Category: Science
Predictions for economics, given AI
With math essentially being delegated to OpenAI, here’s what I predict for economics and the social sciences more generally:
The top tier of research will just become better and it will be normal human-led research where AI is used for scale (e.g. conducting qualitative interviews with relevant populations, running behavioral interventions in the field, analyzing massive text data, etc.)
Field experiments will rise in value and generally making “connections” with firms and being able to run stuff (potentially testing AI pipelines) will be in high demand
Research with administrative data will become even more valuable, but the benefits might be concentrated among some prolific authors who are allowed by the government agencies to use local models to analyze the data at scale. PhD students can probably forget about it
Economic history will have a very exciting boom and there will be an enormous race and big rewards for digitizing archives as a source to both identify new research strategies and document new descriptive facts.
Review and verification systems will become massively improved with AI assistance and it will become standard practice to do a 360 review of the paper + code + data at the *submission* stage.
Research that in principle can already be almost completely outsourced to the AI (download and analyze public data, run simple survey experiments, write theory models) is in for a big shock. This would be very bad from the perspective of researchers who depend on this “bread and butter” research, but I think this type of work will just be outsourced to public agencies who can answer their own questions without the need for “peer-reviewed” research.
All of those make sense to me.
Rising concentration for economics awards
We analyze the academic affiliations of nearly 6,000 award-winning researchers in 18 major fields in the natural sciences, engineering, and social sciences from the 1820s to the 2020s, focusing on the 1960s onward. The analysis reveals a trend of declining concentration in the institutional affiliations of award-winning researchers, shifting from a few science-strong universities in high-income countries to a more diverse set of institutions across the world. The decline in concentration is observed in all fields except one: economics. The institutional affiliations of prizewinning economists have become more concentrated over time, making economics the most concentrated field. We associate the higher concentration of prizewinning work in economics with the field’s stronger sorting by institutional prestige, its lower reliance on specialized equipment and instruments, and its assessment of findings based on a synthesis of evidence rather than on decisive experiments or proofs. We discuss the benefits and costs of this high and rising institutional concentration of prizewinning economists.
Here is more from
AI in science
Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements—LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.
That is from a new paper by Mihai Codreanu, et.al.
What should I ask Terence Tao?
Yes, I will be doing a Conversation with him. And he has a new book coming out Six Math Essentials. So what should I ask him?
The Price of Intelligence is Falling Rapidly
An amazing Epoch AI report from Emberson and Roodman:
- Over the past three years, the cost of a given level of AI performance has fallen an average of some 47% per quarter. That is a 13-fold drop every year – a faster rate than any other transformative technology in history.
To give an example, OpenAI o3 cost about $0.30/question to attain 75% on GPQA Diamond in January 2025, while GPT-5.6 Luna attained roughly the same score for $0.0004 in mid-2026—a roughly 725-fold decline in under 18 months.
Thus, it’s not just that the models are getting smarter. A given level of intelligence is also requiring dramatically less inference expenditure. This is one reason the open-model threat is not as large as it appears: frontier models don’t merely outperform older models; they are rapidly becoming cheaper to run at any given level of performance. Smarter and cheaper.
Reimagining research papers as interactive and reliable AI agents
That is a new Nature paper by Jiacheng Miao, Joe R. Davis, Yaohui Zhang, Jonathan K. Pritchard, and James Zou. Here goes:
Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper’s code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent’s effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.
As I have been saying for a while now, there is much more of this coming down the pike. Via Charles Klingman.
The mathematicians rebel against AI
Here is the statement, signed by Terry Tao among many other math notables, most of you probably have read it by now. I do not accept the most cynical interpretations of this proclamation. Some of you for instance may recall that I made and indeed stressed a similar point in the last chapter of my recent “generative book” on marginalism. In some near future, perhaps fewer economists will carry around marginalist insights and modes of thought in their heads, since you can just get the right answer by pressing the proverbial button on the AI.
I find this future disturbing, and not altogether pleasant for me personally, given how much personal status I have wrapped up in particular modes of economic thought. Yet I also know the Bastiat distinction between the seen and the unseen, and I expect the benefits to economic science from AI will be enormous, even if current practitioners cannot foresee most of those benefits today.
I do very much differ with at least one part of the mathematicians’ proclamation. They write: “…whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology.” There is no actual argument for that proposition, and I would sooner expect that the main “action variable” is how well the mathematicians adapt to the new reality. For instance there is nothing stopping the mathematics community from awarding status, pay, and promotions to people who “fill in the important blanks in math understanding,” even if an AI already has proven or disproven the underlying theorems. If that kind of work is so important, we still can do it and reward it professionally. In the meantime, I expect the funding for mathematics, and the interest in the topic, to rise considerably, at least in the medium term. All of a sudden, math matters much more than it used to, all the more so if P vs. NP happens to go the wrong way, or if the distribution of the primes turns out to be a little too predictable.
The mathematicians may not in every way enjoy being the subordinates or handmaidens of the AIs, but that is a change in status they simply will have to get used to, just as I realize AIs someday will end up as better column and blog writers than I am. I do not look to the companies — which I fully expect to “act like companies” — to somehow manage, moderate, or assuage that pending trend. It really is up to me to parlay my current intellectual portfolio into new, more AI-compatible intellectual and yes also marketing approaches. I’ve been given plenty of “legs up” along the way already, as is true for the Fields Medal winners as well, and it is up to me to figure out how to contribute in the future.
Might someone not invent/discover/prompt a way to use AIs to produce, articulate, and teach “more mathematical understanding” along the way? I get that solving famous dramatic math problems is the current commercial priority of the major AI companies. But as the AI space grows, these other paths hardly seem unlikely to me, and in fact the human mathematicians are the ones who can do the most to lead the way along those dimensions.
In this regard the current manifestation of complaints seems oddly early. “I didn’t like the first week or two of your intellectual revolution” is an accurate, and perhaps better reframed way of putting it. At which point perhaps a bit of patience is needed before anything else? These days, we all have more mathematical resources at our disposal, and so a bit of celebration is in order as well.
The Prediction Archive
The Prediction Archive is a public database of tens of thousands of world predictions. Using AI it tracks predictions over many decades and marks to market. I was surprised to discover that I am currently the highest ranked individual predictor in the world! Huh, I would not have predicted that.
Ranks are based on the Wilson score so you get credit not just for accurate predictions but for making enough predictions so that uncertainty about accuracy is reduced. Bryan Caplan was more accurate than I was but makes fewer predictions. Tyler made more many predictions than I did and was only somewhat less accurate. What the AI marks as predictions seem sometimes to be more about contemporary events, so take the numbers with a grain of salt. I expect to fall in ranking as the archive expands. Other people the archive covers include Peter Zeihan, Scott Alexander and Warren Buffett.
Some Navier-Stokes updates
Some are calling this “a Deep Blue moment” for AI.
All via T., a mathematician.
Update from OpenAI: “Our internal model group arrived at the Navier–Stokes solution in 88 hours, using around 10,000 coordinating AI agents.” And this. And from Noam Brown: “Yes, this result cost millions of dollars. But remember that when @OpenAI announced o3 it cost ~$500,000 to score 87.5% on ARC-AGI 1. Today, Astra scores higher for ~$20. In 2025 it took us and GDM an enormous amount of compute to achieve IMO gold. For the 2026 IMO, anyone with a $20/month ChatGPT subscription could do it. Massively scaling test-time compute gives us a glimpse of the future. I believe that a year from now everyone will have an AI at their fingertips capable of solving problems of this caliber.”
Overreaching causal language in the social sciences
Across the social sciences, many studies use cross-sectional designs that reveal associations but are generally unable to support direct causal claims, yet authors of such articles may make or imply causal claims anyway. Here, to examine the prevalence of such ‘overreaching’ causal language, we analysed 194,631 cross-sectional articles using large language models. Over the period 1980–2024, an average of 46% of articles contained causal language in their titles or abstracts, where the annual rate has risen almost threefold since 2000 from 20% to 60%. To examine the effects of such language, we conducted a human-subjects experiment (N = 1, 105), finding that readers frequently indicate abstracts with this phrasing provide causal evidence but that methodological labels (β = −0.4, 95% confidence interval −0.56 to −0.19) and associational wording (β = −0.3, 95% confidence interval −0.43 to −0.07) reduce this tendency. Experiments with five LLMs revealed that model summaries of these articles (N = 100 each) can amplify causal overstatement, removing hedges and introducing causal claims where articles used strictly associational phrasing; however, prompting caution diminishes this pattern.
That is from a recent paper by Calvin Isch, Timothy Dörr, Neil Fasching, Grace Jennings & Duncan J. Watts. Note that Isch is on the job market this year, working with Tetlock and Watts.
Indian documentary covers EV winners
A new short documentary (22 mins) film called The 22nd Century Indian by Shaurya Sinha offers an optimistic take on India, and also covers five (!) Emergent Ventures winners. Congratulations to them, and to Shruti too.
EV India winners featured: Naman Pushp https://x.com/therealnamzoo?s=11
Khushi Mittal: https://khushimittal.com
Shreeporna Rao: https://x.com/shreepoorna365?s=11
Samay Sanghvi: https://www.thealmanac.ai/article/samaysanghvii
Angad Daryani: https://www.linkedin.com/in/angaddaryani?utm_source=share_via&utm_content=profile&utm_medium=member_ios
How economics is changing
As a proportion of the literature, research about econometric theory, monetary policy and corporate governance has slumped. Meanwhile, papers on development, crime and gender are on the up. This could perhaps be because of changing intellectual interests, or maybe demand-side pressures, such as policymaker priorities and external funding…
Research from Prashant Garg, a postdoctoral researcher at Bocconi University, and Thiemo Fetzer, economics professor at Warwick University, finds causal claims in economics have jumped. In 1990, 7.7 per cent of claims made in the literature were causal. In 2023, that hit 32.6 per cent. Additionally, papers with more causal claims are more likely to receive citations and wind up in top five journals, the research suggests.
Here is more from Harvey Nriapia at the FT.
How well does AI peer review work?
Claude and I planted 100 known errors into 10 open-access psychology papers and then ran them through frontier models and two commercial AI review tools. In brief:
- The best single system caught 71 of 100 errors, while the worst caught 30.
- Pooling every system’s output caught 93 of 100. Models are only partly correlated in the errors they find, making ensembling a big lever for finding issues in papers. Check your papers against multiple models!
- Seven errors could not be caught by any system. All were omissions — information deleted from a paper rather than mistakes inserted into it.
- Refine.ink contributes more unique catches than any other single system, though it’s expensive.
- I didn’t measure false positives and I don’t know how this error distribution compares to the distribution of errors in real papers.
- I’ve made the papers, errors, model outputs, and the full experiment log public. I hope people can build on this work to create a comprehensive eval benchmark across disciplines.
That is from Paul Litvak, here is more. Note that is not even using the very latest generation of models.
The Queen song ’39
I only recently learned what it is really about, namely very rapid travel into space and time dilation mattering for the return voyage.
I had never before listened carefully to the lyrics. I heard the “’39” reference at the beginning, the mention of volunteeers sailing away, and the general nostalgic British music hall mood to the piece, and assumed it concerned the Second World War. But no, the volunteers sailing away are going to the stars, and their eventual return to the Earth will be for them a sad and tragic event, as everything they had known will be gone. You can listen to the song here.
Here is one of the most direct excerpts:
In the year of ’39 came a ship in from the blueThe volunteers came home that dayAnd they bring good news of a world so newly bornThough their hearts so heavily weighFor the Earth is old and grey, little darling, we’ll awayBut my love, this cannot beOh, so many years have gone though I’m older but a yearYour mother’s eyes, from your eyes, cry to me
Here are the full lyrics. Brian May, who wrote the song, is not only a wonderful guitarist but he has a PhD in astrophysics.
p.s. no synthesizers!
Solve for the (Refine) equilibrium
We’re proud to announce that Refine has signed partnerships with two leading publishers in economics. Both the American Economic Association and the Econometric Society now use Refine’s AI-assisted technical verification as part of their publication processes.
Here is the thread. And more comments here.