In the park looking out

The American Way of War

I've got a short series of posts from awhile back on the American way of war. I decided to add to it with this short conversation with Claude.

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American wars since the collapse of the USSR

Give me a chronological list of the wars that America has been involved in since the collapse of the USSR.

The project knowledge touches on this theme thematically but doesn't provide a factual list. Here's a straightforward chronological account from general knowledge, using 1991 (the USSR's formal dissolution) as the starting point:

Gulf War (1991) — Already underway when the USSR collapsed in December; the US-led coalition expelled Iraq from Kuwait. A short, decisive conventional war.

Somalia (1993) — Humanitarian intervention that escalated into urban combat, culminating in the Battle of Mogadishu ("Black Hawk Down").

Haiti (1994) — Military intervention to restore the elected government of Jean-Bertrand Aristide.

Bosnia (1995) — NATO air campaign (Operation Deliberate Force) in which the US played the lead role, ending the Bosnian War.

Kosovo (1999) — NATO air war against Yugoslavia to halt ethnic cleansing; again US-led.

Afghanistan (2001–2021) — Invasion following 9/11, ousting the Taliban; the longest war in American history.

Iraq (2003–2011, then resumed 2014–) — Invasion that toppled Saddam Hussein, followed by a prolonged insurgency. US forces returned in 2014 to fight ISIS and remain in smaller numbers today.

Libya (2011) — US participated in NATO air operations that led to Gaddafi's fall.

Syria / ISIS (2014–present) — Air campaign and special operations forces deployed against the Islamic State across Syria and Iraq.

Iran (2020) — The drone strike killing General Qasem Soleimani; a targeted killing rather than a sustained war, but a direct military act against a state actor.

Yemen (ongoing, indirect)** — The US has provided intelligence, refueling, and arms to the Saudi-led coalition, with direct drone strikes against Al-Qaeda in the Arabian Peninsula.

Iran (2025) — You noted in your War Series writing (with a 2026 addendum) that a Trump-era attack on Iran occurred, which fits this list as the most recent major escalation.

A few observations relevant to your own analytical framework: this list illustrates the pattern you describe in your War Series — the post-Cold War redirection of American aggression outward, particularly toward the Arab/Muslim world as the new "Other" following the loss of the Soviet scapegoat. Nearly every conflict after 1993 takes place in the Islamic world, which tracks your 2014 addendum about the "well-crafted Orientalist mythology of the exotic and crazy Arab Other."

Talcott Parsons 1947

At the heart of that War Series is a 1947 essay by Talcott Parsons, “Certain Primary Sources of Aggression in the Social Structure of the Western World.” I read that early in my undergraduate years at Johns Hopkins. I regard it as speculative, plausible but speculative. How would one gather empirical evidence on it? It’s not like any nation ever announces, “We’re going to war to let off some steam and ease tensions at home?”

That's exactly the right epistemological challenge — the mechanism Parsons proposes operates at the level of collective unconscious motivation, which is precisely what makes it both compelling and resistant to direct verification. But there are several indirect empirical strategies that could build a cumulative case.

Quantitative correlational approaches

The most tractable starting point is looking for statistical relationships between domestic stress indicators and the initiation of military action. You'd want to examine whether wars or significant military escalations cluster after periods of elevated internal tension — economic downturns, spikes in domestic unrest, rising inequality, political polarization indices. The null hypothesis would be that war timing is random with respect to domestic conditions; a systematic pattern would be suggestive. Turchin's work on political stress cycles (his "cliodynamics" project) is actually well-suited to this — he's already built quantitative indices of internal instability across centuries, and one could ask whether war-initiation correlates with his stress peaks.

The "rally around the flag" literature

There's actually a substantial body of political science research on this, though it approaches the question from a slightly different angle. The "rally" effect — the well-documented spike in presidential approval following military action — shows that war does relieve domestic political pressure in measurable ways. John Mueller's work on war and presidential popularity is foundational here. The harder inferential step is from "war relieves pressure" to "anticipated relief motivates war-making," but the effect at least confirms the direction of the mechanism.

Elite communication analysis

Parsons' theory doesn't require that anyone consciously decides to go to war for psychodynamic reasons — the motivation can be largely unconscious and rationalized afterward. This actually makes elite rhetoric more useful as evidence, not less. You'd look for patterns in how war is framed domestically — whether war rhetoric systematically spikes domestic us-versus-them language, whether it correlates with reduced internal scapegoating (your Klinkner & Smith thesis about civil rights gains during wartime), and whether the timing of hawkish escalation by elites tracks with rising domestic discontent. Content analysis of congressional debates, newspaper editorials, and presidential speeches across the full arc of conflicts could reveal structural patterns invisible to any individual case.

The comparative/historical method

Parsons' thesis should predict that nations with other mechanisms for handling internal aggression — stronger corporatist institutions, more robust social contracts, functioning scapegoats already in place — should be less prone to foreign military adventurism. Scandinavian nations, for instance, would be a useful comparison class. You could also look at cases where domestic conditions that should predict war didn't produce it, and ask what was different. The comparative method won't confirm the mechanism, but systematic cross-national patterns would be hard to explain otherwise.

The Klinkner-Smith natural experiment

The civil rights thesis is actually the most testable piece, and as you note in your writing, they've already done substantial work on it. Their finding — that Black Americans' civil rights gains cluster tightly around periods of external military threat — is exactly the kind of pattern that's hard to explain without something like Parsons' mechanism. It's not proof of the psychodynamics, but it's consistent evidence that war functions to redirect internal aggression in measurable ways.

The fundamental limitation

None of this gets you to the causal mechanism Parsons actually proposes — that repressed aggression generated by Western child-rearing practices flows through nationalist scapegoating and finds periodic release in war. That specific chain is probably permanently beyond empirical reach, because the mediating variable (unconscious aggressive impulse at civilizational scale) isn't directly observable. What you can do is show that the functional relationship holds — that war operates as if it were serving this purpose — and that alternative explanations (pure rational calculation of national interest, for instance) leave systematic residuals.

Which is, frankly, roughly where Freudian-influenced social theory in general sits: plausible, structurally coherent, supported by functional evidence, but not falsifiable in any clean Popperian sense. The honest assessment is that Parsons is probably tracking something real but operating at a level of abstraction that empirical social science can approach but not fully close.

Commencement speakers: Out to lunch & out of touch

Molly Jong-Fast, Why Those Commencement Speakers Deserved Those Boos, NYTimes, June 5, 2026.

Commencement address season hasn’t been going well — for the commencement speakers. [...]

When Eric Schmidt, a former chief executive of Google, told graduates at the University of Arizona about their A.I.-shaped future, the shouting got so intense that he paused and said that graduates feared “that the future has already been written, that the machines are coming, that the jobs are evaporating, that the climate is breaking, that politics are fractured, and that you are inheriting a mess that you did not create.” Mr. Schmidt told them to make the best of it. “The question is not whether A.I. will shape the world. It will. The question is whether you will help shape artificial intelligence.”

Mr. Schmidt’s solution to world-upending technological change is … what? To pull yourself up by your bootstraps? His approach is peak billionaire brain, directed at the young people who have, for the better part of a decade, been treated as woke, lazy, avocado-toast-eating snowflakes. All these speakers just don’t get it. The problem isn’t woke; the problem is work. It’s a lack of social mobility. It’s that college may no longer elevate a graduate to the middle class. It’s that nobody even bothers to pretend that a house, a good job and the ability to start a family are at all guaranteed.

Think of this from the graduates’ perspective: Wealthy old people telling you your future is being pulped by acres and acres of electricity-sucking, water-guzzling data centers feels dystopian because it is. Companies are trying to automate your future away. No wonder you’re furious.

The truth about AI:

Right now, A.I. is in its dark hype period — great for Anthropic’s I.P.O. — but who knows how useful any of this actually will be in the end in creating efficiencies (a.k.a.: replacing the youngs with bots). It’s within young people’s power to stop. Demand regulation of tech companies. Elect people who will legislate that regulation. Organize against data centers in your hometowns.

Don’t just boo — do something.

There's more at the link.

11th St., a red flower, and Washington St. [Hoboken]

Microsoft AI builds a “hill-climbing” machine

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The Microsoft AI Team, MAI-Thinking-1: Building a Hill-Climbing Machine

Abstract: Progress in AI is driven not by a single model, but by the ability to continually improve upon the current state of models. Achieving this requires treating model development as a system-level optimization problem, for which the solution is building a hill-climbing machine for rapid improvement. Our process includes a scaling-focused framework for pre- training modeling decisions, as well as a robust reinforcement learning recipe and infrastructure that sustains long, log-linear performance improvement. The first model developed using our process is MAI-Thinking-1, a 35B active / 1T total parameter MoE that stands among the strongest models of similar size on STEM reasoning and coding tasks (e.g., 52.8% on SWE-Bench Pro, 97.0% on AIME 2025, and 87.7% on LiveCodeBench v6). MAI-Thinking-1 is trained from-scratch, exclusively on clean, enterprise-grade data, without distillation from third-party models. In this technical report, we offer a deep dive into the development of MAI-Thinking-1. By sharing our technical details and learnings we hope to cultivate a transparent and science-driven approach to further development in AI.

Final paragraph of the introduction:

MAI-Thinking-1 is the first model developed using our hill-climbing machine: the integrated process of building data pipelines, training infrastructure, reinforcement learning environments and rewards, evalua- tion suites, and safety tests that turn model development into an empirical optimization loop on a specified domain. The hill-climbing machine allows us to advance AI while grounding progress around human needs from the ground up.

Was kitty wishing upon a star?

When dreams become true... pic.twitter.com/0VIPyCpQ17

— The Figen (@TheFigen_) June 4, 2026

Seeing China Clearly | Robert Wright & Tianyu Fang

YouTube page:

Trans-Pacific tech watcher Tianyu Fang joins Robert Wright to take a close look at China—including how the government works (and doesn’t), online censorship, how China sees AI and the chip war, the legacy of the one-child policy, and more. Plus: Silicon Valley’s hawkish turn. [...]

0:00 Teaser
0:45 How Tianyu became a “China explainer”
3:15 American vs Chinese exceptionalism
8:09 What you can and can’t say in China
18:32 Is the government more accountable in the US or China?
27:03 How China sees America
36:32 The one-child policy and its consequences
42:53 Tianyu: The biggest misconception about China is…
46:20 Heading to Overtime

Fountain in Shipyard Park

Does waning interest in the World Cup signal a thinning of genuine nationalist sentiment?

David Wallace-Wells, Why Does No One Care About the World Cup This Year? NYTimes, June 3, 2026.

They used to call the World Cup, unequivocally, the planet’s biggest sporting event. But it is about to start, right here in North America, and no one much seems to care. Thousands of tickets remain unsold, and just weeks ago, others were being resold well below their official price. [...] And I actually do think this might be telling us something, beyond the world of sports, about the global landscape of politics and culture.

In the States, the indifference might not be surprising, even though the event is being played mostly on U.S. soil. The U.S. team is more talented than in the past but hasn’t looked impressive for years. Soccer is still a growth sport rather than a dominant one in this country, and many Americans aren’t exactly feeling the flush of simplistic patriotism these days. On top of which, the tickets have been priced punishingly high.

What is more striking to me is the muted interest of the rest of the world, which every four years for decades seemed almost to pause for a month to engage in a truly global but appealingly low-stakes performance of tribal nationalism. [...]

What makes this shift so striking is that it has happened alongside a rising tide of political nationalism around the world, which you might think would produce a great surge in soccer nationalism, too. Instead, the age of global populism has coincided with intense interest in the biggest club teams — for-hire rosters assembled largely from international talent by megacorporations boasting jersey sponsorships from foreign conglomerates. [...] But no one could even pretend to illustrate the age of global populism by talking about the intensity of popular feeling about national teams.

That’s from the beginning of the article. After this, that, and the other, Wallace-Wells concludes:

Namely, that what we identify as nationalism in global affairs might be better described as a form of parochialism, with populists making particular claims not about the nation per se so much as the ways it should be reformed — presumably toward some reactionary ideal, its contours often more local than genuinely national. In this reading, globalization hasn’t just generated a backlash among those who resent deindustrialization, capital flight and the stateless lives of the world’s billionaires. It has also made the nation itself seem like a somewhat untrustworthy unit of political and social organization to many people on the right. For them, what might once have served as a source of patriotism and pride now produces feelings of resentment and regret. Not that liberals aren’t queasy about nationalism these days, either. For all of us, rooting for Arsenal or P.S.G. might now be more appealing precisely because it’s essentially meaningless.

Ed Zitron says AI is a losing bet – "doing exit liquidity for venture capital"

To go out publicly and say what he is saying, @edzitron has balls the size of the Las Vegas sphere pic.twitter.com/oHjv3LCvXh

— JustDario (@DarioCpx) June 4, 2026

From Saturday's walk in NW Hoboken

Correcting Cowen’s misleading presentation of large language models [MR #10]

Surprise! There’s been a change of plans. The last time I’d posted about Cowen’s monograph on marginalism I figured I had one more (longish) blog post, one about the fourth and final chapter, “Why Marginalism Will Dwindle, and What Will Replace It?” But the more I thought about it, the longer and more convoluted it got. So I’ve decided to simplify things by writing three posts, each substantial, but focused, instead of a long rambling affair like the one I did on biology. So, I ‘m writing one post about large language models (this post), which Cowen brings up at the end of the chapter. Then I’m writing one about high dimensional models in economics, which Cowen introduces early in the chapter. My final post will be a general response to Cowen’s ideas about where this is all headed.

In this post I want to do three things: 1) First I’ll talk about the surprise nature of the success achieved by GPT-3 and then ChatGPT. 2) Then I will present three passages from Cowen’s text and comment on them. 3) Finally, I want to give a brief rundown of tradition of statistical work that stands behind LLMs.

Surprise!

OpenAI released GPT-3 in 2020 to a limited audience of insiders, who recognized that it represented a breakthrough. This level of performance came as a surprise. No one predicted it. GPT-3 was scaled up from GPT-2, which was in turn scaled up from GPT-1, but no one was making explicit predictions about the level of performance to be achieved at each step. These were experiments: “Let’s try it and see what happens.” That’s fine. That’s a good way to make progress, to try things out and see what happens. But don’t mistake a lucky trial for genuine knowledge.

Cowen mentioned GPT-3 on Marginal Revolution on July 19, and then published a Bloomberg column on it on July 21, which he excerpted in Marginal Revolution the next day: “...think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.” I published a working paper in August, GPT-3: Waterloo or Rubicon? Here be Dragons, in which I both acknowledged about the breakthrough and cautioned about becoming too satisfied with the technology that occasioned the breakthrough.

Two and a half years later, in November of 2022, OpenAI released ChatGPT to the general public. It spread like wildfire. Now the proverbial everyone witnessed what only a small group had witnessed in the summer of 2020. The machine speaks. Sorta’. But more convincingly than any machine had spoken before and in a way that had unimaginable implications for the future.

A threshold HAS been crossed, but it is not, so far as I can see, a threshold in our understanding, either of AI or anything else. It is a threshold in performance along a continuous line of scientific understanding and engineering design and construction, something I have documented in some detail in a recent working paper, The Origins of LLMs. As far as I can tell, there has been no paradigm shift, in Thomas Kuhn’s sense, no rank shift, in terms of cognitive rank theory. There were no fundamentally new ideas in the world by, say, late July of 2020 as a consequence consolidating GPT-3 and making it available in limited release.

“What about the scaling hypothesis,” you might ask. “Isn’t that new?” Ilya Sutskever first explored the idea in 2014. Rich Sutton’s famous 2019 essay, The Bitter Lesson, generated broad discussion about the issue. Then OpenAI published a paper in 2020 that cemented matters, “Scaling Laws for Neural Language Models.”

Given the nature of computing, scaling up is not trivial. Hundreds if not thousands of technical details need to be worked out as the size of the training corpus increases by factors of 10 or more, time after time, and as more and more GPUs are ganged together to assemble the computing power needed. The scaling hypothesis gave researchers a reason to expect improved performance with scaling, but without having to make fundamental breakthroughs in understanding, not of machine learning, artificial neural nets, and certainly not about language and cognition. Consequently our sense of possibility has expanded enormously. Our knowledge and deep understanding have remained the same and the scaling hypothesis made it easy to believe that that was just fine.

Passages from Cowen’s Text

Unfortunately Cowen seems to have bought this story. Not only that, but he doesn’t even acknowledge that there is considerable current debate about whether or not LLMs will be sufficient to achieve AGI (artificial general intelligence) when they are scaled up enough. The most visible opponent of this idea is Gary Marcus, a student of Steven Pinker, who argues that we need to incorporate insights and technology from “old school” symbolic computing (sometimes known as GOFAI, good old-fashioned AI). Marcus is certainly not alone, there are many others. But I don’t want to reprise that debate. I just want to mention that it exists and that Cowen completely ignores it.

What I would like to do in this section is quote some passages from his text and comment on them.

The Marginal Revolution: Rise and Decline, and the Pending AI Revolution, pp. 106-107:

Suffice to say, LLM construction has for the most part ignored linguists and philosophers, and that also means ignoring their intuitions. LLM construction also ignored a lot of people in the AI field who insisted neural nets were a dead end. Instead, in a relatively short number of years humans invented new ways of modeling language and reasoning through language. That research program has proven wildly successful, as we have much better models of language and reasoning than almost anyone had been expecting.

That first sentence is true, sorta’. It is also misleading. As I have documented in that working paper, The Origins of LLMs, this technology is based on a continuous line of statistical thinking that extends back to the 1950s (I take a brief look at this in the next section) . It is the syntacticians, semanticists, and cognitive scientists who been ignored. The second sentence is a bit of an exaggeration. AlexNet put neural nets firmly back on the agenda in 2012.

The big problem is Cowen’s use of “model” in the last two sentences. Large language models are not causal models like those economists use. They don’t tell us anything about how language and thought work. They are algorithmic models. They are about turning input into output; just how that is done is a mystery. Until we understand the internal operations of LLMs they tell us almost nothing about language and reasoning. They give a boost to the idea that some kind of statistical process is involved, but that’s it.

This situation is deeply paradoxical. These algorithmic models perform much better than the computer models created during the “classical” era of cognitive science, the 1960s and 1970s, models that were based on linguistic theory. We knew how those models worked. We don’t know how these models work. We have purchased performance at the cost of ignorance – a formulation I have from the late Martin Kay.

AI won't unfold in society as fast as the Silicon Valley pundits think it will [Tyler Cowen]

From YouTube: 

Economist and author Tyler Cowen delivers a provocative keynote on how AI will reshape growth, work, status, and geopolitics. Mixing clear‑eyed realism with long‑run optimism, he argues that AI is both our “plan A” for avoiding fiscal crisis and a technology that will leave many people disoriented—and some high‑status winners of the old world worse off.

What’s in this video:
—Why AI will radically change jobs and status without causing mass unemployment
—Two big new job categories: running experiments and gathering data for AI
—The “human bottlenecks” that limit AI’s impact to ~2% → ~2.5% growth
—How AI could be “plan A” for stabilizing public debt and avoiding fiscal crisis
—Who gains and who loses: global poor and initiative‑takers vs. elite professionals

Cowen’s message: AI’s benefits are enormous—higher growth, longer lives, more opportunity for the poor—but they come with psychological, political, and institutional friction. If you work in or care about AI, you’re not just building products; you’re helping write the only credible plan for a sustainable and prosperous future.

Recorded live at Sana AI Summit 2026, New York, May 21st, 2026.

Three diverse flowers

Mathematicians are concerned that exploitation by the AI industry threatens the long-term intellectual interests of the field

Siobhan Roberts, As A.I. Makes Strides in Mathematics, Mathematicians Urge Caution, NYTimes, June 2, 2026.

Mathematicians issue a declaration:

On Tuesday, a group of 16 mathematicians, in consultation with colleagues and math organizations worldwide, published the Leiden Declaration on Artificial Intelligence and Mathematics. It aims to “frame the conversation about future directions,” said Dame Ursula Martin, one of the authors, and a mathematician and computer scientist at Oxford.

This effort comes as A.I. models have been making headlines with successful results in research-level mathematics. In late May, OpenAI, the maker of ChatGPT, announced that one of its models had disproved a notable 80-year-old mathematics conjecture in the field of combinatorial geometry.

The conjecture is one of some 1,200 problems posed by the Hungarian mathematician Paul Erdos. While some of these “Erdos problems” are considered throwaway questions of narrow interest, others have proved influential and field shaping. Along with a research paper describing the proof, OpenAI released a companion paper by several independent mathematicians. Jacob Tsimerman of the University of Toronto, an expert in the adjacent subfield of number theory, commented: “This is a really impressive piece of work, and I would accept it for any journal without hesitation.”

Potential problems:

Among the potential threats that the Leiden Declaration authors articulate are accuracy and reliability: Journal editors are already complaining about a flood of plausible seeming A.I.- generated papers and proofs that have turned out to be incorrect, and in ways that are difficult for mathematicians to discern.

Perhaps most pointedly, the authors raise the question of whether the many A.I. companies tackling mathematics — major players such as OpenAI, Google DeepMind and Anthropic, or start-ups such as Harmonic, Math, Inc. and Axiom Math — are keeping the field’s best interests in mind. “Technology companies’ involvement in research,” they write, “raises the risk that research questions are prioritized and incentivized because of their amenability to A.I. methods and models, rather than their deeper significance to understanding.” In turn, they point out, this disadvantages researchers who choose not to use the technology, and those who do not have access to it.

For Rodrigo Ochigame, a historian and anthropologist of computing and artificial intelligence at Leiden University in the Netherlands, and one of the statement’s authors, the latest OpenAI proof illustrates why this sort of collective reckoning in the discipline is necessary. “The story follows the same pattern as many other announcements by commercial A.I. developers,” Dr. Ochigame said. “The A.I. model is proprietary and unavailable to anyone outside the company. We get a flashy promotional video, while basic information needed to assess the scientific meaning of the result is kept secret. The company disclosed nothing about the methods, human-written prompts, training data, or computational resources consumed.”

Much of the article consists of a videoconference and email dialog with Dr. Ochigame, Dr. Martin and mathematician Michael Harris of Columbia University:

MARTIN: What OpenAI has done is throw a great deal of resources at Erdos problems, and got lucky with this one. That’s remarkable, and impressed the experts. We are not told about the model’s failures. [...]

To think of mathematics in terms of precise and neatly stated problems, like high school exams or the list of Erdos problems, is to misunderstand and diminish what makes mathematics so powerful and significant. Mathematics is not just about solving problems — it is also the cultivation of ideas, understanding, judgment, and human insight.

HARRIS The purpose, from my perspective, is to recover control of the narrative about the values and the goals of mathematics from the A.I. industry. Mathematicians are concerned that the values of the profession are being misrepresented, not intentionally but due to the media campaign on the part of the industry, which seems to want to promote the belief that they are in a position to transform mathematics — “the A.I. revolution in math,” as one headline put it not long ago. [...]

We want to affirm certain values that have characterized the profession: openness, honesty, giving credit where credit is due, sharing, transparency about methodologies, and access for independent verification of results.

An aspect of mathematics that is cherished by mathematicians is that it is one of few successful examples of a gift economy — that is to say, its economy is somehow an island of idealism in our society.

OCHIGAME Several A.I. companies are investing in dedicated teams focusing on mathematics, using problems as benchmarks and publications as training data. They are training their models to prove theorems not because they want to advance mathematical knowledge, but because they hope that such training will improve the models’ reasoning abilities more generally. [...]

MARTIN It’s important not to lose sight of the fact that what the A.I. companies are doing, what you can achieve with this technology, is absolutely extraordinary. I don’t think we’re challenging that. We’re challenging the framing, we’re challenging the behaviors around it.

I share the concern that these mathematicians express, that the commercial exploitation of mathematics is inimical to long-term research interests.

There's more at the link.