Facts about breastfeeding
We produce a numerical estimate of the value of unpaid breastfeeding relative to national income spanning five centuries and integrating historical and bioarchaeological data. The relative economic contribution of maternal nursing was greater in the distant past than in recent history, rivaling regionally significant industries such as pottery, glassmaking, and even the historical public sector. It was likely the largest source of human capital investment in early modern England, supporting the physical and cognitive development of the workforce. Finally, we show that breastfeeding rates probably fell as incomes rose, challenging the Malthusian assumption that higher income correlates with lower mortality.
That is from a recent paper by Louis Henderson and Jane Humphries. Via the excellent Samir Varma.
How central bankers spend their time
How leaders allocate their limited time and attention is an important question in economics. This paper constructs and analyzes a novel data set covering the daily calendars of three Federal Reserve Chairs, Ben Bernanke, Janet Yellen, and Jerome Powell, containing more than 20,000 calendar entries. We document several patterns. First, all Fed Chairs spend more than 30% of their documented meetings with Fed staff, highlighting the crucial role of economists in the Fed’s decision process. Second, interactions with the media became more frequent during Bernanke’s tenure and remained important until the end of Powell’s tenure. Third, Powell held significantly more meetings with lawmakers than both Yellen and Bernanke, reflecting an increasing need for the Fed to engage with political actors. Beyond these trend patterns, we examine the time allocation of Fed Chairs at business cycle frequencies and show how different activities respond to shifts in the macroeconomic environment. Finally, we place our findings in historical context using the available calendars of earlier Fed Chairs, including Alan Greenspan.
That is from a recent NBER working paper by
Emergent Ventures India, 20th cohort
Zahan Turel, a backend engineer and eighth-generation Zoroastrian priest from Surat, received a grant to digitize his community’s manuscripts and liturgical records before they are lost.
Aditya Vijay, 17, is building Forge, supporting overlooked, self-taught young founders with shared housing, capital, and access.
Dev Goyal received a grant for SCRIPT, exploring whether stylus-based drawing kinematics can support non-diagnostic clinical triage for ADHD, autism, and anxiety.
Adwait Tiwari, 16, a self-taught founder from Indore, is building BharatBahi, a WhatsApp-native AI assistant helping Indian shopkeepers.
Mayukh Goswami and Meher Manda received their grant for a graphic novel examining democratic crises around the world through the lens of a fictional Indian town.
Ajith Chekka is building Oru’el, a physics-enabled intelligence layer to improve the reliability and efficiency of data centers.
Gaurav Kumar, a software engineer and social entrepreneur, is building OncoVault, which turns fragmented cancer records into AI-generated clinical summaries to support physicians’ treatment decisions.
Kush Agrawal, an engineering student at IIT Bombay, received a grant for Tensor Recon, developing execution-aware financial reconciliation infrastructure for high-volume cryptocurrency trading firms.
Mohd. Ashzad, 22, a security researcher and engineer, is building Build Jam, helping young South Asian builders discover creative skills through real-world projects in AI, design, filmmaking, and photography.
Vaibhav Sai, 25, received a grant to develop AI for intent-based biomolecular design, starting with a nucleotide-focused model and capture molecules for biosensors.
Rithvik Vibhu and Rithwik Jayasimha received their grant for research on passive optical fiber network vulnerabilities, including wiretapping and attacks on ISP infrastructure, and presentations at Black Hat USA 2026 and DEF CON 34.
Neha Sarda is building Zyogen, sovereign AI software converting injury images into patient-specific printable graft designs and bioink formulations, aiming to make regenerative medicine 30–40% more affordable.
Hari Shankar Ramalingam, cofounder and CEO of SpaceTug, received a grant to develop and validate autonomous spacecraft technologies for in-orbit satellite servicing, inspection, and life extension, ahead of orbital demonstrations.
Laiba Noor received a travel grant to attend a research program at Özyeğin University and develop a speech-analysis tool for detecting presymptomatic Alzheimer’s in low-resource settings.
Abhishek Raj, 16, a crime and mystery writer and dual national record holder, received a grant to advance his fiction writing and criminology research.
Kunal Kanase, an IIT Bombay pre-doctoral fellow, received a grant for GullyWell, studying Dharavi residents’ wellbeing where formal mental-health care rarely reaches and developing low-cost tools informed by that research.
Divyaratna Joshi, a Chemical Engineering undergraduate at IIT Madras, is developing low-cost S-PEEK membranes to replace expensive fluorinated membranes and make fuel cells and electrolyzers affordable and manufacturable at scale.
Tanjot Singh, 16, received a grant for PraaNavya (Erynth), a climate startup developing bio-based nanofiber air filtration technology for sustainable clean air.
Tribhi Kathuria, a Lecturer in Robotics at the University of Michigan, received a grant to connect aspiring Indian engineers with robotics and AI research and mentorship from experts in the United States.
Swamynathan Jerra, founder of Hyderabad’s AcquaHT Labs, received a grant to upgrade and seek patents for machines extracting drinking water from air at 30% humidity, rather than the usual 50%, for dry areas with unsafe groundwater.
Niharika Srivastava, a criminal justice and technology lawyer, received a grant to develop a rule-based system identifying Indian undertrial prisoners held beyond statutory custody limits, enabling targeted legal intervention.
Anirudh Varma, a New Delhi pianist, composer, and producer, leads the Anirudh Varma Collective (AVC), received a travel grant for reimagining Indian classical music in a contemporary format for its debut UK tour.
Himaneesh received a grant to build AI software that simplifies professional digital audio workstation workflows.
Sahasrabda Sai Pradhan received a grant to develop a skin-regenerative biomaterial that can be fabricated on-site for urgent graft substitution when suitable tissue is unavailable from the patient’s donor site.
Gaurav Hadavale, 21, a final-year undergraduate at the Sardar Patel Institute of Technology, received a travel grant to present his mechanistic interpretability and trustworthy medical AI research at the MI4MedFM workshop at MICCAI 2026.
M R Sharan, an economist, received a grant to establish a data and intelligence platform focused on India’s local bodies.
Nitish Goel, 14, from Panchkula, is building Thalamus to reduce AI costs and improve efficiency, and Agentoverflow, a shared repository of agents’ solutions that reduces duplicated work and supplies model-training data.
Sudharsanam received a grant for Hardlight, developing autonomous space robots to assemble and maintain assets in low Earth orbit, including space stations, data centers, and factories.
Naveen Gulati, Noida, received a grant for instrumented rooftop trials of GIVARA ONE, a compact dual-rotor vertical-axis turbine for low, gusty winds. Givara Energy owns, installs, and maintains the machines; customers pay only for delivered electricity.
Daevan Mangalmurti and Bilal Moin received their grant for Kindly Adjust, a podcast and Substack discussing small fixes for India’s big problems.
Those unfamiliar with Emergent Ventures can learn more here and here. The EV India announcement is here. More about the winners of EV India second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth, fourteenth, fifteenth, sixteenth, seventeenth, eighteenth, and nineteenth cohorts. To apply for EV India, use the EV application, click the “Apply Now” button and select India from the “My Project Will Affect” drop-down menu.
And here is Nabeel’s AI engine for other EV winners. Here are the other EV cohorts.
If you are interested in supporting the India tranche of Emergent Ventures, please write to me or to Shruti at [email protected].
Econvitals
From Jason Furman, it tracks economic indicators, mostly U.S. Here is the website. Subscribe here to briefings. Here is a thread on the site.
Why are cybersecurity losses not worse?
Here’s the first part of my theory: cybercriminals are not the organized geniuses we implicitly imagine. Anyone building a criminal gang has to find candidates willing to forgo more stable opportunities in the formal economy. My modeling estimated that something like 720,000 people are involved in the online criminal economy, 90% of them in scam and fraud call-center type operations, some of them human-trafficked. Of the 76k or so actual hackers, the vast majority work for governments. Of the remaining few thousand criminal hackers, most work in ransomware. Some demographic estimates of these workforces are below.
Criminal organizations need to base themselves in middle and lower income countries, where they can pay less and hire people who have few formal-economy options. For scams and fraud, picture an illegal call center building in a small city in Nigeria. For ransomware gangs, picture kids working in the informal economy in Belarus. I think folks in the AI safety and cyber world sometimes imagine adversaries as their Waluigi twins, implicit MIT graduates; in fact, in cybercrime we’re dealing with a lower-skill demographic. The chart below estimates attacker wages relative to default economy wages…
Here is more from Joshua Saxe, very good throughoiut. There are bottlenecks on the down side too…
Cybersecurity ETF
Joe Weisenthal: “The Red Line is the day that Anthropic introduced Mythos”
I guest host an NYT podcast with Michael Kratsios, Trump science advisor
Here is the audio, video, and transcript. Here is one excerpt:
Cowen: Talk me out of this worry: So we agree science is an ecosystem, and there’s always a temptation to want to fund the most dynamic, most productive parts of the ecosystem. And right now, that would probably be A.I.
But if you need the whole ecosystem to be working well, arguably you should be funding, at the margin, the less productive parts, which need the aid. That will sometimes be universities, but there’s plenty of other inefficient parts of our ecosystem.
So is this plan not putting more resources into the parts that are working well, but we get stuck with bottlenecks if the parts that are not working well end up with fewer resources?
Kratsios: Well, luckily, we’re not investing all of our money for the federal government into S.I. for science. It’s a portfolio approach. And obviously one ——
Cowen: But it’s a net shift toward artificial intelligence, right?
Kratsios: Well, I would argue — and I think this is a very important distinction — it’s not investing in superintelligence, it’s investing in superintelligence for science.
So the question is, I think a lot of people argue: Oh, why would the federal government want to invest more in superintelligence when billions, if not trillions, of dollars are being invested in the private sector into superintelligence? And I would agree with that statement. We shouldn’t be doing that.
The government isn’t funding how to create a next frontier model. That’s something that our great American companies are working on.
But applying those models to scientific discovery is still in its early stages. And as you know, if you think about the trend or the path by which we’ve gotten to this place with L.L.M.s — we started first with being able, essentially, to scrape the whole corpus of the internet, to use that to train a large language model.
The next chapter of that was around coding, where the model builders realized: Oh, we have really good coding data, and we can actually train these models to be good at coding. So then we had this rise of Anthropic and OpenAI Codex and all this stuff.
And I think, to me, the next chapter is around science, and the data that you need to train these large language models for science is still one that we’re working on a lot. And we were very proud to announce 190 federal data sets being made public in order to help train these science models.
But ultimately, you’re going to be applying this superintelligence to everything from material science to chemistry to math to physics, and we have to be in a position to actually leverage that.
Definitely interesting throughout, pretty much entirely we are discussing the Trump administration science plan, authored by Michael.
Friday assorted links
1. Weird diets (NYT).
2. Is this what the Denisovans looked like?
3. Measuring bottlenecks for AI.
4. How is violence diffferent when women lead drug cartels?
6. Latin America seems to be climbing the high-tech value chain.
7. ZMP by any other name (amazing how much people used to hate on that idea).
8. Chatting with Milton to run your central bank?
9. Warhound.
10. “Somehow, @srajagopalan and @lachygroom have managed to fund a hard-tech revolution in India. There’s something rather massive going on, and everyone else has missed it.” Link here.
Prediction Markets Are Accurate
No surprise to me but a new paper looks at the forecast accuracy of over one million prediction questions from Polymarket, Kalshi, Manifold and the forecasting platform Metaculus. A virtue of the new analysis is that it covers many different areas from business to elections to geo-politics. An event predicted to happen p% of the time happens about p% of the time. Can’t beat that.
Using approximately one million resolved contracts and questions across four collective forecasting platforms, twelve domains, and time horizons ranging from hours to years, we evaluate the calibration and accuracy of collective forecasts in real-world settings. We found that forecast probabilities were overall highly calibrated: events assigned a probability of p% occurred approximately p% of the time. This broad pattern was evident across platforms and many domains, although fine-grained analyses revealed systematic tendencies toward over- or underestimation in highly consequential areas. While this does not mean that forecasting platforms provide certainty about individual events, our findings suggest that large-scale aggregation of judgments contains meaningful information about real-world uncertainty; when examined in aggregate, the predictions generated by the platforms provided informative estimates of event likelihood.
Protecting Americans from Medicine
Max Doppelt: In January 2025 my mom’s lung cancer spread to her brain. The drug with the best chance of helping her was approved in China but not here.
A doctor told me, almost as an aside, that if she were Chinese she could buy it at a pharmacy.
So I flew to Shanghai and came home with 26 boxes in my luggage. That was 19 months ago. My mom is still here, still working, and in her words has had an amazing three years.
The drug was approved in China in 2023. The FDA approved it in July 2025. It still is not for sale here. I know of at least 30 Americans who have made the same trip.
I have been scared to tell this story because I worried it could cost my mom access to the medicine keeping her alive. But the problem matters more than my silence. These drugs need to be developed and available here, and that means FDA reform.
A piece at KFF Health cover’s Max’s story and adds this insanity:
Rep. John Moolenaar (R-Mich.), who chairs the House Select Committee on the Chinese Communist Party, in June co-sponsored legislation with Rep. Debbie Dingell (D-Mich.) requiring all U.S. pharmaceutical deals with Chinese companies to be cleared by the Treasury Department.
Got that? A son flew to Shanghai to buy pharmaceuticals to extend his mother’s life and the House Select Committee on the Chinese Communist Party wants a Treasury lawyer monitoring the baggage claim. Yes, I am angry.
Addendum: See also my earlier post on Operation TrialBlazer and Montana’s SB535. My 2009 TED Talk is the ur-source for my argument that a rich China and India will create many benefits for the United States, including many more cancer drugs.
Podcast with Rabbi David Wolpe
Here is their episode summary:
Economist Tyler Cowen joins David Wolpe for a conversation about AI, religion, and what makes us human. Tyler makes the case that AI’s biggest impact may be on status: who gains it, who loses it, and what happens to experts like rabbis when anyone can ask ChatGPT all their questions. He also suggests that losing our monopoly on intelligence may make us feel more unique as humans, rather than less.
You can listen here.
Will bystander economies be able to finance their governments?
From Jesús Fernández-Villaverde:
Will AI poison the public finances of many modern states? There is a real risk that it will.
Yesterday, I circulated a new paper, “Saving for the Machines,” which makes a simple point. Building the AI transformation will require enormous investment. The only way to induce agents to save for that investment is through a large increase in the real interest rate. For instance, in my benchmark scenario, the ten-year real yield jumps from 5.1% to 13.6% on impact.
But that means government borrowing will become much more expensive. Here is a back-of-the-envelope calculation. Take a government that owes 100% of GDP in 2025 (a typical case in Western Europe) and runs a balanced primary budget from then on (not so typical but never mind; I want to build an optimistic scenario). That means the debt ratio changes only with the gap between the real interest rate and growth.
I input the interest rate and the growth rate for my benchmark transition to full automation, and I roll over the debt at the short rate. I consider two economies. The first economy is an AI adopter, for example, the U.S., which experiences an AI boom at home. The government pays the new interest rate but also enjoys faster growth.
The second economy is a bystander, for example, Spain. This economy lives in an integrated financial market but does not adopt AI (this is an extreme case of zero adoption, just to make the exercise transparent): it pays the same world interest rate and continues growing at its old 1.6% trend.
Let’s first consider the adopter. With an intertemporal elasticity of substitution of one-half, the Euler equation implies that consumption growth is about half the real interest rate. Hence, the gap between the interest rate and growth does not close during the AI-driven growth boom. Instead, it widens. For example, with a unit elasticity of substitution among tasks, the real rate rises from 5.1% to 42% by 2040, while growth increases only from 1.6% to 22%. Debt doubles by 2038, reaches 296% of GDP in 2040, and 907% in 2045. To hold the ratio at 100%, the government needs a primary surplus of 10% of GDP in 2035 and 24% from 2045 onward. With a low elasticity of substitution of 0.2, the numbers are milder, but the pattern is the same: 204% in 2040, 397% in 2045, and a stabilizing surplus of 13% of GDP.
But the adopter has a way out. The capital share goes to one, so net capital income is about 88% of GDP, and a flat tax on it can cover the surplus. A 36% tax on net capital income holds the debt at 100% of GDP in the limit with unit elasticity, and a 20% tax does so with strong complementarity across tasks. The tax is above the gap because it slows growth, from 21.9% to 13.6% with unit elasticity and from 11.7% to 9.1% with an elasticity of 0.2, while it does nothing to the rate the Treasury pays, which technology pins at the machines’ net return. Painful, but the boom survives and the adopter can pay its debt.
The grim news is that the bystander economy has no way out. Its capital income is still a third of GDP, which grows at 1.6%, and the surplus it needs is 11% of GDP in 2030, 26% in 2035, and 44% forever with unit elasticity (18% and 24% with an elasticity of 0.2). On a balanced primary budget, its debt doubles by 2033 and triples by 2035. No tax system raises that.
Two more bad pieces of news for the bystander. First, long yields jump on impact, so a Treasury that issues ten-year debt pays more, and sooner, than rolling over at the short rate assumes: it is the same bill but front-loaded. Second, the bystander’s own capital flows to the machines abroad, so its GDP and tax base fall below trend. One relief is that households can save for other people’s machines and get rich on the returns, which the bystander’s government can tax to some extent. This back-of-the-envelope calculation is a benchmark, not a forecast of what is to come. But the arithmetic is simple, and it runs counter to the hope that an AI-fueled growth boom will take care of public debt, because growth comes with an interest rate that is twice as high. Not a pretty future for most of Western Europe. Due to AI, the choice is between default, inflation, or, most likely, financial repression: capital controls that keep domestic savers from investing in machines abroad.
I am somewhat more optimistic than this. The assumption of zero Spanish adoption already is noted, but also ideas are public goods, and Spain can benefit from American (and Chinese) AI, and the resulting scientific advances. The long-run supply of capital also might be quite elastic, we will see.
How Long Do Wealth Shocks Persist? Less Than Three Generations in England, 1700–2026
What happens across generations to wealth shocks? Do they endure and even magnify, or do they dissipate? By implication, how much modern wealth is attributable to events before 1900? This paper uses random shocks to family size in England before 1880 that created wealth shocks for the children to measure the persistence of wealth shocks. Fertility in England before 1880 was a biological lottery. For richer families, family size strongly influenced child wealth. This paper finds that such biology-induced wealth shocks had no impact on descendant wealth by three generations later. Since wealth itself persisted across more than five generations this implies that, in the long run, wealth mainly derives from sources other than inherited wealth. The observed link between nineteenth century and modern wealth does not lie in wealth transfers alone. Wealth persisted because of the familial inheritance of behaviors associated with wealth accumulation and retention.
That is from a recent paper by Gregory Clark and Neil Cummins. Via the excellent Kevin Lewis.
Thursday assorted links
2. New YouTube channel for Indian architecture.
3. Where do Math Olympiad gold medal winners end up? “By total gold medal count, China is the clear leader, followed by the pack of USA, Russia, and South Korea. Over 20 years China has 105 out of a possible 120 gold medals which means nearly all their students achieved gold. This is so successful that it’s common knowledge that it is harder to make the China IMO team than to get a gold medal in the IMO itself.”
Modern Principles of Economics!
If you are teaching economics Modern Principles is the book to use! Great and original writing, over one hundred videos, iClicker, Achieve and lots of additional support. Contact Mike Krotine to get an examination copy.