What are we meant to take away from the 8000 word Zitron post?
In any case, here is what Anthropic CEO Dario Amodei said about DeepSeek:
"DeepSeek produced a model close to the performance of US models 7-10 months older, for a good deal less cost (but not anywhere near the ratios people have suggested)"
"DeepSeek-V3 is not a unique breakthrough or something that fundamentally changes the economics of LLM’s; it’s an expected point on an ongoing cost reduction curve. What’s different this time is that the company that was first to demonstrate the expected cost reductions was Chinese."
https://www.darioamodei.com/post/on-deepseek-and-export-cont...
We certainly don't have to take his word for it, but the claim is that DeepSeek's models are not much more efficient to train or inference than closed models of comparable quality. Furthermore, both Amodei and Sam Altman have recently claimed that inference is profitable:
Amodei: "If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume, in this cartoonish cartoon example, that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model, in this example, is actually profitable.
What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's much more expensive and requires much more upfront R&D investment. And so the way that it's going to shake out is this will keep going up until the numbers go very large and the models can't get larger, and then it'll be a large, very profitable business, or, at some point, the models will stop getting better, right? The march to AGI will be halted for some reason, and then perhaps it'll be some overhang. So, there'll be a one-time, 'Oh man, we spent a lot of money and we didn't get anything for it.' And then the business returns to whatever scale it was at."
https://cheekypint.substack.com/p/a-cheeky-pint-with-anthrop...
Altman: "If we didn’t pay for training, we’d be a very profitable company."
https://www.theverge.com/command-line-newsletter/759897/sam-...
In terms of sources, I would trust Zitron a lot more than Altman or Amodei. To be charitable, those CEOs are known for their hyperbole and for saying whatever is convenient in the moment, but they certainly aren't that careful about being precise or leaving out inconvenient details. Which is what a CEO should do, more or less, but, I wouldn't trust their word on most things.
I agree we should not take CEOs at their word, we have to think about whether what they're saying is more likely to be true than false given other things we know. But to trust Zitron on anything is ridiculous. He is not a source at all: he knows very little, does zero new reporting, and frequently contradicts himself in his frenzy to believe the bubble is about to pop any time now. A simple example: claiming both that "AI is very little of big tech revenue" and "Big tech has no other way to show growth other than AI hype". Both are very nearly direct quotes.
Those two statements are not contradictory, and thinking that they are belies a pretty fundamental misunderstanding of his basic thesis.
The first statement is one about the present value of AI. The second statement is about their belief of the future value of AI.
It is not about the present and future value of AI at all. It is about the present and future value of things other than AI. Here is the full quote:
"There is nothing else after generative AI. There are no other hypergrowth markets left in tech. SaaS companies are out of things to upsell. Google, Microsoft, Amazon and Meta do not have any other ways to continue showing growth, and when the market works that out, there will be hell to pay, hell that will reverberate through the valuations of, at the very least, every public software company, and many of the hardware ones too."
I am not doing some kind of sophisticated act of interpretation here. If AI is very little of big tech revenue, and big tech are posting massive record revenue and profits every quarter, then it cannot be the case that "there is nothing left after generative AI" and they “do not have any other ways to continue showing growth” — what is left is whatever is driving all that revenue and profit growth right now!
Grok 3.5: 400M training run DeepSeek R1: 5M training run Released around the same time, marginal performance difference.
I suspect that says more about Grok than anything else.