Podcast with Parker Conley
About “learning, history, and investing in ideas”, and what if there were ten Tyler Cowens?:
Here is the transcript.
Tuesday assorted links
1. The new Brink Lindsey book is out.
2. Markets in everything: “Wasp nests have become a surprisingly sought-after home décor commodity, with some priced at up to $250 per specimen.” (NYT) The shipping fees to get them can be pretty high.
3. New charter city, crypto-based in St. Kitts and Nevis? (FT)
4. “Specifically, TGIF, Nellie Bowles’ witty take on the week’s news, is being censored in the UK.” (TFP, beware Australia…)
5. A case where the Peltzman effect might apply.
6. Glenn Loury in memory of Thomas Schelling.
7. Purdue University Approves New AI Requirement For All Undergrads.
8. Robert J. Samuelson, RIP (NYT).
Robot Lab
Google’s Deep Mind Lab is going to build a materials science lab in the UK, manned by robots and humans:
To help turbocharge scientific discovery, we will establish Google DeepMind’s first automated laboratory in the UK in 2026, specifically focused on materials science research. A multidisciplinary team of researchers will oversee research in the lab, which will be built from the ground up to be fully integrated with Gemini. By directing world-class robotics to synthesize and characterize hundreds of materials per day, the team intends to significantly shorten the timeline for identifying transformative new materials.
This is a very big deal. Gemini won’t just read papers. It will design the experiments, run the experiments, learn from the successes and failures and then recursively improve. It’s an attempt to learn the game of material science in the same way AlphaGo learned the game of Go.
Two more notable books from 2025
Ken Belson, Every Day is Sunday:
Tom MacTague, Between the Waves: The Hidden History of a Very British Revolution 1945-2016.
Both are excellent. I didn’t read the first one right off, because I do not care very much about the topic. The book is good enough to overcome that problem. I did not read the second one right off because I care about the topic a lot, but thought I already knew enough about it. The book is good enough to overcome that problems.
Noah Smith on AI existential risk
Superintelligent AI would be able to use all the water and energy and land and minerals in the world, so why would it let humanity have any for ourselves? Why wouldn’t it just take everything and let the rest of us starve?
But an AI that was able to rewrite its utility function would simply have no use for infinite water, energy, or land. If you can reengineer yourself to reach a bliss point, then local nonsatiation fails; you just don’t want to devour the Universe, because you don’t need to want that.
In fact, we can already see humanity trending in that direction, even without AI-level ability to modify our own desires. As our societies have become richer, our consumption has dematerialized; our consumption of goods has leveled off, and our consumption patterns have shifted toward services. This means we humans place less and less of a burden on Earth’s natural resources as we get richer…
I think one possible technique for alignment would give fairly-smart AI the ability to modify its own utility function — thus allowing it to turn itself into a harmless stoner instead of needing to fulfill more external desires.
And beyond alignment, I think an additional strategy should be to work on modifying the constraints that AI faces, to minimize the degree to which humans and AIs are in actual, real competition over scarce resources.
One potential way to do this is to accelerate the development of outer space. Space is an inherently hostile environment for humans, but far less so for robots, or for the computers that form the physical substrate of AI; in fact, Elon Musk, Jeff Bezos, and others are already trying to put data centers in space.
Here is the full post.
Emergent Ventures India, 14th cohort
Avani Agarwal, 18, high school senior, received her grant for Synthera, to accelerate personalized medicine using AI-powered drug discovery.
Sushan Bhattarai received his grant to map archaeological sites linked to the Khasa-Malla kingdom across the Himalayas.
Utkrisht Singh Chauhan, 19, and Yash Chavan, 22, received their grant for InTacht, to speed up and reduce costs for edge and private AI systems.
Tanuj Pandya, 20, received his grant to build gloves bringing realistic touch to XR devices.
Dhanush Bakthavatchalam, 24, received his grant to build fully automated AI-driven factories for metal fabrication.
Rashi Bhavsar received her grant for Algaevity, to develop an all-natural, zero-electricity mosquito-killing bio-device.
Rounak Banerjee received his grant to develop affordable technology for converting standard wheelchairs into electric wheelchairs.
Vasisht Dilip received his grant for Seric Steel, to turn iron ore mining waste and crop residue into steel without fossil fuels.
Mohammad Mahean Hasan, 22, studying at Minerva university, received his grant for travel and general career development.
Syed Irfan Ahmed received his grant to develop non-invasive devices monitoring posture in real time.
Kumari Anushka, 20, received her grant for RAD-Scan, to build a citizen-led biomarker testing system for radiation and heavy metal exposure.
Prince Rawat, 19, received his grant for Falken Aerospace, to build autonomous cargo UAVs for logistics.
Joy Agrawal, 19, sophomore at University of Chicago, received his grant for general career development.
Nikhil Kashyap, 20-year-old high school dropout, received his grant to build an affordable robotics kit and visual coding platform making STEM education accessible across India.
Ansh Saxena, 21, received his grant for Aquanode, to build an AI-native cloud helping teams deploy and train models with lower costs.
Mateo Escalante, 24, received his grant for Horus Prosthetics, to develop machine learning models generating perfectly fitting prosthetic leg sockets.
Dhruv Bathla, founder of Ezbeat, received his grant to build a copilot preventing cardiovascular disease through early risk identification.
Rishi Rathi, 25, received his grant to develop a marine carbon dioxide removal solution.
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, and thirteenth 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].
Markets in everything?
If you don’t yet have a REAL ID, you can continue to fly, but it’s going to cost you. Beginning Feb. 1, 2026, the Transportation Security Administration (TSA) will start collecting a $45 fee from travelers using non-compliant forms of identification at airport security checkpoints.
The agency previously proposed a fee of $18 to cover the administrative and IT costs of ID verification for those traveling without a REAL ID or passport but increased the total to $45 in an announcement released earlier this month.
Here is the full story, via the excellent Samir Varma.
Monday assorted links
Growth Matters
Between 2011 and 2023 India’s GDP per capita grew at a rate of about 4.8% per year so in those 12 years GDP per capita, a good measure of the standard of living, nearly doubled (77%). Shamika Ravi and Sindhuja Penumarty look at what this means on the ground.
The percentage of the budget spent on food has declined–dropping below 50% for the first time ever–and that has enabled significant purchases of consumer durables.
It will perhaps not be surprising that mobile phones have become universal among both the poor and the rich but vehicle ownership is also converging with rural ownership of a vehicle (2 or 4 wheeler) nearly tripling from (19% to 59%).
Another standout is refrigerators which reflects growing income and reliable electricity. In the 12 years across the survey, refrigerator ownership in rural areas more than tripled from 9.4% circa 2011 to 33.2% in 2023. In urban areas refrigerator ownership went from less than half (43.8%) to more than two-thirds (68.1%) of urban households. Overall, only two states Bihar (37.1%) and Odisha (46.3%), had less than 50% of urban households owning a refrigerator in 2023-24.
Economists are often accused of “line go up” thinking but the truth is that line go up matters. The 4.8% annual growth matters because it shows up as a broad, visible upgrade in how people live.
Cats, dogs, and babies, in Taiwan
Using newly linked Taiwanese administrative datasets, including an annual census of dog and cat registrations from 1999 to 2020 matched to complete personal tax records from 2009 to 2020, we revisit the claim that “pets crowd out babies.” We exploit two quasi-experimental price shocks: a childbirth subsidy and large receipt lottery windfalls. These allow us to estimate cross elasticities between childbearing and pet ownership. The results reveal a Marshallian cross elasticity of −1.32: as the effective cost of children falls, pet ownership rises. Combined with income elasticity estimates, we recover a child price elasticity of fertility of −0.21, suggesting that pets and children are complements, not substitutes. Event study evidence reveals dynamic asymmetry. Acquiring a dog sharply increases subsequent births among previously childless adults (a “starter family” effect), while a new baby temporarily depresses further pet acquisitions, likely due to time constraints. Overall, our findings challenge popular narratives and suggest that pet ownership may support, rather than displace, fertility.
That is from an AEA session paper by Kuan-Ming Chen, Ming-Jen Lin, Hau-Hung Yang, and Shirley Yen, here is the online abstract.
Is involuntary hospitalization working?
From Natalia Emanuel, Valentin Bolotnyy, and Pim Welle:
The involuntary hospitalization of people experiencing a mental health crisis is a widespread practice, as common in the US as incarceration in state and federal prisons and 2.4 times as common as death from cancer. The intent of involuntary hospitalization is to prevent individuals from harming themselves or others through incapacitation, stabilization and medical treatment over a short period of time. Does involuntary hospitalization achieve its goals? We leverage quasi-random assignment of the evaluating physician and administrative data from Allegheny County, Pennsylvania to estimate the causal effects of involuntary hospitalization on harm to self (proxied by death by suicide or overdose) and harm to others (proxied by violent crime charges). For individuals whom some physicians would hospitalize but others would not, we find that hospitalization nearly doubles the probability of being charged with a violent crime and more than doubles the probability of dying by suicide or overdose in the three months after evaluation. We provide evidence of housing and earnings disruptions as potential mechanisms. Our results suggest that on the margin, the system we study is not achieving the intended effects of the policy.
Here is the abstract online at the AEA site. I am looking forward to seeing more of this work.
“AI is everywhere but in the productivity statistics…”
These people are saying it is there too. Though I am not quite sure what they (or anyone, for that matter) mean by AI:
First, we argue that AI can already be seen in productivity statistics for the United States. The production and use effects of software and software R&D (alone) contributed (a) 50 percent of the 2 percent average rate of growth in US nonfarm business labor productivity from 2017 to 2024 and (a) 50 percent of its 1.2 percentage point acceleration relative to the pace from 2012 to 2017. Second, taking additional intangibles and data assets into account, we calculate a long-run contribution of AI to labor productivity growth based on assumptions that follow from the recent trajectories of investments in software, software R&D, other intangibles, and productivity growth in both US and Europe. Our central estimates are that AI will boost annual labor productivity growth by as much as 1 percentage point in the United States and about .3 percentage point in Europe.
That is from Bontadini, Corrado, Haskel, and Jona-Lasinio, here is the complete abstract online.
Sunday assorted links
1. What Stanley Kubrick learned from chess.
3. Rob Wiblin and Dean Ball podcast on AI. And ChinaTalk surveys Chinese AI in 2025.
4. Stagnant construction productivity is a worldwide problem.
5. “Announcing Progress in Medicine, a high school summer career exploration program.”
How bad was British “austerity” anyway?
Chris Giles writes in the FT:
The main periods of measurement error came in the austerity years of 2012 to 2014, in 2017 during the early period after the Brexit referendum and in recent post-pandemic years. The truth is that a huge pessimistic bias in our national accounts has led us to be fed with contemporary reports of doom and gloom, which subsequently turn out to be nonsense.
But it is the first version of economic events that enters the national debate — and the national consciousness — for the entirely understandable reason that initial releases of economic data make news. You cannot expect people to care deeply about a revision to data that is three years old. Psychologically, they have made up their mind by then.
We are still told that 2010s austerity destroyed growth, but the data no longer supports that story: growth between David Cameron’s election victories of 2010 and 2015 now registers an annualised average of 2 per cent.
Somehow I am not seeing people jumping all over this story? Is it even correct? I have not seen anyone refute or counter it. Here is the analysis from 5.2 Pro, largely confirming, though it suggests 1.8% to 1.9% is a better estimate than 2%. I am very open to alternative points of view here, but at the moment it appears the correct stance was a) the British economic problems were largely structural and would not just be fixed by an aggregate demand boost, and b) fiscal consolidation was necessary, and while done imperfectly, not a disaster relative to the alternatives available.
The dust has not yet settled, but perhaps most of you were basically just wrong on this one?
Art as Data in Political History
From Valentine Figuroa of MIT:
Ongoing advances in machine learning are expanding opportunities to analyze large-scale visual data. In historical political economy, paintings from museums and private collections represent an untapped source of information. Before computational methods can be applied, however, it is essential to establish a framework for assessing what information paintings encode and under what assumptions it can be interpreted. This article develops such a framework, drawing on the enduring concerns of the traditional humanities. I describe three applications using a database of 25,000 European paintings from 1000CE to the First World War. Each application targets a distinct type of information conveyed in paintings (depicted content, communicative intent, and incidental information) and a cultural transformation of the early-modern period. The first revisits the notion of a European “civilizing process”—the internalization of stricter norms of behavior that occurred in tandem with the growth of state power—by examining whether paintings of meals show increasingly complex etiquette. The second analyzes portraits to study how political elites shaped their public image, highlighting a long-term shift from chivalric to more rational-bureaucratic representations of men. The third documents a long-term process of secularization, measured by the share of religious paintings, which began prior to the Reformation and accelerated afterward.
Here is the link, via the excellent Kevin Lewis.