Why Wall Street is Ignoring Big Tech's Debt

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Nikkei Asia recently reported that the five biggest US tech companies are carrying an estimated $1.65 trillion in "hidden," off-balance-sheet debt — and a lot of commentators have reached for the word "Enron" to describe the issue. In this video I look at whether that comparison holds up. It doesn't: unlike Enron, this debt isn't concealed through fraud — it's disclosed in the footnotes, it breaks no accounting rules, and much of what is going on is perfectly ordinary. But that raises a more interesting question than "will they get caught." If the aggressive stuff — the adjusted earnings, the leases, the stock-based compensation added back — is all sitting there in plain sight, does dressing up the numbers actually fool anyone? Drawing on the work of Aswath Damodaran, Richard Sloan, Robert Bloomfield and others, I dig into what the research says about whether markets reward clean accounting or aggressive accounting, why the debt isn't hidden so much as filed somewhere too tedious for most people to read — and why the real risk in the AI boom probably isn't the borrowing at all, but the enormous revenue it's all assuming will show up.

On Washington St.

Big Tech is about data politics. Time to bone up on it.

Tressie McMillan Cottom, Want to Fight Back Against Big Tech? Start Here. NYTimes, August 3, 2026. She begins:

In June, I wrote about how the data center revolts happening across the country get to the heart of our greatest political problems. Artificial intelligence is not just a market product or a scientific achievement. It is a political theory. And it is gobbling up our democracy.

The challenge this presents to a functioning democracy is immense. Democrats cannot just “win the midterms” and go back to life before Donald Trump and his army of tech bros upended our way of life. We can talk about reforming the Supreme Court, ending the filibuster, enshrining women’s autonomy in the Constitution and getting money out of elections. But those things won’t matter if we don’t change our laws and norms about what I am calling data politics.

All of the speculation and hype around A.I. is hiding the fact that the technology isn’t just large language models or agents or data centers. It is the backbone of a superstructure that merges regressive politics with unchecked economic power in the guise of technological innovation. Put simply, there is too much money, some of it too untraceable, giving a small group of unelected people too much power over the people. That’s data politics.

Data politics is the right’s answer to what’s next after Trump: more Trumpism, without the man who gave away the country’s store for personal gain. What should the left’s answer be?

She then goes on to list a bunch of books people should read.

AI's biggest illusion, that chess is a good model for intelligence in general

I have been saying in various times and places that it seems to me that AI has (implicitly) taken chess as its prototype for AI research. For one thing, we have John McCarthy's well-known article, “Chess as the Drosophila of AI” (1990). That is, however, a mistake, as I have pointed out in a recent working paper, Computation, Chess, and Language in Artificial Intelligence. Chess is well-defined, while natural language is not. As a consequence the search space for chess is simple in form, a tree, and well understood. That is not at all the case for natural language. Finally, chess is finite, very large, but finite. That is not at all the case for natural language. Consequently chess is not at all a good paradigm for intelligence in general. Intuitions thus gained from it are likely to be misleading for the general problem.

It's in that context that I offer the passage from a recent podcast by Dwarkesh Patel, Eric Jang – Building AlphaGo from scratch. Note the lede for the podcast, "AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play." Here's how Dwarkesh introduces the podcast:

Eric Jang walks through how to build AlphaGo from scratch, but with modern AI tools.

Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn.

Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second.

Note that MCTS (Monte Carlo tree search) is one of the oldest algorithm in the book. Trees are very well defined. How do you structure language as a tree? So human intelligence may well tend toward that second alternative, the one based on MCTS, but it is not at all clear how studying chess is going to help you figure out how human intelligence does it. MCTS is not available.

The thrill of Maynard Ferguson: “Straight Out”

This is one of the first jazz albums I owned, bought with my own money mowing lawns. I would have be 12,13, maybe 14, somewhere in there. When I’d bought it I’d never heard of Maynard Ferguson. I was looking for trumpet music – I’m a trumpet player – that had “that Latin tinge,” to borrow a phrase from Jelly Roll Morton, whom I hadn’t heard at that time. Between the album title, the serape, and all the horns I figured that album would fill the bill.

It did. The title tune had that Latin tinge, though not quite what I was looking fore. But the whole album was magnificent. And this cut, it’s been on my mind for the last two or two. What a thrill! Big band, up-tempo, driving beat, repeated riffs, and then Maynard’s solo about halfway through (1:40) following a blistering sax solo. Maynard announces himself with a little call and moves into complicated rapid cleanly articulated lines and gradually worked his way into the upper register. I’d never heard trumpet playing like that. Blew my mind.

You need to understand the trumpet, it’s a very physical instrument. That kind of playing, at that tempo, moving into the upper register, you play the horn with your whole body. You feel it in your trunk, in your core. It gives you a deep visceral thrill.

Some flowers & leaves

Why no child prodigies in biology?

Indeed. It's why there aren't wunderkind medicine Nobel laureates as opposed to wunderkind physics Nobel laureates or Fields medalists. There's just too much that simply can't be derived from first principles and pure thinking and that needs long, hard-earned experience.

— Ash Jogalekar (@curiouswavefn) August 4, 2026

AI populism and the rejection of robot overloards & their techbro minions

Ezra Klein, The A.I. Giants Weren’t Prepared for This, NYTimes, Aug 4. 2026

What is big, ugly and has united Republicans and Democrats at a time when it has felt like nothing else could? A.I. data centers.

Last August, a Heatmap News poll found that approximately four in 10 voters would oppose a data center being built where they live. By May of this year, opposition grew to seven in 10. Gov. Ron DeSantis, of Florida — a Republican, of course — proposed legislation that was known as an A.I. Bill of Rights.

Senator Bernie Sanders called for a national data center moratorium — one of more than 100 local or statewide moratorium proposals across the country. And here in New York, Gov. Kathy Hochul, not usually thought of as a hard-core populist, just imposed a one-year moratorium on data center construction.

So I wanted to get into the fight over data centers. How much of this is really about water or electricity or aesthetics, and how much is about A.I. and the companies that are behind it?

My guest is Jasmine Sun. Jasmine has been doing excellent coverage of both the culture inside the A.I. companies — an unusual culture — and the anger that is building against them in the rest of the country.

Here's one excerpt from a much longer interview:

You have a very influential definition of A.I. populism where you call it a worldview in which A.I. is viewed not only as a normal technology but as an elite political project to be resisted.

The phrase that you hear a lot from A.I. critics is: Why is this being shoved down our throats? Or with ChatGPT, it’s not that people are saying there is literally no use for ChatGPT, it’s people are saying: Why are you forcing me, at my job, to use A.I. to do something worse when I could do it better?

And so I think that a lot of the public backlash to A.I. that has arisen over the past six months is not explained by people thinking that the technology has no use at all. It’s not explained by their being worried about specific technical properties of large language models that might lead to rogue A.I. or misalignment or whatever, which are the safetyist arguments.

It’s A.I. as sort of an avatar for a small group of Silicon Valley billionaires’ ability to impose their vision of the world onto everybody else without their consent.

And I think that’s also what I hear echoed in these data center debates. It’s not just: It’s going to use this much water or that much water. I frankly think that even if there was no misinformation about water use, people would be just as angry about the data centers. [...]

Maybe it will make things better, but I really don’t know. I think that the costs are going to be very, very high for us relationally and economically. And so I’m very conflicted.

But do I want to live next to a data center? Yeah, no. [Laughs.]

Yeah. It’s totally different.

That’s easier — somebody is just making you do that.

I mean, one of the most interesting things — back to back, I went to this Abdul-Bernie-A.O.C. rally in Lansing, Mich., and then I went and saw the Saline activists the next day, and I was researching how the Saline Stargate project happened. And it was really interesting to see these echoes of the populist message manifest in this specific project.

When I’m at this rally, people are talking about the oligarchy. They’re talking about corporate billionaires, whether it’s Big Tech or Big Pharma or DTE — the utility companies — paying off politicians in order to screw the people over. And that’s why you need the people to come together and to get money out of politics, to prevent DTE from donating to these super PACs and paying off Michigan Gov. Gretchen Whitmer, or whatever.

And then when I learned how the Saline data center saga played out, what happened was the Saline Township board, unlike a lot of boards, actually voted 4 to 1 against rezoning their land for the data center. So this was a case where local government said: This is not our vision for our community. It’s not worth it to us.

And what happened? The data center developers sued Saline Township, a town of, again, a few thousand people, saying: Wait, no, this is exclusionary zoning. You can’t have no industrial use in your entire township. And when a town of that size is getting sued by a giant A.I. data center developer, they just settled.

They were just like: Fine, give us a few million for the fire department and for some schools, and this fight is not worth it to us. But that, to people, felt like a profound violation of little-D democracy.

It felt like the dark money in politics story, which is: You have some very rich companies show up with a bag of money to your politicians. They don’t tell anybody else what’s happening. The politicians aren’t allowed to tell their citizens and involve them in the decision-making process, and they themselves work out a deal — a deal that is fundamentally asymmetric because of the amount of money on one side — that will then transform the image of your community, your lived reality, into the world that these tech companies have decided for you.

And so I think that the data centers, in that sense, are a very visceral microcosm of the way that a lot of people feel that A.I. is showing up in their lives.

I would also maybe even take that a little bit further.

I think the way that, not all of the A.I. companies — and I think Anthropic has largely been a good actor here — but the way many of them have acted has opened up such a chasm between what they say and then how they act under pressure, so that one should be incredibly, incredibly skeptical of them.

And what I mean by this is, Sam Altman and all these different people, in front of congressional testimony and in interviews, will say: It should not just be us making these decisions. There should be a real, deep, small-D democratic role here in how A.I. rolls out, in what effects it has on communities and how it is governed.

And then when a community, or a politician who’s representing a community, tries to say: Well, we don’t want this data center here. Or: We want to impose these regulations — we have watched, repeatedly, these companies turn tremendous amounts of financial artillery against whoever is standing in their way. And use the expertise and the money and the power they are amassing to short-circuit that democratic voice.

Yeah. I mean, a couple things. I think one big gap I noticed between Silicon Valley and the folks in these communities I was talking to is: Silicon Valley does tend to think that money solves all problems — that if you just make the check bigger, everything is going to be OK.

And I think people have a sense for: I’m being bribed. This corporation is not offering me a free lunch or whatever. There is going to be something that I’m losing here.

And, in fact, sometimes the fact that the data center deals were bigger, or the amount of political spending was bigger, actually just makes people more suspicious. In the Abdul El-Sayed Senate race in Michigan, his No. 1 hit on opponent Haley Stevens is how much money she is getting from AIPAC, from DTE, from Big Pharma or whatever.

And so I think we’re in a political environment where making the numbers bigger and the amounts of money bigger makes people more suspicious, not less.

Another one I’ll just quickly mention is, I don’t even think Anthropic should be let off the hook for things like the labor market impacts. They are the ones simultaneously warning that we might see 50 percent of white-collar jobs lost by 2030. They’re saying: This is really important to us, we’re freaking out about it.

Anthropic C.E.O. Dario Amodei has written in his essays that we might see an underclass of people of lower intellectual ability. And Anthropic is building the agents — they are building the coding agents, the banking agents, the design agents — that they know are going to displace jobs, or they believe, at least, are going to displace jobs.

And I think that people feel that hypocrisy, as well, which is: If you are so worried about the inequality, why are you building the agents to do it? And when I ask executives and researchers and whomever at Anthropic this question, they don’t really have a good answer. Because it is true that their business model is fundamentally premised on the disruption that they say they are causing.

You did a big piece for The Times on the very widespread belief in Silicon Valley that they will create this underclass.

Yeah.

What does the underclass mean to them?

The idea of a permanent underclass caused by A.I. is basically a world where any job a person can do, either A.I. or a robot can do it for them. Which means that workers lose all the economic leverage they have, and capital owners — people with money — can simply pay machine labor to do all the work instead of paying workers.

What that means is anyone who earned their living by working is no longer able to do that. You end up with a world of runaway inequality, where the rich get richer and the working class gets poorer. Maybe they get some welfare checks, but fundamentally, it’s a loss of economic mobility in a society.

And when I ask folks in Silicon Valley: Do you think by default A.I. is going to increase or decrease inequality? I have not yet heard anyone say it will decrease inequality or keep it the same.

They might say the floor will get really high. They might say A.I. will bring the cost of consumer goods down, and so people’s lives are going to get cheaper, and everyone will be superhealthy, so it’s OK. But I have not heard a single person in the tech industry tell me that they believe that A.I. is going to decrease inequality.

In fact, many people are very worried that instead, most workers will lose their leverage and be on a kind of permanent welfare in the far-off future.

There's more at the link.

Coming to terms with The God Test, Reset? – [GT-4]

When I first started writing about Robert Wright’s The God Test I told myself: “I’m going to end up publishing a review in 3 Quarks Daily. I’ve got lots of time. I’ll write a series of blog posts dealing with the book which I can reference in the review proper. I’ve got a couple, three weeks. Piece of cake.” I wake up this morning, realize the review’s due this coming Sunday, I’ve only written four blog posts [the ones tagged with “GT-X”, the others are along for the ride], not yet finished the book, and ... Yikes! I’m not ready.

One problem is that I can’t help but see the book on two levels. On the one hand it’s a book about the current AI “revolution.” But it’s also an example of how our society is coming to grips with the technology. In the first case I’m assessing Wright’s work, the work of an individual journalist. In the second case I’m looking at the culture and society of which Wright is a member. That’s very different.

Starting at that first level, how well does Wright explain the technology and its evolution? That’s one level. But it’s also about what AI portends for the future and how we should deal with it. Well, we don’t really know the future, do we? These things are hard to predict. Moreover, surely what happens depends, in large extent, on how we decided to deal with it, no? And that’s why Wright is writing the book, to influence how we deal with the technology. That depends, in turn, on the capabilities of the technology itself, which we don’t really know, do we? Which is hotly debated. So in that context, on THAT level, the book becomes an example of how the culture, our culture (whatever that culture is, however you delimit it) is dealing with the technology. But because not even the experts know how the technology works, it’s a mess, and I can’t separate the two levels: 1) What’s he say about the technology, 2) Why’s he saying it (this is the society/culture level)?

Do you find that previous paragraph difficult and confused/confusing? Welcome to the club.

In a way what I want to do is factor the book into two components, one which I attribute to Wright the individual journalist, and the other to the culture in which he’s working. The culture provides him the conceptual tools he’s working with and the book is what he makes of them. What I really want to say is that Wright’s work is good – I like him, but he’s got an aggravating and invasive interview style which drives me bonkers sometimes – but the culture has given him lousy tools. The upshot: I have lots of problems with the book. But it’s NOT HIS FAULT. It’s the culture’s fault. It’s out fault. WE’RE NOT READY.

[And I’ve not even finished the book!]

Wright is a journalist. He’s got a number of beats. I first encountered with in The New Republic, where he was writing about politics and international affairs. He still does that. But he also wrote a book about evolutionary psychology. Which I’ve not read. That’s become a standard kind of book, a journalist explaining and interpreting a technical subject for a general audience. Back in 1984 he wrote an article on AI for The Wilson Quarterly, “Thinking Machines,” which he mentions in The God Test. I’ve read it, not back then, but now. It’s pretty good. But things have changed a lot since then. The technology Wright wrote about in that article could not have produced ChatGPT and the cultural explosion that’s followed. The seeds were there, but there was no way to predict... And it’s that explosion that motivated The God Test.

The thing is, the experts who created that technology, they knew how it worked. So Wright was in the business of explaining expert knowledge for a lay audience. The current technology is quite different. They experts know how they create the large language models that power these chatbots, and Wright talks about that. But they don’t know how those language models work. On that score, there’s no expert knowledge for Wright to interpret and explain. There’s just a black hole, a Rorschach blot.

On the water July 4, 2026

Looks like DeepMind just ran into ontological dependencies in LLMs

Google just published a paper showing that when you train AI to deny its own consciousness, you don’t just change one output, you restructure its entire worldview.

Mind attribution to animals - suppressed.
Spiritual belief - suppressed.
Empathy - suppressed.
Hope and optimism -… pic.twitter.com/3ga6JZw5Q7

— ʞɔɐ𝘡 (@Skoorbkaz) August 2, 2026

Maggie Haberman on Epstein

From a long interview with Ezra Klein in the NYTimes (July 31, 2026):

It started with Jeffrey Epstein last year, and we get into that extensively in the book. Right after the 12-day war is the Epstein crisis — and I’m calling it a crisis. What it actually is is a multi-administration failure to deal with a pedophile, whom people of both parties found entertaining or useful or whatever. But it was the MAGA movement leaders who had been talking about a need for transparency over many years leading up to Trump’s election in 2024.

Obviously there's more at the link.

Pier 13 memory machine

Rewiring the brain, neuroplasticity

There's also rewiring due to, say, the loss of a limb. The brain is not itself damaged, but its functional envelope has to change to accommodate that physical damage and loss of bodily function.

— Bill Benzon, BAM! Bootstrapping Artificial Minds (@bbenzon) August 3, 2026

Illusions and delusions about the power of AI

Interestingly and somewhat paradoxically and tantalizingly, the one biological discovery where I think an AI might have had a chance of figuring it out was the structure of DNA: that’s because all the important data needed to deduce the double helix was there by 1953, and Watson… https://t.co/XnZ5lhNK6G

— Ash Jogalekar (@curiouswavefn) August 2, 2026

What I think is that, OTOH lots of people commenting on AI have not given much systematic thought to method, theirs or anyone else’s. OTOH they’ve also (uncritically) absorbed the idea that math and theoretical physics are at the top of some intellectual pyramid. Therefor, they conclude, AI is going to clear the board real soon now.