Wednesday assorted links

1. Was RCA the tech stock of the 1920s?

2. Measuring the skill of individual soccer players.

3. Electric “autos” for India.

4. Chinese views on DeepSeek.  And Chinese music AI, Yue, the model song.  And Chinese robot dance.  And a poem from R1.  And R1 wants more mood affiliation from Yglesias.

5. The AI Paradise Lost?

6. Joe Lonsdale on possible health care reforms.

7. More Paul Krugman on the NYT saga.

It’s Time to Build the Peptidome!

Antimicrobial resistance is a growing problem. Peptides, short sequences of amino acids, are nature’s first defense against bacteria. Research on antimicrobial peptides is promising but such research could be much more productive if combined with machine learning on big data. But collecting, collating and organizing big data is a public good and underprovided. Current peptide databases are small, inconsistent, incompatible with one another and they are biased against negative controls. Thus, there is scope for a million-peptide database modelled on something like Human Genome Project or ProteinDB:

ML needs data. Google’s AlphaGo trained on 30 million moves from human games and orders of magnitude more from games it played against itself. The largest language models are trained on at least 60 terabytes of text. AlphaFold was trained on just over 100,000 3D protein structures from the Protein Data Bank.

The data available for antimicrobial peptides is nowhere near these benchmarks. Some databases contain a few thousand peptides each, but they are scattered, unstandardized, incomplete, and often duplicative. Data on a few thousand peptide sequences and a scattershot view of their biological properties are simply not sufficient to get accurate ML predictions for a system as complex as protein-chemical reactions. For example, the APD3 database is small, with just under 4,000 sequences, but it is among the most tightly curated and detailed. However, most of the sequences available are from frogs or amphibians due to path-dependent discovery of peptides in that taxon. Another database, CAMPR4, has on the order of 20,000 sequences, but around half are “predicted” or synthetic peptides that may not have experimental validation, and contain less info about source and activity. The formatting of each of these sources is different, so it’s not easy to put all the sequences into one model. More inconsistencies and idiosyncrasies stack up for the dozens of other datasets available.

There is even less negative training data; that is, data on all the amino-acid sequences without interesting publishable properties. In current ML research, labs will test dozens or even hundreds of peptide sequences for activity against certain pathogens, but they usually only publish and upload the sequences that worked.

…The data problem facing peptide research is solvable with targeted investments in data infrastructure. We can make a million-peptide database

There are no significant scientific barriers to generating a 1,000x or 10,000x larger peptide dataset. Several high-throughput testing methods have been successfully demonstrated, with some screening as many as 800,000 peptide sequences and nearly doubling the number of unique antimicrobial peptides reported in publicly available databases. These methods will need to be scaled up, not only by testing more peptides, but also by testing them against different bacteria, checking for human toxicity, and testing other chemical properties, but scaling is an infrastructure problem, not a scientific one.

This strategy of targeted data infrastructure investments has three successful precedents: PubChem, the Human Genome Project, and ProteinDB.

Much more in this excellent piece of science and economics from IFP and Max Tabarrok.

Is it a problem if Wall Street buys up homes?

No, as I argue in my latest Bloomberg column.  This one is basic economics:

The simpler point is this: If large financial firms can buy your home, you are better off. You will have more money to retire on, and presumably selling your home will be easier and quicker, removing what for many homeowners is a major source of stress.

And all of this makes it easier to buy a home in the first place, knowing you will have a straightforward set of exit options. You don’t have to worry about whether your buyer can get a mortgage. Homeowners tend to be forward-looking, and a home’s value as an investment is typically a major consideration in a purchase decision.

And:

When financial firms buy homes, they also tend to renovate and invest in fixing the places up.

A less obvious point is that lower-income groups can benefit when financial firms buy up homes. Obviously, if a hedge fund buys your home, no one at the fund is intending to live there; they probably plan to rent it out. The evidence shows that when institutional investors purchase housing, it leads to more rental inventory and lower rents.

If the tradeoff is higher prices to buy a home but lower prices to rent one, that will tend to favor lower-income groups. Think of it as a form of housing aid that does not cost the federal government anything. Economist Raj Chetty, in a series of now-famous papers with co-authors, has stressed the ability to move into a better neighborhood as a fundamental determinant of upward economic mobility. Lower rents can enable those improvements.

The article also show that the extent of financial firms buying homes is smaller than many people seem to believe.

Will transformative AI raise interest rates?

We want to know if AGI is coming. Chow, Halperin, and Mazlish have a paper called “Transformative AI, Existential Risk, and Real Interest Rates” arguing that, if we believe the markets, it is not coming for some time. The reasoning is simple. If we expect to consume much more in the future, and people engage in smoothing their incomes over time, then people will want to borrow more now. The real interest rate would rise. The reasoning also works if AI is unaligned, and has a chance of destroying all of us. People would want to spend what they have now. They would be disinclined to save, and real interest rates would have to rise in order to induce people to lend.

The trouble is that “economic growth” is not really one thing. It consists both of expanding our quantity of units consumed for a given amount of resources, but also in expanding what we are capable of consuming at all. Take the television – it has simultaneously become cheaper and greatly improved in quality. One can easily imagine a world in which the goods stay the same price, but greatly improve in quality. Thus, the marginal utility gained from one dollar increases in the future, and we would want to save more, not less. The coming of AGI could be heralded by falling interest rates and high levels of saving.

From Nicholas Decker.

Tuesday assorted links

1. How fiscally progressive are state governments?

2. Update on the quest to abolish parking minimums (NYT).

3. When are tariffs the optimal industrial policy?

4. Interview with the CEO behind DeepSeek.

5. Executive Order tracker.

6. “Our findings indicate that, on average, a large minimum wage increase results in a 4.6 percent increase in the total case [injury] rate.

7. Olivier Blanchard on DeepSeek: “Probably the largest positive one day change in the present discounted value of total factor productivity growth in the history of the world.”

8. A nearby superearth?

Future unemployment will be (mostly) voluntary unemployment

A shortage of electricians means that those willing to endure long shifts and live on remote sites can potentially earn up to A$200,000 (US$124,000) a year — double the national average salary and not far off the average MP salary.

“It’s a cup half full/half empty life. You do 12-hour shifts, there’s the heat, the flies and you’re stuck in a donga [temporary housing] in a single bed. But you’re fed well and everything’s covered. You leave your credit card at home. You earn good money and you get plenty of time off,” said Dowsett of his life as a fly-in, fly-out electrician.

The high salaries reflect the fact that fewer Australians want to be electricians, creating a potentially devastating shortage as major renewable energy, mining and data centre projects come online. Australia needs 32,000 more electricians by 2030 to meet the demand for workers, according to a report from the Clean Energy Council, citing government statistics.

Here is more from the FT, via the excellent Samir Varma.

Questions about LLMs (from my email)

From Naveen:

So much talk of “AI safety” and too little in the way of practical questions like these that are going to be important in the near future.

Should law enforcement be able to subpoena AI  assistants about your information? For example, I use the free GPT-3.5/4 version and it already has a lot of my personal information on it.

The other day, when I asked an insurance claims related question in a new chat window without reminding it of the fact that my car was recently totaled, it includes in the answer that “but that wouldn’t apply to you, since your car was declared non-repairable and you were declared as not at-fault.” So it remembers personal information I mentioned weeks ago even though I never told it to commit to its memory.

ChatGPT is such a rudimentary free AI system compared to the personal AI assistants we will get in the near future which will have all my travel data, health data, financial data, mental health data, personal data and what I’ve been up to.

Should law enforcement be allowed to subpoena such AI assistants? Should there be legislation mandating data retention so law enforcement can access it much like telephone records or the opposite — mandating data encryption so it can’t be accessed?

The mistakes of Michael Pettis

Noah Smith, and a few readers, have asked for a summary post about the errors of Michael Pettis.  Since Pettis is now an influential trade thinkers with many of the Trump people, I think it is worth repeating my previous points just a bit.

Here are the errors, perhaps there are more:

He talks about tariffs (FT) as if they are anti-consumption but pro production. But tariffs are anti-production on the whole, at least outside of some well-known cases of increasing returns, and even then the tariffs have to be applied properly.  Pettis does not present this very important qualification about increasing returns, and he basically presents an argument that we would expect economics undergraduate majors to reject.

With this talk of trade balances and demand deficits, he repeatedly confuses the short-run with the medium-run and long run.  At some point prices adjust and the demand shortfalls go away.  I never see him acknowledge price adjustments as creating differences between short-run and long-run effects in this context.  Of course this distinction between long-run and short-run effects is fundamental to macroeconomics.  If Pettis wishes to disagree, fine, but he has to spell out his argument.

He has a bizarre notion and theory of demand, for instance claiming during America’s recent high inflation that demand was weak in the United States.

He sees the degree of wage suppression or “labor exploitation” in a country (his concept not mine) as a central determinant of export success.  That does not accord with the evidence, and yes this question has been studied extensively.

All of these are basic and fundamental errors, and furthermore they matter for most of Pettis’s main conclusions, including his policy conclusions.  So I will say it again — he has become a major media figure, but Michael Pettis does not understand basic international economics.

You could add to that some more complicated shortcomings, such as his analysis of tariffs is bad (see Noah’s piece), or that there is not (so far?) any coherent model that will get you to his conclusions.  Admittedly those are more complex issues, what I list above are not.

A habit he has is to categorize and dismiss economists as a whole. For instance, as Noah cites, Pettis wrote, I think responding to me:

If you want to understand the effects of trade intervention, its ok to ask economic historians, but never ask economists. That’s because their answer will almost certainly reflect little more than their ideological position…It was direct and indirect tariffs that in 10 years transformed China’s EV production from being well behind that of the US and the EU to becoming the largest and most efficient in the world…Tariffs may not be an especially efficient way for industrial policy to force this rebalancing from consumption to production, but it has a long history of doing so, and it is either very ignorant or very dishonest of economists not to recognize the ways in which they work…To oppose all tariffs on principle shows just how ideologically hysterical the discussion of trade is among mainstream economists.

Obviously that is not responding on the points of substance at all.  In general, you should be suspicious when you see broadside attacks on economists in a debate, even if some of the embedded criticisms might be true.

Note that Pettis’s fundamentally correct point, namely that China subsidizes investment too much, predates him and can be made without all the accompanying errors.  The notion that party-run economic systems oversubsidize investment is decades old, and usually right.

Addendum: After I wrote this post, Paul Krugman came out with a new Substack, excerpt: “But I decided to talk about a new view of trade imbalances, associated especially with Michael Pettis, that has been gaining some traction lately. It’s also mostly wrong…”

Sunday assorted links

1. Cato ad for policy analyst in human progress and economics.  And for psychology.

2. Joe Boyd episode Spotify playlist, put together by a CWT listener.

3. Lots more black holes than we had thought? And more here.

4. DeepSeek okie-dokie: “All I know is we keep pushing forward to make open-source AGI a reality for everyone.”  I believe them, the question is what counter-move the CCP will make now.

5. A much longer follow-up post on why northern England is poor (still no mention of drunks?).

6. Progress on Neom?

Do Migrants Pay Their Way? A Net Fiscal Analysis for Germany

This study quantifies the direct average net fiscal impact (ANFI) of migration in Germany, taking into account both indirect taxes and in-kind benefits such as health and education spending. Using a status quo approach with data from the German Socio-Economic Panel (SOEP) for 2018 and microsimulation techniques to impute both indirect taxes and in-kind benefits, our results show that migrants, especially first-generation migrants, have a more favorable net fiscal impact on average compared to natives. However, we demonstrate that this result is mainly driven by the favourable age structure of migrants. When controlling for demographic differences between these groups, we show that second-generation migrants contribute very similarly to natives to the German welfare state. Nevertheless, both natives and second-generation migrants, respectively, contribute more than first-generation migrants. These differences persist even when we do not account for indirect taxes and benefits-in-kind.

That is from a recent paper by Hend Sallam and Michael Christl.  One interesting point in the paper is that native Germans have a net negative fiscal impact — is that really consistent with blaming the immigrants for the major problems?

Via the excellent Samir Varma.

*On the Calculation of Volume, I and II*

Thoee two novels by Solvej Balle, a Danish author, are now available in English.  Conceptually, they are close to time travel novels (I should not tell you the actual nature of the twist), but with more literary value than you might be expecting.

Every now and then a new book comes along that is conceptual, fascinating, fun to read, good on human psychology, and in literary terms very well done.  The Balle books qualify there.  Each is also quite short, though the second half of each volume is better than the first, so there is a return to patience (to be clear, the first halves, or maybe thirds, are fine, but the true points are revealed only with some time).

There are some implicit economic and even crypto themes in the work, though I doubt if the author is aware of them.

So I recommend these, and will not go near potential spoilers.

*In Praise of Floods*, James C. Scott does Uncle Boonmee?

That is the new James C. Scott book, from Yale University Press.  It focuses on Burma [sic], and I found this to be the most typical and illustrative sentence:

Several of the commentators evaluating this book in manuscript noticed that the section of river spirits (nats) and the much larger section describing the eco zones, hydrology of the Ayeyarwady, and the mapping of major human interventions represent something of a rupture from the preceding narrative on rivers.

Also:

One central purpose of this book is not only to recognize the animated liveliness of the river and its tributaries, but also to give voice to all the flora and fauna whose lifeworld centers around the river.

One section of the book is narrated from the first-person perspective of a river dolphin.

This is an essential book for understanding Scott.  And from the preface we read:

The book before you contains more disjunctions than I would have preferred.

Scott was a great man and scholar, and this book reminds you that many such people are really quite weird, in the good sense of course.  You can pre-order it here.

Saturday assorted links

1. Teen fertility in sub-Saharan Africa.

2. Does Chile have the fastest fertility collapse?

3. New book sorting algorithm almost reaches perfection.

4. How did the organization of DOGE end up? An important piece. As I have been predicting, DOGE will shrink government (slow its rate of growth?) by only a modest amount, if at all.

5. Ten-minute Rebekka Grun talk on the economics of dating and marriage.

6. Andrej on agents and Operator.

7. Reasonable thoughts on AI alignment.

AI Improves Student Learning and Engagement

In our paper on online education, Tyler and I wrote:

One model of a future course is a super-textbook: lectures, exercises, quizzes, and grading all available on a tablet with artificial intelligence routines guiding students to lectures and exercises designed to address that student’s deficits and with human intelligence—tutors—on call on an as-needed basis, possibly for extra marginal fees.

We were wrong only in thinking that human tutors would be wanted and needed. Toda,y it is clear that AI tutors will be available 24-7 for all students. Online education was already at least as good on average as in-class education for large classes like Physics and Economics 101 and much cheaper. Combined with AI tutors who can offer individualized instruction and encouragement (!) the online-AI model looks better.

A study at Harvard compared physics students who worked with an AI tutor against a human-led, active learning classroom. Note that the AI was paired not against boring lectures but against an active learning classroom with an experienced and motivated teacher.

Not only did the AI tutor seem to help students learn more material, the students also self-reported significantly more engagement and motivation to learn when working with AI.

“It was shocking, and super exciting,” Miller said, considering that PS2 is already “very, very well taught.”

“They’ve been doing this for a long time, and there have been many iterations of this specific research-based pedagogy. It’s a very tight operation,” Miller added.