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AI as the next political divide
We have a guest post today from our friends at Focaldata, with whom we have done polling before. They have been doing a lot of work on attitudes towards AI and how this is affecting politics on both sides of the Atlantic. In this post, they explore exclusive new polling – with a whole set of fascinating charts on how voters’ views are developing and why. In particular, the authors explore why the debate is currently so much more polarised by party in the US than in the UK, to the benefit of the Democrats. Subscribe Focaldata HQ is located in Shoreditch, east London. To access the office from the Shoreditch High Street Overground station, you have to walk under the bridge at Braithwaite Street. We mention this not as a navigational aid, but because the posters above caught our eye over the summer. Since June, the area has been the centre of a fiercely contested debate around the construction of a data centre on the Truman Brewery estate on Brick Lane, less than 200 metres from our office. It was one of the first ‘US-style’ protest movements around AI data centres the country has seen, and may not be the last. These protests served as the catalyst for our forthcoming report, examining whether AI could become the next political divide on both sides of the Atlantic. We wanted to understand what was driving the emerging politics of AI and whether the dynamics at play in the US offer clues as to how the British debate will evolve. Over the last month, we polled 3,851 people across the UK and US about their views on AI. Our research found striking similarities between the two countries: for instance, both electorates are evenly split on their views towards AI development, with just over a third in favour (37% in each country), a similar number opposed, and the remainder somewhere in the middle or unsure. Statistically speaking, there is no difference in overall sentiment towards AI between the UK and US. Under the hood, demographic patterns also look largely the same in the two countries, with millennials, degree holders, full-time workers and those who describe themselves as financially comfortable more pro-AI than the rest of the country. Despite this convergence in overall attitudes, there is a striking divergence in how much of a politicised issue AI has become. In the US, attitudes are increasingly split by political affiliation and the issue has become the latest front in the seemingly interminable culture war, playing a role in the upcoming midterms. This stark political polarisation of AI has not happened in the UK – at least not yet. In what follows, we test several hypotheses for why the two countries are so far apart on a political basis. Understanding which hypothesis is correct will give us an indication of where the British dynamics go next.
Brazil: Lessons from a Disaster
By Antonio Martins (Outras Palavras) Defeats become deeper and more prolonged when we misunderstand what caused them, avoid correcting course, and embrace “solutions” that only compound our mistakes. Lula’s government and the Brazilian left suffered a disaster in Sunday’s election. Contrary to every poll, the president finished the first round 2.2 million votes behind Flávio Bolsonaro—and, more significantly, 3.4 million votes below his total four years ago, when the machinery of the state was working against him and in favor of his opponent. His vote share declined in 98 percent of Brazil’s municipalities. The PL, the main party of the far right, elected 121 deputies and 28 senators, becoming the largest caucus in both houses of Congress. The parties aligned with the Centrão—Brazil’s bloc of transactional center-right parties—were somewhat diminished, but will still hold roughly 250 seats in the Chamber of Deputies and 35 in the Senate. That will make it easier to assemble the majorities needed to amend the Constitution or impeach Supreme Federal Court justices. If elected, Flávio Bolsonaro could appoint four members of the Court, substantially altering its internal balance of power. What produced these results? How can the far right be prevented from consolidating them into a prolonged political hegemony? And what is the essential change of direction the left now needs? Explaining the Disaster Across the commercial media and the exuberant chaos of digital platforms, interpretations are multiplying. Some focus on specific episodes: Lula’s absence from the Globo debate, his very close relationship with Alexandre de Moraes, or his emphasis on issues supposedly remote from most people’s everyday concerns, such as rare earth minerals. Among the more systemic interpretations, three seem especially questionable. One argues that the principal defeat was not the left’s, since its congressional representation declined only slightly. The decisive movement captured at the ballot box, according to this view, was a shift within the right itself—from the more moderate and transactional forces of the Centrão toward the more ideological and radicalized forces of the PL and Novo. A second interpretation holds that the nature of electoral and political processes has changed. The material interests and aspirations of society, it argues, no longer play the central role they once did; they have given way to forms of subjective self-identification that the left has yet to understand. And a third concludes that, because of these two developments, we should expect a political winter: a prolonged period in which the far right and the broader right dominate Brazilian political life. There are important elements in all three interpretations. The problem lies in what they deny or leave out: Lula’s third government was a small-bore government. It offered the majorities that elected it neither a clearly better life nor reasons to believe again in politics and its power to transform society. These failures flowed from two fatal accommodations, which I will examine below. As a result, the government suffered a slow but steady erosion of support. And, as has happened in so many Western countries, it gave the far right the opportunity to present itself as anti-system and to benefit from popular frustration and distress. The interpretation that emphasizes the migration of votes from the center-right and traditional right toward the far right has been advanced, among others, by the courageous journalist Leonardo Sakamoto. In his UOL column on Monday, October 5, he highlights the miserable behavior of the traditional conservative parties. Over the past four years, they repeatedly joined forces with the “neo”-fascists, helping to “normalize” them—a criticism that could equally be directed at almost the entire corporate media. Believing they could use the far right as a stepping stone, they instead ended up partly devoured by it. This was especially true in the Senate, where the PL’s gain of 13 senators and Novo’s gain of two came at the expense of the PSD, which lost nine; the MDB, which lost two; the PSDB, which lost two; and the PP, which lost one. Sakamoto’s observation is accurate and necessary, but it leaves uncomfortable questions unanswered. Why did the erosion of the Centrão benefit the far right rather than, for example, producing growth for the left—as has happened so many times before, both in Brazil and elsewhere? The answer is not difficult. Throughout its four years in office, Lula’s third government was almost invariably associated with the Centrão. Here lies its first accommodation: accepting the limits imposed by conservative institutions; avoiding, as much as possible, any attempt to strain those limits through popular pressure; and believing only in the modest changes that could be squeezed through openings in Congress. The failure of the campaign to end the six-day workweek is emblematic. Lula could have electrified the presidential race by pairing it with a popular campaign for passage of the measure. Instead, he chose to place his hopes in the hands of Senate president Rodrigo Alcolumbre. He generated no mass mobilization, did not force his opponents onto an uncomfortable defensive, and won no victory. In September 2025, the government squandered the greatest of its many opportunities to shake up conservative power in Congress. Popular opposition to the so-called Shielding Amendment—which, if approved, would have restricted criminal proceedings against members of Congress—brought crowds into the streets, mobilized primarily by artists and social movements. This opened the possibility for a broader political education: a campaign that could begin exposing the limits and dysfunctions of Brazil’s political system while putting Bolsonaro’s movement on the defensive for its predictable support of political privilege. The amendment was defeated. But the executive branch then backed away from the fight. It remained associated with an institutional world widely seen as corrupt, elitist, and indifferent to ordinary people’s struggles. In doing so, it forfeited its credibility as a vehicle for the protest vote. On Sunday, it reaped the bitter fruits. Are We Doomed? Taking a different approach from Sakamoto, the philosopher Moysés Pinto Neto and other writers have drawn attention, in their first analyses of October 4, to the fragmentation of the public sphere produced by Big Tech platforms. Through their algorithms, these platforms present each person with a different view of social reality. As a consequence, it becomes much harder to identify common material problems and demands—for example, the fight for labor rights or for a properly funded public health system. Voters no longer embrace such demands, these authors argue, because they no longer see themselves as part of a collective. Instead, they tend to value the identities assigned to them by neoliberal subjectivity—the role of the entrepreneur, for example. On this interpretation, the left’s possible error lies in clinging to outdated illusions. Moysés’s warning is highly relevant and resonates with arguments such as Naomi Klein and Astra Taylor’s “Apocalyptic Fascism and the Billionaires’ Bet.” Unlike a century ago, the far right no longer promises a radiant future. It concentrates instead on resentment—especially that of young men. “They belong to a generation that fears it may never own a home, but compensates with the pleasure it gets from constantly provoking progressives online. Convinced their future has been stolen, they share memes romanticizing historical authoritarian regimes,” the authors write. But Moysés seems to confuse this danger with inevitability. He fails to see that the left’s answer cannot be adaptation. On the contrary, it must strengthen the possibility of politics as collective action. Nor does he recognize that precisely such a reversal is already becoming possible in some of the most promising mobilizations of the twenty-first century: the election, at the end of 2025, of the young Muslim Zohran Mamdani as mayor of New York, on a platform centered on the intensely material fight for an affordable city; the Left Party’s victory in Berlin two weeks ago, built around the demand to expropriate private real-estate corporations; and the very recent wave of demonstrations in Spain—including an occupation of Madrid’s Puerta del Sol—in which tens of thousands of young people mobilized by the Tenants’ Union have demanded limits on rents. By putting itself in the straitjacket of the “fiscal framework” in the first months of 2022, Lula’s third government adopted the second fatal accommodation of its term. Faced with conservative institutions and a country devastated by four years of Bolsonaro, increased public investment was the government’s remaining weapon for offering a more dignified life to the majority and forging an anti-conservative pact with them. The same state that disburses 1 trillion reais a year to enrich a small class of rentiers could instead spend money to extend the Family Health Strategy to every Brazilian, at a cost of 30 billion reais; provide full-day public schooling and childcare to all adolescents and children—and, especially, relief to their mothers; achieve universal sanitation while cleaning up polluted urban rivers; multiply metro systems; begin transforming the urban peripheries; and undertake many other urgently needed projects. In April 2023, an article in Outras Palavras warned that the fiscal framework could “shrink the Lula government and preserve the forces that condemn the country to inequality and regression.” A year later, another article showed that restraints on public spending were strangling policies vital to ordinary people and undermining the president’s popularity. In November 2024 came a reality check, in the form of the government’s humiliating defeat in the municipal elections. Even then, the fiscal lock was neither removed nor loosened. On the contrary: throughout the government’s four years, fiscal restraint remained central to its political narrative. The administration proudly proclaimed that it had “put the state’s accounts in order,” without grasping either how misleading that phrase was—given the payment of 1 trillion reais in interest—or how badly the message was hurting it politically. Lula finally woke up in September 2026. The increase in Bolsa Família benefits, after four years of being frozen; the announcement that anti-obesity drugs would be offered through Brazil’s public health system; and the ban on online betting showed that the state has the power to act decisively on behalf of the majority when those in government are willing to confront obstacles. The huge marches that closed the campaign in Fortaleza, Salvador, Rio de Janeiro, and São Paulo on the eve of October 4 showed how such initiatives can mobilize activists and the broader public. But it was too late—and too little to change the outcome of the first round. History has not ended The possibility of a turnaround in the three weeks leading up to November 25 cannot be ruled out. It will not be simple or easy. Lula must not only make up a deficit of 2.2 million votes; he must also offset the movement of voters who backed other right-wing candidates in the first round, most of whom are likely to move toward Flávio Bolsonaro. Pulling that off will require a broad, grassroots effort to win over voters, potentially reinforced by new initiatives from Lula. The coming hours and days will be decisive. If Bolsonaro’s movement also wins the presidency, the result will create a high-risk situation for Brazilian sovereignty and democracy. The enormous challenges posed by such an outcome would require the left to reexamine, in depth and with a genuine willingness to engage in self-criticism, its political projects—or its lack of them—and its practices. But would that mean we are condemned to the long winter of far-right hegemony that some analyses are beginning to forecast? It is far too early to say, suggests a recent text by Álvaro García Linera, the thinker who served as vice president of Bolivia under Evo Morales. His central hypothesis is that this political current will prove short-lived. At a moment when the entire world is once again talking about sovereignty and protecting national economies, the far right offers a stale, recolonizing neoliberalism. Its submission to the United States is as blatant and pathetic as Flávio and Eduardo Bolsonaro’s overtures to Donald Trump. Its project amounts to something like every man for himself: greater inequality, capitalism without limits, devastation, and supremacism. Will it work? In the United States, just two years into his second term, Trump himself is reaching record levels of unpopularity and may be on the verge of losing Republican control of both the House and Senate in November. Every new beginning is difficult. Whatever the outcome of the second round, the defeats of October 4 show how profoundly the Brazilian left will have to rethink and rebuild. But just as it would be foolish to underestimate the seriousness of its mistakes, it would be equally foolish to conclude that rebuilding a political project is no longer possible. History has not ended. The times are moving faster than ever. Originally published in Portuguese by Outras Palavras. English translation by Eric Blanc; I’ve added the subtitles. More We can’t stop the far right at home or abroad without a revitalized labor movement, which is yet another reason why you should become a sustaining supporter of the Emergency Workplace Organizing Committee (EWOC) — one of US labor’s pivotal bright spots. Relatedly, Jacobin published a short piece of mine reflecting on a decade of Bernie Sanders inspiring a whole new generation of young worker organizers.
Adaptation delivers book’s tone, forgets some important details
For every novel, there must be an equal or opposite adaptation – that’s how the rule goes, right? Author Ali Hazelwood’s latest film, “The Love Hypothesis,” is Hollywood’s most recent
Argonne: Building Scientific Computing Ecosystems for AI-Enabled Discovery
Trustworthy AI-enabled scientific discovery depends on software, people, institutions and community action. Oct. 1, 2026 — Artificial intelligence (AI) is changing scientific computing rapidly. AI systems can now generate and analyze code, synthesize information, assist with hypothesis generation and carry out complex reasoning tasks within scientific workflows. Alongside advances in computing architectures and data-intensive research, […]
'This is what happened with Claude and me': AI's failed attempt to crack the Riemann hypothesis led mathematician to a breakthrough
An attempt by Anthropic's Claude failed to prove the Riemann hypothesis, but it did produce something genuinely useful: inspiration.
Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
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Is metascience being co-opted?
In July, the White House released a report that argued academia is deeply dysfunctional and in need of radical reform.Science: A New Golden Age sets out a litany of problems many academics will recognise: interminable waits for grants; short-term funding rather than support for longer, more ambitious projects; ballooning administrative requirements; pressure to publish, and many more. “The scientific machine is getting bogged down,” it says. At least on the face of it, Science: A New Golden Age is a triumph for the metascience movement, a loose-knit, global group of academics, philanthropists and think tankers that want far more testing and experimentation in how we fund public research and innovation. The report tells US research funders to set up internal metascience units to do just this. But it has also set alarm bells ringing that metascience is being conveniently deployed to help justify the defunding of US universities, which US vice president JD Vance has openly labelled “the enemy.” “Just because you have an issue with your plumbing in your house doesn't mean you light the entire house on fire and burn it to the ground,” said Colette Delawalla, chief executive of Stand Up for Science, a group campaigning against what it says are attacks on scientists by the Trump administration. No one is arguing that academia doesn’t need reform, but there’s debate over how far and how fast the system should be shaken up. Does public R&D need slow and steady evidence-based adjustment, or radical new experiments?One of the key arguments in Science: A New Golden Age is that there’s not enough funding for what it calls “mid-level” science: engineering-heavy projects that require tens of millions of dollars, involve ten to a hundred people, and last around five years. Such a project might develop a brain-computer interface for people with Alzheimer's disease, for example. As such, the US National Science Foundation (NSF) has already announced $1.5 billion to set up so-called X-Labs, effectively new research organisations to pursue breakthroughs in areas such as quantum sensing and new scientific instruments. Funding divertedBut there’s a catch: this money has squeezed NSF budgets for more traditional, fundamental research grants, Science reported in June, likely meaning less cash for university-based scientists. Science: A New Golden Age also makes various swipes at universities, accusing them of extracting overheads from scientists to fund administrative bloat. “We have become dependent on a narrow set of legacy institutions,” it says. The report doesn’t mention it, but the US government is simultaneously seeking greater powers over federal funding, which would allow political appointees at funding agencies to screen or cancel research grants mid-project. The White House is also seeking to slash the NSF’s budget by more than half in 2027, although similar cuts proposed for 2026 were blocked by Congress. Convenient hookThis has worried some that the metascience agenda could be misused as a kind of smokescreen to further weaken universities. James Wilsdon, executive director of the Research on Research Institute at University College London, said that the report contains lots of “interesting ideas” and agrees that in places, academia is a “pretty broken system.” But the risk is that metascience is being deployed as a “convenient hook” to support cuts and a “political land grab” to transfer grant power from scientists to political appointees in US funding agencies.So far, the metascience agenda hasn’t been seized on by radical right parties in Europe. But Wilsdon worries it will cross the Atlantic, politicising research reform efforts and making them more difficult to carry out. The report is already making some in Europe sceptical of the entire idea. “The system is not without flaws, but the metascience discussion is not likely to improve the system but rather to generate munition to undermine it,” said Claes de Vreese, a professor of AI and society at the University of Amsterdam. Peter Thiel protégéOfficially, Science: A New Golden Age has just one named author: Michael Kratsios, the director of the Office of Science and Technology Policy and a former chief of staff to Silicon Valley mogul Peter Thiel. He also served as chief financial officer of Thiel’s Clarium Capital Management. Since the 1990s, Thiel has criticised US universities for moving away from teaching what he views as the classic works of western civilisation, and drifting towards “anti-Western zealotry.” And since 2011, he has offered two-year fellowships of $250,000 to young people to skip or drop out of university, and instead start companies or pursue scientific research. “College can be good for learning about what’s been done before, but it can also discourage you from doing something new,” according to the Thiel Fellowships website. In other areas, the Trump administration has promoted academics with a longstanding interest in metascience. For example, the head of the National Institutes of Health, Jay Bhattacharya, in 2020 co-authored a study arguing the funder backs overly conservative proposals, although he achieved wider prominence as a critic of pandemic lockdowns. Careless talk costs fundingEven before the Trump administration embraced the metascience agenda, some metascientists privately wondered whether they should be careful not to overstate dysfunctions in academia, in case this gets used as ammunition against universities. But others think being cautious with criticism is pointless. After all, last year, the Trump administration sought to cut funding from US universities on an entirely different premise: supposed antisemitism on campus. “I don't think these governments need a technical metascientific excuse to attack the universities, they will want to do so anyway,” said Will Stone, a researcher at Science Works, a new think tank advocating for a “metascience revolution” in the UK. “I think researchers should report what they find, even when the findings may be used to advance policies they disagree with,” echoed Russell Funk, a researcher at the University of Minnesota, who in 2023 co-authored a highly influential paper that argued scientific papers were becoming less disruptive over time. No justificationThat said, Funk thinks that metascientific research certainly doesn’t justify a weakening of universities. Instead, scientists need “time, resources and stability to pursue unfamiliar ideas,” he said. “Taking away those conditions works against the very goal the reforms are supposed to achieve.”The Institute for Progress, a Washington DC-based innovation policy think tank that has championed X-Labs and metascience units, also says that reforms should complement, not supplant, universities. “Unpredictable, unreliable, and reduced federal funding to researchers is destabilising to the very foundations of research and development and is bad,” said Jenn Gustetic, the institute’s director of metascience and R&D policy.Evolution or revolution?This points to a much bigger divide in the metascience community: how far and how fast should public R&D be shaken up? Does it need evolution or revolution? For example, Science: A New Golden Age says the science system relies too heavily on traditional “workhorse” project grants, the type that pay out around $250,000 a year to fund “hypothesis-driven science by small teams on tractable questions.” However, in criticising project grants, the report “decisively picks one side of a debate that is far from settled,” David Hilmer Rex, a metascientist based in Denmark, has written. In June, Pierre Azoulay, a metascientist at the Massachusetts Institute of Technology, defended project grants, saying that the alternative of X-Labs “merits scepticism.”A-B testingOne challenge in metascience is that it’s easier to generate evidence for some types of change than others. Most straightforwardly, research funders can run A-B tests on different ways of awarding money. For example, Germany’s Volkswagen Foundation recently trialled distributed peer review, where applicants rate each other’s proposals, and measured its results against traditional review panels. Distributed peer review made quicker decisions, although awarded money to largely different ideas. However, much of the focus of the metascience movement has been on founding fast-moving and free-acting new research organisations and funders, in contrast to the alleged stodginess of mainstream academia or public innovation grants. A culture of speed and freedom are the animating principles behind Germany’s Sprind or the UK’s Advanced Research and Invention Agency (Aria).Related articlesViewpoint: it's time to experiment with how Europe funds science European researchers sound the alarm that Trump rule changes will hit cooperationHowever, while it’s possible to closely study previously successful bodies, such as the US Defense Advanced Research Projects Agency (Darpa), for historical clues to their secret sauce, it’s impossible to guarantee in advance that the likes of Sprind or Aria will actually work as hoped. But advocates of new organisations say this inherent uncertainly shouldn’t stop us trying. “If we wait for definitive proof that something will work before we try it, I don't know where your proof is coming from,” said Stone. Trade-offsUp until now, experiments in metascience haven’t generally come with hard trade-offs over money. In the US, new ideas such as focused research organisations have often been funded by billionaire philanthropists. In Europe, new innovation agencies such as Sprind and Aria, or the Netherland’s new National Agency for Disruptive Innovation, haven’t needed explicit cuts elsewhere in the research system. But with the $1.5 billion establishment of X-Labs by the NSF, the trade-offs are becoming clearer. Some metascientists privately wonder whether a smaller pilot would have been more appropriate, given that the idea is untested. The Institute for Progress counters that the $1.5 billion X-Labs budget is spread out over a decade, so will account for less than 2% of the NSF’s annual budget. “It’s important to remember that [Darpa] was once an institutional experiment as well. Now it’s one of the most cited innovative funding models in the world,” said Gustetic.As for Europe?The US isn’t the only country placing metascience at the heart of its research strategy. Science: A New Golden Age looks admiringly at the UK, which in 2024 set up a Metascience Unit to scrutinise how it funds research. In Brussels, it’s more of a mixed picture. In 2018, the EU set up the European Innovation Council to funnel grants and equity to start-ups, although it’s sometimes criticised for awarding money too slowly. Recently, the council has started experimenting with challenges, where teams of inventors compete to overcome a technological challenge. The European Commission also wants to experiment with partially randomising grant winners, to cut down on bureaucracy. However, the Commission’s own advisers have said the EU’s own research schemes are too conventional. They want the Commission to establish a new dedicated unit to scrutinise and experiment with funding. But there’s no sign of a such unit emerging in Brussels’ plans for the next Horizon Europe programme, which from 2028 is set to distribute more than €160 billion over seven years. Sam Bogerd, director of breakthrough innovation at the Brussels-based Arq Foundation, an AI-focused think tank that also looks at metascience, says that Europe might have the opposite problem to the US: universities and basic research funding are still reasonably well supported, but there’s not enough experimentation. “We're not taking a close enough look at where this money can achieve the most, and I am worried that the good ideas around metascience will be tainted politically by their association with the Trump administration,” he said. He’s still lobbying for the Commission to set up an experimental unit. But in the meantime, the Arq Foundation plans to do its own research, and is currently hiring two metascience researchers to run the rule over how Brussels spends research funding.
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AI needs science’s search history
“Actually, that’s where the gold is,” said Alasdair Russell, my graduate school friend who leads a pre-clinical genome editing group at Cambridge. He was talking about the winding road of science that is omitted from published papers. “When you’re discussing how to do the experiment, and why this way is better than another way, and what does the data really mean? I know what it shows, but what does it mean?” At RAAIS 2026, he described how his group has begun logging the twisting path of discovery as it happens, recording the verbal and written exchanges that normally disappear. Ideas become nodes in a living graph: they branch when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar dilemma. The bottleneck is that the record we have kept for centuries of science might be insufficient to get us there. The experiments that never make the paper Peter Medawar, 1960 Nobel laureate and “father of transplantation,” asked in 1963 whether the scientific paper was a fraud, and answered yes: the form of the paper misrepresents the thinking that produced it. Sixty years on, the diagnosis is unchanged. A paper presents a clean progression from hypothesis to result to conclusion. Lost along the way are the unconventional theories, the abandoned or unaffordable methods, and the underwhelming and inconclusive data. It reads like a browser history with every dead end deleted: the ten open tabs, the four rephrased queries, the wrong turn down a subforum. All scrubbed, leaving one clean path from question to answer as the canonical path. What has changed since then is that the discarded material is now worth something. No high-impact journal wants these artifacts, but raw trial and error is the most likely source of the training data required to develop scientific intuition, or what researchers call taste. Earlier attempts to capture the discards were motivated by scientific integrity. The Journal of Negative Results in Biomedicine launched in 2002 to publish rigorous studies that disputed established models or exposed ineffective treatments. Its archive preserved negative conclusions after they had become papers, rather than the live alternatives and arguments that produced them. BioMed Central closed it in 2017, saying the mission had been served now that other journals publish null results. A less generous reading is that in fifteen years it published around 200 papers because almost nobody wants to read a negative result, let alone write one up when it will not count toward academic tenure. AI models are different. Even an uninteresting negative result can be useful, provided it is labeled. Earlier this year, Anthropic put a version of this to the test. The company pulled 129 decision points from real Claude Code sessions between January and March 2026, showed models the work up to a human detour, and asked what should happen next. Claude Mythos Preview beat the human choice 64% of the time, while Opus 4.5 managed 51%. The comparison was tilted toward the models because Anthropic deliberately chose moments where the human decision had room for improvement. On 127 further scenarios where the human action was already strong, the models improved on it only about 20% of the time. The study was possible because Anthropic’s researchers work inside a tool that logs by default. The reasoning, detours and outcome are produced in the same working environment. Biology has no equivalent. Its reasoning happens in hallways, at benches, in Slack threads and on whiteboards, while the outcome arrives weeks later somewhere else. The record has to emerge from the work itself. Ask scientists to reconstruct it afterwards and we’ll create another polished account. Subscribe A log is not a label Before any of this becomes training data, a negative result has to say what failed. The first paper published in the Journal of Negative Results in Biomedicine examined 234 negative studies across five leading medical journals. Only 30% discussed statistical power, and half clearly defined a primary outcome. Tell a model an experiment failed, without telling it whether the assay was underpowered, a reagent had degraded, or the hypothesis was simply wrong, and it will learn noise with confidence. Decision histories have a second missing label too. When a lab considers five experiments and runs one, only the chosen branch returns an outcome. The other four are experimental counterfactuals. Researchers call this the selective labels problem: the data reveal results only for the actions someone chose to take. Such a record can teach a model to imitate a lab’s taste, but it cannot establish that the taste was good. It also smuggles local constraints into general lessons. A lab that never ran cryo-EM because it did not own a microscope teaches a model that cryo-EM is rarely the right call. Alasdair’s nodes preserve the candidate set, which is the necessary first step. To be useful, each decision record needs six fields: The evidence available at that moment, and nothing that arrived later. The candidates considered, including the ones dismissed in a sentence. For each candidate: the expected result, the confidence attached to it, and the cost in time and money. The route chosen, and the reason. What happened next. How the evidence changed the scientist’s view. These must be timestamped before the outcome, because hindsight turns uncertainty into inevitability. Disagreement also has to survive. Averaging three scientists into one clean rationale reproduces exactly the information loss we are trying to fix. But there is an obvious failure mode here. Once decisions are logged and scored, people log for the record post-facto. Anyone who has watched an electronic lab notebook fill up with retrospective tidying knows how quickly a research tool becomes a compliance exercise. A record that costs a scientist ten minutes of honest reflection per decision is worth far more than one that costs an hour of performance. A publication initiative called Registered Reports offers a useful starting point. Here, researchers submit their rationale, methods and analysis plan for peer review before the data exist, and the journal commits in principle to publish if they follow the approved plan. Nature has now expanded the format across every field it covers. This approach to paper writing timestamps intent before the outcome, but it freezes one plan. A useful search history must also preserve how the plan changed, which alternatives were rejected, when, and on what evidence. Subscribe Run the runner-up Autonomous labs show what happens when decisions and outcomes are connected in a loop. Liverpool’s mobile robotic chemist ran 688 experiments over eight days in a ten-variable formulation space. A batched Bayesian search used each result to choose the next experiments and found photocatalyst mixtures six times more active than the starting formulations. Every completed experiment changed what the system did next. The robot optimized within a goal and search space that humans had already chosen. It did not decide which scientific question mattered. So what this work demonstrates is narrower, but still useful: a search history pays off when the options return standardized outcomes quickly. Open-ended biology is harder because branches can take weeks and the discarded alternatives may never be run. Some exploration therefore has to be bought. Where two branches are plausible and the stakes justify the cost, a lab should sometimes run the runner-up. Otherwise the record captures what today’s scientists usually chose and stays silent on what they systematically overlooked. A funder could ring-fence a small fraction of a grant for the branch not taken, provided that the decision record and the outcome are both deposited. That costs money, but so does having every lab rediscover the same abandoned path. Test whether taste transfers When it comes time to evaluate if our new AI system exhibits taste, the comparison should run across laboratories and fields. At selected decision points in a live research campaign, we should freeze the six fields: the evidence, the candidates, expected results, confidence, costs and the choice made. Experts then rank the options before the outcomes are known, and those outcomes are checked independently later. We could give models one of two diets - papers alone, or papers plus decision histories - and ask them to rank the next experiment on unfamiliar projects. Then, we score information gained per dollar and week, calibration, and how quickly weak branches are abandoned. If histories improve choices only inside the lab that produced them, they amount to useful organizational memory. If they improve choices on unfamiliar problems elsewhere, Alasdair’s group will have captured something science has never managed to write down: a transferable record of taste. Until that happens, a scientific search history is a promising record, not yet a training set.
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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.
How Science Works
The 2nd Weather and Climate Livestream is now underway. It is organized by a group of early-career researchers concerned about declining funding for the field and eager to demonstrate how weather and climate research makes a difference in our lives. Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in. Share I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below): And please tune into the livestream here. It runs through tomorrow at 6 pm ET. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe Cleaned up transcript: Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works. Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through. So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with. But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth. Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper. Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word. That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge. Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole. When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere. Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause. Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate. So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it. There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it. This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated. To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible. You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available. So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English. Let me open the floor to questions. Thank you.
Google launches Gemini for Science AI tools | ETIH EdTech News
AI in education and edtech research shift as Google launches Gemini for Science, with Google Labs tools for literature insights, hypothesis generation, computational discovery, and Science Skills in Google Antigravity. ETIH edtech news looks at Google DeepMind, Google Cloud, NotebookLM, Co-Scientist
Google launches Gemini for Science AI research toolkit
The Gemini for Science suite includes three experimental tools covering hypothesis generation, computational testing, and literature review
Are your memories real? Physicists revisit the Boltzmann brain paradox
A new analysis of the “Boltzmann brain” paradox suggests our memories and sense of reality could, in theory, be random illusions born from cosmic chaos. By uncovering circular reasoning in how physicists think about time and entropy, the study raises fresh doubts about what we can truly know about the past.
The disunity of science is a feature, not a bug
Mathematical models wield miraculous power, but mistaking them for reality has trapped science in a destructive quest for a “Theory of Everything.” Physicist and philosopher Ragner Fjelland argues that this leads to bloated, Frankenstein theories kept alive by clumsy, ad-hoc amendments, which reduce rather than boost science’s power. Fjelland calls for a radical shift in our attitude: we must reorientate towards creating entirely new theories, celebrating rather than lamenting disunity and diversity in science. Science, from the scientific revolution in the seventeenth century to the present day, has more or less developed in a “reductionist” way. The fundamental assumption is that all sciences may be ordered in a hierarchy that can be represented as an upside-down pyramid. At the top we find the human sciences, and at the bottom physics. Strong (or “ontological”) reductionism maintains that all theories at one level can be completely reduced to the lower level, all the way down to theories of atoms and elementary particles, or whatever is “at the bottom.” In the end, all diversity in the world can be unified in one fundamental theory. Hence it is called a final theory, or a Theory of Everything.One of the central figures in developing the idea of a Theory of Everything is the Dutch theoretical physicist Gerard 't Hooft, who received a Nobel Prize in physics in 1999 for his work. He emphasizes the non-empirical status of the theory:There exists no closely resembling alternative theory. This means that any slight change brought about in the rules would make the theory unlikely or inelegant. The theory will be a "package deal": take it, or leave it. This should hold both for the local laws and for the boundary conditions.This endeavor is not new. On the contrary, it goes all the way back to the birth of Western philosophy 2,500 years ago. The Babylonians made regular astronomical observations during a period of 600 years, from 747 BCE until 150 ACE. They developed mathematical techniques to predict astronomical phenomena, but they made no attempts at explaining them. However, the Greeks developed models, both material and theoretical, to explain the observed phenomena.The best example is Plato’s late dialogue Timaeus. Here he introduces a divine craftsman, the Demiurge, who has constructed the universe according to an original plan. Plato seems to have thought that the original plan is what is actually real, while the material world is merely an imperfect realization of it. He therefore does not, in the Timaeus, reconstruct the material universe, but the original plan, which can then be used to explain the material universe.Plato’s aim in producing a theoretical model is to reconstruct the original plan. That is what he carries out in Timaeus—and some modern physicists have embarked on the same project. For example, Einstein’s basic attitude is reflected in the title of Abraham Pais’ biography: “Subtle is the Lord...,” echoing Einstein’s comment, “Subtle is the Lord, but malicious He is not.” In an interview Einstein compared our situation to a child who enters a huge library. The child knows that someone has written the books, but he does not know the languages in which they are written. He suspects that there is a mysterious order, but he does not know what it is.___“We have all been working in what Husserl called the Galilean style; that is, we have all been making abstract mathematical models of the universe to which at least the physicists give a higher degree of reality than they accord the ordinary world of sensation.”Steven Weinberg, Nobel Prize in Physics (1979)___This Platonist view is contrary to a widespread impression of science, symbolized by the picture of the “father” of modern science, Galileo Galilei, with his telescope. According to this impression, Galileo liberated himself from the dominating metaphysics of his day—basically Aristotelian philosophy—and constructed a telescope that he directed towards the sky, showing that Jupiter has four moons. He also performed experiments with bronze balls rolling down inclined planes and found the law of freely falling bodies.However, in other cases Galileo emphasized the unreliability of our senses. It is easy to see why. He defended Copernicus’ heliocentric hypothesis. According to this hypothesis, we are not at rest at the center of the universe, but moving at a tremendous speed through space. Why don’t we observe this motion? Galileo praises Copernicus because “with reason as his guide he resolutely continued to affirm what sensible experience seemed to contradict.” In other places he directly refers to Plato. SUGGESTED VIEWING The quest for a final theory With Michio Kaku Galileo’s Platonism goes back to his early writings. In The Assayer, published as early as 1623, Galileo compared the universe to a grand book that is written in the language of mathematics, and “its characters are triangles, circles, and other geometric figures without which it is humanly impossible to understand a single word of it.” Therefore, things that cannot be described by mathematics—for example, colors, smells and odors—are not real. They are merely subjective.This division into subjective and objective, advocated by Galileo and his younger contemporary René Descartes, is normally associated with a mechanistic world view, which is regarded as the opposite of Platonism. When we reduce something to, for example, molecules and atoms, we move “downwards.” In contrast, the Platonist moves “upwards,” to abstract entities. However, it only shows that when we address fundamental questions, we should be careful in applying everyday spatial metaphors like “up” and “down.”___Nature is complex, and we cannot in general construct mathematical models that adequately describe the world.___The endeavor of finding a Theory of Everything has been heavily criticized. In 2006, the theoretical physicist Lee Smolin published “The Crisis in Fundamental Physics.” He argued that one of the most fundamental principles of science is that we should only regard theories as scientific if we have the possibility to show that they are false by experiments. Although he did not refer to Karl Popper, other critics have referred to Popper and argued that a theory is not scientific if it cannot be falsified (see for example Ellis & Silk 2014).These were not the first physicists to be anti-reductionists. In 1972, the journal Science published an article by the physicist Philip Anderson with the title “More Is Different.” This is the kind of article a scientist would normally write only after he has won a Nobel Prize. However, Anderson won the Nobel Prize for physics five years later, in 1977. The article is amazing, not primarily because it argues in favor of anti-reductionism in general, but because it argues for anti-reductionism within physics itself. Anderson’s conclusion was:At each stage entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one. Psychology is not applied biology, nor is biology applied chemistry.We may say that Plato started the “unity of science movement,” based on Euclidean geometry as the model of all knowledge. A distinguishing mark of geometry is that all possible constructions can be reduced to a few fundamental principles. We have to acknowledge that this approach has limitations. In general, we can easily model technological systems. In addition, we may construct simplified and idealized models that capture important aspects of systems, from various perspectives and for various purposes. But nature is complex, and we cannot in general construct mathematical models that adequately describe the world. In a complex system, each level has its distinct, emergent, properties that are characteristic of this level. Therefore, a complex system should be investigated at different levels and from different perspectives. Instead of exclusively pursuing a scientific ideal based on the unity of science, we should acknowledge the diversity—or even the disunity—of science.
Why Is Ice Slippery? A New Hypothesis Slides Into the Chat.
A newly proposed explanation for the slipperiness of ice has revived a centuries-long debate.
Science must move from materialism to mystery
Theoretical physicist and neuroscientist Àlex Gómez-Marín argues that modern science has become trapped in a framework that mistakes matter for the whole of reality. In this wide-ranging interview, Gómez-Marín challenges the foundations of materialism, defends the scientific study of near-death experiences, and calls for a new type of science grounded in mystery and a renewed sense of the sacred. He suggests that abandoning materialism could open the door to a deeper understanding of consciousness, death, and the purpose of human existence. Simon Custer: You are both a theoretical physicist and a neuroscientist, and you have also been fiercely critical of materialist theories of mind and consciousness. What do you think the ultimate nature of reality is?Àlex Gómez-Marín: I don't know what the ultimate nature of reality is, but what I try to first assess is whether the stories that they [mainstream science] have told us about it are right, or maybe whether there are other alternatives. That's why I've been a fierce critic of materialism. As a scientist, I realized that they had sold us this idea that to be a good scientist you also had to subscribe to many other -isms, like materialism, reductionism, and even secularism. And so first I think one needs to unmount these -isms, and then, as is happening today in consciousness studies, we have a huge landscape where there isn't only one game in town, the idea that matter is the only thing that really exists. But because we are studying the hard problem of consciousness, it may be the case that other views of reality, like idealism, or even dualism, or other theories like dual aspect monism... these are philosophical ideas that now, I think, have room in science to be taken seriously. How does something like materialism relate to dualism or idealism? What is materialism in the first place?Well, materialism is the philosophical position that matter is the only really ‘real’ thing in the universe. And I think that worked kind of well because science started 400 years ago and Galileo made that split, I call it the foundational wound. It was a great business move.He said, let's start doing science on that portion of reality that lends itself more easily to mathematization and measurement. So, for some reason, the solidity of this table or the movement of planets lend themselves very well to this foundational idea of science. But as time went on, 400 years later, we realized that we had forgotten the other side, which is what really matters.What really matters is pain, is love, is feelings, sensation, consciousness, which is very hard to define, but very intimate. We all know what it is. It's that thing that disappears when we go into deep sleep and comes back in the morning. SUGGESTED VIEWING On the edges of knowledge With Michael Shermer, Rupert Sheldrake, Güneş Taylor It's what it is like to be, as Nagel said. It's the feeling of being alive. So, materialism is good to study certain things, but then when it ossifies into this ideology and then is dressed like science, now it's preventing us from understanding these key questions that are really that everybody wants to know.Why are we here? Why do we feel reality? Where do we go when we die? And so we need other ways. We need pluralism. I'm only asking for having more options on the table and taking them seriously. Why does materialism fail to explain consciousness? For example, if you were to lobotomize me, I would not be a functional human being. What do you say to someone who might think that materialism adequately explains the mind?Well, here we could speak about different things. We could talk about physics or about neuroscience. I happen to have training with both.For instance, for neuroscientists, 99% of them, I'm not included in that portion, believe that the brain can only be a productive organ. It's an organ that somehow, somewhere, consciousness emerges. It's like a lamp. And then something happens, magic happens, and then consciousness emerges.That's the position you need to be if you're a materialist because if the only really ‘real’ thing is matter, then you need to say, just give us some more time and money and we'll figure out how mind emerges or is produced from matter. There are many philosophically sophisticated ways of criticizing this.Now there's another option, which is the brain as a permissive organ. This was said by William James more than 100 years ago.Now then the brain wouldn't be like this machine, like for instance, a heat engine whose smoke is consciousness, which is what we would have in the first big model, the productive model. But in the permissive model, the brain would be a kind of a filter. And so the brain would still have a very important role in consciousness, but it wouldn't be a productive role.It would be permissive, much like a filter or much like a prism which would receive light. And - I'm speaking metaphorically - it would receive the light of the mind and then it could reflect it and refract it.Now, with these two big options on the table, I think we can make progress into what I call the edges of consciousness, which are all these experiences, weird experiences that many people have, but we don't talk about enough, like near-death experiences, precognitive dreams, synchronicities. These experiences are very important for humans and they have a place in the permissive hypothesis and they are literally impossible in the productive hypothesis. So that's yet one more reason to explore alternatives to materialism. There may be anecdotal evidence for near-death experiences, but is there robust evidence that would be accepted by the majority of scientists who subscribe to the scientific method?Sure. And the plural of anecdotes, someone said, is data.And there's a lot of data. Actually, this year is the 50th anniversary, half a century, since Dr. Raymond Moody realized that we have two ears and one mouth and started listening to what his patients had to say about their experiences while they were clinically dead.The heart had stopped, breathing had stopped in these patients. These patients would come back and tell him these amazing stories that some people may say are hallucinations, but for the patients were hyper-real, like seeing light at the end of the tunnels, seeing deceased family members waiting for them, feeling bliss.___I would say the evidence suggests that the mind can survive the death of activity of the brain.___And so he and others started taking them seriously and writing their accounts down. And then you can do a science based on that material of experience that people have. Now, on the more objective side, more recently people have started to measure what's going on in the brain when people are clinically dead and brought back. This has opened a proper field of study, which is near-death studies.Evidence is a difficult word, because sometimes people think that when you say evidence, this is just the final proof. Evidence is like in a jury. You bring testimony, you bring pieces of data that point in one direction or another.I would say the evidence suggests that the mind can survive the death of activity of the brain. When brains are broken or shut off, there can still be mind going on. And that's revolutionary.That would entail that when we die, it's not game over, which, by the way, is the thesis of materialism. If consciousness survives, materialism dies. It's game over for materialism if something of us continues after we die. Do you think that there is mental continuity after death?Well, I think so, and I believe so, and I could also say I may have experienced it myself, because four years ago I had a near-death experience. I was in the hospital, I lost a lot of blood, and I saw those beings at the end of the tunnel, three beings, and there was yellow light. I was in a well, I was looking upwards, and I hadn't read about NDEs by then, but when I came back from this experience, I decided I would try to integrate this also in my professional life. Not just my personal life, of course, but my professional life as a physicist and as a neuroscientist. So I may have had the experience of glimpses of what an afterlife would be, and I'm not ashamed to say it in this way, and then one can do science of it, and then one can do philosophy of it.One can think about what -isms we were discussing, what views on reality would allow those experiences to be really ‘real,’ as opposed to, well, poor guy, the brain was kind of broken, it must have been a hallucination. Do you have a view about whether individuality or “the ego” persists after death, or whether we sort of merge into some sort of cosmic consciousness instead?I don't know, but my view would be along those lines you're suggesting. I think we could speak about a kind of universal mind, and then the problem is not how we emerge from matter, but how we fragment from a more holistic consciousness into these individual eddies and then when we die, this kind of dissipates.But I'm not sure. For instance, idealism would promote that view, but then what would survive of us after these eddies just vanish and become again the big Consciousness, I'm not sure what that would be. I don't know for sure, but I would tell you I believe something of us survives.And there are other lines of research, not just near-death experience, that can address this really hard question of survival. For instance, there are children who remember previous lives. This was studied by Ian Stevenson at the University of Virginia for more than 50 years.___Research takes place within a selection mechanism that also sometimes reinforces things that are not very scientific.___It's amazing research. He went all over the world, especially in the East, and collected all these case studies, all these stories. In plural, these were data of kids, very young age, who would describe up to 40 items that then he could go and verify.There was no internet. There was really no way the kid could know. Why would the kid say that when I was living in this other body and I was a woman and that's how my house would look like or that's how I would die, right? Again, there is evidence, not proof, but there's evidence not only that something weird and interesting is happening as we cross the threshold, but also when we come back to this life.Scientists should not be afraid of studying this, but they are because there's stigma. There's usually stigma upon the enigma, and this is so unfortunate. And one of the reasons is because of the ideology of materialism, because those things cannot be possible. What is wrong with the politics of science such that they have generated this stigma?So many things. Science is a human activity to begin with.So it falls prey to all our passions and ego and greed. That's one thing. Science is a job for the good and for the bad.You need to get a salary. You need to get grants. Research takes place within a selection mechanism that also sometimes reinforces things that are not very scientific.And then, we didn't have time to unpack this, but science for many years become entangled, as I was suggesting before, to other -isms, materialism, secularism. So anything that smells like the afterlife, people may think has to do with God, and then they freak out.Anything that suggests that there's something about the mind that may not need a physical material substrate to exist, it smells like non-materialism. So, all these -isms are ideologically blocking that kind of research, and then there are all these sociopolitical constraints as to what should be studied and what shouldn't be studied.I call it the backstage of science. Sometimes we scientists or science communicators seem to speak as if the data is the only thing that matters. We say, well, data and theory.But then there's a third leg of this stool, which is all the politics that goes behind the scenes. And we often don't speak about this, and it's quite vicious and petty, and it's very real. Why is materialism so attractive to most scientists?Well, an easy answer is because we've grown up with it.It's what the educational system promotes, and that's what you hear when you go to university. So it's like growing in a culture. It feels like the air in which you breathe.So it's very hard to begin with to detect, well, maybe there's another alternative. Maybe there are other ways of doing science, other environments in which one could do science. That's one reason.And other reasons maybe are more profound, and I'm not qualified to explain them, but I could say, looking at this 400-year history of science, since it started around 1623 with Galileo's book The Assayer, that science went and coupled with all what's going on in the West.And so the fall of religion, you could say, and science presented us this new alternative that then could be coupled very well with other -isms, and this idea of growth, and then science losing its good twin brother, which was philosophy. There's a whole natural history of science starting as philosophy, kind of emancipating or breaking out from philosophy, and then marrying technology, and then marrying other social systems.From that point of view, materialism is a very convenient idea to propose. There were attempts, like the German idealists tried to do a more romantic science. But no, the idea prevailed, like Francis Bacon said: You should pursue nature through all her corners, he said, and make her spill her secrets.Our attitude towards nature in the science that dominates is very much reflecting how we treat nature in our society, in our culture, like a resource to extract secrets by force and then dominate it. How is materialism related to the notions of growth and progress? What's wrong with having a productive citizenry?This is another very interesting and very difficult question to answer.But there's this recent book coming out by Paul Kingsnorth. In it, he explains this idea of the machine and this idea of progress. And this, again, this conflation of more is better, of we need to go forward always, and convincing us that the way to do that is to continually extract more from nature. But because we don't have the religious context anymore, because God is dead, then what do we have? We have what we can do as humans with our own gadgets.It's kind of the story of the Promethean fire. We will just build anything to make ourselves God.A good example today is transhumanism. Transhumanism is this pseudo-religion dressed in technoscientific language whose goal is very religious. They promise immortality, they promise redemption, they promise transformation of the entire natural world, but it's done by human means through technology. Are you suggesting that we can't have ethics without religion?Well, we should have ethics with or without religion. That's another pruning that has happened in science. We say, forget about aesthetics, although some physicists still think that beautiful means probably true, and forget about politics. We pretend there's no backstage of science, and even let's forget about ethics.___Science needs to be in the service of mankind.___Scientists, when they become more like engineers, they're just thinking how to make things work so that they can go to the moon, go to Mars, make machines that think, but they don't think whether that's a good idea.And also consider metaphysics. Many scientists for a long time pretended, and maybe that comes from the stream of positivisms, that really one doesn't need metaphysics at all, except that materialism is always the metaphysics running the system.You see, it's not just ethics. It's the pruning of all of these, you could say, five main legs of philosophy that just makes a kind of unconscious science, a science that doesn't really know what it's doing. It's doing it very well locally, but it just doesn't know what it's doing globally. Do you think science has to have an ultimate vision and purpose behind it for it to be effective or for it to be meaningful and transformative?Yes, a way I would say that is that science needs to be in the service of mankind. And it hasn't been that. And we can see it through, you know, when we discovered the secrets of the atom, and all of a sudden, the Manhattan Project, and then we create nuclear weapons.And now we're trying to crack how machines may think, and now we're creating this mess, right? So science needs to be in service of mankind, not in service of a few ideas that maybe want to make more money or just dominate upon nature. What about planes, trains, automobiles, and modern medicine? Hasn't science served mankind by helping us develop those technologies?Yes and no. I see where you're going, and it's fair.I came here by plane, and it's a blessing. When I almost died, the surgeons did their job, and they saved my life. But these two forced choices between Luddites and Silicon Valley types, I think, of course, it's a false dichotomy.Progress, in a way, it's good, but we often don't speak about the dark side of the moon. We say the great things that progress has brought, and we don't speak about the disasters, the side effects that it has come with, and the same when we promise new technologies. We tend to think that any technology is great, that email is going to save us so much time because we don't need to write letters and send them anymore.Is that true? I spend so many hours a week writing emails. Let's just say there are great things about it and bad things about it. People like Harvard psychologist Steven Pinker, for example, argue that we've made moral progress over time because of the Enlightenment. Do you think that's totally wrong-headed?I think he's emphasizing a side of the moon, and he does that very well, and you can find tons of plots where there's a slope going up and say it's great. But I think he should also tell the not-so-friendly story.But if your ideology is just to resist and continue to explain that the Enlightenment is the best thing ever and reason is the best thing ever… this feels to me like you're worshipping these abstractions.I'm very critical of it and also of these, again, science communicators and scientists that say, Oh, science says, in the name of science, experts say. It's like, come on, let's be careful because we can shoot ourselves in the foot by putting forth to the people an image of science that we know is not really true. Recently there's been a resurgence of spirituality and of people who say that they're spiritual but not religious, especially in the United States. If materialism is going away, what is going to fill that gap? Are we going to return to traditional religion? Do you see that being connected with the rise of the political right?I'm not a fortune teller, so I don't know what's going to happen. But, yeah, we could say we're in this post-materialist age and even post-secular age. One cannot come back to the past, but maybe just to contact with the roots is a good idea.What do I mean by that? I think we need to do a science 2.0, a science that says thank you to Galileo and thank you to materialism, but we need to reinvent even a new kind of scientific method. We need to do so in a culture where we realize humans cannot thrive in a vacuum where there's this idea that the sacred has to be removed.Now, how do we do that without bringing in back all the vices of religion? I don't know. I'm from a Catholic country.Here we are in England, so there are differences and interreligious dialogue has always been a challenge. But I think maybe a way of putting it is: after all these conflicts and branchings I was quickly alluding to, science from philosophy and then also from religion, now there's a time of reconciliation perhaps and maybe debates in the proper sense, like exchange of ideas in service of learning and getting into something higher than both poles. We need science to talk to religious people and it's interesting when people say they're spiritual and not religious.I think this is just a way of saying, no, I don't want to go back, I want to go forward. But at the end of the day, religions have offered us practices and grounding that we now desperately need. Everybody can and should just meditate or pray or do whatever they want, but we're in a crisis and it's time for reconciliation. What do you say to critics that say you are merely projecting our need for meaning onto a fundamentally indifferent universe?Yeah, I know that the typical science communicator says something like this. But we're not projecting. Human beings feed on meaning.It's the other way around. If you're a materialist, because the only thing that there is is matter, all that comes next, free will, consciousness, meaning, this is a kind of illusion that we need to explain.They're just craving for meaning, but there isn't really for them. I would also like to remind my materialist friends that for decades, if not centuries, they've said in the name of science, we know that this and this and this doesn't really exist. I think now they sense they're forced to say, well, actually, we don't know. We've been saying, for instance, that there's nothing after you die, and now probably they need to admit that they don't know the answer.___We need to do a science 2.0, a science that says thank you to Galileo and thank you to materialism, but we need to reinvent even a new kind of scientific method.___So it's a moment to open up possibilities and also to gently but fiercely critique these claims that I think harm society. If you say in the name of science, get on with it, when your father dies, you'll never see him again. Well, what the F do you know about it? You haven't studied the literature, you haven't had any of those experiences, and basically what you're doing is you're just doing marketing on ideology. This needs to stop. Do you think that it's scarier for some people to entertain the thought that there might be life after death, compared to a materialist worldview where you just die, given that there might be consequences for unsavory behaviour?Well, death and dying is scary. It's maybe the ultimate ending. It's a limit on life, and when we're in this conquest mode, we don't like limits.And it also has, there's a shockwave backwards, so I think if you change your notion of what death is or may be, you live differently. It also has consequences, as you're saying, for the future because this is not our only goal here and we come back in whatever form. And again, different religions would say different things. I the East, they are more happy with the idea of reincarnation, and here in the West, there are other preferences. But in any case, it changes how we live, and that's really what matters.It's not about just projecting or imagining what we'll be after, but how we live now and also how we treat those who are dying. So it goes from the individual to the families, to the hospitals.Now in the near-death research I do, this has ramifications at all these levels, all the way to society.But our society is thanatophobic. It has a phobia of death. We don't want to hear about death, and this is another thing we need to put on the table and be willing to talk about it. Do you think that we might get closer to answering ultimate questions like why is there anything at all if we jettison materialism?That’s an excellent question, Simon. Two things: we should stop treating reality, I believe, as a great sudoku, as an enigma, as something that with intelligence, money, and time that we will crack and instead treat reality more like a mystery. And what you do for in front of a mystery is not to be the clever guy in a class and say “I’ll solve you” but more like “I will bow and you will transform me.”I know this sounds a bit like a scientist speaking like a mystic. But I think this reorientation is needed. In the end, we won’t crack the big questions, but they will transform us. If we free ourselves from this prison of materialism, the way we will explore these questions will be freer, more creative, and in service of mankind. Do you think exploring mystery is the reason why we are here?I think we are here because, and this pseudo-theological argument I’m making off the cuff, But the One, the Absolute, if there is such a thing, had everything except limitation. We are that absolute experiencing that contingency. It’s like an act of divine proprioception. It’s a wonderful adventure, it’s just incredible to be alive. Perhaps we have forgotten why we are here and the whole point is to experience the flesh.Let me end by saying this. I criticize materialism harshly. I bang on this dead horse, because I think it’s dead. But matter is a great mystery. So maybe my invitation is as follows: hey materialist friends, come here, let’s work on materialism 2.0! Consciousness is a mystery, but matter is also a mystery. The fact that we can have an intersubjective experience consensus, the fact that we can apply mathematics, that we can measure things... the physical world we take for granted, but it is a great mystery too.Maybe that’s related to your last question: why are we here? Why did spirit decide to incarnate? Being in the flesh may be a great thing if spirit decided to do so.Àlex Gómez-Marín, thank you very much.My pleasure.