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Marcus believes that the underlying technology doesn't work. If that's true, then of course the whole thing will crash as soon as everyone realizes this. This

Marcus believes that the underlying technology doesn't work. If that's true, then of course the whole thing will crash as soon as everyone realizes this. This article is mostly making a different argument (though it contradicts itself in some places), which is that even if the underlying technology does work, and is ultimately going to create quadrillions of dollars of value and transform society, if it takes more than another 1–2 years for that to happen, then there'll still be a crash, because that's when the data center construction bills come due and the labs (especially OpenAI) don't yet have the money to pay them. It argues primarily against a hypothetical optimist who believes that everything is fine because the cash flow numbers currently work out, on the grounds that this hypothetical optimist hasn't realized that the labs' recurring expenses are scheduled to spike in 1–2 years when the data centers come online and the labs have to start paying for them. It also spends a lot of words comparing the situation to the 2008 financial crisis, because that's everyone's favorite morality tale. I am not sure that anyone is actually making this mistake (i.e., trying to predict the future by looking at labs' present cash flows). The better counterargument is what Matt Levine used to call "Netflix Theory": if the large capital investors who own stakes in the labs still believe in their valuations (which they should, if the technology works and the quadrillions are coming, which we're assuming here for the sake of argument), then they will be very highly motivated not to let their investment be seized by the labs' creditors. So the labs will not have too much difficulty raising or borrowing enough money to pay the bills.

dumberquestions 20d ago
Yes, the labs will be fine as long as investors believe in them, and they overwhelmingly do, trying to draw comparisons based on traditional market wisdom will fail because none of this is precedented.
michaelchisari 20d ago
There's a third possibility between works and doesn't work: Works but not quite good enough to make the case against commoditization. If open source or on-device AI gets good enough for 80% of consumers, then this stops being a consumer product and the only real market is people who need the high-end models. If those models are slow and expensive, certain tasks like scientific and math research can tolerate slowness, but they run up against the costs. If they're expensive, the tech industry can afford them but runs up against their inefficiencies. We need to talk about how well these improving models work in multiple dimensions: Accuracy, performance and cost. All three have to improve considerably before the debate dies down.
- ameliaquining 20d ago
  Yes, if the tech works but in a way that doesn't let the labs command premium prices for inference, then that also means a crash. But that's a fundamentals-based argument like Marcus's (despite being based in economics rather than ML science), so distinct from the one the article's mostly making.
w10-1 20d ago
> large capital investors [...] will be very highly motivated not to let their investment be seized [...] So the labs will not have too much difficulty raising or borrowing enough money to pay the bills So: sunk costs of large investors mean they'd be willing to pay high interest costs? Thus small investors can rely on this dynamic, shielded by big ones? Remember, the large investors got large in a near-zero interest rate environment (where exact timing doesn't matter as much), but those days are not coming back. Soon margins will matter most, and while NVDA and Apple have the internal discipline for that, OpenAI et al do not.

dumberquestions 20d ago

Yes, the labs will be fine as long as investors believe in them, and they overwhelmingly do, trying to draw comparisons based on traditional market wisdom will fail because none of this is precedented.

michaelchisari 20d ago

There's a third possibility between works and doesn't work: Works but not quite good enough to make the case against commoditization. If open source or on-device AI gets good enough for 80% of consumers, then this stops being a consumer product and the only real market is people who need the high-end models. If those models are slow and expensive, certain tasks like scientific and math research can tolerate slowness, but they run up against the costs. If they're expensive, the tech industry can afford them but runs up against their inefficiencies. We need to talk about how well these improving models work in multiple dimensions: Accuracy, performance and cost. All three have to improve considerably before the debate dies down.

ameliaquining 20d ago
Yes, if the tech works but in a way that doesn't let the labs command premium prices for inference, then that also means a crash. But that's a fundamentals-based argument like Marcus's (despite being based in economics rather than ML science), so distinct from the one the article's mostly making.

w10-1 20d ago

> large capital investors [...] will be very highly motivated not to let their investment be seized [...] So the labs will not have too much difficulty raising or borrowing enough money to pay the bills So: sunk costs of large investors mean they'd be willing to pay high interest costs? Thus small investors can rely on this dynamic, shielded by big ones? Remember, the large investors got large in a near-zero interest rate environment (where exact timing doesn't matter as much), but those days are not coming back. Soon margins will matter most, and while NVDA and Apple have the internal discipline for that, OpenAI et al do not.