Saturday assorted links
1. A new approach to pain management?
2. Geoengineering the ocean? (NYT)
3. Some dogs learn words by eavesdropping on the conversations of humans (NYT). I know one such dog.
4. John Ford’s American justice.
6. A practical guide to a PhD in economics.
7. GronlandsBANKEN.
AI, labor markets, and wages
There is a new and optimistic paper by Lukas Althoff and Hugo Reichardt:
Artificial intelligence is changing which tasks workers do and how they do them. Predicting its labor market consequences requires understanding how technical change affects workers’ productivity across tasks, how workers adapt by changing occupations and acquiring new skills, and how wages adjust in general equilibrium. We introduce a dynamic task-based model in which workers accumulate multidimensional skills that shape their comparative advantage and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-specific productivity, (ii) the law of motion for skill accumulation, and (iii) the determinants of occupational choice. We use the quantified model to study generative AI’s impact via augmentation, automation, and a third and new channel—simplification—which captures how technologies change the skills needed to perform tasks. Our key finding is that AI substantially reduces wage inequality while raising average wages by 21 percent. AI’s equalizing effect is fully driven by simplification, enabling workers across skill levels to compete for the same jobs. We show that the model’s predictions line up with recent labor market data.
Via Kris Gulati.
Profile of George Borjas and his influence
More recently, his research has found new attention and urgency in President Donald Trump’s second term: Borjas, 75, worked as a top economist on the Council of Economic Advisers, a post he stepped down from last week.
Borjas is an immigrant and refugee who escaped Cuba for the United States in 1962 and later obtained citizenship — a point of tension he has referenced in his writing.
“Not only do I have great sympathy for the immigrant’s desire to build a better life, I am also living proof that immigration policy can benefit some people enormously,” he wrote in a 2017 opinion piece for the New York Times. “But I am also an economist, and am very much aware of the many trade-offs involved. Inevitably, immigration does not improve everyone’s well-being.”
One of Borjas’s direct contributions to the Trump administration this past year was his extensive behind-the-scenes work on Trump’s overhaul of the H-1B visa system for highly skilled workers that added a $100,000 fee, according to three people familiar with his work and a White House official, who all spoke on the condition of anonymity because they weren’t authorized to share internal deliberations. Borjas had previously written about the “well-documented abuses” of that program over the years.
The White House official said Borjas was among many Trump administration members involved in redesigning the H-1B visa program and confirmed that Borjas provided intellectual support for other Trump immigration initiatives last year.
Here is more from The Washington Post.
Sentences to ponder
France will delay this year’s Group of 7 summit to avoid a conflict with the mixed martial arts event planned at the White House on Donald Trump’s birthday.
Here is the article.
Friday assorted links
1. The New Aesthetics and a call for new stories.
2. Seb Krier.
4. How Dean Ball uses coding agents.
5. The golden age of vaccine development.
6. Why is British politics so chaotic?
7. Update on the political science paper written by Claude.
8. “EU countries have approved the Mercosur trade deal after 25 years of talks.” And winners and losers. There are still plenty of restrictions in the deal of course, but better than nothing.
Part of the new job market report
The US continues to lose manufacturing jobs—payrolls are down 75k over the last year, & another 8k jobs were lost in December Transportation (especially auto manufacturing), wood, and electronics/electrical manufacturing are the biggest losers, but few subsectors are doing well
Here is the link.
It is time to back off from Greenland
I do hope it falls eventually into U.S. hands, as I explain in my latest Free Press piece. But now is not the time and furthermore that should happen voluntarily, not coercively. Here is an excerpt:
The better approach is to let the Greenlanders choose independence on their own. They may be ready to do so. In a survey last year, 56 percent of Greenlanders favored independence from Denmark, with just 28 percent opposed. This should not be a tremendous surprise. The Danes have not always treated Greenland well; the legacy of Denmark taking away the children of Greenlanders 75 years ago still remains—and similar issues crop up to this day.
If and when Greenlanders do choose independence, the U.S. should, when conditions feel right, make a generous offer to Greenland. If they do not take the offer, we might try again later on, but we should not intimidate or coerce them. We should respect their right of independence throughout the process. That would increase the likelihood that the future partnership will be a cooperative and fruitful one.
The courtship could take 20 or 30 years, but I am pretty sure that eventually Greenlanders will see the benefits of a stronger U.S. affiliation.
I do not think that simply trying to “buy” Greenland is going to work. I am reminded of my own fieldwork, roughly 20 years ago, in a small Mexican village in the state of Guerrero. General Motors wanted to buy most of the land in and around the village, for the purpose of building a racetrack to test GM cars. It had a lot of money to offer, and at the time a family of seven in the village might have earned no more than $1,500 a year. But the negotiations never got very far. The villagers felt they were not being respected, they did not trust the terms of any deal, and they feared their ways of life would change irrevocably. The promise of better roads, schools, and doctors—in addition to whatever payments they might have negotiated—simply fell flat.
These are very important issues, so we need to get them right.
Ken Opalo outlook on Africa 2026
(4) Keeping with the theme of growing during hard times and in difficult contexts, Nigeria is projected to grow by at least 4.3% in 2026, with consumer demand rising by over 7%.
Tinubu’s strong medicine may have nearly killed the patient, but after two painful years Nigerians seem poised to get relief from improving macro conditions. The Naira will remain stable (despite downward pressure on oil prices), with inflation projected to decline to under 14% — down from over 20% in 2025. Also, by now we can conclude that Dangote Refinery’s $20b bet on the Nigerian economy is a success. He appears to be winning the war against the entrenched interests that for decades fed at the trough of crude exports, imports of refined products, and fuel subsidies. The impact of the refinery will be felt in the further stabilization of fuel prices in 2026.
Nigeria’s reform momentum will slow down ahead of the 2027 elections. It’s not yet clear whether the reforms knocked the economy into a growth path, or if the projected growth is just recovery from the initial steep contraction after Tinubu took office.
(5) South Africa, too, will grow in 2026 despite tariff and political pressure from Washington. The GNU is holding; and Pretoria has weathered geopolitical storms (including the rift with Trump’s America) much better than I anticipated.
After years of stagnation, there is an emerging consensus that South Africa will see improvements in its growth rate over the next three years (averaging 1.7%). The reform momentum will continue, including in the power sector and entrenchment of the rule of law. Local elections later this year, including the big one in Johannesburg, will likely put further pressure on the ANC to improve service delivery and overall quality of policymaking.
The whole post is of interest, interesting throughout.
My Microsoft podcast on AI
Here is the link, here is part of their description:
Economist and public thinker Tyler Cowen joins host Molly Wood to explore why AI adoption is so challenging for many employees, organizations, and educational institutions. As he puts it,”This may sound counterintuitive, but under a lot of scenarios, the more unhappy people are, the better we’re doing, because that means a lot of change.”
In passing I will point out that the AI pessimism that started around 2023, with the release of GPT-4, is looking worse and worse. I am not talking about “the end of the world” views, rather “the stochastic parrot” critiques and the like. Dustbin of history, etc.
Thursday assorted links
1. Advancements in self-driving cars.
2. Infosys partners with Cognition. Rolling out Devin. Significant.
3. ChatGPT Health is launched.
5. Excellent year in review from Sebastian Garren.
6. AI begins prescribing medications in Utah.
You’ve got Sebastian’s excellent letter, and then a whole bunch of okie-dokie for the day.
*Resurrection*
That is the new Chinese movie, noting that the original title translates better as “Feral/Wild Age,” and you can think of it as a retelling of the history of the 20th century, from a Straussian Chinese point of view. Are parts also a retelling of the Buddha story, but what if a Buddha came to earth in contemporary times? Toss in “Chinese Ghost Story” and some vampires, and you have a pretty strange mix. Here is a good critical overview, including an interview with the director Bi Gan.
Scott Sumner noted he may well end up considering this to be his favorite movie of the decade. Visually, it is one of the most interesting movies of the last twenty-five years. Also, the attentive viewer will catch visual references to Dreyer, Uncle Boonmee, Stalker, Enter the Dragon, Rashomon, David Lynch, Matt Barney, and much more. Resurrection is also a homage to cinema, and to the passing of cinema, I would say.
As for the plot, I still am not sure. Perhaps it demands repeat viewings? I do not feel it is a spoiler to tell you there is one character taking five different guises. In any case, this is a major work of creative art and I am very glad I saw it. Large screen is mandatory of course.
The Tyranny of the Complainers
Some years ago, Dourado and Russell pointed out a stunning fact about airport noise complaints: A very large number come from a single individual or household.
In 2015, for example, 6,852 of the 8,760 complaints submitted to Ronald Reagan Washington National Airport originated from one residence in the affluent Foxhall neighborhood of northwest Washington, DC. The residents of that particular house called Reagan National to express irritation about aircraft noise an average of almost 19 times per day during 2015.
Since then, total complaint volumes have exploded—but they are still coming from a tiny number of now apparently more “productive” individuals. In 2024, for example, one individual alone submitted 20,089 complaints, accounting for 25% of all complaints! Indeed, the total number of complainants was only 188 but they complained 79,918 times (an average of 425 per individual or more than one per day.)
What I learned recently is that it’s not just airport noise complaints. We see the same pattern in data from the US Department of Education’s Office for Civil Rights which enforces federal civil rights laws related to education funding. In 2023, for example, 5059 sexual discrimination complaints came from a single individual–from a total of 8151 complaints. Thus, one individual accounted for 68.5% of all sexual discrimination complaints in that year.
In the annual reports for 2022-2024 the OCR identifies what type of complaint the single-individual with multiple complaints was making, a sex discrimination complaint, while in previous years they just give data on the number of complaints from single individuals compared to the total of all types of complaints. I’ve collated this data in this graph which presents totals compared to multiple complaints from a single individual without regard to the type of complaint. Do note, that there are also single individuals filing hundreds of other types of complaints such as age discrimination complaints so the data from more recent years may actually be an underestimate.
In any case, it’s clear that a single individual often accounts for 10-30% of all complaints! These complaints have to be investigated so this single individual may be costing taxpayers millions. It’s as if a single individual were pulling a fire alarm thousands of times a year, mobilizing emergency services on demand, and never facing repercussions.
Does this strategy work? Probably. When complaints are summarized for Congress or reported in the media, are totals presented as-is, or adjusted for spam?
Increasingly, public institutions seem to exist to manage the obsessions of a tiny number of neurotic—and possibly malicious—complainers.
My excellent Conversation with Brendan Foody
Here is the audio, video, and transcript. Here is the episode summary:
At 22, Brendan Foody is both the youngest Conversations with Tyler guest ever and the youngest unicorn founder on record. His company Mercor hires the experts who train frontier AI models—from poets grading verse to economists building evaluation frameworks—and has become one of the fastest-growing startups in history.
Tyler and Brendan discuss why Mercor pays poets $150 an hour, why AI labs need rubrics more than raw text, whether we should enshrine the aesthetic standards of past eras rather than current ones, how quickly models are improving at economically valuable tasks, how long until AI can stump Cass Sunstein, the coming shift toward knowledge workers building RL environments instead of doing repetitive analysis, how to interview without falling for vibes, why nepotism might make a comeback as AI optimizes everyone’s cover letters, scaling the Thiel Fellowship 100,000X, what his 8th-grade donut empire taught him about driving out competition, the link between dyslexia and entrepreneurship, dining out and dating in San Francisco, Mercor’s next steps, and more.
And an excerpt:
COWEN: Now, I saw an ad online not too long ago from Mercor, and it said $150 an hour for a poet. Why would you pay a poet $150 an hour?
FOODY: That’s a phenomenal place to start. For background on what the company does — we hire all of the experts that teach the leading AI models. When one of the AI labs wants to teach their models how to be better at poetry, we’ll find some of the best poets in the world that can help to measure success via creating evals and examples of how the model should behave.
One of the reasons that we’re able to pay so well to attract the best talent is that when we have these phenomenal poets that teach the models how to do things once, they’re then able to apply those skills and that knowledge across billions of users, hence allowing us to pay $150 an hour for some of the best poets in the world.
COWEN: The poets grade the poetry of the models or they grade the writing? What is it they’re grading?
FOODY: It could be some combination depending on the project. An example might be similar to how a professor in English class would create a rubric to grade an essay or a poem that they might have for the students. We could have a poet that creates a rubric to grade how well is the model creating whatever poetry you would like, and a response that would be desirable to a given user.
COWEN: How do you know when you have a good poet, or a great poet?
FOODY: That’s so much of the challenge of it, especially with these very subjective domains in the liberal arts. So much of it is this question of taste, where you want some degree of consensus of different exceptional people believing that they’re each doing a good job, but you probably don’t want too much consensus because you also want to get all of these edge case scenarios of what are the models doing that might deviate a little bit from what the norm is.
COWEN: So, you want your poet graders to disagree with each other some amount.
FOODY: Some amount, exactly, but still a response that is conducive with what most users would want to see in their model responses.
COWEN: Are you ever tempted to ask the AI models, “How good are the poet graders?”
[laughter]
FOODY: We often are. We do a lot of this. It’s where we’ll have the humans create a rubric or some eval to measure success, and then have the models say their perspective. You actually can get a little bit of signal from that, especially if you have an expert — we have tens of thousands of people that are working on our platform at any given time. Oftentimes, there’ll be someone that is tired or not putting a lot of effort into their work, and the models are able to help us with catching that.
And:
COWEN: Let’s say it’s poetry. Let’s say you can get it for free, grab what you want from the known universe. What’s the data that’s going to make the models, working through your company, better at poetry?
FOODY: I think that it’s people that have phenomenal taste of what would users of the end products, users of these frontier models want to see. Someone that understands that when a prompt is given to the model, what is the type of response that people are going to be amazed with? How we define the characteristics of those responses is imperative.
Probably more than just poets that have spent a lot of time in school, we would want people that know how to write work that gets a lot of traction from readers, that gains broad popularity and interest, drives the impact, so to speak, in whatever dimension that we define it within poetry.
COWEN: But what’s the data you want concretely? Is it a tape of them sitting around a table, students come, bring their poems, the person says, “I like this one, here’s why, here’s why not.” Is it that tape or is it written reports? What’s the thing that would come in the mail when you get your wish?
FOODY: The best analog is a rubric. If you have some —
COWEN: A rubric for how to grade?
FOODY: A rubric for how to grade. If the poem evokes this idea that is inevitably going to come up in this prompt or is a characteristic of a really good response, we’ll reward the model a certain amount. If it says this thing, we’ll penalize the model. If it styles the response in this way, we’ll reward it. Those are the types of things, in many ways, very similar to the way that a professor might create a rubric to grade an essay or a poem.
Poetry is definitely a more difficult one because I feel like it’s very unbounded. With a lot of essays that you might grade from your students, it’s a relatively well-scoped prompt where you can probably create a rubric that’s easy to apply to all of them, versus I can only imagine in poetry classes how difficult it is to both create an accurate rubric as well as apply it. The people that are able to do that the best are certainly extremely valuable and exciting.
COWEN: To get all nerdy here, Immanuel Kant in his third critique, Critique of Judgment, said, in essence, taste is that which cannot be captured in a rubric. If the data you want is a rubric and taste is really important, maybe Kant was wrong, but how do I square that whole picture? Is it, by invoking taste, you’re being circular and wishing for a free lunch that comes from outside the model, in a sense?
FOODY: There are other kinds of data they could do if it can’t be captured in a rubric. Another kind is RLHF, where you could have the model generate two responses similar to what you might see in ChatGPT, and then have these people with a lot of taste choose which response they prefer, and do that many times until the model is able to understand their preferences. That could be one way of going about it as well.
Interesting throughout, and definitely recommended. Note the conversation was recorded in October (we have had a long queue), so a few parts of it sound slightly out of date. And here is Hollis Robbins on LLMs and poetry.
The Molly Cantillon manifesto, A Personal Panopticon
I find this piece significant, and think it is likely to be one of the most important essays of the year:
A few months ago, I started running my life out of Claude Code. Not out of intention to do so, it was just the place where everything met. And it just kept working. Empires are won by conquest. What keeps them standing is something much quieter. Before a king can tax, he must count. Before he can conscript, he must locate. Before he can rule, he must see. Legibility is the precondition for governance…
The first thing Claude solved was product blindness. NOX now runs on a cron job: pulling Amplitude, cross-referencing GitHub, and pointing me to what needs building. It handles A/B testing, generates winning copy, and has turned customer support into a fully autonomous department.
Once I saw this was possible, I chased it everywhere. Email, hitting inbox zero for the first time ever, with auto-drafted replies for everything inbound. Workouts, accommodating horrendously erratic travel schedules. Sleep, built a projector wired to my WHOOP after exactly six hours that wakes me with my favorite phrases. Subscriptions, found and returned $2000 I didn’t know I was paying. The dozen SFMTA citations I’d ignored, the action items I’d procrastinated into oblivion. People are using it to, I discovered, run vending machines, home automation systems, and keep plants alive.
The feeling is hard to name. It is the violent gap between how blind you were and how obvious everything feels now with an observer that reads all the feeds, catches what you’ve unconsciously dropped, notices patterns across domains you’d kept stubbornly separate, and—crucially—tells you what to do about it.
My personal finances are now managed in the terminal. Overnight it picks the locks of brokerages that refuse to talk to each other, pulls congressional and hedge fund disclosures, Polymarket odds, X sentiment, headlines and 10-Ks from my watchlist. Every morning, a brief gets added in ~/𝚝𝚛𝚊𝚍𝚎𝚜. Last month it flagged Rep. Fields buying NFLX shares. Three weeks later, the Warner Bros deal. I don’t always trade, sometimes I argue with the thesis. But I’m never tracking fifteen tabs at 6am anymore.
It feels borderline unfair seeing around corners, being in ten places at once, surveilling yourself with the attention span of a thousand clones.
A panopticon still, but the tower belongs to you.
There is more at the link, or this link, and yes she is related to the 18th century Irish economist Richard Cantillon.
Who gets an “RIP” on Marginal Revolution?
A few of you have been asking me this. The core standards are as follows:
1. Most notable figures in economics. MR has many economist readers, and these deaths are not usually well-publicized by mainstream media.
2. A cultural figure I feel more of you should know about. Various figures from say African music or foreign cinema might be examples here.
3. A person who, if even at a distance, has played some special role in my life.
Someone who would not get a mention would be, for instance, a leading footballer. I figure any of you who care already will be hearing about the death.
I also discriminate against suicides, as I consider suicide both a sin and as having (in most cases) high negative externalities on others. I am not so inclined to honor or glorify those who have killed themselves.