Usually companies will publish an “earnings before interest, taxes, depreciation, and amortization” or EBITDA as a way to show whether their core business is actually profitable (even if shareholders aren’t the ones to get the benefit of that business’s operating profit). But that’s not a standardized metric, and Generally Accepted Accounting Practices (GAAP) won’t define earnings in that way.

The big problem is that the more a company has to deal with high cost of borrowing and huge capital expenditures, the more disconnected EBITDA is from actual GAAP earnings. I can build a hotel for a billion dollars and rent out a bunch of rooms, while each room transaction brings in way more money than it costs for me to have a guy at the front desk give them a key and housekeeping to clean their room, I need to actually make a lot more in order to cover the cost of all the furniture I put in the room, the construction costs on the building, and the interest payments to the bank that financed the whole thing. And if the furniture and the building won’t actually last long enough to where I made all my money back before I have to reinvest a bunch of money into renovations and new furniture, then I don’t have a profitable business. Or if it can be profitable on those numbers but I can’t cover the interest payments to the bank, then the bank will eventually take it over and I’ll lose the business entirely, so owning stock in that company isn’t worth all that much.

So if they’re saying they’re barely profitable when you take out the very expensive costs of training new models, entering big expensive deals with data center companies to build out infrastructure, the stock they keep issuing to employees and partners, etc., that might keep things going a bit longer, but it’s still not actually a path to true GAAP profitability.

This right here is how all of this will come crashing down

No, this is pretty normal, and more importantly, doesn’t actually cost the company cash. They gave lenders bonds that can be converted to stock, so this loss on their books is pure paper loss, the same way wealth in owning stock in this money losing company is pure paper wealth.

These numbers are just illustration for simplicity, but imagine a company borrowing $20 billion and telling the lender that you’ll either pay back the $20 billion plus interest, or they can opt to convert it into stock worth 10% of the company. When the company is worth $100 billion, the bondholders won’t convert to stock. But if the value of the company shoots up on paper to where that same stock would be worth $100 billion, the lenders would rather take the stock than the cash repayment. And because it’s worth $80 billion more, the company will have to record that on the books as replacing $20 billion in bonds for $100 billion in stock, and an $80 billion loss compared to their last statement.

But look at the underlying financial cash flow. When that happens, the company actually takes $20 billion in debt off their books. They don’t have to repay that loan anymore. So it improves their cash flow position at exactly the same time that they’re booking a loss.

The whole thing will come crashing down for totally different reasons, relating to operational cash flow, not the actual accounting shenanigans where one kind of paper number gets converted to another kind of paper number.

“we lose money on every sale but we make it up in volume!”

Does it?

Reuters is reporting that Anthropic’s Investor Day event a few days ago, where they showed their audited Q3 financials to investors, caused institutional investors to shift their sentiment and that they are being pushed to lower their IPO valuation target down from $2 trillion to $1.5 trillion.

Coreweave is down almost 20% this week. Nebius is down more than 15%.

Investor sentiment is shakier than it was a month ago. And for hype-fueled valuations, investor sentiment is a two-way street.

That’s basically what AI is best at: incorporating lots and lots of data points (every single pixel on an image, many trillions of pixels on previous images used to train the model) to determine the best answer to a question with clear definitions of what is better or worse on a single-dimensional quality axis that most people would agree on. In this case, whether a de-noised photo looks good and accurately represents the image that could have been captured of the exact same scene using more light or a better sensor or whatever.

It’s generative AI where things fall apart. The analogy I always use is that these math techniques are great for inferring what is between pixels in a low res image, but starts going off the rails when it starts guessing what’s beyond the edges of the image. These AI algorithms are just math, and math with this many data points needs to start considering the limitations described by chaos theory (in the mathematical sense).

Once there’s a public expectation for profit from the two biggest AI labs

I don’t think that it’s necessarily the order things will go.

Anthropic and OpenAI have bills to pay, including obligations to pay a ton of money to partners/vendors in the coming years. They need cash to do that. And the cash can come from 3 main places: revenue from customers, loans from lenders (usually in the form of issuing bonds), and issuing new stock/equity to raise funds.

The IPOs are basically their last chance to raise funds by issuing new equity. Even if each of them raise $100 billion, that’s probably not enough to hit self sustaining profitability. As lenders start to get more skeptical, in a macroeconomic environment with high interest rates and high energy prices, they start to run into a very real possibility that they hit illiquidity issues, even if their core business model actually works by then (and I don’t think it will). There’s just not enough runway for them, even in the most optimistic long term projections.

Annualized Revenue rate is a made up number

It is fundamentally a projection about the future using recent results, and transparent in its methodology. It’s shit methodology, but it’s not hiding anything. So I don’t know that I’d classify it as fraud.

Playing games with “revenue” is another problem, but because that’s misleading about the past, and making an active decision on whether to classify money that was never gonna end up in their pockets as revenue share to partners or cost of revenue. Companies sometimes engage in shenanigans where they give discounts but classify the discounts as marketing/sales expenses rather than foregone revenue. That kind of stuff strikes me as more squarely in the category of accounting shenanigans.

Financial Times smells blood in the water. They’ve been reporting on leaked OpenAI documents for a bit the last few weeks.

If the major financial outlets start reporting on the negative, there may be a sentiment shift that does actually destroy the companies propped up by hype rather than fundamentals.

The number they used for annualized revenue for these rapidly growing companies is taking the most recent month’s revenue and multiplying it by 12. It’s not a guarantee about the future, but it is misleading.

The extreme version is taking a good week’s revenue and multiplying it by 52, or even worse, multiplying a day by 365. Those are outright dishonest. But not accounting fraud, because it’s not accounting at all.

The tablet one eventually came true. Desktops and laptops aren’t anywhere near as popular for personal use by non-technical people.

Conway’s Game of Life is Turing Complete and that fact has been fucking with my mind for like 25 years.

Yeah, nothing quite like visiting a channel for the first time in a few weeks, while your client spends minutes trying to load the data to actually catch up with what you missed.

It is believed that pi is “absolutely normal” in the strict mathematical definition: that in any base numbering system, any finite sequence of digits appears as frequently as any other sequence of that length. If that’s true, then yes, a particular binary string, including one that corresponds to a particular digital file (like a particular mp4 file), can be found in pi.

That said, it hasn’t actually been proven that pi is normal. If it’s not normal, then the number can be infinitely non-repeating and still never hit that particular sequence of digits.

Yeah, you’re probably right. Still, if big tech’s ad behavior (ads for stuff you’ve already bought, ads for things you’d never buy but might have looked up information about for non-consumer reasons) is any indication, I’m not actually sure they’re using really sophisticated techniques here that can’t be thrown off by one-off bored sessions.

When I’m bored, I get price quotes from Lyft and Uber to the airport. I’ll click around and look at the different tiers of service. Then I choose not to actually request a fare, and close the app.

I fly enough to where both apps probably have me as a regular airport customer, but that I’m price sensitive enough where I just won’t use their service sometimes.

I do the same with Doordash and other food apps, where I just put together potential orders to get a sense of how much something would cost if I ordered it, and then just never order it, letting that cart expire. I’m hoping this poisons their data or adds something to the price sensitivity metrics.

I also look at airfares, hotel prices, etc., a lot. It’s not for the purpose of manipulating their algorithms, as I just like to get a sense for how prices move over time. But if there’s a side effect that these variable pricing algorithms take into account people who window shop and walk away, maybe I’m helping push prices down.

If they’re gonna create a panopticon, we should fuck with their data.

Most CPUs implement a strategy called “race-to-sleep,” where it throws all the resources it has at a computing task, to finish it as fast as possible, so that it can hurry back to a low power sleep mode.

For that reason, optimizing speed often improves battery life.

Technically these are all System in Package (SiP) rather than systems on a chip (SoCs). It’s not cost effective to waste silicon area on things like memory and storage in the latest and greatest TSMC nodes, so they have several different silicon dies that they connect together using advanced packaging techniques, but where each silicon die in that package might come from a different foundry/process.

Not exactly, the bootpicker just won’t show partitions that are not flagged as bootable. This is a departure from previous versions, that would check to see whether partitions were bootable without looking at the flag, and took a little bit of investigating to figure out this undocumented behavior, but it is an easy fix:

https://asahilinux.org/2026/06/progress-report-7-1/

The “ninjas stole my dog/wife/kids, need money for karate lessons” joke itself predates smartphones. I used to see this sign from panhandlers all throughout the 2000’s.

What if Claude was one of us?