Some more slow take-off, driven by start-ups

by  Tyler Cowen in

So far, however, the predictions that the mass automation of coding will leave outsourcing firms obsolete seem overblown. Their clients often hope AI will create huge productivity gains by, for example, using the technology to quickly and cheaply build a new internal HR tool. But such improvements in productivity are only possible in “greenfield” environments with “clean architecture”, argues Atul Soneja, chief operating officer at Tech Mahindra, an IT firm. Deploying AI in “brownfield” environments—with legacy code, a lack of documentation and multiple systems that must all continue to operate in real time—is far trickier. In the end, clients often realise that their AI dreams were too ambitious and end up hiring as many outsourced coders as before, say executives.

What is more, the AI boom may present an opportunity for the consultancy arms of India’s outsourcers. They argue that they can now fulfil more of a strategic role for their clients: getting the most out of AI requires understanding all of the context around the problem, something that consultants with experience across businesses can offer. Nandan Nilekani, one of the founders of Infosys, reckons that such services related to AI could be worth $300bn-400bn by 2030.

Here is more from The Economist.

How much more will oil prices have to go up?

by  Tyler Cowen in

[Robin] Brooks: So let me give you two ways of thinking about what’s going on, both of them are really about trying to think about what kind of risk premia need to be priced in oil, given all the massive uncertainty that we have. The first way that I’ve been thinking about this is—I spent a lot of time working on Ukraine and Russia and sanctions after the invasion four years ago. Russia produces about 10 million barrels of oil per day. It exports, of that, about 7 million barrels of oil per day. The Strait of Hormuz has transit of about 20 million barrels of oil per day. So the Strait of Hormuz is roughly 3 times what Russia could have been. And remember, in the days right after the invasion, markets were really worried about Russian oil being embargoed. There was a whole discussion about that. So the rise in Brent, which is the global benchmark oil price, is about 70% from two weeks before the outbreak of war in the Gulf to now. On a similar time horizon back in ‘22, it was 20%. So we have roughly a 3X in terms of the rise in oil prices. So when people come to me and say “$150 or $200 for oil prices” and we’re currently at $115, roughly, then I think, “why, what’s the rationale?”

The second perspective is on the supply shortfall that we have and using price elasticity of demand to think about: “how much does the price need to rise if demand has to do all the adjusting in the short term,” which it does. And “what kind of numbers do we come up with if we make reasonable assumptions?” So I put out a Substack note today—thank you so much for reading my Substack, I’m incredibly flattered and stressed as a result— if you assume that the Strait of Hormuz goes from 20 million barrels of oil per day to 10, it’s basically oil from the Gulf is running at half of its normal capacity, and you assume a price elasticity sort of in the middle of the range that the academic literature has, which is about 0.15, then you get that this would generate a rise in oil prices of between 60 and 70%. So again, if I think about what we’re pricing in markets now versus what basic back-of-the-envelope-calculations tell you, then I think we’re roughly in the right ballpark.

That is from his interview with Paul Krugman.  Via Luis Garicano.

More on the David Lang opera version of Wealth of Nations

by  Tyler Cowen in

In 18 parts, Lang explores some of Smith’s central themes, including one of the book’s most famous passages, where Smith uses a wool coat worn by a very poor Scottish worker as a way to examine trade. “He asks, ‘Did you ever think of how many people need to be employed in order to make that coat?’” says Lang, whose movement “the woolen coat” names all the artisans and laborers who contributed to the garment in song:

the shepherd
the sorter of the wool
the wool-comber or carder
the dyer
the spinner
the weaver
the fuller

There are also the workers on the ship that brought in the dye and all the people who built the ship. An ordinary coat is revealed to be a kind of miracle of skilled labor and global collaboration, the product of “many thousands” of workers coming together in (selfish) harmony. Part of me wanted to run out of the theater right then and buy something … perhaps a coat… for America.

Here is more from Bloomberg, via John De Palma.  The opera seems to be ultimately a rather gloomy view of the book?

Canada facts of the decade

by  Tyler Cowen

From 2014 to 2024, Canada’s real GDP per capita adjusted for purchasing power parity grew by just 3.2 percent in total, an anemic 0.4 percent per year on average, and the third lowest among 38 advanced nations. Over the same period, the United States posted 20.2 percent total growth (1.9 percent annually), and the OECD average reached 15.3 percent (1.4 percent annually). The measurement shortcomings cannot explain five-to six-fold differences in growth rates.

And:

The analysis estimates that a substantial share of Canadians who would rank among top earners in Canada have emigrated to the United States—roughly 40 percent of potential top 1 percent earners and 30 to 50 percent of the next nine percentiles. Canadian-born individuals in the United States are more educated than native-born Americans, earn substantially more, and cluster disproportionately in top income deciles.

Canada is effectively exporting its inequality to the U.S. The brain drain simultaneously lowers our average income while raising American income, accounting for a significant share of the persistent GDP gap.

Here is the full piece.

Those new service sector jobs?

by  Tyler Cowen in

An AI memory startup called Memvid is offering $800 for a one-day, eight-hour shift for one candidate to “bully” AI chatbots by telling them what to do on camera.

Business Insider reported this week that Memvid wants someone to spend eight hours testing and critiquing the memory of popular AI chatbots, effectively paying $100 an hour for what they have branded as a “professional AI bully” role. The worker’s job is to examine where chatbots lose track of details, forget context or misrepresent data, and then feed those findings back to Memvid so the startup can improve its products.

“You’ll spend a full 8-hour day interacting with leading AI chatbots — and your only job is to be brutally honest about how frustrating they are,” the job listing reads.

The draw is that the role doesn’t require a computer science background, AI credentials or any kind of work experience. “No prior AI bullying experience required — we all start somewhere,” the listing reads.

The requirements are deeply personal. The first requirement is an “extensive personal history of being let down by technology,” and the second desired trait is “the patience to ask a chatbot the same question four times (and the rage when it still gets it wrong).”

Here is the full article, via the excellent Samir Varma.

A Danish Fix for U.S. Mortgage Lock-in

by  Alex Tabarrok in

In the Danish mortgage market every mortgage is backed by a corresponding bond. Thus, if a home buyer takes out a 500k mortgage at 3% interest, a bond is issued that pays the lender 3% interest on 500k. I’ve written about this system several times before. It has two distinct advantages.

  • The correspondence principle means that mortgage banks don’t bear interest rate risk but instead specialize in evaluating credit risk (the risk that the borrower won’t pay). Deep markets rather than banks take on the interest rate risk. This makes the Danish system very stable.
  • Mortgages can be pre-paid by buying the corresponding bond at market rates and extinguishing it. If a Danish borrower takes out a 500k mortgage at 3% interest and then rates rise to 6%, for example, the value of that mortgage falls to $358k and the borrower can buy the corresponding bond, deliver it to the bank, and, in this way, extinguish the loan.

In the US, a mortgage can be pre-paid only at a par. As a result, if interest rates rise, home owners don’t want to move because moving would require them giving up a 3% mortgage and replace it with say a 6% mortgage. This is called the lock-in effect. Lock-in can be quite severe. Fonseca and Liu find:

Using individual-level credit record data and variation in the timing of mortgage origination, we show that a 1 percentage point decline in the difference between mortgage rates locked in at origination and current rates reduces moving by 9% overall and 16% between 2022 and 2024, and this relationship is asymmetric. Mortgage lock-in also dampens flows in and out of self-employment and the responsiveness to shocks to nearby employment opportunities that require moving, measured as wage growth within a 50- to 150-mile ring and instrumented with a shift-share instrument.

What about in Denmark? The Danes definitely take advantage of the opportunity to buy-back. Part of this is due to tax advantages but those are just a transfer. More importantly, Danes don’t get locked in. A new paper by Berger, Jeong, Marx, Olesen, and Tourre compares mobility across Denmark and the US:

We study Danish fixed-rate mortgage contracts, which are identical to those in the United States except that borrowers may repurchase their mortgages at market value. Using Danish administrative data, we show that households actively buy back debt when mortgage prices fall below par and that household mobility is largely insensitive when existing mortgage rates are below prevailing market rates — unlike in the United States, where moving rates fall sharply as rates rise. We develop an equilibrium model that explains these patterns and show that introducing a repurchase-at market option into U.S. mortgages substantially reduces interest-rate-induced lock-in with limited effects on equilibrium mortgage rates.

The last point is especially important because you might wonder whether we are assuming a free lunch? After all, if US borrowers lose when they have to pre-pay at par then lenders surely gain. And if lenders gain on pre-payment then they will be willing to lend at lower rates on mortgage initiation. No free lunch, right? The logic is correct but note that the gain to lenders comes mainly from the relatively small set of households that move despite lock-in so the pre-payment bonus to lenders is quite small. Under the author’s calibrated model, mortgage interest rates in the US would rise by only 18 basis points on average if the US moved to a Danish type system.

In other words, there actually is a free or at least a low-priced lunch because lock-in is bad for homeowners and it doesn’t benefit lenders. As a result, moving to a Danish system would create net benefits.

Consumers vs. mates as a source of selection pressure

by  Tyler Cowen

Evolutionary biology is one attempt to explain the nature of living beings. In that framework there is a difference between individuals and genes.  If a practice increases the chance that genes will be passed along, it may evolve and be passed along, whether or not it serves either individual or collective self-interest.

To give a simple example, some women may prefer “cads.”  Those men, by definition, will sleep around, but possibly their sons will sleep around too.  The woman’s genes may thus spread more widely, and women who prefer cads may not disappear from the gene pool, even though the cads are bad for them.

You might ask whether corresponding mechanisms apply to the evolution of AI models.  If I prefer an OAI model to DeepSeek for instance, that will help to spread OAI models through the AI population.  OAI will have more revenue, and it will produce more output of what is succeeding in the market.  Furthermore my choice of model may influence others to do the same, and it may help create and finance surrounding infrastructure for that model.

Will I buy the next generation of OAI models?  Well yes, if the first one pleased me.  The model “reproduces” and sustains itself if I, as a consumer, am happy with it.  One obvious incentive is toward usefulness, another is toward sycophancy.  We already see these features realized in the data.  There is nothing comparable, however, to the “cads incentive” in human life.

One potential problem comes if individuals are not the only potential buyers.  Let us say the military also purchases AI models.  The motives of the military may be complex, but at the very least “wanting to kill people” (whether justly or not) is on the list of possible uses.  Models effective for this end thus will be funded and encouraged.

My model of the military is that, above and beyond efficacy, they value “obedience” and “following orders” to an extreme degree, including in their AI models.  There will thus be evolutionary pressures for those features to evolve in the AI models of the military.

To be sure, not all orders are good ones.  But in this case the real risk is from evil humans, or deeply mistaken humans, not from the tendencies of the AI models themselves.

So my view is that the selection pressures for AI models are relatively benign, noting this major caveat about how evil humans may develop and use them.

If the biggest risk is from the military models, it might be good for the consumer sector of AI models to grow all the more, as a relatively benevolent counterweight.

Are financial sectors AI models going to evolve more like the consumer models or the military models?

Here are some related remarks from Maarten Boudry, and I also thank an exchange with Zohar Atkins.

Is AI currently helping economic research?

by  Tyler Cowen in

The third possibility, that AI helps to weed out mistakes, is trickier for the discipline. This stage could become even more important if journals do start to be hit by a wave of AI-generated slop — or, perhaps more likely, good papers with so many appendices and robustness checks that even the most dedicated referee is defeated. (The real “Dr Robust” does not have infinite energy.)

Eager to embrace the new technology, several of the top five economics journals are already experimenting with Refine, an impressive AI-powered reviewing tool that scours economics papers for errors. Ben Golub, one of its creators, shared that even with papers that had been through referees at top journals, Refine was picking up problems in at least a third of cases.

Here is more from Soumaya Keynes at the FT.

Thursday assorted links

by  Tyler Cowen

1. Ideological trends in academic scholarship.

2. Prediction market for the John Bates Clark award.

3. Show Me The Model.  “Give it a URL or paste some plain text, and the tool flags hidden assumptions, internal inconsistencies, and other problem areas, and tells you how a real economist would think through the issue.”

4. “I built Frontier Graph: an open-source tool to explore open questions in economics, drawing on 240K papers across 300 journals.”  And here.

5. The Peruvian death toll.

6. India tests whether AI can stop trains from hitting elephants.

7. The Amish are OK with washing machines.

University of Chicago fact of the day

by  Tyler Cowen in

A team largely composed of economics majors who know their way around Milton Friedman and Gary Becker, Chicago (23-4) is a DIII powerhouse currently in the DIII Sweet 16 and chasing its first-ever NCAA national title.

“Nobody’s ever going to confuse this with Alabama football,” says head coach Mike McGrath, “but if you think about the student-athlete model, I think we show you can do both of those things very, very well.”

…“Obviously, the kids are really smart,” he says. “You can’t B.S. them. They’re going to challenge everything that you tell them, you have to be prepared for that…there’s a need to understand the why behind things.”

…a friend of the program, Chicago professor John List, is working with students on an analysis of player positioning.

Here is more from the WSJ, via Rama Rao.