AI firms are on the ropes, having spent way too much money building infrastructure for tools that are valuable, but not nearly valuable enough to support the money already spent, let alone what they’re planning to spend over the next two or thee years. This is bound to come to a bad end.

As Jerry Holkins puts it:

They can only loan each other money for so long. Then, they’ll socialize the losses through nationalization.

Source: Cyberbullies – Penny Arcade

At least, that’s their plan. Oliver Jutel and Gil Duran have a name for this plan: “exit through the state.”

Because I’m at heart an optimist, I like to imagine a more hopeful solution—one where this plan fails. And I legit think it might.

If Congress changes hands, and Trump becomes even more toxic (two things that seem very likely), there might not be anybody in a position to lead the charge for socializing the losses. A toxic Trump trying to hand another bunch of taxpayer money over to billionaire tech bros might actually be very unpopular. And if a Republican minority in Congress can’t get it together to come up with a plan that a significant number of Democrats will support, socializing the losses just might not happen.

But it has to “not happen” right then—with a Democratic (or divided) Congress.

If the AI firms can hold things together (with circular financing, SPVs, and the like) until there’s a Democrat in the White House, that guy will probably not be able to resist the pressure to “do something.”

If—as I hope, and kind of expect—it comes to a head before that, the Republicans might well not be able to come up with a plan that meets the demands of all their different constituencies, while the Democrats refuse to join in any plan that a large subset of Republicans will agree to. The result might just be that we just let the sucker go down.

Letting the sucker go down is what George W. Bush wouldn’t do in 2008. Except, of course, he kinda did, as far as homeowners were concerned. Banks, investment firms, and insurance companies got saved. Homeowners got hung out to dry.

My point being that the government is totally willing to let some suckers go down. The Republicans would like those suckers to be ordinary investors, computer users, and (in particular) tax payers. But I like to imagine that there’s at least some chance that the politicians will simply be unable to cobble together an arrangement to accomplish that, with the result that the AI firms go down, a bunch of AI firm executives get prosecuted for investment fraud, and all that infrastructure (data centers and large language models) gets sold off in bankruptcy, ending up in the hands of people with a certain amount of rationality (and much less debt).

I’m using “AI” here in the older sense, rather than the newer sense where it’s just another way to say LLM.

In this older sense, I don’t have anything against AI (even though I generally try to avoid LLMs). So, I thought I’d talk a little about the things I actually object to, when it comes to what people call AI these days. Specifically, what I object to (in order of objectionableness) are:

  • Using them to generate anything that looks like creative output. (It isn’t creative output, but it resembles it enough that I can waste a lot of time realizing that. That’s what I object to.)
  • The copyright theft at the base of LLMs. (I think half the profits (perhaps 40% of the gross revenues) of every AI company should be distributed to holders of the copyrights that were violated in the generation of the models).
  • The resource usage needed to run the inference engines. (Also the resource usage that went into doing the training, but that’s already sunk, so there’s no more point in complaining about it than there is in complaining about the resources that went into building your house.)
  • The fact that AI is unnecessarily used to do stuff that used to be better without it (such as web search).

I do also have some good thoughts. Generally speaking, there’s all kinds of stuff that (I hope) is going to get a lot better. Here’s an almost random sampling of ideas I’ve had. This list is most definitely not comprehensive. It’s not even the most important stuff. It’s just a few things I have been thinking of, because they’re things I want.

I would like an AI to keep track of everything I read (including whether I finish reading it, or give up part way through), and then (insted of trying to sell me something), guess what I’d like to read next. I’d pay money for this. (Not much money, but a little.)

I’d like an AI that picked up domain information what what I read. When I read an economics or finance article, I’d like it to put a little note over on the edge of the screen that I could click on, and then it would apply the information in the article to my situation. “That article, and three others that you’ve read in the past two weeks, suggest that European stocks might do better than U.S. stocks over the next year. Your portfolio is 43% U.S. stocks and only 16% European stocks. Click here for steps you could take to boost your European stock holdings.”

Of course, it should also track future results of each of those hypotheticals and compare them to both what I had before and what I actually did.

I’d like an AI to look at a blog post I’ve written and then from the taxonomy of categories and tags I’ve already created, suggest which ones I should use for that post. (There have long been “tag recommending” plugins for WordPress, but the last time I checked, none of them preferred the tags I’ve already got. Most of them seem intended for a completely different purpose from supporting your own internal tagging system. It seemed like maybe they were intended for finding keywords for maximizing ad revenue?)

I couldn’t think of a good picture for this post, but didn’t want to post it without a picture, so I thought I’d use this picture of my dog. It’s been hot here.

A dog panting, sprawled out on the carpet

AI generated image from the prompt Make an image to suggest "immanentizing the eschaton?”

My brother asked me today, “Which author do you think best immanentizes the eschaton?”

Not being a moron, I immediately replied, “Obviously it’s Charlie Stross.”

Before I could finish typing that, he suggested a Mastodon poll to ask people, to which I replied, “The poll should be ‘After Charlie Stross, what author next best immanentizes the eschaton?'”

We speculated a bit, throwing out names like William Gibson, Bruce Stirling, Neal Stephenson, and Cory Doctorow.

I assume this was all prompted by the most recent Pluralistic, in which Cory Doctorow says, after explaining why, if their AI is so close to having god-like powers, anyone would be asking for money for their AI, when they could just be using the AI to make money for them, says:

So it’s not surprising that we don’t ask why these AI god-botherers need our stupid money while they’re immanentizing the eschaton. Why would they operate a hospital if they could go meta and sell the doctorbots to the MBAs running the hospital?

Source: https://pluralistic.net/2026/07/13/go-meta-meta/#meta-meta-meta

Anyway, at this point I put the question to confer.to (so I don’t know which AI model responded), but it gave me a great reply:

The Core “Eschaton Immanentizers” (Closest to Stross)

1. Vernor Vinge

  • Why: He literally coined the term “technological singularity” in his 1993 essay. Stross stands on Vinge’s shoulders.
  • Key Works: A Fire Upon the Deep (zones of thought = different physical laws based on intelligence density), A Deepness in the Sky, Marooned in Realtime.
  • Vibe: More optimistic than Stross, but equally committed to the idea that superintelligence changes everything, including physics.

2. Greg Egan ⭐⭐

  • Why: If Stross is the journalist of the singularity, Egan is the mathematician. He goes harder into the ontological breakdown. His characters upload, fragment, merge, and cease to be human in ways that make Stross look cozy.
  • Key Works: Permutation City (simulated reality, consciousness as substrate-independent), Diaspora (post-human civilization across dimensions), Quarantine (reality modification).
  • Vibe: Colder, more rigorous, less humor. The eschaton isn’t just immanentized; it’s proven.

3. Hannu Rajaniemi

  • Why: Writes about societies that already live in the post-singularity world Stross describes arriving.
  • Key Works: The Quantum Thief trilogy (solar system transformed by post-human technologies, memory as currency, gogolized consciousness).
  • Vibe: Dense, playful, requires work—but depicts the aftermath of eschatonization brilliantly.

4. Ken MacLeod

  • Why: Stross’s contemporary and friend; they share a intellectual milieu (Edinburgh SF scene). MacLeod explores similar themes but often from a more political/ideological angle.
  • Key Works: The Fall Revolution series (The Star Fraction, The Stone Canal), The Execution Channel.
  • Vibe: More focused on how ideologies (libertarianism, socialism, etc.) interact with singularity tech.

I mean, really, I don’t know why we didn’t have Verner Vinge in mind, or Greg Egan. I’ve read both, and I’m sure Steven has as well. They’re obviously on top.

I don’t know if Steven has read Hannu Rajaniemi, but I’ve at least read The Quantum Thief, and, yes, I’d put him on this list.

The AI also rather dismissed our guesses:

Your Guesses, Evaluated

AuthorVerdictNotes
William GibsonAdjacentCyberpunk is pre-singularity. His later work (The Peripheral) touches on it, but he’s more about near-future tech’s social impact than ontological rupture.
Bruce SterlingAdjacentSame as Gibson—cyberpunk pioneer, but more interested in design, aesthetics, and near-term futures than the actual singularity event.
Neal StephensonPartialThe Diamond Age and Snow Crash have elements, but he’s more interested in memes and social structures than the hard break of singularity. Anathem gets closer.
Cory DoctorowPartialWrites about these themes (especially in Walkaway and Makers), but with a more accessible, activist, optimistic lens. Less “reality breaks,” more “how do we organize after tech changes?”

The AI actually goes on a bit, mentioning several other authors including Richard Morgan, Ted Chiang, and others, but I’ll just go hang my head in shame rather than copy and paste more AI output.

(Normally my posts are entirely my own writing. This post is an exception, in that it includes a bunch of copy/pasted AI output. I think it’s adequately tagged, though, and it’s clear that I’m not trying to pass off AI output as my own writing. Because I thought it was funny, I also generated an AI image to be the “featured image” for this post. Once again, I hope it’s clear that I’m not trying to pass off AI output as my own.)

I just heard a teaser for a story on how PDFs have become ubiquitous, with the supposed downside that AIs have a lot of trouble reading a PDF. The implication was that was bad, but I thought “Awesome! I’m going to have to switch to PDFs for more of my output! Oh, and I think I’ll start using TeX to produce more of that output!”

If you’ve ever read the contents of a PDF file produced by TeX you’ll understand.

Block of unreadable text at the beginning of a PDF file produced by TeX

Update: Turns out it was a story in the Economist. Here’s a gift link to the story (should get the first few people who click on it past the paywall):

https://www.economist.com/business/2026/02/24/the-war-against-pdfs-is-heating-up?giftId=OTNkOGVmNTgtN2ZmMi00NjAzLWExMmQtMDg0NjU5YzM1ZTY2&utm_campaign=gifted_article

And here’s the money quote:

The large language models underpinning generative AI are often bamboozled by PDFs, reading a page set in columns from left to right rather than top to bottom, say, or getting confused by headers and footers. Trouble parsing PDFs is one of the reasons AI chatbots occasionally “hallucinate”, generating nonsense.

I mean, for values of “money” that are totally confused about why LLMs hallucinate.

A graph showing a line that goes up to the right, with a big drop in April 2025 that is quickly reversed

There’s a broker offering a new product, that I’d have been all over 30 years ago. I think they’re calling it a “generated asset,” where they create a personal stock index just for you (based on a prompt to an AI), and then create an imaginary index fund for that imaginary index, and then (I assume) invest in the underlying stocks on your behalf.

Of course, you could do that yourself, but it would be awkward, probably expensive, and definitely fiddly, with a need to track the index over time.

So, instead of that (I assume), the company just promises to pay you the value of your index, and (I assume) hedges its promise by holding the underlying shares—and since this synthetic investment is a service, they can lump all these promises together and invest in all the underlying shares, and be generally sure that their hedge will be close enough that even very large stock moves won’t cost them more than investors are paying for access to this service.

As I said, I’d have been all over that 30 years ago. Now, not so much. I’ve gotten lazier and more conservative with my investments as I’ve gotten older.

But it did make me think what I’d want to invest in, if I were to create a synthetic index, and it occurred to me that the companies that are putting the least amount of money into AI crap will do much better over the next few years. So I asked confer.to if it could give me “a list of large corporations ordered by how much they talk about AI in their reports to investors.”

Sadly, the answer was no:

Based on the available data, I can’t provide a specific ranked list of large corporations ordered by how much they discuss AI in their earnings reports. While the information confirms that AI is a major topic in 2025 earnings calls across the S&P 500, the detailed company-by-company ranking you’re looking for isn’t publicly available in the sources I checked.

Here’s what we do know:

  • AI is a dominant topic: In Q3 2025, “AI” was cited on 306 S&P 500 earnings calls, the highest number in the past decade.
  • Significant increase: This is a large jump from the five-year average of 136 mentions and the 10-year average of 86.
  • Sector leaders: The Information Technology (95%) and Communication Services (95%) sectors have the highest percentage of companies mentioning AI.
  • Market impact: Companies that mentioned AI in their Q3 2025 calls saw a higher average stock price increase (13.9%) compared to those that didn’t (5.7%) since the end of 2024.

While sources like Yahoo Finance and MacroMicro track the overall trend and volume of AI mentions, they don’t publish a specific, ordered list of corporations ranked by the frequency or depth of their AI discussions in earnings reports.

This type of granular, ranked data is typically compiled by specialized financial data and analytics firms (like FactSet, which provided the data to Yahoo Finance) and is often part of a paid subscription service.

Perhaps the specialized AI tool that this broker uses to create its imaginary indices has access to the fine-grained data about AI mentions in earnings calls with investors. But I don’t care enough to go to the trouble of looking.

Poking around at the St. Louis Fed’s Fred graphing tool (to come up with a graphic to include for this post), though, led me to the graph at the top, which is of the “Nasdaq Global Artificial Intelligence and Big Data Index,” which “is designed to track the performance of companies engaged in the following themes: Deep Learning, NLP, Image Recognition, Speech Recognition & Chatbots, Cloud Computing, Cybersecurity and Big Data.”

So one option to get what I want would be to just go short on that index.

I don’t think I’ll do that either.

Turns out Cory Doctorow and I think a lot alike about the AI bubble, but he also has stuff to say about how to speed along the popping of the bubble, which would be a good thing. (Bubbles that pop sooner do less damage when they do.)

so I’m going to explain what I think about AI and how to be a good AI critic. By which I mean: “How to be a critic whose criticism inflicts maximum damage on the parts of AI that are doing the most harm.”

Source: The Guardian

Back in May, I wrote an article about AI journaling. The idea (which I had stolen from some YouTuber) was that you write your journal entries as a brain dump—just lists of stuff—into an LLM, and then ask the LLM to do it’s thing.

. . . ask the LLM to organize those lists: Give me a list of things to do today. Give me a list of blind spots I haven’t been thinking of. Suggest a plan of action for addressing my issues. Tell me if there’s any easy way to solve multiple problems with a single action.

Now, I think it’s very unlikely that an LLM is going to come up with anything genuinely insightful in response to these prompts. But here’s the thing: Your journal isn’t going to either. The value of journaling is that you’re regularly thinking about this stuff, and you’re giving yourself a chance to deal with your stresses in a compartmented way that makes them less likely to spill over into areas of your life where they’re more likely to be harmful.

I still think that’s all true, and I still think an LLM might be a useful journaling tool. My main concern had to do with privacy. I didn’t want to provide some corporation’s LLM with all my hopes, dreams, fears, and best ideas, and hope that none of that data would be misused. I mean, bad enough if it was just subsumed into the LLMs innards and used as a tiny bit of new training data. Much worse if it was used to profile me, so that the AI firm could use my ramblings about my cares as an entry way into selling me crap. (And you know that selling you crap is going to be phase two of LLM deployment. Phase three is going to be convincing you to advocate and vote for the AI firm’s preferred political positions.)

Anyway, I figured it wouldn’t be long before local LLMs (where I’d actually be in control of where the data went) would be good enough to do this stuff, and I was willing to wait.

But I didn’t even have to wait that long! A couple of days ago, I saw an article in Ars Technica describing how Moxie Marlinspike of Signal fame had jumped out ahead with a really practical tool: confer.to. It’s a privacy-first AI tool built so that your conversation with the LLM is end-to-end encrypted in a way that keeps your conversation genuinely private.

I’ve started using it for journaling exactly as I described. Because of the way the privacy is inherent to Confer, I can’t actually keep my journal within Confer—all the content is lost when I end the session. So, I’m keeping the journal entries in Obsidian, and then copying each entry into Confer when I’m ready to get its take on what I’ve written.

[Updated 2026-01-20: This turns out not to be true. Conversations in Confer do last through browser restarts. Until I delete the key for that session, I can go back and see everything that was in that session.]

I wanted some sort of graphic for the post, and asked Confer to suggest something. It came up with 5 ideas, including this one, which (bonus) actually illustrates my process:

Anyway, I’ve already written three journal entries that I otherwise wouldn’t have, and gotten some mildly entertaining commentary on them—some of which may rise to the level of useful. We’ll see.

(Asked to comment on a previous draft of this post, Confer.to mentioned the “Give me a list of blind spots I haven’t been thinking of,” prompt above, and said, “But LLMs can’t actually know your blind spots — they can only reflect patterns in what you’ve said.” Which I know. And so, of course, once I started using an actual AI tool instead of just an imagined one, that ended up not being something I asked for.)

If I keep doing this (and I think I will), I’ll follow up with more stories from the AI-enhanced journaling trenches.

The main entrance of the Federal Reserve Bank of Chicago

I don’t usually worry much about investment bubbles. There have been a lot of them over the past few hundred years, and most of them (railroads, telegraph, dotcom…) were expensive disasters largely only for the people who invested in them. Some though, such as the Great Financial Crisis of 2007–2009, were expensive disasters for lots of other people as well. So it’s worth thinking a bit about whether the current AI bubble is of the former sort or the latter—and how to protect your finances in either case.

Bad just for investors

One big difference between bubbles that are going to be wretched for everybody when they pop and those that’ll end up mostly okay except for the foolish investor’s portfolio, is whether the excess investment got spent on something of enduring value.

For example, railroad lines got enormously overbuilt in the 1840s in the UK and in the 1880s in the US, leading in both cases to a stock market bubble, followed by a stock market crash and a banking panic. But (and this is my point), the enormously overbuilt railroads were of some value. As the firms went bankrupt, the people who had over-invested lost a lot of money, but the railroad tracks, rights-of-way, and rolling stock all still existed. The new firms that got those assets, free of the excess debt, were often viable firms that went on to be successes—hiring workers, providing transportation, and eventually providing a return to the new investors. The people who got screwed were the old investors. (And not even all of them, as the original investors often saw the overbuilding happening early and sold out just as the clueless people who knew nothing about running a railroad, but just saw stocks soaring and wanted to get in on it, started piling in.)

Much the same was true of part of the dotcom bubble. A lot of money got spent on a lot of things. To the extent that it was spent on buying right-of-way and burying fiber, there was something of enduring value that ended up owned by somebody, making it one of the less-bad bubbles.

The key to avoiding catastrophe in bubbles of this sort is largely just a matter of not investing in the bubble yourself.

Bad for the economy

But some bubbles have produced horrible, wretched, prolonged difficulties for the whole economy. The other part of the dotcom bubble, besides the dark fiber build-out, was the bubble in companies with no profits and no prospect of ever having profits, whose stock prices went up 10x based on nothing but a story that sounded compelling until you thought about it for 10 seconds. As usual, that ended up being very bad for the people who invested in those companies, but it also was bad for the whole economy, because when those firms went bankrupt, they left behind nothing of enduring value.

The result was that the imagined wealth of those companies just vanished. The stock market went down, which was bad for (almost) everybody, and it produced a general economic malaise, because post-dotcom crash it became hard even for legit companies with real assets, a real profit, and a real business plan for growth, to raise money, which made actually producing that growth much harder.

Really bad for the economy

There is, however a step beyond just pouring a bunch of money into a bubble that doesn’t actually produce anything of enduring value, like a fiber optic network or a railroad. That’s when the money is raised with leverage (i.e. debt).

The 1929 stock market crash was a rather drastic example. People invested in stocks not because there was an underlying business that was worth what the investors were paying for it, but purely because the stocks were going up. That might have been okay in other times, but stock brokers had recently started allowing ordinary people (as opposed to just rich people) to buy on margin—where you just put up a fraction of the price of the stock you want to buy, and the broker lends you the rest.

In the 1920s you could buy on 90% margin, where you only put down 10% of the price of the shares. That meant that, if the stock price went down by just 10% your whole investment was wiped out, and the broker would sell you out to raise money to pay off (most of) the loan. And of course, all those sales into a falling market produced more losses, leading to the crash.

Since the 1930s you could only buy stocks on 50% margin, making it much less likely that your broker will sell you out into the teeth of a general stock market crash—although it can still happen.

Bubbles with leverage

A great example of a bubble with leverage is the Great Financial Crises of 2007. (Most people date it from 2008, because that’s when Lehman Brothers collapsed. I date it from 2007 because that’s when my former employer closed the site where I worked and I ended up retiring rather earlier than I’d planned.)

That was a particularly bad bubble. A whole lot of money was raised, with leverage, to buy housing. But very little of the money ended up being spent to build more housing (which would have been something of enduring value that would have lasted through the subsequent collapse). Instead, the money was spent bidding up the prices of existing housing, which then fell in value after the bubble popped.

So we had two of the classic producers of bad bubbles: Nothing of enduring value created, and leverage. The whole things was made even worse by the structure of the leverage in question.

This is getting rather far from my main point, so I won’t go into much details, but to raise the large amount of money that was going into houses, the rules on housing market leverage were being eased over a period of time. It used to be that you had to put 20% down on a house. Then you still had to put 20% down, but only half of it had to be cash, with the other half being funded with a second mortgage on the property (at a higher interest rate). Then they started letting people put just 3% down. Then they started letting people with good credit put nothing down. Then they started letting people with no credit put nothing down. At the same time, “structured finance” obscured just how risky all those mortgages were, meaning that when the bubble went pop lots of “mortgage-backed securities” ended up being worth zero.

Which kind is the AI bubble?

This brings us to the current AI bubble. A whole lot of money is pouring into building two things:

  • Data centers (buildings filled with computer chips of the sort used to train and run AI models)
  • Large language models (non-physical things that are basically just a bunch of numeric weights of a bunch of tokens which can be used to produce streams of plausible-sounding text)

Each of those may have some enduring value.

Data centers will have some. They will probably have a lot less than a network of fiber optic cables, which can be buried and will have value for decades with minimal cost or maintenance. Since newer, faster chips are coming out all the time, a data center is well behind the cutting edge as soon as it’s finished. Plus, training or running an AI model runs those chips hard, meaning that they probably only last a couple of years (due to thermal damage on top of regular aging).

Large language models probably have even less enduring value, because so many people are training new ones all the time. People are always trying to make them bigger (trained on more data) while also making them smaller (so they can run without a giant data center). All that means that your two-year-old LLM probably isn’t worth what you paid to build it, and a four-year-old LLM probably isn’t worth anything.

That’s how things looked a year or so ago—a perfect example of a bubble that would burn the people who sank money into it, but leave the broader economy untouched.

Sadly, that’s been changing.

First, the structure of the leverage has been changing. It used to be rich people and rich companies were building data centers and hiring software engineers to build LLMs. But lately that’s been getting screwy. Those large companies are raising off balance-sheet money with Special Purpose Vehicles (small companies that big companies create and provide some capital to, that then borrow a bunch of money to make something, with the loans collateralized by the things they’re making—but importantly, not an obligation of the big company that created them). Any particular SPV can blow up, if it turns out that the things it built don’t earn enough to pay the interest on the money the borrowed to build them. And large numbers of SPVs can blow up if financial conditions change to make it harder for all the SPVs to roll over their debts as they constantly have to keep their data centers running.

Second, they’re also engaging in weird circular investing and spending arrangements, where company A buys stock in company B which then turns around and pays all that money back to company A to buy chips, letting company A treat it as both income and an investment, while company B can pretend it got its chips for free.

Finally, there’s all the non-financial obstacles that may well throw a wrench into the whole thing. The fact that LLMs are all built on copyright violations. The fact that running data centers requires huge amounts of power and water (that has to be produced and paid for). The fact that producing that water and power brings with it horrible environmental impacts.

What to do

So, if AI is a bubble, and its one of the bad sort that will produce a panic and a recession when it pops, what should you do?

There are a lot of little things you can do that will help. I wrote an article with suggestions at Wise Bread called Are your finances fragile? It talks about what financial moves you can take to put yourself in a better position if there’s a general financial crisis. (If you’re interested in my writing about this stuff more broadly, I wrote a overview of my perspectives on personal finance and frugality called What I’ve been trying to say, that includes a bunch of links to other of my posts at Wise Bread.)

Besides that general advice, there are also a few things to strictly avoid. In particular, strictly avoid thinking that you can find some very clever investment strategy that lets you make money off the popping of the bubble. Yes, after the fact there will be some investments that make a lot of money, but no amount of keen insight will let you find and make those investments, as opposed to the thousands of very reasonable-seeming investments that will blow up just like all the rest.

Along about the end of the Great Financial Crisis I wrote an article called Investing for Collapse, which explains why any such effort is pointless. It holds up pretty well, I think.

Short version? Avoid debt. Keep your fixed expenses as low as possible. Build a diversified investment portfolio that limits your exposure to the most obviously stupid investments, but doesn’t do anything too weird or wacky in an effort to get them to zero—it’s pointless, and will probably do more harm than good.

Good luck when the AI bubble pops!