Warsh’s Low/No Guidance Approach at Fed Makes Market Participants Nervous – – Which May Be a Good Thing

Under Jerome Powell, a typical FOMC meeting had become almost a market event in itself. Traders didn’t just care about the rate decision. They dissected every word of the statement, every sentence of the press conference, and especially the “dot plot,” looking for clues about where rates might be six months or a year from now. The Fed wasn’t simply setting monetary policy—it was guiding expectations. Markets often moved as much on hints about future decisions as on the decision itself.

The first two FOMC meetings under Kevin Warsh have felt very different. The dot plots are gone. Forward guidance has largely disappeared. Instead of trying to signal the likely path of policy, Warsh has repeatedly stressed that the Fed will respond to incoming data when it arrives, not commit itself to forecasts that could prove wrong. At his latest press conference, he described avoiding forward guidance as “prudent” given current uncertainty, while reminding reporters that “There is no soft or alternative inflation target—only 2%.”

This is a huge change in communication style, which is having real world consequences.

Warsh long argued that forward guidance can box policymakers into decisions based on yesterday’s forecasts instead of tomorrow’s realities. That is, once their tentative plans had been put out in public, there was a psychological bias among Fed members to lock in on those projections, which would inhibit their ability to rationally interact with the most recent data and situation. So now, rather than telling markets what the Fed expects to do, he wants investors to make decisions based on fundamental economic conditions, knowing that the central bank will react only after the facts justify it. At the latest FOMC meeting he said, “Market participants are learning to play the ball, not the referee—and market prices will continue to respond in the direction and magnitude they see fit. This is, in my view, a change for the better—and we are just getting started.”

That approach chips away at what investors have come to call the “Fed Put”—the belief, built up since the 2008 financial crisis, that the central bank will fairly quickly and forcefully step in to support markets whenever things get rough.

If that belief fades, financiers may think twice before taking excessive risks. Leverage becomes more dangerous if there is less confidence that easier monetary policy will quickly arrive to cushion losses. Risk premiums may better reflect actual economic uncertainty rather than expectations of future Fed support. That is the possible good side of Warsh’s more hands-off approach. Ideally, business people will exercise more prudence on their own, lessening the odds of financial catastrophes that would require Fed intervention.

On the other hand, markets hate uncertainty, and less guidance means more volatility around Fed meetings. I think Powell tried to use sheer talking (jaw-boning) as a tool to influence market rates, lessening the need for the Fed to actually employ its blunt instruments there. Warsh seems to have taken that tool off the table.

Also, I think some (not all) the causation for the rise in 30-year Treasury bonds to twenty-year highs, and of home mortgage rates to one-year highs accrues to Warsh. First, by eliminating dot plots and forward guidance, he has increased uncertainty about the future path of policy. Investors can no longer confidently assume the Fed will ease at the first sign of economic weakness. That uncertainty can raise the term premium, pushing long-term yields higher.

Second, if markets believe the “Fed Put” is weaker, they may demand higher yields to hold long-term bonds because they perceive less protection from adverse economic or financial shocks. In other words, investors require more compensation for risk.

Whether today’s higher long-term rates are a healthy reflection of economic realities, or an unhealth drag on growth, is a matter of debate.

Service Industry Exodus and the ACA: Anecdata

Within my social network the exodus from the service industry is now almost complete. Ten years ago I had no fewer than 7 good friends in the restaurant busines, now only one remains (and he is, by his own classification, 40% retired). The reasons were both myriad and similar. The physiscal toll is substantial, the hours long, and lack of weekends, the separation from non-industry people working diametrically opposed schedules. What really keeps pushing people out, however, that seems to tip the scales over and over, is the lack of health insurance consistent across most restaurants. With the expiration of the ACA subsidies driving up premiums for those without an employer pool to participate in, the calculus has shifted. Who’s leaving? Is it just the friends of economists?

No, it’s everyone over 35. It’s not really more complicated than that. They are entering the age where health insurance has a lot more marginal value, so they are leaving. Sometimes for substantial paycuts.

Between the ACA subsidies expiring and ICE enforcement targeting the keep service industry labor pools, the business that make our meals are going to look very, very different. Will they be worse? I guess I can’t say for sure…no, scratch that, I absolutely can. It’s worse. Everything is going to be worse. Younger, less experience, fewer immigrants? Yeah, that’s the formula to make everything worse.

AI Innate Preferences Paper on Arxiv

Please check out my new paper, with Joshua Foster

The Innate Economic Preferences of Language Models (arXiv link)

Abstract: Language models increasingly settle real resource tradeoffs on behalf of principals yet their economic preferences remain unobserved. We demonstrate their generation rule is isomorphic to the random utility model of discrete choice. This allows internal logit scores to structurally identify preferences. Estimating risk attitudes across twelve models in a portfolio task reveals universal but heterogeneous risk aversion. Although models reject strictly dominated options, their elicited preferences fail invariance tests and violate the independence of irrelevant alternatives across varying experimental prompts. Finally, fine tuning establishes that a principal can explicitly engineer a target risk attitude.

I hope you will refer to the manuscript for details, but I will share one picture here. This is panel (a) of Figure 3: Empirical indifference curves for open-weight models mapped over the portfolio space.

In simple language, what the red/blue picture shows is that the Qwen language model is picking the portfolios that offer more money (in expectation, with a distaste for excessive risk). That’s basically what a rational actor should do. We find that the language models make fairly consistent choices and rarely violate the monotonicity requirement for a well-behaved utility function.

How we describe this figure in the paper: “Starting from a base bundle with expected return µ = 10 and risk σ = 30, we sweep over the dense grid of alternative portfolios from our experimental protocol and record the position-corrected logit gap between each grid portfolio and the base. The yellow dashed line overlays the indifference curve implied by the mean-variance structural estimates, and the heatmap colors encode the sign and magnitude of the logit difference, with blue regions preferred to the base and red regions dispreferred. Several patterns emerge from these plots. All six models produce upward-sloping indifference curves, confirming that higher risk must be compensated by higher expected return.”

We think this basic research on behavior is important, for alignment research and for business applications with delegating work to AI agents. The first question to ask, before testing whether we can impose our preferences on AI agents, is whether those agents have preferences at all in a consistent sense.

Suggested citation: Buchanan, J., & Foster, J. (2026). The innate economic preferences of language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.26288

So Many Prime Ministers

There has been a lot of shade thrown at the United Kingdom recently from economists and political scientists. Economic growth has gone down the tubes and there have been six prime ministers over the past decade. As an American, I didn’t really know if that was a lot. The social media says that we’ve had a lot of turnover recently. Is six prime ministers in ten years a lot in the UK’s parliamentary system? I grew up watching Tony Blair on TV for a ten-year stretch. But I had no context for the historical norm or whether there is any precedent. Here I look at the data.

Right now, there are a record number of former prime ministers still living (PM). Prior to the recent spike, the maximum number of living people who had left office was five. Right now in 2026, there that number is nine! And if the current PM, Andy Burnham, follows the recent trend of short stints in office, then they’ll hit ten. As an American, it’s hard for me to imagine having 10 living former presidents. According to the below charts, the British are probably a bit jarred too!

Why So Many?

In last week’s post I noted that we’re tied for the most living former presidents. But truly, the UK’s numbers are what inspired me to look at this topic in the first place. To recap, the number of living ex-executives can be caused by 1) Longer lifespans, 2) Leaving office at a younger age, and 3) More unique executives. In the US, being currently tied for the record is overwhelmingly driven by longer lifespans. What about the UK?

Continue reading

The Academic Data Project That Turned Into $375 Million

What could be better than creating data so valuable that an institution is happy to host and update it forever, like the Sean Lahman baseball database?

Creating data that sells for $375 million, like the Center for Research in Security Prices. University of Chicago professors assembled this series of finance datasets over decades, starting in 1960 with an effort to track every transaction of every publicly traded security. U Chicago sold CRSP to Morningstar last year for $375 million.

Why could they sell it for so much? It helps to be working in finance, where the willingness to pay is the highest. It also represents 65 years of work from what became a large team that included Nobelists like Eugene Fama. The data was valuable enough to become widely used by key institutions even though CRSP charged for it:

Today, $3 trillion in fund assets are linked to CRSP Market Indexes, including U.S. equity ETFs run by Vanguard, and more than 600 subscribers across 35 countries use CRSP Research Data Products.  

Did U Chicago sell CRSP at the right time? On the one hand, I wonder if this was a fire sale driven by federal grant cuts putting pressure on the U Chicago budget. On the other hand, assembling datasets like this is only going to get easier in the age of AI, so perhaps Chicago sold at the top.

For now though there is still an edge in having restricted datasets that AIs haven’t trained on and can’t access. When I ask myself what advantage my human research assistants have over AIs in 2026, the most obvious answer is that they can legally access restricted databases like CRSP or, in my current case, HeinOnline.

GDP Growth in the Second Quarter: Updated Forecasts

GDP growth data for the second quarter of 2026 comes out tomorrow. As I have been doing for the past several quarters, here is an update on two model forecasts (Atlanta and NY Feds), betting market implied estimates (Kalshi), and an average from a survey of economists (WSJ). Yellow shading indicates which forecast was closest to correct in each quarter (green is if two forecasts were about the same).

In the past two quarters, the WSJ survey has been the best predictor. The Atlanta Fed GDPNow model used to be my favorite, but it has performed pretty poorly in the past 3 quarters. As I have discussed before, an average of the Atlanta Fed and Kalshi was better than any single predictor. I continue to include the NY Fed estimate, even though it seems to be a very terrible predictor, because some people like to talk about it.

The Atlanta Fed, Kalshi, and the WSJ survey are all showing very similar estimates for Q2. If I was a betting man, I would bet on 1.8% for the BEA advance estimate.

What Is So Special About Object-Oriented Programming Languages Like C++ and Python?

I first learned computer programming about 1974, using FORTRAN running on an IBM 360 system that, yes, filled a whole room. And yes, my source code existed in the form of a stack of cards with holes punched in them, which got run through a physical card reader. FORTRAN and similar old-school languages were efficient (b/c computer resources were so constrained) and syntactically simple for solving well-specified problems.

C++ started to become popular in the 1980s, and Java in the 1990s. A big part of their appeal was that they were “object-oriented programming” (OOP) languages. I repeatedly asked my computer-programming professional friends back then to help me understand the difference between OOP and conventional Fortran type programs. They would get misty-eyed and rhapsodize about how their program components were modularized.  I guess I just failed to ask the right questions, because I never could understand why what they were talking about was so very much better or different than a good clean FORTRAN program, where most of the work was compartmentalized into well-defined functions and sub routines.

So I had a good talk with Claude about all this, and achieved enlightenment.. The differences seem to come down to a couple of key concepts:


(1) Data Compartmentalization

 In FORTRAN, you can modularize the data manipulation steps into subroutines, but the data tends to be more in common. Thus, for a very large programs, it is hard to keep some far-distant subroutine from accidentally altering your data. But with OOP, the data and the manipulation methods are “encapsulated” into one airtight thing, so no outside routine can mess with that data.

(2) More Robust Relations Among Chunks of Code

With OOP, there is also a feature called “inheritance”, where some new method can take advantage of an existing method, in a cleaner way than (in the FORTAN world) having a new subroutine call an existing subroutine, which would involve explicitly passing a bunch of parameters back-and-forth (which is very easy to mess up).

For doing fairly straightforward scientific calculations, even big ones, I think FORTRAN is still easier and more efficient. But for modern financial programs, involving millions of lines, written by huge teams of people that cannot all talk to one another, the win goes to OOP. Besides C++ and Java (still popular), in OOP we now have C# (standard for many Windows and gaming applications), and the crowd favorite, Python.


(That’s about it simply as I could put it, without getting long-winded and technical… If you want more details, you can always ask my buddy Claude)

The Odyssey

It is very good. See it in IMAX if you can, though I strongly recommend wearing concert ear plugs (i.e. the kind that let you still hear dialogue clearly). Minor spoilers ahead, if such a thing is even possible with a 2,800 year old epic poem.

The themes of the adaptation/translation are wonderful and poignant. The layers of shame and trauma never, to me, felt forced. As someone who spends a lot of time thinking about the fragility of civiliation as solution to the grand collective action problem, the idea that a single betrayal can unravel an entire society and that the “heroic” cenceiver of that betrayal might feel shame, well, that is not without current relevance.

So yes, the film as story is great. But, sitting here now 4 days after viewing it, what I find myself constantly returning to is the sheer, overwhelming competence of the film. The acting, costuming, set and prop design, lighting, sound design, editing, musical scoring. It all just worked. As a champion of practical effects, the texture of the film was transporting. Yes, there is CGI, but it blends in seemlessly, always complementing the practical elements in a way to never let the imagery fall into the uncanny valley. The final product coordinates a vast array of individual and team efforts that, together, create an experience that always felt purposeful, decisive, and real.

Given the small city that must be erected, populated, struck, and moved to create each element of a film like The Odyssey, it’s a great reminder of what can be accomplished when all of the people involved actually and truly know what they are doing. In an age of carnival barkers and con men, there is nothing more epic than grand demonstrations of competence.

Top EWED Posts of 2026

These are notable posts from 2026, roughly presented in descending order, starting with the post that got the most views.

  1. The US Has One of the Highest Fertility Rates Among Peer Countries

By Jeremy Horpedahl (https://x.com/jmhorp)

“Does the US face a falling birth rate? Yes. Is this as dramatic as most other countries? No.”

Another good follow for issues of the family is Melissa Kearney (https://x.com/kearney_melissa)

2. Claude Mythos Is Such a Dangerous Hacker Engine That Anthropic Has Withheld Broad Release

Scott Buchanan released a timely post in April.

3. What is an AI Skill?

Zachary Bartsch: “A skill can be just plain text written conversationally, it can be a list of rules, mathematical expressions, or even the foundational code that you want your AI to readily modify and apply. Essentially, saying ‘skill’ is the same as saying ‘pre-prompting’ with various degrees of specificity. Rather than writing a prompt each time, you can recycle a set of prompts that you’ve stored in a file. That’s all that a skill is.”

Plus, Zachary provided some useful history of “Explainer text files”

4. Although published in a prior year, this post from Zachary has also done well in 2026: The Mythology of Rice and Beans

“Not a single one of these foods is an ‘incomplete protein’. Yes, the mass that you’d need to eat differs, but there is not much that is exciting about legumes and grains as a combination.”

Anyone who has gone grocery shopping in 2026 knows this is the year of protein.

5. Scott Buchanan considered the price trajectory of silver: Is the Silver Bubble Bursting?

Out of curiosity, I checked the price. Within a week of this post, the price of silver had actually gone up. But after a final peak in late January, the price has declined. As of today, it is down from any of the prices posted in January of 2026.

6. Another bubble post from Scott: Chipmaker Stock Prices Explode: The Latest Bubble?

In addition to financial speculation, these chip prices also affect consumers trying to buy a high-performance laptop.

7. Scott on AI news: Oops: Anthropic Accidently Leaked the Entire Code for Its “Claude Code” Program

“Gleeful researchers, competitors, and hackers promptly downloaded zillions of copies. Anthropic issued broad copyright takedown requests, but the damage was done.”

8. James Bailey considered: Is a US Oil Export Ban Coming? in light of the conflict with Iran

9. Mike Makowsky wrote this haunting poem “Oh, what shall all the candlemakers do now that the sun has risen?”

The actual AI problem in academic economics

He talks about the referee process, since that is where the main decisions happen, as much as the “writing process.” No one has all the answers, but Mike is doing us all a favor by getting some of this real talk out in the open. Please comment if you have more ideas on where to go from here.

I’ve seen chatter about this topic on Twitter/X, but I’d love to see some more blog posts from tenured folk because it helps with the hidden curriculum problem.

10. Even though it was posted in 2025, this post by Jeremy got more attention: Spending on Necessities Has Declined Dramatically in the United States

“Would you have guessed that in the “good old days” of the 1950s and 1960s, the average US family was spending 30-40% of their income on food and clothing, something that today we spend barely over 10% on? To understand the challenges we face today, it’s important to have the context of how bad the past was.”

Jeremy has been telling this story for years. Interestingly, world cup tourist discourse seemed to push a few more people over the fence (why hadn’t they just read our blog?). Most Americans are rich.

11. Humanity’s Last Exam in Nature by James.

“We start asking it questions we don’t know the answer to.” is reminiscent of my recent post Fable on Legibility

12. Scott: SaaSmageddon: Will AI Eat the Software Business?

Since the ChatGPT launch, I have heard conflicting stories on the impact of AI on white collar jobs such as software engineering. There have been layoffs and, for example, ex-Meta employees who struggle rematch in at their old salary. I have also heard claims that the demand for software engineers is actually increasing, perhaps because AI makes them more productive.

13. One of the first posts of 2026, from Zachary Bartsch: Tariffs Are Not Smart Industrial Policy

14. From Scott, to file under things you didn’t expect (and yet should have seen coming): Allbirds, Inc. Attempts Pivot from Making Wool Sneakers to AI Computing

15. Jeremy is still right, as the foreign tourists saw this summer: Average Wealth for Younger Generations Continues To Exceed Past Generations

16. Joy Buchanan: arXiv will ban authors who submit papers with LLM mistakes

The problem echos Makowsky’s post, which ultimately rests on readers and the referee process. I like to say “readers are that which is scarce,” meaning that it’s not difficult to produce writing.

17. Sometimes I just like to highlight a Jeremy post that made me laugh, even if it did not get top views: Berries Are Probably Not Making Parents Go Broke (Probably)

We’ve been cited in most of the major news outlets at this point, but this year was a first with: EWED cited in Top Demography Journal

Blogs are not niche anymore. More people than ever, including many researchers at top schools, have decided to start a Substack. Of course, peer-reviewed and prestige-published research still has a primary place in the discourse. Many of the blog posts are ABOUT the primary objects of research.

I saw something called InTheWeights in 2026 that made me think folks at research schools might be strategic in starting to blog now. ChatGPT reads our blog. One reason I think that to be true is that some of our reader traffic comes from ChatGPT.com and Claude. I think our work is getting repackaged as LLM answers to millions of people, some small percentage of those answers provide attribution to us, and then a small sliver of those answers results in users clicking over to us as the primary source for an answer.

It will be a long time before tenure decisions are based on where you are In the Weights. But our crew would do well on that metric. Our work is legible to AI because we have been blogging ungated here for years.

And me

To find prior year “top post” lists, start with: Updated List of Top Posts for 2025

So Many Living Ex-Presidents

If you count president Trump, the number of living former presidents is at a historic high of five (Clinton, G.W. Bush, Obama, Trump, Biden). The number of living ex-presidents can increase for three reasons. 1) More people becoming president, 2) ex-presidents having longer lifespans, and 3) presidents leaving office earlier in life. Why do we have so many right now?

The historical maximum number of people who both 1) leave office and 2) live simultaneously with others is five. It first happened in 1861 when Abraham Lincoln (16th) was president for just under a year before John Tyler (10th) died in 1862. Since then, the number of living ex-presidents has been mostly below four if not below 3.  The figures below graph the number of people living who have been US president. The left graph uses daily data and the right uses the annual average (weighted by day).

In fact, besides Washington, we’ve had four other periods when there were ZERO ex-presidents living. The first was under Grant (18th). This changes my perspective of that period. Living ex-presidents provide a sense of continuity – that something from the past continues today. They give us hope that our country will continue into the future. Grant presided over part of the reconstruction era. For part of this presidency, there was no one else who knew how he felt and no living person who had been in his position. What a tenuous time!*

The other presidents who, at some point, had no living predecessors were Theodore Roosevelt (26th), Herbert Hoover (31st), and Richard Nixon (37th). But since 1981, we’ve had three or more living presidents. So, most of us feel like that’s “normal”. Imagine if there was just Trump, and that’s it. That’d feel jarring.

1) Are More People Becoming President?

A presidential term is four years and only one president was in office for more than two terms, Franklin Roosevelt (32nd). Let’s take a 24-year trailing average. With 8-year tenures, the least number of presidents is 3. With 4-year tenures, the greatest number of presidents is 6. Assassinations and other deaths of sitting presidents can push the number higher. The graph below is the number of unique people to act as head of state over the prior 24 years (I say ‘unique’ because Cleveland (22nd & 24th) and Trump (45th & 47th) both served two non-consecutive terms).

We can conclude that the number of unique presidents is not exceptionally high at this time. The historical average is about 5.2 unique presidents. We’ve been below that since 1998 owing to a higher proportion of two-term presidents since then. Before Biden (46th), Bush (41st) was the last time that we had a one-term president. So, in terms of executive regimes, the 21st century has been unusually stable. But this stability also places downward pressure on the number of surviving ex-presidents. So, we’ve had many living ex-presidents despite our few regime changes. Reason 1) doesn’t explain why we have so many living ex-presidents now.

2) Are President Lifespans Longer?

Continue reading