trivial: verbal filler deployed in elevators, at conferences and beside bowls of chips at parties. It is the conversation people imagine they must endure before reaching something more meaningful, or escape before it becomes awkward.

And because many people assume they are bad at it, small talk has generated an enormous cottage industry of advice. Memorize a list of topics. Maintain the right amount of eye contact. Ask open-ended questions. Never talk too much. Be interesting. Be confident. Have a story ready.

The scientific evidence points toward something simpler.

Good small talk appears to depend less on performing well than on responding well. People tend to like conversation partners who ask follow-up questions, listen in ways that demonstrate attention and reveal enough about themselves to make the exchange reciprocal. They also appear to underestimate how much strangers will enjoy talking to them, how warmly they will be received and how satisfying a conversation can become if it moves beyond the most superficial level.

That does not mean there is a scientifically validated formula for charming anyone in five minutes. Much of the research involves brief conversations, often among Western participants, and usually measures immediate outcomes such as liking, enjoyment or connection rather than whether two strangers become friends a year later. But taken together, the findings offer a useful correction to how people often approach conversation. The central task may not be to impress another person. It may be to give that person evidence that you are paying attention.

We Expect Strangers to Be Less Pleasant Than They Are

One obstacle appears before a conversation even begins: people often expect social interaction to go badly.

In a series of experiments published in 2014, the behavioral scientists Nicholas Epley and Juliana Schroeder examined what happened when commuters were instructed either to interact with a stranger, remain disconnected or behave as they normally would. Many participants predicted that talking would make the trip less pleasant. Instead, those assigned to connect with another person generally reported a more positive experience. The strangers they approached did not, on average, react with the hostility or irritation people seemed to anticipate.

This gap between expectation and experience matters because avoidance can reinforce itself. If you believe talking to strangers will be uncomfortable, you may avoid doing it. Because you avoid it, you never accumulate much evidence that your prediction was too pessimistic.

Similar pessimism appears after conversations as well. In research on what psychologists call the “liking gap,” people consistently underestimated how much their conversation partners liked them. After an interaction, participants tended to think they had made a less favorable impression than their partners actually reported.

That finding will be familiar to anyone who has replayed a conversation on the way home: Why did I say that? Was I talking too much? Did that joke land? Did they actually want to leave? The other person may be performing an entirely different post-mortem, possibly worrying about what you thought of them.

The lesson is not that every interaction goes wonderfully. Some conversations are awkward. Some people are distracted, tired or simply uninterested. The evidence supports a narrower conclusion: our forecasts of social failure are often too negative.

That suggests a useful first principle of small talk: lower the standard for beginning. An opening question does not have to be brilliant. “How do you know the host?” can be enough. So can “Have you been to one of these before?” or “What brought you here?” The opener is not the performance. It is the door.

The Power of the Second Question

Once a conversation begins, one of the better-supported findings in the literature concerns questions.

Researchers led by Karen Huang at Harvard examined how question-asking affects interpersonal liking. Across several studies, people who asked more questions tended to be better liked by their conversation partners. But the most revealing finding involved follow-up questions.

A follow-up question is different from simply producing another question from a mental list. It responds directly to something the other person has just said.

Consider two conversations. In the first:

“What do you do?”

“I’m a nurse.”

“Oh. Where are you from?”

“Chicago.”

“Do you have siblings?”

The questions keep coming, but the conversation barely develops. Each answer is treated as a dead end.

Now compare:

“What do you do?”

“I’m a nurse.”

“What kind of nursing?”

“Emergency medicine.”

“That sounds intense. What made you choose it?”

The second exchange does more than collect information. It communicates that the first answer mattered.

This is one reason the distinction between being “interesting” and being “interested” can be useful. A person who is preoccupied with appearing interesting may spend much of a conversation searching for the next clever thing to say. A person who is interested has an easier task: notice what was just said and become curious about it.

The research does not imply that asking endless questions will make someone universally likable. A conversation consisting of relentless interrogation would hardly feel warm. The more defensible conclusion is that questions, particularly responsive ones, can signal attention and interest, which in turn are associated with liking.

The practical implication is to follow threads rather than cycle through topics. If someone says, “I just got back from Japan,” there are dozens of possible directions: “What was your favorite place?” “What surprised you?” “Was it your first time there?” “What made you decide to go?” If the person says Kyoto stood out, the next question can emerge from Kyoto. There is little need to abandon that thread so you can deploy the next item on a prewritten conversational checklist.

Listening Has to Be Visible

“Listen” is among the most common pieces of social advice, but it can be misleadingly vague.

A person can be listening internally while appearing disengaged. Good conversation therefore depends not just on receiving information but on demonstrating that it was received.

That might mean briefly acknowledging what someone said before asking another question. Suppose a colleague tells you she spent the weekend moving apartments. One response is: “Where did you move?” Another is: “That sounds exhausting. Did the move go smoothly?” The second response contains something small but important: evidence that you have registered the emotional meaning of what she said.

Research on high-quality listening has increasingly focused on such observable behaviors, including showing attention, responding appropriately and making speakers feel understood. The evidence base here is newer and less settled than some of the research on question-asking, but it fits a broader pattern: people respond positively not simply to being heard, but to feeling heard.

That does not require theatrical displays of empathy. Constantly saying “That must have been so hard” can sound formulaic if the situation does not call for it. Often, a simple reaction is enough: “That’s hilarious.” “I didn’t know that.” “That sounds like a lot.” “I can see why you’d like it.” Then continue.

A Conversation Is Not an Interview

There is an obvious danger in telling people to ask more questions: they may conclude that the ideal conversation involves saying as little as possible.

The evidence does not support that.

Decades of research on self-disclosure, the process of revealing information about ourselves, suggests that sharing can contribute to liking and connection. A major meta-analysis found several related patterns: people tend to disclose more to people they like, people often like those who disclose to them, and disclosure itself can sometimes increase liking.

But reciprocity matters. If one person continually asks questions while revealing nothing, the interaction can feel less like a conversation than a screening interview.

Suppose someone tells you she loves hiking. A purely interrogative response might be: “Where do you hike?” “How often?” “What’s your favorite trail?” “Do you go alone?”

A more reciprocal exchange could sound like this: “I’ve only gotten into hiking recently, but I’m starting to understand why people get obsessed with it. What’s your favorite place you’ve been?” The speaker has contributed something, but not so much that the conversation has been redirected entirely toward himself.

A useful rhythm is: ask, listen, react, share a little, continue.

There is no scientifically established ideal ratio of talking to listening. In fact, one experiment that manipulated how much participants spoke found that people assigned to speak more were, within the conditions studied, often liked more. That should not be interpreted as a license to monopolize conversations, but it does undermine the assumption that the socially safest strategy is to stay quiet. Being a good listener and being an active participant are not opposites.

Why Slightly Deeper Conversation Can Work

Many people keep small talk shallow because they assume deeper questions will create discomfort.

There is some evidence that this fear is exaggerated.

In experiments comparing relatively shallow conversations with more substantive ones, participants often predicted that deeper discussions with strangers would be more awkward than they actually were. They also underestimated how connected they would feel afterward.

“Deeper” does not necessarily mean asking a stranger about childhood trauma. The difference can be modest.

Instead of “What did you study?” try “What made you choose that?” Instead of “Where are you from?” try “What do you miss most about it?” Instead of “Did you like your trip?” try “What was the best part?”

These questions invite interpretation, preference or emotion rather than merely factual reporting. They give people an opportunity to say something that reveals how they think.

There is an important qualification: intimacy works best when it is reciprocal and responsive. A personal question that feels natural after 20 minutes may feel intrusive after 20 seconds. Context, culture, personality and power relationships all matter.

The research supports moving somewhat beyond surface-level facts more readily than people often expect. It does not support ignoring social boundaries.

The Conversation You Think Went Badly

Perhaps the most reassuring finding in this literature is that people seem to be unusually poor judges of their own social performance.

After a conversation, you know every thought that ran through your head. You know when you lost your train of thought, when you were nervous and when you nearly made a joke but abandoned it. You do not have equivalent access to the other person’s insecurity.

That asymmetry may help explain the liking gap. We evaluate ourselves using a vast amount of private evidence unavailable to everyone else. Your conversation partner mostly sees what you did: you smiled, asked another question, remembered the name of her dog, laughed when she told the story, mentioned that you had once made the same mistake.

From the inside, you may have felt nervous. From the outside, you may simply have looked engaged.

None of this guarantees that someone likes you. But the research gives people reason to distrust the immediate conclusion that an imperfect conversation was therefore an unsuccessful one.

What Science Cannot Tell You

The science of conversation remains much less precise than popular advice sometimes makes it sound.

There is no strong basis for claims that people should maintain a particular percentage of eye contact, speak exactly half the time, memorize a specific set of topics or always use open-ended questions. Such rules may occasionally be useful as training wheels. They should not be mistaken for well-established laws of human interaction.

The strongest research also has limits. Laboratory and field experiments can isolate certain behaviors, but real relationships unfold across months and years. A tactic that increases immediate liking is not necessarily the same thing that produces trust, compatibility or friendship over time.

Cultures also differ in conversational norms. So do individuals. Some people welcome personal questions quickly; others prefer a slower progression. Someone who is anxious, grieving, busy or socially exhausted may not want to talk, no matter how skillfully approached.

The evidence therefore points less toward a script than toward a stance: pay attention, become curious about what the person actually says, respond to it, offer enough of yourself that the exchange feels mutual and, when an opening appears, do not be afraid to move one step beyond facts toward what someone found surprising, difficult, funny or meaningful.

Small talk, viewed this way, is not really about mastering a collection of topics. It is the practice of showing another person, in small increments, that what they say has consequences for what you say next.

And that may be why the most effective conversational move is often not the perfect opening line.

It is the second question.

Subscribe now

Evidence & Source Transparency

Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.

The list below does not source every sentence. It focuses on the factual claims most important to the argument.

1. Follow-up questions and liking

Claim or topic:
People who ask more questions, especially follow-up questions that respond to what the other person just said, tend to be better liked by their conversation partners.

Source:
Huang et al., research indexed by PubMed

Source type:
Academic research.

What it supports:
Across multiple studies, greater question-asking was associated with greater interpersonal liking, with follow-up questions playing an especially important role because they signal responsiveness.

Important caveat:
The studies mostly examined relatively brief interactions and immediate impressions. They do not establish that asking more questions automatically produces stronger long-term relationships.

2. Talking to strangers is often better than people expect

Claim or topic:
People may underestimate how pleasant interactions with strangers will be and sometimes avoid conversations they would actually enjoy.

Source:
Epley and Schroeder, “Mistakenly Seeking Solitude”

Source type:
Academic research.

What it supports:
In field experiments involving commuters, participants assigned to interact with strangers generally reported more positive experiences than participants assigned to remain disconnected, despite expecting interaction to be less pleasant.

Important caveat:
The findings concern particular short-term social situations. They do not mean every stranger wants to talk or that initiating conversation is appropriate in every context.

3. The “liking gap”

Claim or topic:
After conversations, people often underestimate how much their conversation partners liked them and enjoyed the interaction.

Source:
Psychological Science research on the “liking gap”

Source type:
Academic research.

What it supports:
The research found a recurring gap between how positively people believed they were viewed and how positively their conversation partners actually rated them.

Important caveat:
This is an average tendency, not a guarantee that any particular conversation went well. People can still misread interactions in either direction.

4. Self-disclosure, reciprocity and liking

Claim or topic:
Sharing information about yourself can contribute to liking and connection, particularly when disclosure is reciprocal rather than one-sided.

Source:
Collins and Miller meta-analysis indexed by PubMed

Source type:
Academic research.

What it supports:
The meta-analysis found several related patterns: people tend to disclose more to people they like, people tend to like those who disclose to them, and disclosure itself can sometimes increase liking.

Important caveat:
Self-disclosure and liking influence each other, so not every association is purely causal. The appropriate depth of disclosure also depends heavily on context and relationship stage.

5. Speaking more is not necessarily socially harmful

Claim or topic:
The common assumption that people should minimize how much they speak to be better liked is not well supported.

Source:
SAGE Journals study on speaking time and interpersonal liking

Source type:
Academic research.

What it supports:
In an experiment that varied how much participants were instructed to speak, participants assigned to speak more were, within the study conditions, often better liked.

Important caveat:
This does not show that dominating a conversation is beneficial. The study tested a limited range of speaking proportions in a controlled setting, not extreme monopolizing behavior.

6. Deeper conversations can be less awkward than expected

Claim or topic:
People may be too pessimistic about having somewhat deeper conversations with strangers and may underestimate how connected those conversations can make them feel.

Source:
American Psychological Association summary of research on deeper conversations

Source type:
Expert organization summarizing academic research.

What it supports:
The research found that participants often expected deeper conversations to be more awkward and less enjoyable than they turned out to be, while also underestimating the resulting sense of connection.

Important caveat:
“Deeper” does not mean indiscriminately asking highly personal questions. Social boundaries, timing, culture and reciprocity still matter.

7. Responsive listening

Claim or topic:
Listening appears to work best socially when it is visible through attentive, responsive behavior that helps the speaker feel understood.

Source:
Recent high-quality listening research indexed by PubMed

Source type:
Academic research.

What it supports:
The research contributes to evidence that observable listening behaviors and perceived responsiveness are associated with stronger social connection and better conversational experiences.

Important caveat:
This evidence is newer and less established than the research on question-asking, self-disclosure and the liking gap. The article therefore treats it more cautiously.

How to read this evidence

Corrections and updates

Subscribe now