2026-10-22 16:04:41
I’m writing about this a little bit late, but France has been having some pretty bad riots:
After widespread rioting at the weekend – estimated to have cost several hundred million dollars in property damage and theft – France faces a toxic mix of urban rebellion, political drift and economic crisis…There was rampant looting in Paris, Marseille, Bordeaux and other cities, while the city hall in Limoges, which survived two world wars, was badly damaged by fire and its clock spire collapsed.
The violence at the weekend, which led to some 2,400 arrests, followed weeks of protests by French students angry about crumbling schools, a lack of teachers and dismal job prospects.
If there’s a reason I’m writing about this a bit late, it’s because “rioting in France” is a little like “rain in Seattle” — it’s not exactly an unusual occurrence. In 2023, France had a week of nationwide riots over a police killing. From 2018 to 2020, France was rocked by over a year and a half of near-continuous “Yellow Vest” protests and riots over economic issues. I remember two decades ago, reading about weeks of rioting in the French suburbs, accompanied by endless photos of burning cars. And so on.
French people like to riot. But this time, things are a little bit different, for a couple of reasons. First of all, this wave of unrest is accompanied by serious economic troubles. France’s long-term interest rates, like rates throughout the world, are surging:
But unlike some countries whose rates have also risen, investors have serious doubts that France’s government will take the necessary steps to pay back what it owes. The price that investors have to pay to insure against a French sovereign default has shot up even more abruptly than interest rates:
Basically, investors are starting to think that France won’t pay back its debt — that instead of raising taxes or cutting spending, the country will simply pay the cost of rising interest rates by borrowing even more money. Instead, investors are starting to worry that France will simply default on its bonds.
In a widely read post a couple of weeks ago, Paul Krugman argued that the problem lies with France’s old-age pensions, which are eating an increasingly enormous amount of the country’s GDP and seem politically immune to any cuts:
The rise in interest rates on French government bonds is largely driven by heightened perceptions of the risk that France could default on its national debt…France is on a fiscally unsustainable path. Debt is already very high as a percentage of GDP, and the French government is adding to that debt by running large budget deficits even though there is no emergency like a war, a severe recession, or a pandemic to justify such deficits. Furthermore, France has an aging population, which means that pension costs will, other things equal, grow much faster than revenue…
France does stand out, even among fiscally troubled nations, in one main way: its persistent inability to get realistic about retirement…The official French retirement age…was only 62 in 2023…The average actual age of retirement…was lower in France than anywhere else in Western Europe…Macron’s center-right government was on track to gradually raise the retirement age to 64, still low by the standards of other advanced nations. But due to opposition by the extreme right and the extreme left parties in France, this rise has been frozen until after next year’s election.
In other words, France needs austerity, but it can’t get it, because the French people demand ever-increasing spending. France’s government already takes up a larger share of GDP than almost any other rich country — significantly more than Sweden or Denmark:
And indeed, a very hefty chunk of that social spending goes to either old-age pensions or to health (which is heavily weighted toward the old):
Of course, there’s another way to do austerity besides cutting spending — France could raise taxes. But this will also be tough. France’s taxes are already higher as a share of its GDP than any other rich country, including the famously high-tax Scandinavian countries:
Could taxes go up even more? It’ll be difficult. In 2019, I wrote about the underwhelming results of France’s previous experiments:
The wealth tax might have generated social solidarity, but as a practical matter it was a disappointment. The revenue it raised was rather paltry; only a few billion euros at its peak, or about 1% of France’s total revenue from all taxes. At least 10,000 wealthy people left the country to avoid paying the tax; most moved to neighboring Belgium, which has a large French-speaking population. When these individuals left, France lost not only their wealth tax revenue but their income taxes and other taxes as well. French economist Eric Pichet estimates that this ended up costing the French government almost twice as much revenue as the total yielded by the wealth tax…
Another French experiment was the so-called supertax, a 75% levy on incomes of more than 1 million euros. Introduced by socialist President François Hollande in 2012, the supertax added to the exodus of wealthy individuals, most notably actor Gerard Depardieu and Bernard Arnault, chairman of LVMH Moet Hennessy Louis Vuitton. Star soccer players threatened to go on strike, and there was fear that France would become a wasteland for entrepreneurs. Meanwhile, the supertax raised much less money than even the wealth tax had -- only 160 million euros in 2014. The unpopular tax was repealed two years after its adoption.
France is in a difficult position here. Its membership in the EU, and its location near to other French-speaking countries like Belgium, Switzerland, and Luxembourg, make it trivially easy for rich French people to pick up and move to other countries if they see their fortunes under threat of confiscation.
France could, of course, try taxing the middle class to pay for expanded social spending, perhaps by an increase in the VAT. But this burden would fall on many of the same people who are now protesting and rioting in the streets, so it’s probably a political nonstarter as well.
In fact, the current riots are making the strains in France’s bond market worse, by making the politics of austerity seem more intractable. Various groups of protesters and rioters are demanding enormous amounts of additional government largesse — billions of euros of increased spending on schools, increases in pensions, increases in social benefits, and even subsidies for consumer goods like fuel. That demonstrates that there’s still enormous grassroots political pressure for higher government spending; even if France’s leadership doesn’t cave in to this pressure, leaders will probably be averse to making the deep cuts to spending required to put France on a sustainable fiscal path.
And that, in turn, is probably making it harder to enact any of the spending that the protesters are demanding. When protesters demand increased spending, bond markets respond by charging higher rates on French government borrowing. That increases interest costs for the government, which makes it harder to raise social spending — the exact opposite of what the protesters wanted.
Fundamentally, the French riots show the limits of the European left-liberal welfarism that has been the region’s default approach to governance for the last half-century — and which American progressives want to emulate. The French populace — or at least a powerful chunk of it — simply doesn’t seem to be able to recognize the difference between a sustainable welfare state and an unsustainable one. Instead, social benefits seem to be a one-way ratchet — higher benefits raise expectations for even higher benefits, until even the most milquetoast and inadequate of austerity measures end up causing cities to burn.
That one-way ratchet also calls into question the viability of Keynesian fiscal stimulus — the main tool that left-liberals (including me) prefer to combat deep recessions. Few serious macroeconomists now question that stimulus is effective in raising aggregate demand in a severe downturn. But Keynesian policy requires that fiscal largesse during a bust be matched by fiscal austerity during a boom, so that deficits don’t grow unsustainably. The problem is that while “austerity during the boom” is easy to say, it’s politically very hard to enact.
Hanno Lustig, reading Krugman’s post about the unsustainability of France’s borrowing, recalls the years of the Euro crisis, in which Krugman urged France to spend, spend, spend to fight the recession:
Lustig is being a little unfair to Krugman here. In fact, here’s what Krugman actually wrote in 2011, in the first post that Lustig cites:
[W]e won’t always be in a liquidity trap. Someday private demand will be high enough that the Fed will have good reason to raise interest rates above zero, to limit inflation. And when that happens, deficits — and the perceived willingness of the government to raise enough revenue to cover its spending — will matter…At a future date, when we’re out of the liquidity trap, public finances will matter…[T]he biggest source of our long-run deficit isn’t the overhang of debt, it’s the prospective current cost of paying for retirement, health care, and defense. So such a crisis — again, it’s very much hypothetical — could spiral into something very nasty, with very high inflation and, yes, hyperinflation.
Krugman was actually talking about the U.S., but he might as well have been talking about France. Keynesians don’t just say “more government spending is good” — they recognize that there are limits, so they urge governments to cut spending during good economic times.
But Lustig has a point, nonetheless. The Keynesian prescription — deficits during the bust, austerity during the boom — requires that whoever is in power during the boom take the political hit for doing the austerity. In practice, you can sometimes find politicians willing to spend their political capital on taking that hit, but it’s tough. Even Macron caved in the end.
When we look at actual French borrowing and spending during the Euro crisis of the early 2010s, it’s clear that neither the austerity Krugman feared nor the stimulus he desired was actually happening. Look at the charts above, and you’ll see that government spending didn’t rise or dip during the 2010s — it just sailed on, unperturbed, as if the recession didn’t even exist. The same was true of French government debt:
Germany actually did do austerity during the bust. France did not. In fact, they didn’t do anything — they just kept borrowing and spending.
That points to a political-economic problem for Keynesian liberalism. It suggests — as James Buchanan foresaw — that spending is often less likely to be determined by the wise judgement of technocrats, and more likely to be determined by angry ignorant people burning down government buildings.
Finally, France’s riots show the limits of one more pillar of modern left-liberalism — tolerance toward mass immigration.
It’s not obvious that African or Middle Eastern immigrants are disproportionately behind these riots. Viral videos of rioters and looters — including the famous video of the French video game store owner trying to defend his shop against young hoodlums — show a racially mixed crowd of rioters with plenty of white representation. Some past waves of riots have been driven by immigrants and their children — for example, the anti-police riots of 2023. But others, like the much longer-running Yellow Vest riots of the late 2010s, were mostly white.
Still, France’s public appears more and more inclined to blame “migrants” for the chaos in their streets. When the disorder began, the political fortunes of the anti-immigration right-wing candidate Marine Le Pen soared:
In contrast, the chances of the only major left-wing candidate, Jean-Luc Melenchon, didn’t budge. Melenchon is promising lavish increases in social spending, but the public is disinterested; instead, as in Germany with AfD, and indeed all over Europe, the people are gravitating toward whoever promises to stop immigration.
America is not France, of course. Our immigrant mix is very different, and our ability to assimilate large numbers of immigrants is much greater. Our attitudes toward immigration have turned positive again, at least partly as a reaction against Trump’s brutality. But France shows that the left-liberal approach to immigration simply doesn’t work equally well for all countries.
So France’s riots aren’t just one more case of French people going wild in the streets. They demonstrate the limits of European-style left-liberalism — the lavish welfare state, the tolerant attitude toward mass immigration, and the trust in Keynesian demand management. Left-liberalism built some very beautiful and kindly societies, but its powers are not infinite.
2026-10-21 16:45:19
WARNING: This post contains spoilers for the movie You Can See Everything.
A few days ago, I had the great pleasure of seeing Nathan Fielder’s new film, You Can See Everything. I’ve been a fan of Fielder’s work for a long time, so I basically knew what to expect, but it was still incredible — every bit as good as you’ve heard. Go see it if you haven’t.
The film is a documentary about the notorious fraudster Elizabeth Holmes (of Theranos fame) and her partner Billy Evans. But it’s also sort of a horror movie about liars. That was interesting enough that it made me want to write a post about the movie — about the nature of lying, and of belief, and of human communication in general.
If you read even a little bit about the history of Theranos, there is absolutely no question that Elizabeth Holmes is a deliberate fraudster and a prolific, aggressive liar. Holmes presented potential investors with fake financial results in 2015. She personally added fake logos of pharmaceutical companies to Theranos’ own reports. She flatly stated that Theranos didn’t dilute blood samples, then admitted in court that she intentionally concealed the fact that they did dilute blood samples. Theranos employees detailed the whole deliberate fraud in anonymous online posts years before the story broke.
But You Can See Everything doesn’t cover much of this actual history. Fielder doesn’t bother to indict Holmes and prove that she committed fraud — the court case already did that. Instead, he offers a window into the psychology of someone who could commit such an epic fraud. Holmes and Evans allow Fielder into their home with the goal of proving that they’re just regular, good people trying to improve the world. Instead, Fielder depicts the couple — quite accurately — as monsters of deception and manipulation.
It’s doubtful that any other filmmaker could have done nearly as good a job of this. The blurry line between what’s real and what’s fake has always been a major theme of Nathan Fielder’s work — he loves hiring actors to simulate past and future interactions, constructing facsimiles of whole buildings, and so on. And of course he’s very skilled with the tricks of the visual storyteller’s trade, using deliberate cuts, camera angles, and creative edits to make the audience think and feel what he wants them to.
Whereas many filmmakers would have regarded Holmes as a subject to be studied, Fielder seems to see her as an opponent to be challenged and beaten. If he can shoot what Holmes wants him to shoot, but ultimately seize control of the narrative, then he’s the more powerful storyteller. This, I think, is why he doesn’t bring in outside evidence to show what a well-documented liar Holmes is. That would be cheating. Instead, he has to venture into the monster’s lair alone, and defeat her in single combat. He has to film the movie that she asks him to film, and let her show him only what she wants him to see, and still have audiences walk away having seen the story that he wants to tell.
And of course he succeeds, because he’s very good, but also because he gets to edit the film, and editing is more powerful than acting. Score one for the good guys.
(Major spoilers begin here.)
How does Fielder win the contest? Over the course of You Can See Everything, he captures Elizabeth Holmes saying a number of strange and extraordinary things:
Near the beginning, Holmes tells Fielder that she’s never lied in her life. After he presses her, she half-admits that she may have told small white lies out of convenience. But the audience gets to watch her repeatedly assert something obviously, fantastically false, always with the same sincere, relaxed, wide-eyed expression on her face.
Later on, Fielder shows Holmes the famous “Who’s on first?” comedy routine, which she claims never to have seen. After watching it, Holmes claims not to have realized that the baseball player’s name is “Who” — thus missing the entire point of the sketch.
Fielder recruits Amanda Seyfried, who played Holmes in the miniseries The Dropout, to help him figure out what’s going on with Holmes and her partner. At one point, Holmes is calling on the phone from prison. Seyfried asks Holmes if she knew Seyfried would be on the call, and Holmes says “I’m not sure if I knew.”
It’s moments like these, more than any simple lie, that reveal Holmes’ true character. In fact, Fielder seems like he passes up several chances to catch Holmes in a straight-up lie on camera. After all, what would one lie, or even three lies, really prove? Everyone lies sometimes, and capturing a couple of those instances during weeks of living with Holmes wouldn’t be hard. It would be a cheap trick, and it wouldn’t really prove that Holmes is a liar in general.
Instead, the moments Fielder captures demonstrate something deeper and far more unnerving about Holmes’ relationship to the truth. Holmes is a very special and powerful kind of liar — someone for whom deception and manipulation are as automatic and as continuous as breathing.
2026-10-19 12:33:30
Ramez Naam is, in my opinion, one of the best futurists alive. But although he has a Substack (which you should definitely follow), he doesn’t write nearly enough. So I’ve been encouraging him to write more by cross-posting his posts on my blog.
Somewhat to my surprise, Ramez has turned out to be a lot more of an AI skeptic than I would have though. He’s certainly not dismissive of the technology, but he disagrees with many of the more expansive and dramatic claims coming out of the big labs (especially Anthropic). In his first post in this series, he expressed skepticism that recursive self-improvement (RSI) would lead to a fast takeoff of AI capabilities in the near future:
In this post, he expresses skepticism about the potential for agent swarms — large numbers of AI agents working in parallel — to offer dramatic increases in AI capabilities. Agent swarms have been in the news a lot, both because of the Hugging Face attack, and OpenAI’s solution of the famous Navier-Stokes problem is also widely attributed to a large swarm. But Ramez argues that parallelization is just another kind of input that suffers from diminishing returns, and that we should therefore temper our expectations about what agents swarms are able to achieve.
As was the case with his previous post, I’m not sure how much this matters, when the capabilities of the underlying models — swarm or no swarm — are still improving at such a steady and impressive rate. But it’s a very intellectually interesting argument, and Ramez can be very persuasive.
In my previous post, we asked whether AIs could improve themselves all the way to absurdly high levels of superintelligence. My conclusion: today’s feedback loop is probably too weak, because improving AI faces diminishing returns at every step. Even fully autonomous AIs doing all the research and coding don’t make those diminishing returns disappear. We should keep updating that conclusion as real-world parameters and data change.
But we didn’t look closely at another possibility: running millions or billions of AI minds in parallel. How much do they help? That’s what we’ll look at here.
Plenty of people have modeled how more AI agents working in parallel could fuel recursive self-improvement. But the data we have today tell a different story. More agents are useful for speed: they can solve many problems faster. They’re less useful for boosting AI intelligence. Multi-agent gains in solving harder problems often diminish incredibly quickly and may even saturate. Whether multi-agent systems can produce the conceptual breakthroughs RSI might require to become self-sustaining is the harder question.
Without breakthroughs in how well agents parallelize problems - something that may depend on the structure of machine learning problems themselves - it’s hard to see even millions or billions of copies of today’s models making repeated breakthroughs on the hardest problems. These are among the strongest diminishing returns we’ve looked at. Of course, these diminishing returns may change. And we have less experimental data to go off of today than we’d like. We’ll need to recalibrate as new evidence comes in. I’ll end with the things we should track and my predictions about a natural experiment from newly released Anthropic data.
Below is the two-image summary: The experimental observations of how large numbers of agents scale to solve harder problems (what I’m referring to as intelligence) are far far lower than the assumptions in studies that predict that AI recursive self-improvement will lead to an intelligence explosion. The first image is at the scale of 100 parallel AI agents. The second is at the scale of a billion AI parallel agents.
Real world experiments show dramatically lower intelligence gains from parallel agents than hypothetical AI models.
Extrapolated out to a billion agents, the intelligence gain in hypothetical models is orders of magnitude beyond real world experiment gain curves.
To recap the last post: recursive self-improvement (RSI) means AI improvements feeding back into the next round of AI research. Four findings stood out:
Real AI research is harder than benchmark scores suggest. OpenAI’s models needed human help on much shorter research tasks than the coding benchmarks led us to expect.
More AI activity produces much smaller gains in useful work. OpenAI used 124 times as many tokens per researcher but ran only 1.6 times as many experiments per researcher.
Maintaining progress requires ever more resources. Training and thinking time face steep diminishing returns. Anthropic reports that more powerful AI has been key to maintaining the current pace of AI progress.
The measured software feedback loop doesn’t yet look self-sustaining. Our tentative calibration put it roughly five to ten times below the strength needed.
I want to reiterate a distinction here: fully automated AI self-improvement doesn’t at all guarantee an intelligence explosion. Diminishing returns in AI capabilities dampen that feedback loop. Parallel agents can help automate a search across AI parameters, but don’t look nearly as useful in making new conceptual breakthroughs. My expectation is that we’ll need those sorts of breakthroughs for self-improvement to become self-sustaining, let alone to accelerate rapidly and reach truly profound levels of superintelligence.
Today, let’s examine in much more detail what it would mean to run thousands, millions, or billions of AI researchers in parallel.
In this post I’m focusing only on software: AI researchers improving the algorithms that make AI better or cheaper to run. Hardware, investment, and economic growth have their own feedback loops, which we discussed briefly in the last post. Here, we’re asking how much more the AI minds themselves can discover.
Here’s the argument, with links:
In Machines of Loving Grace, Dario Amodei imagines a:
“country of geniuses in a datacenter”
Millions of AI minds, each more capable than a Nobel Prize winner across many fields, working at much higher speed. That would be extraordinary. The intelligence of each mind is a crucial part of the premise.
Tom Davidson and Daniel Eth’s Forethought paper describes the equivalent of “millions of top-notch human researchers.” Benjamin Todd’s explainer points to “millions of competent engineers working on AI research.” Dwarkesh Patel emphasizes AI’s “massive population size and perfect knowledge transfer.”
How does the number of AI agents running in parallel enter these arguments? In Eth and Davidson’s illustrative model, twice as many researchers produce twice as many papers. Cheaper AI lets the same hardware run more researchers, while each software improvement gets harder to find. Alan Chan and colleagues’ 2026 paper, including AI pioneers Geoffrey Hinton and Yoshua Bengio, assumes an even larger effect from adding researchers: doubling effective research labor increases the rate of software improvement by about 2.6 times, holding the software level fixed.
Those are assumptions about what more researchers produce. The growing number of AI researchers must keep finding improvements fast enough to outweigh the increasing difficulty of further progress. We’ll come back to those assumptions after looking at what larger AI teams actually achieve.
Meanwhile, we had OpenAI’s Navier–Stokes announcement: around 10,000 agents working concurrently during an 88-hour run solved a mathematics Millenium Problem. It made the idea vivid. A datacenter full of AI mathematicians, tackling a famously hard problem.
I understand the excitement. But the number of agents tells us how many AI researchers we ran. We also need to measure what those additional researchers contributed.
On MTS Live, I described a ladder of increasingly hard scaling:
Super-linear: Twice the input produces more than twice the output. Accelerating returns.
Linear: Twice the input produces twice the output. Independent jobs can look like this. A power-law exponent of 1 is the same case.
Power-law diminishing returns: Output grows as input raised to a power below 1. At exponent 0.5, doubling output takes four times the input. At 0.2, it takes 32 times. At 0.1, it takes 1,024 times.
Logarithmic: Each multiplication of input buys the same additional gain. Another five points might require another tenfold increase in compute.
Sub-logarithmic: Even successive tenfold increases buy smaller gains. Those gains can keep shrinking, or saturate at a ceiling. A slow rise doesn’t require a hard plateau.
Not all diminishing returns are alike. Some are much harder than others.
The classic pre-training scaling laws describe power-law reductions in prediction loss. Many test-time score curves look roughly logarithmic over the measured range. Some multi-agent results flatten much more sharply.
Let’s illustrate this with numbers. In the figure below, every curve earns the same gain from the first doubling of input. The later differences then become visible. The hardest curves share a vertical scale, and each panel states its endpoint.
Different diminishing returns scaling patterns lead to very different long term results.
For runaway RSI, improvements have to make subsequent improvements arrive faster and faster. Logarithmic returns to the number of AI researchers are too weak to sustain that loop if every further doubling of software efficiency requires a fixed percentage more research. Sub-logarithmic scaling or a plateau are weaker still. The experiments I’ll summarize in the rest of this post aren’t perfect, and I wish we had more data. But the clearest larger-team evidence we have today points to logarithmic or sub-logarithmic intelligence gains. Those look too weak to make RSI self-sustaining, let alone runaway, under the assumptions we’re examining.
There are at least three reasons a larger team might be limited in its ability to produce better answers, and probably more. Let’s start with these:
First, coordination skills. Can the agents divide the problem, manage the work, check each other, and combine their results? Can they communicate what matters without spending all their time communicating? A team needs good project management as well as good individual contributors.
Second, homogeneity of thought. Are the agents contributing useful new ideas, or repeating the same approaches and mistakes? A hundred copies can share the same blind spots. More varied answers help only if some of that variety is right.
Third, the structure of the problem. How much can happen in parallel, and how much has to wait for earlier results? A million independent searches can run at once. A sequential investigation can’t. Even figuring out how to divide a conceptual problem may require an insight the team hasn’t found.
Three places where more agents can run into limits.
These limits interact. A better model can supply an idea its copies previously lacked, find a better division of labor, or recognize a solution the team had been overlooking. A difficult task may become easier to parallelize once somebody discovers the right approach. That’s why the shape of the curve matters, and why it can change.
Before we go further, let’s separate two things: speed, or how quickly agents solve a problem, and collective intelligence, or whether they can solve a harder problem and reach a new level of skill. We’ll use verified benchmark scores as evidence of that intelligence. They aren’t a universal IQ scale.
So far, adding agents seems much more effective at speeding up execution than at raising the level of problem a team can solve.
So far, using more agents buys us much more speed than extra capability.
Let’s start with the best case. A million independent jobs. A million candidates to test. A million places to search. Each agent gets useful work it can do without waiting for the others, and a reliable way to check whether it succeeded.
That can scale extremely well. Ten workers may finish ten jobs in the time one worker finishes one. A huge search may find a rare solution that a smaller search misses entirely.
One concerning use of parallel agents is offensive cybersecurity. Lisan al-Ghaib emphasizes this possibility. I disagree with his broader argument about swarm intelligence, but many attacks running in parallel could be extremely powerful. A swarm may not need a conceptual breakthrough to search many targets or try many possible vulnerabilities at once.
That said, penetrating the most secure targets, such as operating systems and browsers, can require long attack chains. Each step depends on earlier discoveries to get through another layer of defenses. Here again, we might find diminishing returns or eventual saturation, even with respect to speed. Indeed, one of the experiments below suggests that.
Toby Ord analyzed how well GPT-5.6 Sol increases the speed of solving problems on different benchmarks, and at what cost. This gives us a useful way to measure the tradeoff. In general, using more agents can help get tasks done more quickly, but at higher total cost than if you’d used fewer agents.
On top of that, the tradeoff varies by the type of task. On command-line tasks (Terminal-Bench), Ord’s analysis finds that four agents can reach the same result about 1.9 times faster, at about 2.1 times the token cost. On web research (BrowseComp), four agents can reach the same result about 2.6 times faster, at about 1.6 times the cost. I’m using the figure below to show how different tasks differ in that speed vs cost tradeoff.
More speed costs more, and the tradeoff varies by task. Based on Toby Ord’s analysis. Source.
Why the difference? Web research can often split into independent searches. A command-line task may have to change a shared environment in sequence. Some problems are simply easier to divide among agents than others.
Ord uses lambda, λ, for how speed scales with the number of agents. We’ll come back to it. At λ = 0.5, speed and total compute cost rise by the same factor. A higher λ means speed rises faster than cost.
Inspired by Toby Ord’s analysis, I applied the same approach to Anthropic’s published results on program reconstruction, or ProgramBench. Here again, more speed comes with more cost. But we also see that models within the Claude family are getting better at the speed-cost tradeoff. The newest model, Opus 5.5, gets more speedup for a smaller cost increase than Opus 5 or Fable 5.1.
With five agents, Opus 5.5 gets about 2.7 times its solo speed, compared with 2.2 times for Opus 5, and does so at lower cost. [Note: We’re using total agent runtime as a proxy for cost. Token usage and dollar costs could differ.]
Opus 5.5 gets more speedup for a smaller cost increase than older AI models. Source.
Will these speed-versus-cost gains hold up at hundreds, thousands, or millions of agents? Probably not completely, and certainly not for all tasks.
Some problems are easy to split ten ways, harder to split a hundred ways, and harder still to split among ten thousand agents. More of the work may have to wait for earlier results, and coordinating all those pieces takes work too. Smarter models can get better at this. But my best guess is that, on many problems, each further expansion will buy less speed for the added cost.
Anthropic’s formal math proof benchmark gives us one example.
On the Lean formal math task, we can compare how quickly teams reach the same score. Using Anthropic’s published checkpoints, we estimate the speed and cost of reaching 0.50. The chart starts with ten agents as the baseline.
On a math proof benchmark, the speed advantage of adding additional agents starts to taper with scale. Source.
From ten to thirty agents, speed improves faster than cost. From thirty to a hundred, cost rises faster than speed. The local speed exponent, λ, falls from about 0.75 to 0.33. The extra agents still save time, but the tradeoff gets worse.
This suggests that even speed gains can taper as we split the work among more agents, let alone gains in intelligence. More agents may add less and less as we approach the limits of how easily a problem divides into subproblems. Better coordination may push those limits outward; the task itself may still impose them.
So in speed, more agents can buy us time at some additional cost. Newer models are improving, but there are probably limits. What about intelligence?
Most arguments for superintelligence go beyond running huge numbers of today’s agents. They posit individual models becoming vastly smarter. There’s a chicken-and-egg problem here. Getting there may require conceptual breakthroughs in machine learning itself, perhaps as profound as the ideas that made modern machine learning possible, or at least as important as the Transformer and reasoning models. Parallel search may not be enough to find them. Anthropic has identified ambitious research ideas as an area where current models lag behind humans.
In the previous post, I argued that today’s measured gains from smarter models are roughly five to ten times too weak to produce a self-sustaining feedback loop. But what about running enormous numbers of our best models in parallel? Can a massive team of frontier agents that aren’t yet superintelligent invent a new architecture, a new learning method, or the idea needed to build a much smarter successor? That’s the heart of the question.
For runaway RSI, those discoveries have to overcome the diminishing returns we discussed in the last post. Here are three places they appear.
The diminishing returns a runaway research loop has to change or overcome.
The pre-training example comes from the Chinchilla scaling fit. The reinforcement-learning example comes from ScaleRL. The inference row describes the test-time math curves we discussed previously. The goal is better returns to data and compute, and gains that continue longer before flattening.
My expectation is that we’ll get easy AI improvements through work we can automate, parallelize, and search. But to get very large benefits, and runaway RSI, I expect we’ll need conceptual breakthroughs. That’s why the intelligence of each model matters so much.
Bear in mind that we’ve observed these sorts of diminishing returns in machine learning for more than thirty years. Power-law learning curves were measured in 1993. They appear to be a fundamental feature of machine learning as we know it. We shouldn’t assume they’ll be trivially changed, or that we’ll suddenly find a way to improve AI without diminishing returns. That would be a revolution. Discovering such a route might itself require far smarter minds, and we don’t know whether the mathematics of learning would permit it across the problems we care about.
That doesn’t mean there haven’t been big improvements. The transformer and reasoning models have moved the field forward enormously. But, as we saw in the previous post, a runaway feedback loop needs enough acceleration to keep outweighing the obstacles, again and again. As ideas get harder to find, we may need a faster research pace just to maintain today’s rate of progress. Anthropic’s assessment suggests that better AI tools are becoming necessary just to maintain the current pace of AI progress.
OpenAI’s GPT-5.6 Sol results provide a useful example on cybersecurity. SEC-Bench Pro tests the generation of proof-of-concept demonstrations of software vulnerabilities. This is a particular security task, not a measure of coding ability in general. The reported peak scores are approximately 71.2 with one agent, 74.3 with four, and 76.4 with sixteen.
On this cybersecurity test, sixteen times as many agents buy about 5.2 score points. The first fourfold increase adds 3.1 points; the next adds only 2.1. There’s still plenty of room below a perfect score, but the gain on this test is shrinking, even with multiplication of the number of agents. This is sub-logarithmic scaling.
On SEC-Bench Pro, each expansion buys fewer score points, showing sub-log scaling. Source.
These agents could coordinate through OpenAI’s ultra setup. This is already a test of collaboration, not just a stack of independent guesses. The points are the best reported results across different budgets, rather than an equal-compute comparison.
When OpenAI announced its Navier–Stokes result, the 10,000 agents attracted enormous excitement. Interviewing Noam Brown, Dwarkesh Patel said he was “shocked by the scale of cognitive effort” concentrated into such a short run.
But how much did those extra agents contribute? Brown, an OpenAI researcher who helped develop o1 and now works on multi-agent systems, was emphatic:
“I wouldn’t even attribute 10% of the credit to multi-agent.”
He credits the smarter underlying model. OpenAI hadn’t run the single-agent experiment needed to isolate the swarm’s contribution. Brown’s estimate is a judgment, but it directly challenges the idea that the sheer number of agents was the decisive ingredient.
Formal mathematical work seems especially analogous to the conceptual breakthroughs AI research may need. It demands a chain of valid reasoning, sometimes with a new insight that makes the whole problem tractable. Formalizing existing mathematics isn’t the same as inventing a new learning algorithm, but it’s a useful place to look.
And here we find almost no extra gain when Opus 5.5 grows from ten parallel agents to a hundred. Anthropic’s system card tests teams on 49 weighted theorem statements, checked in Lean. Each team gets 24 hours. The first tenfold expansion adds 27 score points; the next adds just two.
On these math proofs, most of the gain arrives by ten agents. This is steeply sub-logarithmic scaling, and possibly saturation Source.
These agents could communicate, share a machine and Git repository, and organize their work. The hundred-agent team even developed a hierarchy with twelve sub-leads. It could also use ten times as much aggregate agent runtime as the ten-agent team over the same day. That isn’t a measured tenfold increase in tokens, but it gives the larger team much more room to work.
We still didn’t get over the hump. This won’t necessarily stay true, but the diminishing returns are incredibly steep here, possibly pointing toward a plateau.
Opus 5.5 starts smarter than the older models and gets more speedup from parallel agents on ProgramBench. It clearly can put additional agents to useful work. So why do intelligence gains nearly stop on these math proofs? Two possibilities stand out:
The remaining work may be hard to parallelize. Later steps depend on understanding earlier results.
The remaining problems may need insights the model can’t find, however many copies we run. Intelligence may matter more than agent population or speed.
We haven’t isolated the cause. But a thousand copies don’t automatically supply the insight one copy is missing.
Looking at the same math results, we see something striking. Ten Opus 5.5 agents get a higher score than a hundred Fable 5.1 or Opus 5.0 agents. Opus 5.5 starts much higher than the older models. It improves substantially with ten agents, then barely improves with a hundred. But at 10 it’s already better than Fable or Opus 5’s results with a hundred agents.
Opus 5.5 starts stronger, but its team gains flatten quickly. Source.
Ten Opus 5.5 agents beat a hundred Opus 5 agents on these math proofs. They beat a hundred Fable 5.1 agents too. The stronger model does more with one-tenth as many agents.
Ten Opus 5.5 agents are able to solve problems a hundred older-model agents can’t. Source.
We saw an analogous pattern with test-time compute in the previous post. OpenAI’s internal model starts far above Astra on the curated math problems below. Even its lowest displayed result beats Astra’s highest. Changing the model gives a much bigger boost than spending more compute on Astra over the tested range.
A stronger model makes the bigger difference than more test-time compute in solving previously-unsolved math problems. Source.
A rough extrapolation of Astra’s line suggests it would need about 300 times as much compute as the internal model to match the internal model’s lowest plotted score. And that extrapolation itself is an unknown. It’s quite possible that Astra would just plateau before ever matching the OpenAI internal model’s results with far less compute.
Does adding different minds help? A study of model and persona diversity found substantial gains as diverse teams grew to eight agents. After that, the average across its tasks barely moved.
Diversity of AI models helps, but the gains flatten after eight to twelve agents. Source.
Other approaches, including X-MAS and Aitchison and colleagues’ forecasting study, find useful gains from combining different models. But none of these results demonstrates sustained improvement over many expansions of the team. The first study directly shows flattening; the others test smaller ranges.
It’s plausible that today’s models simply aren’t diverse enough. LLMs often produce strikingly similar ideas. Perhaps we need bigger changes in how these minds are trained. We’ll return to whether more deliberately different agents could change the curves.
All of the above examples show sub-logarithmic scaling, roughly the hardest kind. That said, some experiments show slightly easier diminishing returns, indicating roughly the same gain from each multiplication of parallel work. That’s logarithmic scaling. It’s better, but still steep diminishing returns. If that’s how useful research grows while each further AI improvement gets harder to find, even billions of agents don’t make a self-sustaining RSI loop look plausible.
In Ryan Greenblatt’s ARC experiment, a model generated and checked many candidate programs. More samples kept improving its score, by roughly the same amount per doubling. This is parallel search rather than a communicating research team, but it gives us a useful comparison.
Parallel search keeps improving, with logarithmic returns. Source.
A molecular-exploration study found a similar logarithmic pattern as it grew from one to 128 agents. Multiple agents explored chemical targets more effectively than one agent given the same total evaluation budget. Each agent learned as it searched.
More AI agents explore more chemical targets, with logarithmic diminishing returns. Source.
There are encouraging results from communicating teams too, including Argus on web research and Microsoft and Berkeley’s work on ARC-AGI-3. The latter finds unusually strong gains from one to five agents. Those results deserve attention; we still need to see what happens with much larger teams.
Logarithmic gains are better than the sub-logarithmic curves we saw earlier. Each tenfold expansion keeps buying the same extra increment, instead of less each time. But both shapes rise drastically more slowly than the research gains assumed in hypothetical models of self-sustaining or runaway RSI. Let’s put them side by side.
Some work can be split into independent searches or experiments. Other work needs the previous result before it can proceed. And some problems may need a higher level of intelligence or a conceptual breakthrough before we even know how to divide them. Here’s a very tentative and relatively subjective stab at which problems will scale best and worse with multiple agents. The chart runs from work that seems easiest to parallelize at the top to the hardest at the bottom. I’m sure others have many thoughts here, and that this can be much improved upon.
From work that seems easiest to parallelize to the hardest.
Will a million or a billion parallel AIs get us to self-sustaining or runaway RSI? Let’s do the math.
Hypothetical software-RSI models assume that a growing number of AI researchers produces a much larger stream of discoveries. Those discoveries make AI cheaper or more productive, allowing the next round of research to grow again.
There’s a strong case for gains in speed and for searching more of a well-defined space. Even speed gains, as we’ve seen, can taper. Intelligence is much harder to boost through parallel agents. In several experiments, peak problem-solving performance (what I’m referring to here as intelligence) rises dramatically more slowly than speed.
Here’s what three software-RSI models assume:
Chan and colleagues (2026): doubling effective research labor produces about 2.6 times the rate of software improvement, holding the software level fixed. I find that implausible as a general assumption about adding copies of today’s AI models. The clearest large-team intelligence experiments look nothing like that.
Eth and Davidson (2025): their illustrative team produces twice as many papers from twice as many researchers. The accelerating example assumes that the number of researchers grows faster than the work required for each further improvement.
Tom Davidson and Tom Houlden (2025): their central exponent for parallel research effort is 0.6. Ten times the labor gives about four times the research pace. They cut that exponent in half, to 0.3, when experimental compute is held fixed and becomes a bottleneck. The benchmark reports above don’t appear to have been limited by experimental compute, so we’re using 0.6 here.
The top half of the next chart shows research-output relationships used in hypothetical RSI models where AI self-improvement can sustain itself or accelerate. The bottom half shows real-world intelligence shapes from the benchmark experiments. Those experiments show dramatically less gain from running parallel agents than the hypothetical RSI models assume.
Hypothetical research curves rise much faster than these benchmark shapes.
Let’s put these onto a single chart. We give every curve the same gain from its first expansion from one to ten agents or researchers, so we can compare their shapes.
The same starting gain, very different later returns.
Now let’s zoom in on the gap. The real-world shapes sit in a very different zone from the gains assumed in those RSI models, even when we let the math-proof curve keep rising slowly.
Zooming in makes the gap clearer. Hypothetical RSI models show much faster gains from adding more agents than empirical models based on real-world experiments.
Today’s hypothetical RSI models assume gains from additional AI researchers that we haven’t seen in these real-world experiments. Removing humans from the loop doesn’t remove shared blind spots or sequential dependencies. Adding more copies of today’s models hasn’t shown the sustained intelligence gains that may be needed for repeated conceptual breakthroughs.
I do expect better AI models to get better at spreading work across agents: dividing the problem, managing collaborators, and combining what they find. We’ve already seen progress in speed on program reconstruction. We haven’t seen a clear trend toward better multi-agent intelligence scaling in the math results. More on that in a moment.
First, an encouraging analogy: single-agent thinking-time scaling. Toby Ord’s analysis suggests that humans get more relative benefit from additional time than these AIs get from additional reasoning tokens. That may surprise people. But newer models are getting closer to the human reference on these cybersecurity tasks.
AI agents can get stuck in cognitive ruts, repeating or refining an approach that isn’t getting them far. Newer models seem to be improving at how they use extended thinking.
Newer models make better use of extra thinking tokens on these cyber tasks. Source.
This isn’t multi-agent scaling, but it’s encouraging. AIs were poor at turning more thinking into harder solved tasks, and that scaling parameter improved. They might eventually pass the human reference. That’s part of why I don’t see runaway RSI as imminent, yet think it could become more plausible as these capabilities change.
We’ve also seen new findings on helping agents escape their ruts and use more thinking time productively. The new Fork-and-Flush study periodically creates parallel branches that keep the accumulated work but start with fresh chat contexts, then continues from the best branch. It improved results across thirteen research and engineering tasks at the same compute budget. That’s a concrete research direction for using additional work more effectively.
So, have we seen a similar improvement in multi-agent scaling? For speed, sometimes. For intelligence, there’s no clear direction, so far, at least on formal math proofs. Opus 5.5 gets more speedup for a smaller cost increase on program reconstruction. But the score gains in the chart below are noisy, and newer models don’t consistently get a larger boost from additional agents.
Newer models don’t get consistently large gains from larger math-proof teams. Source.
That doesn’t mean better multi-agent scaling won’t happen. I expect the labs to work hard on coordination. Improving diversity of thought is a more open research question, but I’m sure it’s at least somewhat tractable given sufficient effort. The structure of the problem may impose the hardest limits: some discoveries have to happen before the next useful step becomes possible. We don’t yet know how much each of these constraints contributes.
Summarizing what we’ve seen, here’s an update on the conceptual limits to multi-agent scaling.
What we’ve learned so far, and where the hardest limits might be.
Frontier models share many of the same broad training ingredients: overlapping public data, related algorithms, and similar reasoning and coding exercises. So how different are these minds really?
The Artificial Hivemind study found striking similarities in models’ open-ended responses. A creativity study involving 467 people likewise found that adding AI responses eventually contributed less diversity than adding human responses. These experiments suggest that AI agents can bring less diversity of thought than an equal number of humans on these tasks, which may limit how much we get from parallel agents.
Can we make agents more different from each other? Very likely. Here are four ideas. I’m sure there are others:
Different training experiences: vary data mixtures, curricula, reinforcement-learning environments, and reward objectives to develop strong agents that favor different useful approaches.
Complementary specializations: combine agents trained for conjecturing, finding lemmas, searching for counterexamples, and checking proofs. Test whether their strengths combine into results no member reliably achieves alone.
Independent proposals before discussion: let agents develop approaches before seeing one another’s answers, then exchange evidence and criticism. Test whether this reduces early convergence on the same mistake.
Teams chosen for complementary errors: select members whose successes and failures overlap less, rather than relying only on individual benchmark rank. Measure whether the team finds more verified solutions as it grows.
As we discussed in the previous post, we’re going off of scant data here. This model could be wrong. Or newer AI versions could get sufficiently better at doing parallel work that these diminishing returns would change. If that happens, we need to be aware of it.
How would we track this? I’d propose measuring three things from new AI models across a variety of problem times. Across each task type, and especially across real world AI research, measure three parameters and track how they change as newer models arrive:
G - Intelligence scaling: How much verified task performance does each multiplication of agents add? Measure local score gains, slopes, or exponents as appropriate. This is a task-specific proxy for collective intelligence, not a universal intelligence unit.
λ - Speed-cost scaling: How much faster does the team reach the same result, and how much more total compute or agent-time does it use? Track the speed exponent λ over successive team-size ranges.
N - Where gains weaken or saturate: At what team size do the score or speed curves bend, or approach a ceiling? Does the bend or onset of saturation move outward with newer models?
Track intelligence gains, the speed-cost tradeoff, and where gains weaken or saturate.
G and N answer two different questions. How much extra benchmark score does a defined team expansion buy, and how many agents can we add before those gains flatten? In the illustration below, G is the gain from one to ten agents. N is the agent count where the curve reaches its plateau. A real curve may have a gradual bend rather than a sharp plateau, or no plateau within the tested range.
One hypothetical benchmark curve. G is the added score from a specified expansion of the team; N is the agent count where gains flatten. The illustrated sharp plateau is not assumed for every real task.
Watch these parameters over time, especially G and N. Don’t stop at one to ten agents. Measure the gains from 1→10, 10→100, and 100→1,000 agents, separately, on each benchmark. A newer model might scale better over the first interval and worse over the next. We need to know whether the gains improve and whether the bend moves outward.
Three hypothetical trends for added benchmark score across model generations. Measure each expansion separately: 1 to 10, 10 to 100, and 100 to 1,000 agents. Different intervals can follow different paths.
We especially need to track these trends for actual internal AI research. Are newer models getting bigger gains from larger teams? Are those teams producing more verified discoveries and better successor models? That’s what would tell us whether massive parallel teams are getting closer to the conceptual breakthroughs RSI may need.
Anthropic has given us a concrete test to watch. Its October employment report shows research agents logging about nine hours for every human work-hour by mid-July, and nineteen by late August. The company-wide ratio reached about six.
Research agent runtime rises to about nine hours per human work-hour by mid-July and nineteen by late August. These hours include longer operation as well as parallel agents. Source.
Let’s make a generous assumption: each agent-hour is as valuable as a human research-hour, with the number of human researchers unchanged. Agents can run throughout the 168-hour week. Anthropic’s runtime ratios already count those extra operating hours against a forty-hour human week.
On that assumption, July’s nine agent-hours plus one human hour amount to ten times the labor of humans alone. August’s total is twenty times. But Anthropic already had roughly one agent-hour per human hour in April. Compared with that observed baseline, total labor grew fivefold by July and tenfold by August.
What should that do to research? Treat the added labor as parallel research effort. Davidson and Houlden’s relationships imply about 2–4 times April’s research pace by August, depending on whether experimental compute becomes a bottleneck. Applying the labor exponent used by Alan Chan and colleagues, including Geoffrey Hinton and Yoshua Bengio, gives about 25 times. Log scaling gives about twice the pace. Our sub-log shapes give only about 1.1–1.8 times.
Now translate research pace into the Epoch Capabilities Index (ECI). Suppose software research supplies half of baseline capability improvement, while the compute-growth contribution stays unchanged. Then four times the software pace means about 2.5 times the overall ECI slope, not four times. We’re already discounting the software boost. Other obstacles could reduce it further.
Allow six months from research to public release. Here’s what those assumptions imply for January and February 2027. The top group uses hypothetical RSI research relationships; the bottom group extends experimental benchmark shapes.
What different RSI models predict we should see in the pace of AI improvement (using ECI slope), six months after an AI labor increase. Source.
Why do log scaling and Davidson-Houlden’s power 0.3 nearly tie? This is only a tenfold labor increase from April to August. At that scale, both calibrations give about twice the software pace. By 100 agents and then a billion, the hypothetical power law curve pulls far ahead of the experimentally-observed log and sub-log scaling rules.
Around late May, agents were already logging about three hours per human hour: twice April’s total labor on our assumption. With a six-month lag, that earlier signal would arrive around November.
These tests have many different assumptions, including equal value per hour and treating added runtime as parallel labor. I’m open to refining them on how others think about this. In measurement, we should judge the AI pace of improvement as the rate of at which ECI score goes up over successive releases, allowing for development and publication delays.
Personally, I don’t expect to see sustained acceleration of Anthropic’s AI progress (as measured by ECI) through the first half of 2027, and certainly not one as large as these simple extrapolations from the hypothetical models of Davidson and Houlden or Chan and colleagues suggest. If anything, I expect acceleration at the low end of the sub-log scaling bar at the bottom of the chart.
I could be wrong for many reasons. Parallel search of different AI training parameters, or use of AI to code new RL environments, or gains from AIs combining existing ideas in the literature could all make a bigger difference than I expect. Or, at the next increment of AI improvement, AIs could start making much more major advances in machine learning theory that allow us to bend the scaling laws and other diminishing returns that AI faces, or to add new axis of scaling, as reasoning models did.
On the other hand, while more parallel search and recombination of existing ideas seems highly likely, they may not bring huge advantages, and the AIs we’re making today might not find conceptual breakthroughs. Atop that, ideas probably really are getting harder to find in AI, and that may chew up some or all of the increased capacity of AI to work on improving itself.
We shall see. If we see the rate of AI improvement increase significantly, I’ll update my views in proportion to what the data show.
In summary, for now, multi-agent scaling shows incredibly steep diminishing returns to model intelligence, or the ability to reach conceptual breakthroughs. I expect those sorts of breakthroughs to be necessary for major AI advances and for self-sustaining RSI, let alone a runaway intelligence explosion. And at the same time, we’re all operating on far too little real world data. The most important thing we can do now and for the months and years ahead is to rigorously collect that data, and use it to update our forecasts of the future.
2026-10-18 14:34:00
“Something old and wild and blind at war with man and the works of man” — Poul Anderson
Years ago, I thought it was valuable for me to understand the depth of the human capacity for atrocity, so I made myself look at photos and videos of some of the worst things people had done to each other. So in 2015, I made myself watch the full, unedited video of ISIS burning a man alive in a cage. However horrifying that sounds, it was worse. The man stood in the cage and hung his head while masked ISIS fighters all around him sang grim chants. No scripted scene in Game of Thrones could have approached the raw, atavistic horror of that real-life execution. It was like something out of the ancient world — a human sacrifice on the steps of a temple built to gods long forgotten.
I thought of that video when I heard about the Trump administration’s plan to stage a public televised execution by firing squad. The condemned man, the 2009 Fort Hood shooter Nidal Malik Hasan, certainly deserves death, and a firing squad is a relatively humane way to kill a man. But to televise this event to the nation seemed like an act of barbarism — the kind of spectacle that was already a disgusting relic when Britain abolished it in 1868 (for the U.S., it took more than 60 more years).
I was not alone in this opinion. Except for MAGA Republican men — a slice of a slice of the right-wing base — every segment of American society deeply opposed the idea of shooting Hasan on live TV:
Seemingly swayed by the bad polling, Trump cancelled the idea, declaring that the execution wouldn’t be livestreamed. But the episode has revealed something important about the nature of the men who now rule America. Their values and their worldview are not the same as those of the average American. They are, in many ways, premodern and barbaric. Public executions actually aren’t a new idea on the right — Charlie Kirk was calling for them years ago. Now everyone knows that this wasn’t just empty rhetoric.
It is very important that we understand the New Right. Donald Trump himself is currently in command of the New Right, but not for much longer — he is old, older than Biden was at the same point in his presidency, and already visibly declining. What’s more, Trump is a singular figure — a charismatic but spectacularly corrupt mafia type with authoritarian tendencies and a worldview mashed together from early-1990s CNN and half-remembered stories of America’s pre-WW2 glory days.
There are many reasons Trump is amenable to the New Right, and became their beloved leader and standard-bearer, but he is fundamentally not of them, and when he is gone, it is that ideology, rather than another charismatic figure, which will flow into the vacuum his exit creates. Like MAGA now, it will rule through factional politics — the strategy by which a small minority becomes the majority-of-a-majority-of-a-majority. The New Right will lack Trump’s charisma, but it will also lack the baggage of his corruption and (possibly) the lingering stink of his policy blunders. So it is important that we understand this ideology — not simply because it rules us now, but because we may find a purer version of it ruling us sometime in the not-so-distant future.
I already wrote a post about the New Right, in early 2025:
A slightly abridged version was later published in The Free Press.
In that post, I argued that the key to understanding the New Right was its conception of “Western Civilization” under siege:
To a large-ish number of Americans, [the New Right] told a coherent story about who they were, where they came from, and what commonalities tied them together. The story it told was about “Western civilization”.
When I say “Western civilization”, I don’t mean it in the Cold War sense of “the free world”. It doesn’t include Japan or Latin America, and it very much does include Russia. The Western Civilization that the New Right believes in is defined by three things: 1) European racial descent, 2) Christian heritage, and 3) veneration of the previous two things.
The third of these is absolutely the key. European descent and Christian heritage define “Western” for the New Right, but respect for those things defines “civilization”. If you created an all-white country, and it was full of blue-haired genderqueer atheist decolonial-leftist white people, that would be anathema to the New Right…Nor are the New Right typically pious or born-again Christians…[Christianity is] more of a source of shared heritage, and a boundary that demarcates historical Europe from the rest of the world. It’s less about Christ than about Christendom.
This concept explains why the New Right cares so much about Europe. It’s important to realize that this is new. The Old Old Right — the right wing of 1910 or 1935 — was explicitly isolationist toward Europe. Not only did they vigorously advocate against U.S. involvement in European wars, but they viewed the waves of South and East European immigration as fundamentally alien — a threat to the cultural and racial purity of America. Francis Walker called those immigrants “beaten men from beaten races”, and most of the restrictionism of the period — especially the famous immigration law of 1924 — was explicitly aimed at choking off the flow of people from places like Poland, Italy, and Greece.
You can see faint echoes of that old anti-European sentiment in Trump’s desire to withdraw from NATO. But overall, the New Right cares much more about Europe than any incarnation of the Right in American history ever has before. They view America and Europe as fundamentally unified — belonging to a single civilization with a single common heritage, ethos, and culture. Here’s Marco Rubio in February at the Munich Security Conference:
For the United States and Europe, we belong together. America was founded 250 years ago, but the roots began here on this continent long before. The man who settled and built the nation of my birth arrived on our shores carrying the memories and the traditions and the Christian faith of their ancestors as a sacred inheritance, an unbreakable link between the old world and the new…We are part of one civilization – Western civilization. We are bound to one another by the deepest bonds that nations could share, forged by centuries of shared history, Christian faith, culture, heritage, language, ancestry, and the sacrifices our forefathers made together for the common civilization to which we have fallen heir.
Rubio restated similar sentiments at a recent speech in Athens.
The Trump administration and the MAGA movement are obsessed with the idea that immigration — primarily from Muslim countries — is destroying the birthplace of Western Civilization, aided and abetted by leftist ideology among the European elite. This is from a Hegseth speech in June, commemorating the Normandy landings:1
Sadly, today, different European beaches are stormed by different dangerous ideologies…Beaches in Spain, in Italy, in Greece, and Bulgaria. Boats and men arrive. When will European capitals do something about that invasion, or is it too late?
And this is from Rubio’s February speech:
[I]n a pursuit of a world without borders, we opened our doors to an unprecedented wave of mass migration that threatens the cohesion of our societies, the continuity of our culture, and the future of our people. We made these mistakes together, and now, together, we owe it to our people to face those facts and to move forward, to rebuild…Mass migration…was and continues to be a crisis which is transforming and destabilizing societies all across the West.
And here’s JD Vance back in 2025:
Of all the pressing challenges that the nations represented here face, I believe there is nothing more urgent than mass migration…How many times must we suffer these appalling setbacks before we change course and take our shared civilization in a new direction?…No voter on this continent went to the ballot box to open the floodgates to millions of unvetted immigrants.
The core belief of the New Right, in other words, is that Western Civilization is being invaded by barbarians — people from non-Western civilizations, who come to the West under the guise of immigration and then proceed to try to overthrow the West from within, with the aid of local leftists and progressive elites.
This worldview has been reinforced by a constant stream of news from Europe, which American rightists follow assiduously and comment on constantly. Islamist terror attacks in Europe are rarer than far-right extremist attacks, but cause far more deaths. Migrants from Muslim countries are responsible for a very disproportionate number of sexual assaults in countries like Sweden. And there are periodic riots in France, often initiated by students, leftists, and various unaffiliated angry folks, but disproportionately joined by poor people from immigrant backgrounds living in ghetto-like areas. The most recent round of such disturbances is currently gripping the nation and generating a bunch of apocalyptic videos:
Incidents like this have steadily increased support for the European right, which promises mass deportations and has explicitly positioned itself as the defender of Western civilization:
America’s New Right views itself as closely allied with the European right — perhaps even one and the same. Elon Musk does everything he can to support Germany’s AfD, and JD Vance has met with them. JD Vance campaigned (unsuccessfully) for Hungary’s Viktor Orban. Trump has often voiced support for France’s Le Pen, and so on. It’s a regular Western Civilization Internationale.
The European right has been ambivalent about this support; Trump’s extreme unpopularity in Europe means that his embrace is often detrimental to the European right’s electoral prospects. Nor is there much evidence that Americans link Europe’s immigration problems with the immigration issue in the United States — pro-immigrant attitudes have surged in America since Trump returned to power, and the President is underwater on the issue.
Regular Americans simply do not see Europe and America as a single, unified civilizational entity. They do not view riots on the streets of Paris as a dark portent of riots on the streets of Houston. They do not see their Guatemalan housecleaners as rapists or terrorists. They are not invested in the political fortunes of Alternative für Deutschland or Marine Le Pen or Restore Britain. These are fringe beliefs, core to the worldview of the New Right but utterly peripheral and irrelevant to the typical resident of Michigan or North Carolina.
And these fringe beliefs help explain why the New Right has embraced a savage, atavistic morality that’s deeply at odds with the decency of regular Americans.
The New Right’s embrace of savagery did not begin with the proposed livestreaming of Nidal Malik Hasan’s execution. For many years, I have been watching commentators in this movement claim that the American right needs to become tougher, more vicious, and more brutal — and that conservatism has been hamstrung by excessive niceness and tolerance in the past. For example, here was Jesse Kelly in 2022:
You communists simply got way too comfortable with the ‘live and let live’ Right. You’re gonna have a tough time adjusting to The New Right. Your names and accusations mean nothing to us. You can’t use our values against us. We’re enemies. Game on,” posted Kelly on social media…I’m not a Republican. I’m not ‘better than that’. I’m a monster. I believe in hitting people who hit me.
In 2021, Donald Trump Jr. told a gathering of evangelical Christians:
We’ve turned the other cheek, and I understand, sort of, the biblical reference — I understand the mentality — but it’s gotten us nothing. OK? It’s gotten us nothing while we’ve ceded ground in every major institution in our country.
And if you watch rightists tweet, you constantly see them making declarations like this:
In his famous essay “The Flight 93 Election”, widely viewed as a founding manifesto of the New Right, Michael Anton says conservatives have been meeker than “fearful, beaten dogs”, and declares that they “bend over backward to play by the self-sabotaging rules the Left sets for them”. Less elegantly written screeds by Kurt Schlichter, Glenn Ellmers, and others all make the same argument.
American rightists have also begun to heap praise on historical rightist leaders whom they view as having achieved effectiveness through savagery. The two they typically cite are Spain’s Francisco Franco and Chile’s Augusto Pinochet. Recently, though, we’ve started to see another name creep back into rightist discourse:
One would think that the disastrous results Adolf Hitler produced for Germany, Western Civilization, and the European Right would make American rightists wary of carrying out apologetics on his behalf. But no:
Whether American rightists believe in Hitler’s ideology or want to emulate his policies is beside the point here. The real lesson is that by invoking Hitler, rightists can paint themselves as tough, savage, and ruthless.
A pattern should begin to emerge here. Leftists are famously obsessed with ideological purity; when Democrats lose, leftists always claim that it was because they were too moderate, or compromised their principles, or failed to energize the working class by promising real change, etc. Rightists, on the other hand, are obsessed with toughness. If Republicans lose, it was because they were too meek, too bound by liberal norms. Repeated failures, to rightists, are nothing but an infinite regress of boxing gloves that they were too afraid to take off.
This explains the proposed televised execution of Nidal Malik Hasan. Rightists view Hasan — a Palestinian American who served in the U.S. military but murdered his fellow servicemembers because he was angry about America killing Muslims in Afghanistan — as typical of the invading anti-Western barbarian horde. By executing him on live TV, they probably thought they could demonstrate that the West can be equally savage in its own defense.
It also explains Pete Hegseth’s constant performative toughness:
Physical toughness and traditional masculinity are not the same thing as brutality, obviously, but these antics demonstrate the same general obsession with showing that the Men of the West are no weaklings.
And of course, the rightist obsession with toughness explains part of the romance between Trump and Russia.
There are many reasons that Trump has chosen to shift America’s allegiance from Europe to Russia, of course. Given the President’s stupendous, unprecedented corruption, I would be very surprised if there were not some financial ties involved. But rightists also tend to see Putin’s Russia as the living instantiation of Western toughness — savage, unrestrained, unconcerned with humanitarian softness or the rules of war.
Republicans have been oohing and ahhing over Russia’s hypermasculine military recruitment ads for years, despite the fact that the Ukrainians inflict many more casualties on the Russians than vice versa. Push-ups and big biceps don’t win wars — nor do massacres and war crimes — but they comfort American rightists with the knowledge that somewhere out there are White, culturally Christian men who can match the hordes of the Middle East in savagery.
If you wonder why Trump is canceling Congressionally mandated sanctions on Russia, urging Europe to buy more Russian gas, ordering the military to cut off Starshield satellite communications for the Ukrainians defending their homes from Russian drones, and discussing various joint economic projects with Russia…well, this is the reason.
It’s probably not the only reason. But if it were purely a matter of Trump’s personal corruption, his team would not be so eager to go along with all this pro-Russia stuff, especially given how unpopular it is with the American people. The MAGA ideology views Russia as a civilizational ally against the true enemy — an EU they believe to be captured by leftists and bent on destroying Western Civilization through mass importation of anti-Western immigrants. And Russia’s savagery and attitude of toughness both on and off the battlefield provide America’s rightists with an ideal to aspire to.
This Russian propagandist basically summed up how the rightists see the world right now:
The rightists’ embrace of savagery, of course, is not just about aesthetics, or about psyching themselves up to do the horrible things that deep down they suspect must be done. It’s also a way to give them confidence in the face of their own unpopularity. Rightist figures are deeply unpopular in America. Their main policy idea — remaking American policy to ethnically reengineer the American populace — has very little purchase. Everyone in America still thinks Hitler was the worst. The obsession with Europe, and with their idea of Western Civilization, is a niche, alien thing. And they’re losing the young generation of men:
The only way for rightists to sustain the illusion that they would have the upper hand in a post-democratic America — that they would triumph in the climactic civil war that suffuses their perfervid daydreams — is to imagine themselves as a diamond-hard vanguard, a small fist of ruthless men who can defeat any number of effeminate, emasculated enemies, the “three percent” who are willing to pick up a gun and kill.
That fantasy is a dangerous one. You can end up doing some very stupid things if you think you’re the only wolf in a world full of sheep. A successful movement needs broad popular appeal, not just the willingness to out-barbarize the enemy. But the New Right appears set on pursuing the strategy it has laid out for itself, and all we the rest of us can do is to understand how they think, and prepare for their next mistake.
It’s interesting that Hegseth says that “today…European beaches are stormed by different dangerous ideologies”. What dangerous ideologies did he think were storming the beaches of Normandy in 1944?
2026-10-15 16:47:39
A little over a year ago, a bunch of people were worried that the AI data center boom would result in an epic bust, much like the railroad bust in 1873. I wrote about the possibility at the time:
The basic idea is that even if AI is an incredibly useful technology and creates enormous economic growth in the future, the people building the infrastructure (data centers) might borrow so much money that they can’t pay it back in time. Last summer, the AI industry’s revenue was growing, but not nearly as fast as the amount of money being spent. But the “hyperscalers” building the data centers were mostly spending their own cash instead of borrowing money, so the danger wasn’t imminent. The worry was mostly about the future — if spending continued to increase and revenue didn’t accelerate, there could be an 1873-style bust.
Well, now it’s the future, and two things happened. Spending did accelerate, and hyperscalers started having to borrow money to keep increasing their spending. But revenue accelerated even more. The release of Claude Code, Codex, and other agentic coding tools proved to be the “killer app” of AI, and suddenly Anthropic and (to a lesser extent) OpenAI were rolling in cash:
So like in summer 2025, we aren’t obviously heading for a big AI bust in the near future. But the stakes are higher now — because companies are borrowing a lot more money now, any slowdown in the torrid pace of AI revenue growth could cause a blowup.
The amounts of money being thrown around here are just staggering. A year and a half ago, the AI boom was still significantly smaller than the railroad boom of the late 1800s, as a percent of GDP. Now, there’s an active argument as to which one is bigger.
This debate doesn’t really matter, because the basic message is just “Wow, it’s a lot of money.” But it’s fun to look at the numbers people are throwing around. Stijn Van Nieuwerburgh gives figures of 3.63% of GDP for AI and 2.24% of GDP for railroads:
But those percentages are over different amounts of time — the railroad boom lasted a very long time. According to some data sources, railroad spending peaked at around 5.4% of GDP:
And 19th century data are notoriously patchy and incomplete. Some sources claim much higher numbers for the railroad buildout:
Nor do people agree on the size of the AI investment binge. Some sources yield numbers that are much lower than Van Nieuwerburgh’s:
As far as I can tell, the main difference here is assumptions about costs. The banks and ratings agencies are using announced dollar spending; Van Nieuwerburgh is using announced physical construction projects and then making assumptions about how much those projects will cost. The biggest reason he gets a higher number is that he assumes data centers are going to cost a lot more than the hyperscalers think.
Meanwhile, Dylan Patel claims over 6% for the AI boom already, once you include all types of AI-related infrastructure — fab equipment, chips, energy, and so on.1
But whatever the correct numbers are, the basic point remains the same: AI is now one of the two biggest infrastructure booms in American history, by a large margin.
So it’s understandable that people are worried that the two booms could follow similar paths. For the railroads, that path included a big crash in 1873, which caused a huge decade-long depression in the U.S. economy. A lot of people are watching for something similar to happen with data centers, and the chorus of skeptical voices is beginning to swell again.
OK anyway, on to the real point of this post. The flood of revenue that accompanied the rise of coding agents quieted the talk of an AI bubble for a while, but now it’s back. The first reason is the rise of AI-related debt. In the early years, the hyperscalers — data center builders like Amazon, Google, Meta, and Oracle — mostly paid in cash for the data centers. These companies get tons of cash from their software and cloud businesses, and they ended up spending most of it on AI infrastructure. But now the buildout has gotten so big that they’re having to borrow a bunch of money to keep ramping up spending:
For all of the hyperscalers except Microsoft, free cash flow either went negative in recent months or is expected to go negative late this year. This has been a giant boon for semiconductor companies — Nvidia, TSMC, and the rest:
All this debt creates a financial danger that didn’t exist before now. When they paid in cash, hyperscalers were safe — if AI revenue disappointed, their stock prices would go down, but their businesses would just keep going and they’d eventually recover their profitability. But the more hyperscalers borrow, the more revenue they’ll need just to stay solvent; if the revenue doesn’t arrive, they might have to default on their debt.
2026-10-13 13:28:43
I have a Google Doc with a long list of posts I’ve been meaning to write. Far down the list was a line that read “New Keynesian macroeconomics deserves a Nobel”. Somehow I never got around to writing that post, and now I never will. Because today, it happened! This year’s Econ Nobel went to Bob Hall and Mike Woodford, two of the theory’s most important creators.
First, let me say that this prize is long overdue. In 2009, I asked my PhD advisor, Miles Kimball, who was the greatest living macroeconomist. Without hesitation, he answered “Bob Hall”, and said he would be very surprised if Hall didn’t win the Nobel at some point. Hall’s work is breathtakingly broad; if you want good summaries, I recommend Alex Tabarrok and Tyler Cowen (as usual). As for Michael Woodford, everyone I know assumed that if New Keynesian macro ever did get the prize, Woodford would be included, as he literally wrote the book on it.1
But anyway, let’s talk about New Keynesian economics — what it is, how it came about, why it’s important, and why it didn’t get a Nobel until now. Let’s start with a brief history of modern macroeconomics.
Back in the 1960s and early 1970s, most economists tried to understand the macroeconomy by looking at relationships between macroeconomic aggregates. For example, “If income goes up by 10%, how much will spending go up?”. Or “If inflation goes up by 3 percentage points, how much will unemployment go down?”. 2
The problem with that is that correlation isn’t causation. Suppose you see that when inflation rises, unemployment goes down. That correlation is called a Phillips Curve. So you think “OK, if we raise inflation, more people will get jobs”. But you try that, and it doesn’t work — you push inflation up (by printing more money), but unemployment stays high. The correlation you saw before has now broken down. Now you’ve got two problems instead of one!
This is a very simplified version of what a lot of economists think happened in the 1970s. Whether that’s what really happened is a story for another day. But the stagflation of the 1970s prompted the econ profession as a whole to rethink how it modeled the macroeconomy. A lot of researchers asked: What parts of the macroeconomy are invariant to policy? What are the solid foundations we can stand on, so that when we try to make some sort of change, we don’t cause our own theories to crumble?
The answer most economists settled on was called “microfoundations”. They reasoned that the basic drivers of individual and corporate economic behavior shouldn’t change when macro policies like interest rates and government spending change. Most importantly, those drivers include:
Technology
Consumer preferences
The process by which people and businesses form expectations of the future
Do all three of these really stay the same when economic policy changes? Probably not completely. But it gave macroeconomists a place to start. So in the late 1970s and early 1980s, economists started to build macro models based on theories of individual and corporate behavior. These were called “microfounded” macro models.
The last of those three “microfoundations” — expectations — was particularly thorny to think about. Almost any sort of expectation-formation process that you write down allows policymakers to trick people over and over, by surprising them systematically. Economists thought that eventually, people should wise up to the government’s tricks. So they came up with the idea that consumers and businesses should, on average, be as smart as the economists making the models. If economists write down a model of how the world works, that model should be the average person’s mental model in the model. Basically, people generally know how the economy works, and they plan accordingly.
This idea is called “rational expectations”.
In 1978, Bob Hall wrote a very influential paper that convinced a lot of economists that this was the right track. Hall wanted to test a simple idea, originally developed by Milton Friedman: If people plan out their consumption in advance, then changes in the level of consumption should be completely random.3 Nothing from last quarter should be able to predict the change in consumption this quarter — because if something last quarter mattered for this quarter’s consumption, people would already have changed their consumption last quarter! Hall found that, sure enough, a bunch of variables that seemed like they should be important for consumption — especially last quarter’s income — didn’t actually help predict changes in consumption.
For a more detailed explanation, I recommend this post by John Cochrane:
Brian Albrecht has a good (brief) explanation as well.
At first blush, this might not seem like an important result. After all, absence of evidence isn’t evidence of absence — there might be other variables that do predict changes in consumption, that Hall simply forgot to test. And in fact, Hall did find one variable that predicted future changes in consumption: stock prices. So even in the formal sense, the basic theory didn’t quite hold. Nor did Hall’s result rule out the traditional Keynesian idea of consumption — if government spending surprises people, it can move consumption immediately, without creating any sort of predictable future change.
But it was an important result, for two reasons. First, if people plan ahead when making their economic decisions, it should be possible for policymakers to influence today’s behavior by making promises about what they’ll do in the future. Second, if it’s impossible for economists to predict changes in consumption, that implies that consumers are as good at planning their future consumption as economists are.
If that’s true, it’s an important lesson for policy: If you want to make people consume more, or if you want to make businesses hire more, etc., you have to do it in a way such that people generally understand what you’re doing.
So macroeconomists set out to make models of the economy in which people understand how the economy works. At first, this effort didn’t go well. For example, one very popular early idea — which eventually won a Nobel — was called “real business cycle” (RBC) theory. This was the idea that recessions and booms are driven purely by changes in the rate of technological progress — if engineers happen to invent less useful stuff this quarter, you get a recession.
No one denies that technological inventions can be very important for economic growth — just look at how the AI boom is powering the U.S. economy right now. But the idea that tech progress drives the business cycle in a simple, mechanical way just doesn’t fit the evidence. In particular, productivity booms don’t seem to line up with economic booms, as RBC theory would predict.4
Even more importantly, RBC models predict that monetary policy — raising and lowering interest rates — shouldn’t affect the real economy. Ed Prescott, the creator of RBC theory, famously declared that monetary policy “is as effective in bringing prosperity as rain dancing is in bringing rain.” But our best evidence says that Prescott was wrong — monetary policy almost certainly does affect the real economy.
So something’s going on there. Macroeconomists were missing something big — they didn’t understand how or why monetary policy boosts growth and employment. During the 1990s, a bunch of economists started coming up with possible reasons why having the Fed buy bonds could result in more people getting jobs.
These ideas generally fell under the name of “New Keynesianism”. As John Cochrane and others have pointed out, this was something of a misnomer. Whereas John Maynard Keynes and his successors thought a lot about fiscal policy — i.e. government spending and taxes — the New Keynesians of the 1990s and 2000s focused almost entirely on monetary policy. New Keynesian macro initially owed a lot more to Milton Friedman than it did to Keynes — everything was about the Fed, and the idea of fiscal stimulus initially fell by the wayside.
Anyway, in the 90s and 2000s there were plenty of ideas about how monetary policy could affect the real economy. This is probably one big reason why New Keynesian economics was so slow to win a Nobel — it has so many creators that it’s not even clear who deserves the prize. Greg Mankiw, David Romer, Stanley Fischer, George Akerlof, Janet Yellen, Olivier Blanchard, Nobuhiro Kiyotaki, Jordi Gali, Mark Gertler, John Taylor, Guillermo Calvo, and a bunch of others contributed pieces to the edifice.
Most of these contributions were ideas about why prices are slow to adjust to macroeconomic fluctuations. Basically, if you have something that decreases aggregate demand — say the Fed raises interest rates like in 1980, or there’s a banking panic after a housing bubble bursts like in 2008 — there’s a question of why companies don’t just cut prices and maintain the same level of output. If there’s something causing prices to fall too slowly, that can force companies to produce less instead. A lot of New Keynesian theory is about why companies have difficulty cutting prices.
Bob Hall made very important contributions here too! The basic New Keynesian model explains how interest rates can affect GDP, but it doesn’t explain why that affects employment. After all, if growth slows down, it’s not immediately obvious why a bunch of people should be thrown out of their jobs. In a 2005 paper, Hall made a model explaining how wages might be slow to adjust to changes in demand — meaning that when there’s a downturn, companies can’t just cut wages, but instead have to lay off workers.5
Anyway, New Keynesian economics wasn’t the brainchild of a few famous founders — it was a riot of new ideas. It needed someone to come synthesize all of those ideas into something simple that central bankers could use to set monetary policy in real life. That man was Mike Woodford.
In a paper with the late Julio Rotemberg, Woodford laid out a simple New Keynesian model. On the question of why companies have difficulty cutting prices in a recession, he punted — Woodford simply assumed that for whatever reason, only some companies are able to change their prices each quarter.6 (This idea is called “Calvo pricing”, after its creator, Guillermo Calvo.) The rest of the model is pretty simple. Woodford uses the same basic consumption-smoothing model from Hall’s 1978 paper, in which people’s decision of how much to consume and how much to save depends on the interest rate. He assumes that the central bank (i.e., the Fed) sets interest rates according to a simple rule that balances out the risks of inflation and unemployment. There are a few more assumptions in there, but those are the basics.
This simple model provides a guide for monetary policy. It explains how the central bank can cause a recession in the short term — like Paul Volcker did in the early 1980s, when he raised interest rates to fight inflation — but can’t raise employment forever by keeping rates permanently low. And it explains how the central bank can respond to a drop in private demand — say, from a financial crisis like the one in 2008 — by cutting interest rates to stabilize the economy in the short term.
In other words, Woodford’s model — which he later expanded into a famous book — is a grand synthesis of the general consensus that emerged from the intellectual ferment of macroeconomics from the 1970s through the 2000s. We knew that aggregate demand was a real thing. We knew that the economy had trouble adjusting to negative demand shocks, resulting in a lot of disruption — reduced output, people getting thrown out of work, and so on. And we knew that monetary policy could boost the economy in the short term, but that its effect probably faded in the long term. Woodford gave us the simplest plausible model to describe all of that, and to offer central banks a badly-needed road map for stabilizing the economy.
Because it was so simple and so applicable, Woodford’s New Keynesian model became the standard tool of central banks around the world. No sooner was his synthesis complete, however, than it ran into a huge problem. In 2008 and 2009, the U.S. and European economies were hit by such a huge negative demand shock that cutting interest rates all the way to zero failed to put things right. Something else was needed.
There were three basic ideas for what that “something else” should be. The first is contained in Woodford’s model, and draws on the basic idea in Hall’s papers. It’s the idea of “forward guidance”. If the central bank can promise to leave interest rates low for a long time even after the recession is over, that can prompt consumers to spend more today — thus hastening the end of the recession.7 The second is quantitative easing — having the Fed buy long-term bonds instead of just short-term ones, in order to push down long-term interest rates. QE isn’t in Woodford’s model, but he eventually came around to supporting it.
The third idea was fiscal stimulus. If interest rates go all the way to zero, then government spending can become a more effective way of raising demand (as long as the central bank doesn’t cancel out the stimulus by raising rates again). When the Great Recession hit, New Keynesian economists scrambled to put Keynes back into their theories, by adding a role for fiscal stimulus.
Hall wrote a paper in 2009 explaining how stimulus could be effective:8
[T]he multiplier linking government purchases to GDP may be in the range of 0.7 to 1.0…but higher values are not ruled out. New Keynesian macro models have multipliers in that range as well…Multipliers are higher—perhaps around 1.7—when the nominal interest rate is at its lower bound of zero, as it was during 2009.
In that paper, Hall explains how fiscal stimulus can be effective even in light of his famous result from 1978. Even if there is no simple “consumption function” linking stimulus directly to spending, there are plenty of ways that stimulus can affect the behavior of companies and consumers, especially when interest rates are at zero.
And Woodford wrote a paper in 2011 showing how stimulus works in his basic model:
This paper explains the key factors that determine the output multiplier of government purchases in New Keynesian models, through a series of simple examples that can be solved analytically. Sticky prices or wages allow for larger multipliers than in a neoclassical model, though the size of the multiplier depends crucially on the monetary policy response. A multiplier well in excess of one is possible when monetary policy is constrained by the zero lower bound, and in this case welfare increases if government purchases expand to partially fill the output gap that arises from the inability to lower interest rates.
So in the end, the New Keynesians went beyond the ideas of Milton Friedman, and brought some Keynes back into their theories.
But New Keynesianism’s biggest success arguably came not in the Great Recession, but in the post-pandemic inflation of 2021-23. Inflation spiked to 9% in 2022, but then came down to around 3% in early 2023 after the Fed raised interest rates. But what’s remarkable is that unlike in the early 1980s, this happened without a big rise in unemployment!
It’s not clear exactly why this happened, of course, but it sure looks like a big victory for the kind of New Keynesian macro that Bob Hall and Mike Woodford pioneered. The most likely explanation for this “immaculate disinflation” is that as soon as businesses realized that the Fed would do whatever it took to beat inflation, they stopped raising prices — and inflation simply ended.
It didn’t require demand destruction like Volcker caused in the early 1980s. It didn’t require legions of down-and-out unemployed blue-collar workers cutting back on essential purchases to finally force companies to stop raising their prices. All it required was a clear Fed commitment, and forward-looking expectations — exactly the mechanisms at the core of Bob Hall and Mike Woodford’s models.
It was this episode, even more than 2008-9, that truly represented New Keynesianism’s acid test. And it seems to have passed with flying colors. There was always a question mark hanging over the theory — it was always unclear whether it could shape the future instead of simply telling a story about the past. Since 2022-23, the answer to that question looks like a much more unambiguous “yes”.
I wouldn’t be surprised if that had something to do with this long-overdue Nobel.
Believe it or not, I have read that book cover to cover.
These are simple examples, but sometimes these models could get very big and complicated!
Except for a smooth trend, which people plan out in advance
Here’s another good paper showing the same thing.
This was far from Hall’s only contribution to New Keynesian macro. Another example is his 1986 paper showing how monopoly power could exacerbate economic booms and busts.
Woodford separately wrote down a model of how Calvo pricing might arise from imperfect information. It’s not clear how important this model is, though, or whether Woodford — or anyone else — actually thinks that this is the real reason prices are slow to adjust.
Interestingly, it looks like we did this. The Fed kept interest rates ultra-low even in the late 2010s, when demand had recovered. And it even kept rates low in 2021 as inflation was surging. There’s an interesting paper to be written there about how sluggishness in the Fed’s reaction function might be a substitute for forward guidance, and act as an automatic stabilizer of sorts.
The “multiplier” here is a measure of the effectiveness of stimulus. When the multiplier is greater than one, it means a dollar of government spending raises GDP by more than a dollar.
