New Emergent Ventures tranche on science policy and communication
American science policy is now more important than at perhaps any previous point in history—how science is organized and funded (or not funded) in this country continues to rise in significance.
I have also spoken about the undersupply of people who understand this and are trying to act on it in Washington. Unfortunately the career paths here are neither well-defined nor well-regarded. I would like to help change that.
What we’re looking for:
- Priority 1: Metascience Policy Entrepreneurs in DC
- Funding for individuals working at the intersection of science policy and institutional reform—people who can shape how Congress and federal agencies think about science funding and governance.
- Priority 2: Science and Metascience Communicators
- Funding for communicators via any medium—bloggers, journalists, authors, podcasters, artists, filmmakers, conveners, influencers, event organizers—who can expand the reach of pro-science ideas beyond their current audience.
We are doing this with and thank Renaissance Philanthropy for the support. You can apply through the regular Emergent Ventures portal.
Thursday assorted links
1. The “estrangement” from philosophy of economics.
2. Investing in scientific instruments.
3. New book coming on Carlsen vs. Niemann.
4. Houston economy growing at more than ten percent (and that is even without moving forward on bike paths).
AI, Unemployment and Work
Imagine I told you that AI was going to create a 40% unemployment rate. Sounds bad, right? Catastrophic even. Now imagine I told you that AI was going to create a 3-day working week. Sounds great, right? Wonderful even. Yet to a first approximation these are the same thing. 60% of people employed and 40% unemployed is the same number of working hours as 100% employed at 60% of the hours.
So even if you think AI is going to have a tremendous effect on work, the difference between catastrophe and wonderland boils down to distribution. It’s not impossible that AI renders some people unemployable, but that proposition is harder to defend than the idea that AI will be broadly productive. AI is a very general purpose technology, one likely to make many people more productive, including many people with fewer skills. Moreover, we have more policy control over the distribution of work than over the pure AI effect on work. Declare an AI dividend and create some more holidays, for example.
Nor is this argument purely theoretical. Between 1870 and today, hours of work in the United States fell by about 40% — from nearly 3,000 hours per year to about 1,800. Hours fells but unemployment did not increase. Moreover, not only did work hours fall, but childhood, retirement, and life expectancy all increased. In fact in 1870, about 30% of a person’s entire life was spent working — people worked, slept, and died. Today it’s closer to 10%. Thus in the past 100+ years or so the amount of work in a person’s lifetime has fallen by about 2/3rds and the amount of leisure, including retirement has increased. We have already sustained a massive increase in leisure. There’s no reason we cannot do it again.
LDS fact of the day
The Church of Jesus Christ of Latter-day Saints has grown 66% this century, fueled in part by a record-breaking number of convert baptisms in 2025.
The church had 10,752,986 members at the end of 1999. The church had 17,887,212 at the end of 2025, according to an annual statistical report released Saturday during the church’s 196th Annual General Conference.
Furthermore the growth is coming in every part of the world (as a qualifier I am not sure what the outflow is). Here is the full article, via Tyler Ransom.
Financial Regulation and AI: A Faustian Bargain?
Important work is just flowing these days, and much of it (of course) concerns AI:
We study whether AI methods applied to large-scale portfolio holdings data can improve financial regulation. We build a state-of-the-art, graph-based deep learning model tailored to security-level data on the holdings of financial intermediaries. The architecture incorporates economic priors and learns latent representations of both assets and investors from the network structure of portfolio positions. Applied to the universe of non-bank financial intermediaries, covering nearly $40 trillion in wealth, the model substantially outperforms existing approaches in out-of-sample forecasts of intermediary trading behavior, including in crisis episodes. The model has more than ten times the explanatory power for the cross-sectional variation in asset returns during stress events compared to traditional approaches, and it outperforms existing systemic risk metrics at the institution level. Its learned representations show that the holdings network encodes rich, economically interpretable information about firesale vulnerability. The architecture is fully inductive, producing informative estimates even when entire asset classes or investors are withheld from training. We embed our empirical approach into a macroprudential optimal policy framework to formalize why these objects matter for policy and welfare. We show that even in an equilibrium environment subject to the Lucas critique, the predictive information from the model improves welfare by sharpening the cross-sectional targeting of policy interventions, and we demonstrate a complementarity between prediction and structural knowledge.
That is a new paper by Christopher Clayton and Antonio Coppola, of Yale and Stanford respectively.
Wednesday assorted links
1. Waymo rollout in NYC is halted.
2. Back “plus” is the better answer (NYT). I am glad this is now settled, Alex T. can attest I have been insisting on this for a while. Note my earlier prediction.
3. Nicholas Decker on Ludwig Straub.
4. Crypto and quantum computing. Likely an important piece, here is GPT Pro on that paper.
5. “The Suno upgrade for song generation seems quite good as well.” So much is new!
6. Hollinger on NBA tanking (NYT).
7. Is there an evolving Iran bargain with China? (speculative, mostly we still do not know what is going on, you should discount most of what you are reading on this topic).
Mythos assorted links
Here is Dean Ball on Mythos. And now more from Dean. Here is John Loeber. While I am seeing some likely overstatement, probably this is a real turning point nonetheless, and we need to think further about what is best to do. No b.s. on data center slowdowns and algorithmic discrimination, rather actual thought on how to regulate something that actually will matter. And be glad we got there first. But how long will it be before an open source version, even if somewhat inferior, is available? Will OpenAI and Google soon be showing similar capabilities? (And how will that shift the equilibrium?) Should we upgrade our estimates of the returns to investing in compute? How will the willingness of attackers to pay for tokens evolve, relative to the willingness of defenders to pay for tokens? Which are our softest targets? As a side effect, will this also lead to higher economic concentration, as perhaps only the larger institutions can invest in quality patches rapidly enough? How many things will be taken offline altogether? It was the government of Singapore that started moving in that direction in 2016 with their Internet Surfing Separation. Which of the pending hacks and leaks will embarrass you the most?
And if nothing else, this is proof we are not all going to be jobless, albeit for reasons that are not entirely positive.
AI Risks
Two new papers/initiatives indicate severe risks from AI, interestingly in opposite directions. The first is that the most advanced frontier models are now capable of finding and exploiting software in ways that could be used to crash or control pretty much all the world’s major systems.
Anthropic: We formed Project Glasswing because of capabilities we’ve observed in a new frontier model trained by Anthropic that we believe could reshape cybersecurity. Claude Mythos2 Preview is a general-purpose, unreleased frontier model that reveals a stark fact: AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.
Mythos Preview has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser. Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glasswing is an urgent attempt to put these capabilities to work for defensive purposes.
That’s from Anthropic. The irony is that the company that has developed a frontier model capable of infiltrating and undermining more or less any computer system in the world is the one that has been forbidden from working with the US government. It’s as if a private firm developed nuclear weapons and the American government refused to work with them because they were too woke. Okey dokey.
The second paper on AI risks is AI Agent Traps from Google DeepMind. They point out that AI agents on the web are vulnerable to all kinds of attacks from things like text in html never read by humans, hidden commands in pdfs, commands encoded in the pixels of images using steganography and so forth.
Putting this together we have the worrying combination that very powerful AI’s are very vulnerable. Will AI solve the problems of AI? Eventually the software will be made secure but weird things happen in arms races and its going to be a bump ride.
Herbert Hoover is still underrated
We study the effects of large-scale humanitarian aid using novel data from the American Relief Administration’s (ARA) intervention during the 1921-1922 famine in Soviet Russia. We find that the allocation of relief closely tracked underlying food scarcity and was uncorrelated with subnational politics. We show that ARA rations reduced food prices, raised caloric intake, lowered the prevalence of relapsing fever, and increased rural birth cohorts. The aid benefited poorest peasants most and proved most effective in provinces with higher levels of human capital. Back-of-the-envelope calculations suggest that, absent ARA relief, the 1926 population would have been 4.4 million lower.
That is from a new paper by Natalya Naumenko (my colleague), Volha Charnysh, and Andrei Markevich.
Stephen Pimentel has an excellent review of *The Marginal Revolution*
Here is one very good paragraph of many:
Cowen is excellent on the question of why the marginalist insight had to wait so long, and why it eventually came in a simultaneous eruption across countries and three intellectual temperaments. The answer involves the slow assembly of preconditions: advances in calculus, the rise of statistical thought, the professionalization of economics as a discipline, and certain changes in the philosophy of science associated with the Victorian debate between inductive and deductive methods. Progress in science, Cowen suggests, is rarely a matter of the lone genius, but rather of the alignment of previously dispersed elements. The genius arrives when the ground has been prepared to receive the insight.
And another:
There is a discomforting codicil to all of this. Perhaps, Cowen suggests near the book’s end, the intuitions of 20th-century microeconomics were always a kind of compensation for a deeper ignorance. Perhaps we elevated intuitive reasoning, with its clean parables of marginal utility, and elegant supply-and-demand diagrams, because they were what we had, and we mistook their availability for adequacy. Machine learning models that find hundreds of thousands of factors in financial data are not exactly refuting marginalism. They are revealing the scale of what marginalism was never equipped to see. Our intuitions were always a small corner of understanding, swimming in a larger froth of epistemic chaos. The illusion has been stripped bare.
Here is the full review. Here is the book itself. Via Mike Doherty.
Two assorted links
1. Claims about the role of China, and its economics. And there is a lot of remaining uncertainty, but here is one of the saner Iran war takes.
Andy Hall advice on AI and economic research
Here is the document, excerpt:
In January, I released the results of an experiment showing how Claude Code could helpfully extend old papers “automagically.” It was pretty astonishing to me. Claude was able to come up with a plan, scrape the web, write code, run regressions, create tables and figures, and write a whole memo on what it had found—all in about 45 minutes.
Are AI tools perfect? No. Claude made some interesting mistakes in that extension, and since then, I’ve seen it make a whole bunch more. Are human researchers perfect, though? Hell no.
The evidence that AI tools should now be an essential part of your toolkit is overwhelming—look at the recent work that my Stanford colleague Yiqing Xu has put out, for example, which allows for the automated verification of empirical research. This is so clearly valuable. When it comes to empirical work, we’re never going back to the pre-AI world.
Here is a thread on the paper, heedworthy throughout. If you do not have some kind of decent plan here, other economists will leave you in the dust. Even if it is only a minority of “other economists” their total leverage and impact will be extreme.
Tuesday assorted links
Ludwig Straub wins the Clark medal
Here is his home page:
Ludwig Straub is a professor of economics at Harvard University. His research areas are macroeconomics and international economics. Among his topics of interest are the recent decline in the natural rate of interest, rising levels of private and public debt, and the transmission of monetary and fiscal policy. Ludwig also has an active research agenda solving and analyzing heterogeneous-agent models. Among his most recent papers is a 2025 paper studying the short-run effects of tariff shocks.
Here is his Google Scholar page. The Medal citation gives an overview of his work. Congratulations!
*The Drama* (no real spoilers)
An excellent and highly original movie, I cannot say much without infringing upon the surprise of the basic premise. Exquisitely choreographed in its timing, scene by scene. So anti-Woke that it will make some uncomfortable? The reviews which are very negative are unfair and stem from this fact. I recommend it, but yes some of you will go away feeling offended. I can report that one theme is that couples who are getting married often do not know each other well. Here is the trailer.