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‘We’ll be OK’: Business owner optimistic after tornado damages her shop
Columbia residents continue to pick up the pieces after an EF-1 tornado tore through the borough’s downtown on Thursday.
Schmitt feels optimistic on upcoming vote for Protect College Sports Act
U.S. Sen. Eric Schmitt, R-Mo., spoke to reporters at Memorial Stadium on Thursday regarding the Protect College Sports Act, which he is a co-sponsor of. He met with Missouri coach
Let’s get on the hype train for…Illinois’ opponents?
What the 12 teams the Illini will face in the 2026 regular season have to be optimistic about.
Should Lakers fans be optimistic about the new ownership?
Will the new ownership of Bob Iger and Josh Kushner breed success for the Lakers?
Business owners optimistic after meetings with Savannah police on Starland break-ins
Business owners in the Starland district say meetings with Savannah police have left them feeling more confident about the neighborhood’s security following a summer of repeated burglaries.
Last business on Mohr Concrete property hangs onto hope
True Discount, the last daily-operated store on the Mohr Concrete property, stays optimistic about business while development conversations continue.
Roma optimism grows over move for Porto talent Rodrigo Mora
Roma are increasingly optimistic about reaching an agreement with Porto for highly-rated youngster Rodrigo Mora after stepping up their efforts to sign the Portuguese talent.According to Corriere dell...
Webtoon Entertainment bets on gaming to expand its global IP business
Webtoon Entertainment CFO David Lee says he is optimistic about its business outlook, with the company betting on gaming to expand its global IP business, while leveraging AI to promote human creativity and deepen fan engagement.
Webtoon Entertainment bets on gaming to expand its global IP business
Webtoon Entertainment CFO David Lee says he is optimistic about its business outlook, with the company betting on gaming to expand its global IP business, while leveraging AI to promote human creativity and deepen fan engagement.
'A different feel around Wolves'
Wolves fan writer Dazzling Dave delves into why supporters can feel optimistic about this season.
Results Of The 39th ITMF Global Textile Industry Survey: Business Weakens Slightly, Cautiously Optimistic
ZÜRICH, Switzerland — August 4, 2026 — The International Textile Manufacturers Federation (ITMF) has published the results of its 39th Global Textile Industry Survey (GTIS), conducted from 14 to 22 July 2026 among companies along the entire global textile value chain. Worldwide, 10% of participants rated their business situation as good, 53% as satisfactory and […]
Science|Business summer reads of 2026
As most people in the European research and innovation policy bubble are going on a well-deserved summer break, we have come up with a list of our favourite books, podcasts and essays from the past year that ask deeper questions about science, technology and politics.BooksPasteur’s Quadrant: Basic Science and Technological Innovation, by Donald StokesIf you haven’t read Pasteur’s Quadrant yet, now is the time. In the EU and elsewhere, we’re living through a period of dwindling faith that curiosity-driven research automatically leads to economic growth and technological progress. Pasteur’s Quadrant, published in 1997 by the Princeton academic Donald Stokes, is the most influential critique of the standard “linear model” of R&D: the idea that new discoveries made in ivory-tower labs get neatly passed on for commercialisation by start-ups. In reality, Stokes argues, practical applications often inspire more theorical lines of inquiry. Of course, we have long known that things are more complex than the linear model, but even today, it’s still often assumed in Brussels discourse. The book is a useful corrective, and is being used now to argue for new types of scientific institutions. David MatthewsThe Weaponization of Expertise: How Elites Fuel Populism, by Jacob Hale Russel and Dennis PattersonAre society’s problems solvable by technical expertise alone? “If only the benighted would listen,” say Jacob Hale Russel and Dennis Patterson, two professors at Rutgers Law School who got frustrated with how scientists and politicians handled public health communication during the Covid-19 pandemic. In the book, they argue that clichéd mantras about scientific evidence and expertise perpetrated by academics, journalists and politicians have harmed public trust in the very institutions they were trying to defend. Paradoxically, slogans such as “we believe in science,” “follow the science” and “defer to the experts” can fuel mistrust in science, experts and democratic institutions. The authors criticise “condescension” and “technocratic paternalism” in the public discourse and make the case for a more nuanced approach to scientific and expert policy advice that rests on humility, doubt, scepticism and open dialogue. “Most complex social questions in fact revolve around values, not the recitation of unambiguous facts,” they say. Florin ZubascuBreakneck: China's Quest to Engineer the Future, by Dan WangIn this book, Dan Wang compares the structural differences between the US and Chinese industrial policies, but raises a very timely question for innovation policy wonks: how can advanced manufacturing know-how contribute to technology-intensive economic growth? Wang says technology has three components: the tools, the blueprints and patents, and the process knowledge. The third component represents the “proficiency gained from practical experience, which isn’t easily communicated and requires years of implementation.” According to Wang, rather than seeing tools and blueprints as the ultimate ends of technological progress, policymakers should see them as milestones in the training of better scientists and manufacturers. Over the past decades, China has amassed “process knowledge” because capital flowed to complex fabrication facilities, while investors in the West have parked their money in capital-light businesses such as digital platforms or chip companies that focus on design. As it turns out, thanks to its oversized focus on manufacturing, China has gained remarkable advantages in the global technology race. Florin ZubascuBaltic: The Future of Europe, by Oliver MoodyWhat can Europe learn from the nine countries surrounding the Baltic Sea? Alongside the familiar stories about Estonia's digital transformation, the book examines how the region has used technology, resilience, education, governance and long-term thinking to respond to geopolitical, economic and security challenges. Innovation policy wonks might find fresh perspectives on how innovation can strengthen defence, societal resilience and Europe's capacity to adapt in an uncertain world. Whether or not you agree with all of Oliver Moody's conclusions, the book is a useful guide to a region that is increasingly shaping European debates on security, competitiveness and technology. Goda NaujokaityteThe Invention of Nature, by Andrea Wulf This book chronicles the wild and adventurous life of natural science pioneer Alexander von Humboldt who at some point in the 19th century seems to have been just about the most famous person in the world. Through scientific expeditions to the Andes Mountains and to the depths of Siberia, von Humboldt contributed a host of discoveries and concepts. During his life he met and formed relationships with historical figures as different as Johann Wolfgang von Goethe, Simon Bolivar, Thomas Jefferson, Napoleon Bonaparte and Charles Darwin. He is also claimed to be the person in the world with most things named after him, a list that includes an ocean current, a penguin and a region of the Moon. Still venerated in Germany, with numerous scientific institutions named after him, von Humboldt has slipped out of public consciousness in most of the rest of the world. For anyone not familiar with him, this book does a good job of describing his fascinating life. Ole EllekrogCareless People, by Sarah Wynn-WilliamsYou may have heard this referred to as the book Meta doesn’t want you to read. The 2025 memoir from Sarah Wynn-Williams, former director of global public policy at Meta, then just called Facebook, includes explosive claims about everything from a toxic workplace culture to how the company worked with advertisers to target vulnerable teens, and the lengths it was prepared to go to in order to break into China. It’s a fascinating insight into how and why the tech company gradually shifted its approach to policy, whether in the US, Europe or Asia. Meta has denied the allegations in the book, and last year won an emergency ruling to prevent Wynn-Williams from promoting it. In response, the author is now suing her former employer. If that doesn’t make you want to read it. Martin GreenacrePodcastsThe Europeans: Europe’s anti-ElonThis podcast is a great choice for a light-hearted dose of European policy and culture news. Most of it has little to do with research, innovation and technology, but this particular podcast features an interview with Mastadon’s founder Eugen Rochko. He talks about his experience running an open-source, federated, European-made social media platform, and leaving it all behind. The conversation, which starts 14 minutes in, gently encourages us to reflect on what kind of technology we, as Europeans, want to make. Goda NaujokaityteSolarpunk and how we escape dystopiaNew technologies are very often taken from science fiction. Alberto Santos-Dumont, inventor the airship, was inspired as a child by the adventure stories of Jules Verne. OpenAI’s attempt to build a personal assistant with the voice of Scarlett Johansson was lifted from the 2013 film Her, completely ignoring the movie’s cautionary message. So if we want better tech, we need to imagine it first. Unfortunately, the state of current sci-fi is relentlessly dystopian, giving inventors precious few ideas or hope. However, a new genre, Solarpunk, which envisages a technologically advanced but ecologically harmonious future, is now taking off. The actual tech the genre imagines, such as permaculture, renewable energy or community repair workshops, while nice, isn’t all that inspiring or ambitious. But it’s a first step towards rescuing sci-fi from its pessimistic Blade Runner rut. David MatthewsEveryday AbundanceLaunched in June, Everyday Abundance is a podcast that tells the stories of the inventions we now take so for granted that we barely given them a second thought. For example, the washing machine, which liberated women from a lifetime “bent over washtubs and washboards.” Or modern dentistry, which ended the reality that “tooth pain was simply part of human life.” Historians of technology, Virginia Postrel and Charles C Mann are very much on the optimistic end of the spectrum when it comes to the capacity of invention to improve human life. Here, they set out their case: if it wasn’t for progress, you might be hunched over a washtub with toothache rather than reading these words. David Matthews‘We will not know what we lost’: Conservation fallout a year after USAID shutdownSince his return to the White House, Donald Trump has been on a mission to undermine science. He slashed jobs at federal agencies, terminated grants in some research areas, took unprecedented control over university affairs and, within hours of his inauguration, suspended the US Agency for International Development. While its initial focus was to administer health and food aid around the world, it had since expanded to conservation projects. The result? Some of these projects were stopped abruptly. In this Mongabay podcast, environmental reporter Michelle Nijhuis tells the story of the EcoGuard conservation programme in Liberia, whose local patrols had helped protect rainforests, monitor endangered wildlife and track illegal threats. From one day to the next, these were left without salaries. Luckily, she notes, the initiative was able to get emergency funding from international NGOs, but its future remains “incredibly uncertain.” Juliette PortalaEssaysThe Mars Delusion, by Henry WismayerWhy is humankind so fixated on establishing a colony on Mars? asks London-based writer Henry Wismayer. From space evangelists promoting Martian settlement as an insurance policy, to billionaire Jeff Bezos, who said humans should settle on Mars “because it’s cool,” the mission is not running short of advocates. However, in this essay, Wismayer reminds us that some critics expect any future Mars pioneer to end up “irradiated, sick, mad and dead.” Scientists have already looked into the possible effects of prolonged exposure to Martian gravity, and it’s not pretty: muscle shrinking, bones weakening, changes to the central nervous system and other biological modifications to make us all reconsider the journey. “Some researchers have forecast that the future settler, after generations of evolutionary conditioning, will look less like an Übermensch and more like ET,” Wismayer writes. Juliette Portala
OpenAI hosted creators at a luxe retreat. The internet isn't charmed.
Over the weekend, influencers posted optimistic videos of AI after attending an OpenAI-led content creator retreat.
2026 college football win totals: Bettors again overly optimistic on Deion Sanders' Colorado Buffaloes
Are bettors too optimistic on Deion Sanders and the Colorado Buffaloes?
‘It’s unacceptable’: Cleveland Browns owner says fans ‘deserve better’
The Cleveland Browns have not performed well the past two seasons, and team owner Jimmy Haslam knows it. However, despite the recent struggles, it sounds like Haslam is optimistic about this season. During a recent interview, Haslam acknowledged the team’s frustrating record the past two years, but he made it clear that he thinks the…
PENN Entertainment (PENN) Nears Earnings, Is The 74% Undervalued View Too Optimistic?
PENN Entertainment (PENN) is back in focus as investors look ahead to its upcoming June quarter earnings report on August 6, with Wall Street expecting higher revenue and year over year earnings growth. See our latest analysis for PENN Entertainment. PENN Entertainment's recent share price has held around $20.53, with a 90 day share price return of 18.88% and a year to date share price return of 38.25%. The 1 year total shareholder return of 15.34% contrasts with weaker 3 and 5 year total...
Jim Cramer Is Sad About nternational Business Machines Corporation (NYSE:IBM)’s Performance
International Business Machines Corporation (NYSE:IBM) is another stock that Jim Cramer has been quite optimistic about. However, its shares are down by 23% year-to-date and by 12% over the past year. The primary reason behind the weak performance is International Business Machines Corporation (NYSE:IBM)’s preliminary second-quarter earnings report released on July 14th. The results saw […]
Lions Star Sends Strong Message on Injury Recovery
Detroit Lions safety Kerby Joseph started training camp on the Physically Unable to Perform list. While head coach Dan Campbell said that he would feel good if Joseph were back by Week 1, Joseph has been much more optimistic than his coach.The fifth-year safety told media members that he was super optimistic about his health […] The post Lions Star Sends Strong Message on Injury Recovery appeared first on HEAVY.
I drove Slate's truck. It's more pickup-y than I expected.
Slate will start delivering its roughly $25,000 pickups later this year. After driving it around, I'm a bit more optimistic it'll stick.
Packers kick-off first practice of Training Camp
Micah Parsons is still a ways away from practicing. The prognosis for tight end Tucker Kraft is more optimistic.
'I needed this summer' - what next for Arsenal's Kelly?
Chloe Kelly found 2025-26 hard, but is optimistic about hers and Arsenal's prospects this season.
Attitudes about Southeast’s business climate a bit better, but pessimism in Juneau rises in annual study
62% of regional business leaders say they’re optimistic about current conditions, but big differences in responses exist among communities and industries.
‘He was so optimistic and positive.’ Nashville business owner dies after battle with cancer
The cookie business posted to their social media Friday announced the death of Matthew Rice, one of Pink Door’s owners.
Mahomes back in full practice, 'optimistic' for NFL opener
Kansas City Chiefs quarterback Patrick Mahomes has been cleared to return to full practice as he recovers from tearing knee ligaments late last season.A disappointing season for the Chiefs last year was capped in December when Mahomes tore left knee ligaments, requiring surgery for the first major injury in his nine-year NFL career.
ABI quarterly survey: Employers to invest in their businesses
Many employers expect sales to expand and plan to make capital expenditures. Many are also optimistic about the future and plan to hire more employees. Those were some of the findings of the recent Iowa Association of Business and Industry’s (ABI) third quarter 2026 Quarterly Business Survey. Despite differing views on the broader economy, respondents continue to […]
NFL insider offers optimistic update on health of Giants owner John Mara
A long-time NFL reporter recently sat down with New York Giants co-owner John Mara and shared an optimistic update on his health.
One Word to Describe Colts' Current Vibe? 'Wishful'
The Indianapolis Colts are optimistic and hopeful as training camp approaches.
Truthfully The Fallout I’m Most Looking Forward To Is Fan-Made
As Xbox tries to quell critics by replaying the hits, it’s a fan-made game that has me optimistic for the wasteland
SUD Industry Optimistic Despite Reimbursement Woes
This is an exclusive BHB+ article. A few months ago, I was moderating a panel on M&A in behavioral health, and a theme emerged: savvy buyers could get
U.S. small business owners grew more optimistic in June but inflation worries are climbing
The NFIB's optimism index rose to 97.4 in June, nearing its 52-year average, as 21% of owners named inflation their top business problem
At midyear, Sioux Falls CEOs hold more optimistic view of business conditions, local economy
Sioux Falls CEOs are seeing much the same business conditions as a year ago -- while feeling more optimistic about what's ahead in 2026.
Florida Has Too Many Big Question Marks Surfacing Before Camp
A high-stakes quarterback battle and defensive depth concerns define Jon Sumrall’s debut, leaving the Florida Gators as a volatile wild card despite Steve Spurrier’s optimistic ten-win projection.
Wildwood, NJ businesses see summer momentum after busy Fourth of July week
Businesses in the Wildwoods say activity picked up during holiday week and are optimistic the momentum will continue through the rest of the summer.
Stockton businesses optimistic as new city hall opens on waterfront
Stockton's new city hall opened on the waterfront, bringing hundreds of employees to the area and raising hopes among nearby businesses for increased activity.
These 2 Renowned Financial Experts Have Opposing Views on Debt. So, Who's Right?
Entrepreneurs who understand the difference between predictable free cash flow and optimistic projections can use leverage to build wealth — while others quietly destroy their businesses with it.
The Talos Principle 3 creators are doing their best to stay hopeful in the tech apocalypse
The puzzle series' final installment is staying optimistic, even in the face of death and AI
“I Am an Unabashed Optimist About the Long-Term Future of Science.”
The new president of the National Academy of Sciences talks about learning to see as a scientist and communicating science in a noisy information landscape.
'Cautiously optimistic': Swing voters describe their view of America at 250
As America turns 250, voters from our Swing Shift project talk about the state of the country. Their views ranged from "uncertain" and "concerned" to "excited" and "cautiously optimistic."
'Cautiously optimistic': Swing voters describe their view of America at 250
As America turns 250, voters from our Swing Shift project talk about the state of the country. Their views ranged from "uncertain" and "concerned" to "excited" and "cautiously optimistic."
Croatia criticised for ‘unambitious’ changes to 2022 science law
Croatia’s science ministry has been accused of a lack of ambition, and even incompetence, over proposed changes to the country’s 2022 science law. Critics say an opportunity has been missed to fix wider shortcomings in the research and universities system.“This law, like all the previous ones, will not significantly change the current state of science in Croatia,” Vlatko Silobrčić, a member of the Croatian Academy of Sciences, told Science Business. “A small shift here and there will not have a significant impact.”The 2022 law introduced multi-year block grant financing of research, based on results, and changes intended to boost employment and career progression for researchers and lecturers. But implementation of the law has been uneven. “It became apparent that certain of its provisions were subject to different interpretations by higher education institutions and scientific institutes,” the science ministry said in its proposal for change. “This caused legal uncertainty in the system, administrative difficulties in implementation and, in certain cases, unequal treatment of teachers, scientists and students.”Most of the proposed amendments deal with technicalities in hiring and promoting staff, such as extending the committee deadlines from 60 to 90 days, or allowing applicants to cite all of their achievements and not only those since their last appointment.Both the ministry and Croatian media highlighted changes that would raise the retirement age from 65 to 67 years and allow part-time students 15 year rather than 10 years to finish their studies.The change to retirement age has been criticised for reinforcing a weakness in the system. According to Matko Marušić, professor emeritus at the University of Split, this will “allow unproductive teachers to advance and work longer, while hindering and discouraging productive ones.”A longtime campaigner for excellence-based meritocracy, he sees little hope in the new legislation. “I am not optimistic that things will improve,” he told Science Business. “As recent changes to the law in Croatia show, the legal framework is becoming increasingly hypocritical and harmful.”For Marušić, who analyses Croatia's university system in a recently published book, the situation is “unfavourable and constantly deteriorating” due to the criteria used for academic advancement.“The key criterion for advancement should be scientific productivity,” he said. “In Croatia [. . .] scientific criteria are increasingly lowered, and ineffective and absurd criteria such as waiting times for promotion and age limits for remaining in a job are promoted.” He would also like to see better criteria for the quality of teaching at universities. “In practice, teaching is not monitored or evaluated,” he said, estimating that up to a third of teaching simply does not take place. “Under the guise of university autonomy and bad laws, universities and faculties are allowed to earn additional money by increasing enrolment quotas that have nothing to do with the needs of the labour market.” Lack of ambitionDora Kršul, a reporter at Telegram.hr who covers science policy in Croatia, finds the rationale for changes “bizarre.” “The ministry responsible for supervising the uniform application of the law is changing the law because it is being implemented unevenly,” she said. “This primarily speaks to the power relationship in the sphere of science and higher education in Croatia, that is, the question arises as to who actually manages this system.”Rather than raising quality, she continued, the changes appear to be designed to help vocational and applied universities who have not been complying with the law.The Independent Union of Research and Higher Education Employees of Croatia said that the changes were “unambitious” and represent a missed opportunity for more important changes. “We think some of the changes are even negative,” it said in its statement on June 25. One of these is introduction of an “assistant” job title that allows people to be hired only for the duration of a project. Related articles: Elections to top science bodies test Croatia’s gender equality in researchSlovakia revises R&D approach to increase focus on competitivenessLatvia pilots citizen engagement in research grant evaluationsThe union’s president, Tvrtko Smital, who was part of the 15-member working group tasked with drafting the changes, told the local media there was a “worrying lack of ambition” in the process, and “lack of competence” in terms of research experience and understanding the problems in the system.“There is no clear will to deal with the problems,” said Smital, an assistant professor of marine and environmental sciences at the Ruđer Bošković Institute in Zagreb. “It’s a combination of unambitiousness and incompetence,” he went on, expressing his “deep frustration” with the situation. “There is no strategy, no clear thinking, and money is not being spent optimally.”Croatia’s science ministry has not responded to requests for comment.
3 reasons to like Wisconsin’s new AD hire
Why should Badgers fans be optimistic about their new Athletic Director?
Asheville grants $855K to business accelerator, extends recovery program through 2028
Asheville grants $855,000 to Optimist Ventures, an accelerator for Asheville entrepreneurs affected by Hurricane Helene.
Seattle’s Pioneer Square lands a new retailer
A bespoke diamond jewelry company opened a store in Pioneer Square. The CEO is optimistic about its future in downtown Seattle.
Smart voices in business react to Apple and Xbox price hikes
After Apple and Microsoft's Xbox raised prices due to a shortage of memory chips, tech observers are not optimistic about future prices.
What early Cannes Lions conversations reveal about AI, creators and agencies
After my first day at Cannes Lions, two things stood out immediately: the festival feels busier than it did a year ago, and the mood feels more optimistic.
Amazon, AI and local media at 617 Day small business summit
Speakers were optimists about how small businesses can navigate business and technology trends.
Obama Presidential Center dedication: A nod to a past era in politics and future hopes
All living former presidents gathered for the dedication of the Obama presidential center in Chicago. It was a star studded event that harkened back to a more-optimistic era in American politics.
Mood darkens among German auto suppliers as investment, hiring fall, survey shows
German automotive suppliers expecting business conditions to worsen over the next year now outnumber the industry's optimists, a survey showed on Wednesday, as domestic hiring sinks to a new low and investment in the sector moves overseas.
What a longtime Google AI leader told UW computer science students at their graduation
Jeff Dean, Google's chief scientist and a UW alum, returned to campus Friday with an optimistic but clear-eyed message about AI for Allen School graduates — many of them headed into the industry to help shape the future of technology.
What a longtime Google AI leader told UW computer science students at their graduation
Jeff Dean, Google's chief scientist and a UW alum, returned to campus Friday with an optimistic but clear-eyed message about AI for Allen School graduates — many of them headed into the industry to help shape the future of technology.
In Retrospect: Poland, Russia, Europe
In April I spent a few days with Zryw, a Polish non-profit organization that recruits young people to enter politics and public life. Their aim is to build a new generation of civic leaders in Poland. I’ve followed their evolution from the beginning; my son is one of the founders. (“Zryw” means “impulse” or “burst of speed” in Polish). Julie Chmielewska, one of Zryw’s leaders, interviewed me at the event. Here is the conversation, lightly edited for grammar and context: Julia Chmielewska: Assuming that culture shapes institutions, could we conclude that some cultures are more prone to authoritarianism? Anne Applebaum: I don’t think so. Every culture, every nation, and every country has the potential for change. When we look at history, we can see that nations do change. Poland has changed, for example, from the 1980s to today. There’s a completely different mentality here than the one I encountered when I first visited, in 1988. The current generation sees the world through a different frame. So no, I don’t believe that certain nations are doomed to authoritarianism, or to anything. Of course, there are political and cultural traditions, but they can always be challenged. JC: So do you think there’s something intrinsic in human nature that pushes people to democracy, freedom? AA: I think there is something in human nature, a deep instinct, that pushes people towards freedom, yes. People who live in authoritarian countries – and I’ve heard this from them over and over again – sooner or later find themselves angered by a feeling of helplessness. They can see the unfairness of crooked courts, irrational political decisions. They naturally dislike the fact that somebody has all the power while they have none. In those circumstances, people who have no political background at all sometimes are politicized just by that frustration. I’m thinking of Svetlana Tikhanovskaya, for example, who was a Belarusian housewife, until the circumstances of her husband’s arrest forced her to become a politician. I met a woman who is a Uyghur from China, and who had come to live in Turkey. After several of her family members were arrested, she too was propelled into a political role. These kinds of experiences make people interested in democracy or political reform, even if they thought they didn’t care. I also think that the opposite is true: some people instinctively prefer homogeneity, order, hierarchy. They don’t like the confrontation of opinions or the noise of debate, and they lean towards a single leader or a unified regime. I think that most of modern history is shaped by the clash between those two different sides of human nature. JC: Which force do you find more powerful today? AA: I think they both have a lot of power and there’s no way of predicting who wins. Even in countries that seem very shut off or stuck in an authoritarian system can change. We’ve seen that happen. And vice versa, I think democracies can also begin to fail and slide into authoritarianism. So it’s very important not to be complacent in either direction. JC: I want to go into your history and your experiences. You’ve lived in Russia during a time of incredible change in the late 80s, and then you came back to do research in post-Soviet Russia. AA: I was a student in Russia just before the collapse of the Soviet Union. I then traveled and worked there extensively in the 1990s, and I met people who really were full of hope. They hoped that Russia could become a democracy, or at least a place that guaranteed freedom of speech, freedom of the press. These weren’t ideas that came from Washington. Although now the narrative in the Kremlin is different, I met many people who were working towards exactly that kind of transformation. A friend of mine ran a series of seminars – once a year, and then later several times a year, in cities all over Russia – designed to get people to discuss politics in new ways. She invited young politicians and journalists, as well as foreigners. I went to many of them, in Moscow as well as Volgograd and Novosibirsk. Over two decades, hundreds of people came to these seminars, and all of them wanted Russia to be different. I remain convinced that many Russians still want Russia to be different. It’s just that the old system, in the form of Putin, a representative of that system, returned. The KGB reimposed their power. And this wasn’t because of something to do with culture, this was to do with money and politics, and who was in charge. JC: You’ve partially answered my question... I wanted to ask whether that excitement of the 1990s was an exception from the Russian historical norm which has been rather authoritarian? AA: Of course, if you look at the history of Russia since the 18th century, there have always been liberal groups, reformers and revolutionaries. There were the Decembrists, in the early 19th century, and the rural populists later on, who tried to organize the peasantry. A significant group of liberal Russians appeared at the turn of the 20th century, people like the father of the writer Vladimir Nabokov, who was a Russian politician before the revolution. These groups were small in number, but sometimes had a broader significance. The Russian dissident movement in the 1950s and 1960s actually invented the modern human rights movement. It was their idea to hold the leaders of the Soviet Union accountable for the language they used, to remind them of the treaties they had signed and the promised they had made. They created the Helsinki group, for example, that held Soviet leadership to the language of the Helsinki Treaty, which formally ended the Second World War and which contained guarantees of human rights, at least on paper. That idea spread to Eastern Europe, and similar tactics were used here in Poland. (Benjamin Nathans recently wrote a great book about the Soviet dissidents, To the Success of Our Hopeless Cause) JC: But seemingly, even if they do ever get power, they could never hold on to it. AA: We don’t know, because they have never really had power. But in any case, we also don’t know what will happen in the future. There is no reason to assume that Russia will forever be condemned to totalitarianism. Even during the first fifteen years of Putin’s regime there was some form of democratic politics. Only when the full-scale war began did he bring everything to an end: he shut down all remaining independent organizations and killed his most important opponent, Alexi Navalny. But does that mean he is popular, that he enjoys legitimacy? Even now, we don’t really know what Russians think about him, or about the war. There is no way of doing real polling in Russia because nobody will answer an independent pollster over the phone. JC: Do you think that the flock of young people, especially young men, who fled Russia after the start of the war represent a continuation of liberal movements that refuse to partake in the authoritarian state, or do you think they are moving for the sake of self-preservation – “I don’t want to go to the army, so I leave”? AA: I’m sure it’s both. I actually know many people who left Russia for political reasons either in the very last years before the war, or else just after the war began. JC: And do you see a change in the young generation versus the old one that have fled? AA: It’s hard for me to say because the people who I know were mostly already politicized. I do know some younger people who left, but most of them were already either active in the opposition, or working as journalists. Many of them very self-consciously think of themselves as maintaining Russian culture abroad, just as in the 20th century the Soviet dissidents or the anti-communist White Russians once thought of themselves as people who maintained the true Russian culture, outside of Bolshevik Russia. JC: Let’s move on to Poland. One of your first experiences with Poland was reporting on Margaret Thatcher’s first state visit, in the autumn of 1988. You followed her around the farmers’ market. AA: She was making a symbolic gesture. She wanted to welcome free markets, but the only one she could really visit was the farmers’ market at Hala Mirowska. JC: And I want to talk about this area because today you can look out of any window in that district, Mirów, and see rows and rows of skyscrapers. How does it feel to see a country built from the ground in such a short time, both politically and economically? AA: You know, when you’re living in a period of dramatic change, it’s always hard to know whether the change is positive or negative. The transformation in Poland created winners and losers, as well as new classes of people, new political movements of different kinds. At the time, it sometimes just felt like turbulence. Now, we can look back at the 1990s and early 2000s in Poland and see in retrospect that the country was headed in towards prosperity and European integration. In around 2009 or 2010, I remember speaking to a woman who worked cleaning government offices, and who asked me if her daughter should study in Amsterdam or in London. This seemed to me to be a real transformation, and it happened very fast: within a few years, Poles stopped feeling like second-class Europeans, and a generation of younger Poles appeared who had no qualms about crossing borders. JC: We again live in turbulent times. Do you think we’re heading towards positive outcomes? AA: I can’t make predictions, because everything that happens tomorrow depends on what people do today. Also, history doesn’t have a trajectory. It doesn’t move in a particular direction. You only think it does afterwards. As I said, you can look afterwards at Poland in the 1990s and you could say this was a period of great progress. But not everybody at the time saw that. Sometimes people are very wrong about a given country’s trajectory. I remember people writing about Ukraine in the 1990s, about how dangerous it was that Ukraine was independent, that this was going to be an anti-semitic state, that Ukrainian nationalism was going to come back... and although you can say many bad things about Ukraine, the country made many bad choices, actually it became a pretty tolerant state which now has a Jewish president and until recently a Muslim defense minister. JC: In the 1990s Poland’s and Ukraine’s GDP were roughly similar. Ukraine was even a bit richer. So the starting point was similar for both of these countries, but they’ve turned out very, very differently. Even if we stop in 2014. Why do you think Ukraine “chose East”? Was it external influence? AA: No, I think Ukraine was in a different position. First of all, central and eastern Ukraine had been part of the Soviet Union since the 1920s, and had therefore experienced a longer period of occupation, repression, Sovietization and even Russification. Ukraine did not have, as Poland did, an alternative elite: a large cadre of economists and politicians, who already had some experience in opposition politics and in independent thinking. In Poland, the transformative finance minister, Leszek Balcerowicz, had already thought about how an economy might be decentralized before he took office. We can argue about how successful he was, but at least he was prepared to try something new. In Ukraine, people weren’t prepared. For one, there hadn’t been much space even for an illegal opposition in the previous couple of decades. Ukraine was also in many ways still emerging from colonial occupation. For the previous couple of centuries, many of the most capable Ukrainians had moved to Russia, and the first generation of leaders in independent Ukraine tended to be former Soviet apparatchiks. They didn’t immediately seek to become closer to western Europe. Instead, they looked for some kind of economic and political “third way” and an accomodation with Russia. Ukraine became stuck in this position, half-in, half-out and became pretty profoundly corrupt. JC: The Polish alternative elite had a vision of where they wanted the country to go. I think the general perception now is that this vision ends with the next election, right? Is this a natural process of gaining power? AA: Their vision, and it was shared by the time by politicians who now call themselves both left and right, was that Poland should join the EU and NATO, as fast as possible. My husband (Radek Sikorski) was in the very first government that said it wanted to join NATO. At the time this was taken very badly in Washington: the demand for NATO membership absolutely came from Poland, not from the US. But even then, in the early 1990s, many Poles already understood that the Russian sphere of influence would grow once again, that it was important to become part of the transatlantic alliance and an integrated Europe as fast as possible: NATO, the EU, the Council of Europe, all of the trading and security alliances. That consensus lasted through several different governments and over many years, even governments containing former communists. For twenty years, right, left and center disagreed about many things, but they all agreed on the direction that the country should go. In retrospect, that consensus created the basis for prosperity in Poland. JC: And regarding the Polish-Ukrainian relations. We know that antagonisms, e.g. the myth of Wołyń [a massacre of Poles that took place in what is now Western Ukraine, in 1943] are increasingly used in Polish political discourse today. In Ukraine, nationalist historians make heroes out of UPA, the Ukrainian revolutionary army, which is held responsible for this tragedy. Where do you see a start for burying the hatchet? Is the reconciliation of our nations possible? AA: So here’s a strange thing: For more than a decade after they both gained independence, Poland and Ukraine participated in a number of projects that were designed to solve exactly these kinds of Polish-Ukrainian differences. Books were published that were jointly written by Polish and Ukrainian historians, monuments were built. These projects were successful: If you’d asked me this question in 2010, I would have said that there are no real historical issues between Poland and Ukraine, that the two countries had mostly reconciled. For a number of reasons, this began to shift. In Poland, children and grandchildren of victims of the Wołyń massacre felt their tragedy hadn’t been fully recognized. In Ukraine, nationalist groups wanted the UPA partisans who had fought against the Soviet Union recognized for their heroism, despite the fact that some in this broad and not well-defined group were also linked to Wołyń. In both eastern Poland and western Ukraine, the memory of the ethnic cleansing that took place there in the late 1940s - Poles were moved West, Ukrainians East - to accomodate new borders has left feelings of displacement and bitterness. The wave of Ukrainian immigration, which started before the war, may have provoked some of this. But don’t underestimate the role played by Russia, which has always wanted to create discord between Poland and Ukraine, and pumps out propaganda to that effect in both countries. Vandalism on both sides of the border, damage done to cemeteries and monuments, is sometimes carried out by provacateurs working for Russia, just to create bad feelings. The Polish-Ukrainian conflict also hides a different reality: although there are real grievances and bad memories, the truth is that the biggest tragedies both nations experienced in the twentieth century were inflicted by the Soviet Union and Nazi Germany. That should unite these two countries, not divide them. JC: This week (19.04.2026 – Ed.) in Hungary we saw that using historical narratives to create grievance has no longer worked. Are you optimistic about the direction after Orban’s loss? AA: The Hungarian election was really important because it shows that people can actually resist powerful, dominant propaganda. The ruling party Fidesz controlled all of the TV, 90% of the newspapers, the constitutional court, the bureaucracy... and yet people had the nerve to vote against it and resist it. It’s also an important election because Hungary created a huge network of well-funded think tanks and foundations that were funding other far right groups and projects. So that loss is going to have a knock-on effect in other places too, maybe even in Poland. JC: What you’ve said about resisting propaganda reminds me of one of the interviews you did about talking to Russians in the post-Soviet era who claimed that people knew that the system was lying. Why do people choose to believe in these regimes anyway? AA: Because it’s useful. Stephen Kotkin, an American historian, once described how people would learn how to “speak Bolshevik”: It was just a language that you needed to use in order to get by in society. If you didn’t talk the way the regime wanted you to talk, then you might have problems, or lose your job, or your kids would not get into university. It was a way of getting along. Communist Poland was similar, at least in the early years. When I was writing a book about the Sovietization of Eastern Europe after World War II, I met a lot of people who wanted to talk a lot about the war, when many of them had done really heroic things. But almost nobody wanted to talk about the decade afterwards, between 1945 and 1956. This was when you had the short period of terror, violence and Polish Stalinism. To stay safe, people felt the need to just go along with what others were saying, or stay out of politics, or use communist jargon without necessarily believing it. I don’t judge them for it, and I wouldn’t describe all of those people as collaborators. It’s easy to feel moral certainty in retrospect, because we know that communism ended. But if you were living in 1949, it might not have been clear to you that communism was going to end. In very repressive societies, maybe about 10% of people who are really evil – collaborators, torturers. Maybe 10% of people are really brave and who find ways to resist. And then around 80% of people have kids, they have parents, they have to have jobs and they’ve had to get through it somehow. Strange though it may sound, I’ve found that the work I did on that book is now very useful in explaining some of what’s going on in the United States today. There are plenty of members of the Republican Party in Congress who know perfectly well that Donald Trump says many things that aren’t true. But they avoid criticizing him because it’s inconvenient, because they would get in trouble, because someone would attack them on the internet. So actually you don’t even need mass terror to get people to conform. JC: Is Gen Z reinventing agency? AA: We’ll see. In any generation you can find groups of people who want agency. If you live in a society that’s unfair, for example where a tiny number of people have captured most of the wealth, then at a certain point people rebel. This is always easier for younger people, who have you have less to lose. The question now is how the internet will affect the relationships between citizens and the state, and how it will change organizations. The “Twitter revolutions” people spoke about as early as 2008-9, mass protests organized online, have often proved to be very superficial. Although you can easily join something just by clicking, the work of creating a real movement requires physical participation. Successful mass movements of the past, for example the Civil Rights movement in the United States, were built by people who had spent many years together. They had participated in non-violent workshops together. They practiced and prepared. Just using the you can get everybody to show up at a certain moment, but then when the protest is broken up, nothing happens. It’s also true that the repressive regimes have more recently learned how to use the internet to undermine leaders and movements. Most of all, they now understand how to make people cynical. The form of propaganda that most modern authoritarians use - and I think the Russians really pioneered this - deploys sneering cynicism. Instead of praising the rulers, the regime propagandists mock the opposition movement leadership for being supposedly corrupt or sexually degenerate, with the goal of undermining any kind of idealism. JC: MAGA grew on the internet? AA: MAGA is a real grassroots movement. They had symbols, red hats, they showed up at rallies. But you’re right that the cynicism, the sarcasm and the distrust that the movement embodies came originally from the internet, and this is different. Fifty years ago, most European political parties had a social base in real organizations, in trade unions for Social Democrats, in church groups for Christian Democrats. As people stopped going to church and as the trade unions became smaller, the social base of these parties disappeared. Now people who wouldn’t have known one another in the past meet on the internet and create new movements, many of which don’t last. JC: Is history ending? Going in stale circles? AA: History never ends. In truth, human beings want contradictory things at the same time: order and freedom, stability and change. These things are never resolved, and when a society swings too far in one direction, people always push it the other way. But each time that happens, the result is different. Certainly it is true that we are now in a period when old philosophers and ideas from the 1930s are undergoing a revival. Carl Schmitt, who was a philosopher who was very influential on the Nazis, is back as an influence on both the far left and the far right, once again inspiring people to think that all politics has to be existential, that all politics is about defining and defeating enemies. In the US, some are also bringing back dominionism, the idea that we need a Christian and not a secular state. It’s also true that we are living through a more fundamental shift. The old left-right divide, which was mostly about the size of the state, is much less important now. Instead, we are arguing about more fundamental things: what are our rights, what protects our freedom. Or how much power do we want to give small elites, versus how much power to give ordinary people. In America a small group of very wealthy people now control information, because they control the social media platforms and AI, which means they determine what people will read and learn. Finding a way to preserve preserve zones of autonomy and independent thinking, this is going to be the main task for the next generation. JC: What is the one book that a person interested in Central Europe should read? AA: You could read my book “Iron Curtain: the Crushing of Eastern Europe” which tells the story of how the Soviet Union imposed its culture on the region. Or read Tim Snyder’s history of the region, “The Reconstruction of Nations: Poland, Ukraine, Lithuania, Belarus 1569-1999.” And everyone should read “The Captive Mind” by Czesław Miłosz, which describes exactly the phenomenon we were talking about: why do people collaborate? I joined Scott Galloway and the brilliant Fiona Hill in a discussion about the wars in Ukraine and Iran, the future of global power, and what these conflicts reveal about America's role in the world. Scott told us that this was the 400th edition of his podcast, which is quite a number: My friend Francesco Chiamulera runs a brilliant series of literary events, Una Colline di Libri, A Hill of Books, in the Prosecco Hills, a region of vineyards and elegant hill towns in the Veneto, north of Venice. We did an event in Vittorio Veneto and also drove and biked around the countryside. Buy Autocracy Inc Buy Iron Curtain Open Letters, from Anne Applebaum is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
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We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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Elections to top science bodies test Croatia’s gender equality in research
Croatia’s track-record on gender equality in science has been both questioned and lauded in recent months, following both bad and good news on the topic. On the one hand, a senior awards committee was announced without a single woman researcher included. On the other, the national science academy welcomed its first cohort of fellows with more women than men.Members of the committee, which gives national awards recognising scientific achievement, are proposed by the ministry of science and endorsed by the parliament. The current line-up does include a woman, but she is culture minister Nina Obuljen Koržinek rather than a researcher. Even then, she didn’t make it into the photograph released to announce the appointments on April 16. “The image speaks a thousand words,” says Tanja Rudež, a science journalist for Jutarnji List newspaper. “I am surprised that none of these men, all well-known scientists, saw a problem with the fact that there was not a single successful female scientist on the committee,” she told Science|Business.Ivan Đikić, a prominent Croatian biochemist based at Goethe University Medical School, commented on LinkedIn that the news illustrated a “sad truth” about the state of science in Croatia.“It [. . .] reveals a system where women, despite contributing a substantial share of high-quality, internationally competitive science, are systematically excluded from the very processes that determine recognition and influence,” he wrote. Some 60% of researchers in Croatia are women, according to the Croatian Bureau of Statistics, and they perform well in international competitions. For example, of the 12 European Research Council (ERC) grants awarded to Croatian researchers, seven went to women.“So how is it possible that decision-making bodies are still dominated by men over 60, while not a single female ERC laureate is involved in the selection process?” asked Đikić. “This is not a marginal oversight; it is a structural failure.” The ministry of science did not reply to a request for comment. The awards committee is not unusual. For example, a scientific advisory body announced by the ministry of demographics in April comprises 35 men and five women. The board of the Croatian Science Foundation, newly appointed by the parliament, is a little better, with five men and two women, but still unequal. “This is especially significant because such bodies do not merely distribute prestige. They shape research priorities, funding allocation and long-term scientific development,” Marija Lugarić, chair of the parliament’s committee on gender equality, told Science|Business. “Representation here matters not only symbolically, but materially.”Referring to the awards committee and the board of the science foundation, Lugarić says that while both bodies were appointed by parliament, neither met the minimum 40% gender balance threshold that Croatian law and EU guidelines expressly recommend for public bodies.Đikić wants to see EU institutions put pressure on the government to ensure basic standards of fairness, transparency and democratic governance in science policy are met. “Meaningful change will not come without pressure,” he wrote. “It must intensify both within Croatia and at the level of European institutions. Anything less undermines the credibility of Europe’s commitment to excellence and inclusion in science.”Rudež also underlined the importance of talking and writing about this topic, “because public opinion in Croatia does influence changes in the status and visibility of women in science.”The European Commission has not yet responded to a request for comment.Good news from the academyA more positive story came from the Croatian Academy of Sciences and Arts on 21 May, with the announcement of seven new women fellows and five men. This is the first time in the academy’s 165-year history that women have outnumbered men in a new intake.One of the new fellows, two-time ERC grantee Iva Tolić, credits the academy leadership with trying to be more modern and inclusive, representing the whole community better. “The academy is really aware that so far, before this election, there were very few women,” she told Science|Business. “So, they are actively trying to make this more equal.”Related articlesExplained: How the MSCA is close to achieving gender balanceCall for EU to leverage academic freedom in global talent raceRudež is more cautious, noting that despite the academy’s “breakthrough this year [. . .] it is still a predominantly male institution, with only 18% women.”This reflects a wider imbalance in Croatia’s research landscape. “The key levers of power in science are still in the hands of men,” she went on. “For example, all 14 rectors of private and public universities are men.”Similarly, Lugarić says a structural glass ceiling still exists in Croatian science, which is “most visible precisely where power is exercised.”She cites data from the Commission’s She Figures 2024 report, which shows that Croatia ranks second in the EU for gender equality in research and innovation overall, with an equality index of 79.6 out of 100. It is in the top five for women’s career advancement, participation in research; representation across all research sectors and for scientific publications.In stark contrast, it ranks second to last when it comes to women in decision-making positions in science. “This is not a coincidence,” Lugarić says. “It is a structural pattern.”Informal barriersThe persistent under-representation of women in the highest structures of Croatia’s academies, boards, award committees and leadership positions is not down to formal barriers, Lugarić says, but arises from more subtle obstacles such as “informal networks, persistent expectations regarding traditional gender roles and the unequal distribution of care responsibilities.”Tolić would like to see more support for young women in terms of affordable housing and child support, and higher salaries. Currently, she says, salaries in science stay low for the first 10-15 years, while housing and spaces in kindergarten are limited and expensive. “You have to work a lot, you get very little money, so it’s not great to have a family in this situation,” she says.But despite the challenges, Tolić is optimistic, saying that gender equality in research in Croatia is “definitely improving and going in the right direction.” The situation is particularly promising in biology, she adds, where most full professors, heads of department and deans of faculties are women. Lugarić, too, see the glass ceiling “very slowly, cracking,” and cites the academy election as an example that structural change is possible. Yet the situation in the national awards committee is a “stark reminder that without a conscious, systemic commitment to gender balance, progress will remain slow and uneven.”
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Is the FDA Really Suppressing Science? Plus. . .
It’s Thursday, May 7. This is The Front Page, your daily window into the world of The Free Press—and our take on the world at large. Today: Aaron MacLean says Trump is poised to snatch defeat from the jaws of victory in the Iran war. Joe Klein on the life of CNN founder Ted Turner. Jillian Lederman on why cruise ships are here to stay. And much more. But first: Is the FDA suppressing science? On Tuesday, The New York Times published a story with an alarming takeaway. “FDA Blocked Publication of Research Finding Covid and Shingles Vaccines Were Safe,” read the headline. It is the latest in a string of stories that all have the same message: The Food and Drug Administration (FDA), and other agencies, are engaged in a cover-up—and silencing scientists trying to get the word out about the Covid vaccine. What all these stories suggest—some more subtly than others—is that the outsiders now running the public health establishment, officials such as Marty Makary at the FDA and Jay Bhattacharya at the National Institutes of Health, are on a revenge tour. That they are doing harm to the country to get back at the scientists and bureaucrats who ostracized them for deviating from the public-health consensus during the pandemic. It’s a seductive narrative. But is it true? That’s the question we address in our latest editorial. —The Editors Read [ Marty Makary's FDA Has All the Right Enemies ](https://www.thefp.com/p/marty-makary-vaccine-study-fda) [ Trump Blinks First ](https://www.thefp.com/p/iran-peace-deal-pakistan-trump-strait-of-hormuz) Aaron MacLean In return for no detectable concessions, President Trump this week gave up on forcing open the Strait of Hormuz. Now, the Trump administration looks desperate for a deal to end the war with Iran. It’s easy to understand the desire to end the war, but are they blowing the chance at victory? Aaron MacLean makes sense of the administration’s latest moves—and Iran’s response—in his column today. Read full story Aaron MacLean and Niall Ferguson: Live in New York City Join Aaron MacLean and Niall Ferguson on May 19 for a live recording of School of War at the iconic New York Historical. In a conversation as timely as it is wide-ranging, Aaron and Niall will explore the strategic stakes of the moment, including the Middle East, China, and the future of American leadership on the global stage. Get Tickets Now [ Ted Turner, the Optimist Who Built CNN ](https://www.thefp.com/p/ted-turner-cnn-tribute) Joe Klein The man who gave us CNN and the 24-hour news cycle, Ted Turner, died Wednesday at the age of 87. Today, political writer Joe Klein—who almost ghostwrote Turner’s autobiography—remembers his friend, the American media mogul who stood out for his idealism and lack of a filter—something that got Turner in trouble more than once. “His was not a traditional intelligence, but one of horizon: Ted saw things before other people did,” Joe writes. Read full story [ Nothing Can Sink the Cruise Ship ](https://www.thefp.com/p/cruise-ship-hantavirus) Jillian Lederman A viral outbreak on a cruise ship that left three passengers dead has left some people wondering: Are cruise ships on the way out? Not so fast, says Jillian Lederman, who has been on many a cruise herself. Read her piece on why 38 million people a year take a cruise—and aren’t about to stop. Read full story [ The Great Writers Who Were Terrible People ](https://www.thefp.com/p/the-great-writers-who-were-terrible) Shilo Brooks Throughout history, some of the greatest literary geniuses have also been immoral bigots. In the latest episode of Old School, Shilo Brooks is joined by Eli Lake for a conversation about those geniuses, from Voltaire to Norman Mailer and Roald Dahl—and why we should read their work despite their odious prejudices. Listen on MORE FROM THE FREE PRESS [ Inside the Digital Intifada ](https://www.thefp.com/p/digital-intifada-social-media-hamas-terror) Warren Kinsella Read full story [ This Week in American History: Trump’s Over-Arching Problem ](https://www.thefp.com/p/this-week-in-american-history-trumps-over-arching-problem) Jonathan Horn Read full story [ Humanity Is Still Evolving—and Fast ](https://www.thefp.com/p/humanity-is-still-evolvingand-fast) Razib Khan Read full story THE NEWS Thirteen D.C. Metropolitan Police officers have been placed on administrative leave and could face termination after an internal investigation found evidence that crime statistics were manipulated to make the city look safer. The Republican-led House Oversight Committee previously alleged that former D.C. police chief Pamela Smith pressured officers to skew data. Tennessee Republicans unveiled a redrawn U.S. House map yesterday that would split up Memphis’s black-majority Democratic district, the latest move in a nationwide push to redraw congressional boundaries ahead of the midterms. The effort follows a Supreme Court ruling last week on Louisiana’s districts, which Tennessee House Speaker Cameron Sexton said gave states the green light to redraw maps along partisan lines. Israel struck Beirut for the first time since a ceasefire with Hezbollah took effect last month, saying it targeted a commander of the militant group’s elite Radwan force in the city’s southern suburbs. The strikes risk destabilizing that truce at a delicate moment, as Iran and the U.S. report progress toward a deal to resolve their own standoff. The U.S. Air Force is aiming for a Fourth of July delivery of a Boeing 747 gifted by Qatar, hoping to add it to the Air Force One fleet in time for the U.S.’s 250th anniversary, Reuters reported. (For more on the controversy surrounding the aircraft, read Jed Rubenfeld’s piece “It’s a Terrible Idea, but Trump Probably Can Accept a Free Jet from Qatar.”) Commerce Secretary Howard Lutnick testified privately before Congress that he has no memory of why he and his family dined on Jeffrey Epstein’s private island in 2012, according to Reuters. The admission contradicts Lutnick’s earlier claim that he had cut ties with the convicted sex offender well before that visit. Weeks before a former JPMorgan investment banker filed a lawsuit packed with explosive allegations, the bank reportedly offered him $1 million to settle his sexual assault and harassment claims, according to The Wall Street Journal. The suit, filed by Chirayu Rana, alleges he was sexually assaulted by a female colleague, subjected to racial discrimination, and forced into a degrading dynamic at work. JPMorgan says the claims lack merit, and the female colleague’s lawyers call the accusations fabricated. A new report from the Hartford Institute for Religion Research shows that, for the first time in roughly 20 years, church attendance is trending upward, with more people volunteering and clergy expressing renewed optimism. The study’s authors described the findings, based on a survey of more than 7,400 congregations, as showing signs of widespread recovery from low attendance numbers.
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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
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A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
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