Toward, through, and out: Hoboken Terminal

An AI blizzard is headed our way [Yikes!]

Adam Satariano, Paul Mozur, Jacqueline Gu and Cade Metz, The Impending, Inescapable Deluge of A.I. NYTimes, July 30, 2026.

Boom....

From the American Midwest to the Persian Gulf, hundreds of major data centers now under construction will be turned on in the coming years. They are set to deliver an avalanche of computing power to develop and run A.I. that has no equal in the history of the technology industry, with breakthroughs that once felt revolutionary likely to become increasingly routine.

Behind each leap in A.I. are corresponding jumps in computing power. Today, there are about 20 million A.I. chips crammed into the data centers that underpin the technology’s growing abilities and usage worldwide, according to the research firm Epoch AI. [...]

In size and ambition, this moment compares to the building of the railroads in the 1800s, President Franklin D. Roosevelt’s New Deal in the 1930s, and the Manhattan Project to create an atomic weapon in the 1940s, technologists said.

“This is the largest scale infrastructure build-out in the history of humanity,” said Rob Wachen, a co-founder of the microchip firm Etched, which has raised more than $1 billion to meet the growing demand for A.I. components.

Peter DeSantis, who leads foundational A.I. models at Amazon — which provides computing power to the A.I. firms Anthropic, OpenAI and others — said the Seattle company has doubled its computing capacity since 2022 and would double it again by next year. “It’s hard to get your mind around the scale,” he said. [...]

Confidence in the Scaling Laws has led A.I. leaders to make ever bolder predictions. Dario Amodei, the chief executive of Anthropic, has said that if these laws hold for another year or two, A.I. will be able to perform huge amounts of white-collar work. Demis Hassabis, the head of Google’s A.I. lab DeepMind, wrote recently that A.I. could usher in “10x of the Industrial Revolution at 10x the speed.”

Bust?

Economists and investors have raised concerns that tech firms are spending faster than they can profit from A.I. Past infrastructure booms have been followed by downturns before the benefits of the technology were realized. The railroad boom in the 1800s, electrification in the 1920s and the dot-com bubble in the late 1990s were punctuated by economic recessions and a stock market crash as companies that overspent went out of business.

“Each time you’ve had a technological revolution, this kind of bubble bursting happened,” said Philippe Aghion, who won the Nobel in economic science in 2025 for research on innovation-driven economic growth. “A.I. is like the fourth industrial revolution and it has this aspect to it that generates a bubble.”

Moreover:

With more computing power coming online, geopolitical divisions are only set to widen.

The United States, home to about 5,500 data centers, about 10 times the next closest country, is far ahead of the rest of the world, including China. U.S. companies like Amazon, Google, Microsoft and Meta control about 80 percent of global computing power that drives A.I., according to Epoch AI. Google alone is believed to have four times as many A.I. chips as all of China’s companies, which are racing to catch up by developing new semiconductors and A.I. infrastructure of their own.

The race is on...

The article then goes on to discuss the huge increase in total chip count distributed over a growing collection of ever larger data centers under construction or proposed. We're in an AI arms race. The article then goes on to discuss the race with China, currently a fairly distant second (by an order of magnitude) to the US in chip count and gigawatts.

Mr. Nanos said the U.S. data center lead over China would likely grow over the next four to five years, before China’s domestic chips are produced at scale. After that, China should begin closing the gap.

“The advantage will run out,” he said.

The U.S.-China race threatens to leave the rest of the world behind. France, Germany and other nations are trying to encourage data center construction across the European Union, which has 5 percent of global A.I. computing power, according to a report by A.I. developers and policy experts in the region. Europe has been hampered by electricity and land access, permitting and financing.

Changes in the labor market:

“There’s going to be millions of jobs destroyed, millions of jobs created,” said Erik Brynjolfsson, an economist who is the director of Stanford University’s Digital Economy Lab. “That’s going to be very difficult. Even if new jobs are created, they’re not the same jobs.”

The last three paragraphs are about recursive self-improvement.

Logic and language in the brain

yeah I know everyone’s gonna say it’s an oversimplistic view, LeCunn-pilled, whatever. Clearly we’ve managed to squeeze a lot of reasoning out of scaling words…you’ll talk about hidden CoT layers, etc…but that’s not how humans work at all.

I’ve always been a fan of the cogsci… pic.twitter.com/4VI2efjSN2

— LaurieWired (@lauriewired) July 29, 2026

The abstract of the linked article:

Humans are endowed with a powerful capacity for inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to express complex and structured meanings. Some have therefore argued for a tight relationship between complex thought and language, postulating that reasoning, including logical reasoning, relies on linguistic representations. We systematically investigated the relationship between logical reasoning and language using two complementary approaches. First, we used noninvasive brain imaging (fMRI) to examine neural activity as healthy adults engaged in logical reasoning tasks. And second, we behaviorally evaluated logical abilities in individuals with extensive lesions to the language brain areas and consequent severe linguistic impairment. Our findings reveal that the language brain network is not engaged during logical reasoning, and patients with severe aphasia exhibit intact performance on logic tasks. Instead, inductive reasoning recruits the domain-general multiple demand network implicated broadly in goal-directed behaviors, whereas deductive reasoning draws on brain regions that are distinct from both the language and the multiple demand networks. Together, these results indicate that linguistic representations are neither utilized nor required for inductive or deductive logical reasoning.

Why Adam Hunt has “flipped from being bullish to being bearish about AI.”

Recently I've flipped from being bullish to being bearish about AI.

I think I'm updating my bearishness to be more solidly bearish. Early thoughts (which I hope to be disproven in the next year or so, I would prefer progress) and my reasoning:

The whole 'it turns out if you… pic.twitter.com/NERVF9LiAq

— Adam Hunt (@RealAdamHunt) July 28, 2026

They do it all for us, Mickey D’s!

Recent Chinese Innovation

Lerner, Josh and Narain, Namrata and Papanikolaou, Dimitris and Seru, Amit and Xu, Zunda Winston, Chinese Sputnik Moments? (July 13, 2026). Available at SSRN: https://ssrn.com/abstract=7114818

Abstract: China's technological progress in recent decades has been viewed with admiration, alarm, and (in some cases) doubt. To better understand the Chinese innovation ecosystem, we compile a dataset of almost 14 million domestic Chinese patent publications. We focus on the subset of critical technologies identified by the U.S. Department of Defense. Several surprising patterns emerge from the data: Chinese patenting is strongly associated with other measures of innovative progress; patents are not concentrated in corporate giants such as Huawei; universities have played a key role in innovation, much greater than state-owned enterprises or government-owned facilities; and fewer than one in ten Chinese critical technology patents involves an inventor with U.S. experience or training. Finally, using four text-based measures of patent quality, we show that the rise of Chinese patenting in critical technologies has not been associated with a decline in quality relative to the U.S. awards.

H/t Tyler Cowen.

Three important agents [think about it]

The three most important agents in an agentic bio workflow. pic.twitter.com/sPt4bNr42M

— Ash Jogalekar (@curiouswavefn) July 29, 2026

The Vera Rubin Observatory in Chile has been discovering objects we hadn't even imagined existed

On the YouTube page:

Vera Rubin’s First Images JUST STOPPED THE WORLD! The Vera Rubin Observatory has already discovered objects that scientists never expected to find.

What if the most revolutionary telescope in history isn't looking deeper into space—but watching the universe change in real time? In this video, we explore the astonishing first discoveries from the Vera C. Rubin Observatory, including a 163,000-light-year stellar stream, an impossibly fast-spinning asteroid (2025 MN45), millions of newly detected celestial objects, and why astronomers believe Rubin is about to transform astronomy forever.

Unlike Hubble or the James Webb Space Telescope, Vera Rubin repeatedly scans the entire southern sky every few nights, creating a living timeline of the cosmos. That unique capability has already revealed hidden galactic structures, strange asteroid behavior, and a flood of discoveries that previous generations of telescopes completely missed.

You'll learn how Rubin's Legacy Survey of Space and Time (LSST) works, why it generates millions of alerts every night, what makes asteroid 2025 MN45 seemingly impossible according to current physics, and how the observatory is expected to map nearly 20 billion galaxies during its decade-long mission. Every image is adding new pieces to one of the biggest scientific puzzles of our time.

Could these discoveries change our understanding of dark matter, galaxy formation, planetary evolution, and even the future of our Solar System? The first images suggest we may only be witnessing the beginning.

Rounding the turn into Newport

Framing my discussion of The God Test, Part 1: Rorschach, reason, and whaling – [GT-3]

I’ve got to bite the bullet: I’m just going to have to go through a bunch of (preliminary) stuff before I can really engage with The God Test. My current target is to be in a position to publish a proper review of the book in 3 Quarks Daily for the week of August 9.

Rorschach Recap

I want start by recapping the Rorschach metaphor I introduced in the previous post, More on how I’m approaching The God Test – Rorschach! [GT-2]. What I like about it is that has a shape, there’s something there, but it’s not clear what. So we have little choice but to project onto it in order to (begin to) make sense of it.

First: It is a new kind of thing, an artifact we can converse with in an open-ended and natural way. The steam engine was the same kind of thing. It was an inanimate object that moved over the surface of the earth under its own power. Previously only animals (& humans as animals) had that power. So it becomes an iron horse. Just what are AIs? What’s their nature? That’s one thing.

Second: How it works is opaque. We know how to create large language models (LLMs), but we don’t know how they work. That’s new. We may not have understood the deep physics of the steam engine, but we certainly knew how they worked.

Third: We don’t know what they portend for the future. To some extent this is a function of the first two: How can we, how should we, interact. But it is also a function of the future, which is undetermined. We just don’t know.

Rhetorical force over reason

This is an argument I made in the first working paper I published after the release of ChatGPT in November of 2022: ChatGPT intimates a tantalizing future; its core LLM is organized on multiple levels; and it has broken the idea of thinking (February 6, 2023).

What do I mean by that, has broken the idea of thinking? Prior to ChatGPT it was obvious that humans could think and computers could not. [Yeah, I know, there’s Deep Blue defeating Kasparov in chess. That just changes the dates, not the argument.] The difference in performance was so obvious that the fact that we don’t really know how humans think wasn’t much of an issue. Now it is. Sure, we can still say that we can think and the AI’s can’t, but that’s just a line and without good explanations on both sides of the line, it seems a bit arbitrary, if not desperate.

I made a particular argument about Searles’ (in)famous Chinese Room thought experiment. I read it when it was first published in Brain and Behavioral Science in 1980. I wasn’t impressed. Why not? He didn’t say anything about any of the techniques used in AI or computational linguistics (CL). How could anyone possibly take that seriously?

He talked about intention, that’s how. Meaning requires intention and only living things can have intention, a remark he made at the end of the article. Without intention the most you get is syntax, but no meaning. Searle could get away with that because, in the first place, the concept of intention has a long history within philosophy – it has a subtle meaning, but that can wait for a later post – and so philosophers, his main audience, were comfortable with it. That’s one thing.

But there’s something more important, something that we can see only in retrospect, and that’s the simple fact computers very obviously could not translate from Chinese into English or into any other language. That difference carried tremendous weight. We don’t have a subtle behavioral difference between computers and humans that requires a subtle and sophisticated argument. To a first approximation, almost any argument would do. As far as I was concerned, “intention” was just a fancy word for something we don’t understand. But that’s not an argument anyone needs to take seriously. The behavioral distance is quite sufficient to carry the argument for those who insist that computers can’t and will never be able to think like humans.

Now the behavioral evidence has changed. Sure, differences remain, but the evidence is shifting. The old arguments remain and those who believed them still do so, but it’s getting harder. The need for explicit arguments grounded in explicit accounts of computers, and also brains, is growing.

Whaling and expertise

What’s an expert in machine learning and LLMs actually expert in? For some time now I’ve been arguing that investing in AI is like investing in a whaling venture where the captain and crew of the ship know all there is to know about the ship and how to handle it but know little or nothing about whales and their behavior and about navigating around the Cape Horn and in the South Pacific, where the whales live. What are the chances of that voyage being successful? Not very good.

The people who have created the current AI technology are like that captain and crew. The know how to sail the ship. But they don’t know much about language or cognition. They don’t actually know much about the human mind. Here my point is not about the fact that the models are opaque, but that human language and cognition are highly structured and they don’t believe that one needs to know (much of) anything about that not only to build AI but to make confident prediction about the future of AI.

Gary Marcus, Subbarao Kambhampati, and others have been consistently arguing that, yes, the current technology is remarkable, but we are going to have to adopt classical symbolic techniques if we are to fully develop the technology so that we have accurate and safe systems. Marcus is arguing from his knowledge of human language and cognition. As far as I can tell, Wright doesn’t take that seriously. I know that he had Marcus on his NonZero podcast, and that he lists Marcus in his acknowledgements, but that he doesn’t discuss Marcus’s ideas. I conclude that he doesn’t take that line of argument seriously.

That’s a mistake, but this is not the place to make my own arguments on this issue. My point is simply that expertise in AI is no generally construed to encompass knowledge of, expertise in, human cognition and language. I can’t see how that is going to work out well in the future.

[Note: If you’re curious about my views, on this subject, read the article linked in the first paragraph of this section. My views all over the place here at New Savanna, particularly around the work of the mathematician Miriam Yevick. Also, check out the experimental work I’ve done with LLMs.]

The odd destruction of books en masse by AI companies [Homo economicus on a binge]

I'm a second-generation bookseller. My family runs Houston's largest used & rare bookstore and I'm building an AI tool for used bookstores. We got hit by exactly these orders, including a single order for 70 obscure books that made us pause online sales entirely. So I dug in. I… https://t.co/IK7OE8f1DG

— Charlie D. Becker (@charliedbecker) July 28, 2026

Firetruck

Behavioral similarities in the way chatbots and oral poets perform

Kush R. Varshney, An Annotated Reading of ‘The Singer of Tales’ in the LLM Era, https://arxiv.org/html/2502.05148v1 Feb. 2025.

Abstract. The Parry-Lord oral-formulaic theory was a breakthrough in understanding how oral narrative poetry is learned, composed, and transmitted by illiterate bards. In this paper, we provide an annotated reading of the mechanism underlying this theory from the lens of large language models (LLMs) and generative artificial intelligence (AI). We point out the the similarities and differences between oral composition and LLM generation, and comment on the implications to society and AI policy.

Varshney develops his argument by interlacing passages from Albert Lord's The Singer of Tales with comments on LLMs. This is a very interesting way of reviewing your understanding of LLMs in relation to a specialized kind human language performance.

You might want to consider two of my blog posts:

GPT-3, the phrasal lexicon, Parry/Lord, and the Homeric epics, July 16, 2022.

In some ways, some contexts, LLMs may provide a useful model for human language, March 24, 2026.

In this more recent post I discuss empirical evidence about human memory for F.C. Bartlett's classic book, Remembering: A Study in Experimental and Social Psychology (1932), David C. Rubin, Memory in Oral Traditions: The Cognitive Psychology of Epic, Ballads, and Counting-out Rhymes (Oxford 1995).

The Impact of the Sewing Machine on Women

Philip Ager and Davide M. Coluccia, The Impact of the Sewing Machine on Women

Abstract: This paper provides novel evidence on how technological change shaped women’s labor market participation, fertility, and marriage in 19th-century Massachusetts. We distinguish between the sewing machine’s dual role as a manufacturing technology and as a household appliance. Using rich town-and individual-level longitudinal data, we show that this innovation induced divergent responses across the wealth distribution. Women from lower-wealth households increased labor supply, delaying marriage and reducing fertility. In contrast, for wealthier women, the sewing machine functioned as a domestic efficiency tool, enabling earlier family formation and greater civic engagement while reducing market work. Our findings demonstrate how household constraints and social norms mediate the effects of labor-saving technologies, suggesting that technological progress can reinforce inequality by influencing women’s economic and social roles.

H/t Tyler Cowen.

On Washington St. in Hoboken