alt Hacker NewsThe entire premise on whether it is a bubble depends on if LLMs can be more than a next token prediction machine or not. Adding reasoning agents, agent swarms, and so on to enhance the results from what is essentially just predicting the next token still means this technology at its core only predicts the next token. It is not AGI and unless something fundamental changes, it never will be. There are certainly valuable applications for this technology as we have seen in coding, math, and other sectors. But it is still not more than taking in a lot data and predicting the next token.
Meanwhile, the major tech companies first financed this build out using the cash flow from their existing revenue. Once this became more difficult to justify, they literally set up companies to keep this off their books directly, and these companies are ultimately designed to take a loss and fail. But before reaching this failure stage and defaulting on debt and obligations, they went to the criminals on Wall Street and designed a financial product structured not all that different from mortgage backed securities. Also, as the "AI" aspects of their existing businesses expand, it eats into the traditional revenue generated by their core business, which if we remember, is the original collateral being used to justify all the debt in the first place. Now these core tech companies have to win "AI", whatever that ultimately means, to not only pay the debt from the build out, but to also replace their existing collateral from their original products. Think in terms of Google Search being replaced by AI. Maybe a clean transition, maybe not. Really just depends on the technology living up to the hype. If not, the hyperscaler companies designed to take a loss a fail, will fail and with it, the circular financing will pop the very real bubble that exists.
Speaking of IPO valuations as well, it's not to say that LLMs are not a valuable technology. Just look at what they have done to computer science already. But there may be increasingly limited returns on investment if every valuable problem to be solved isn't as well defined as logical computer science problems. Without solving the hallucination problem (as a result of the next token prediction limitations), the wide application of LLMs as being sold will never happen.
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Is there any evidence that you yourself are more than a 'next action predictor with a multi-modal context window'?
I don't think the nature of 'predicting the next token' is the problem. One could argue that any organism works the same way. The problem is that we've had million years of evolution to stabilize the platform such that the important parts of it work without that many hallucinations -- and even then that sometimes fails and you get failure modes like e.g. schizophrenia, where the context window gets confusing and the 'next actions' don't make that much sense anymore, seen from the outside.
An LLM never experienced evolutionary pressure the same way. There's no fundamental difference between how it was incentivized to answer correctly when asked about how many Rs there are in strawberry vs whether it is safe to leave a house through the second story window, even though for any physical organism, those two would be _very_ differently weighted during training.
> entire premise on whether it is a bubble depends on if LLMs can be more than a next token prediction machine or not
I don't think current valuations need AGI to work. They just need enterprise and personal spending on AI to keep rising exponentially for a while.
We could absolutely wind up in a case where these companies are fairly close to a trillion dollars each, but not multiple trillions, at a reasonable multiple, all without AGI.
> The entire premise on whether it is a bubble depends on if LLMs can be more than a next token prediction machine or not
A theoretically perfect next token prediction machine has limitless utility. Predict the weather, stock market, lottery number, the next step of groundbreaking cancer research, etc etc. That's not the argument against current LLMs being capable of being AGI.
Predicting the next token well is indistinguishable from intelligence. The bubble is financial, as you point out, just like railroads, radio and dotcom bubbles were. The technology didn’t die, only the companies did. LLMs are in the exploit phase, that doesn’t mean there isn’t more to explore and there may be an architecture-hardware combination that takes us an order of magnitude or two higher in a step-function-like fashion just like LLMs did 2024-2026.
But despite getting better and better at not making stupid mistakes, the models are still just next-token predictors. There’s plenty more room for them to improve without a totally different architecture.
The entire premise on whether computing is a bubble depends on if computers can be more than a bit-flipping machine or not. Adding operating systems, compilers, distributed clusters, and so on to enhance the results from what is essentially just flipping bits still means this technology at its core only flips bits. It is not a general-purpose machine, and unless something fundamental changes, it never will be. There are certainly valuable applications for this technology, as we have seen in ballistic tables, census tabulation, and other sectors. But it is still not more than taking in voltages and toggling transistors between 0 and 1.
> Adding reasoning agents, agent swarms, and so on to enhance the results from what is essentially just predicting the next token still means this technology at its core only predicts the next token. It is not AGI and unless something fundamental changes, it never will be.
A lot of people have dismissed LLMs four years ago for this reason. "It cannot possibly do coding since it doesn't think, it just predicts the next token." On the other hand, Scott Alexander did say in 2019 that next token prediction is a brilliant idea and might cover a lot, including math proofs [1]. So I wouldn't say it is impossible for either 1) LLMs to be all the way to AGI or 2) LLMs to become creative enough to tell us what the AGI architecture is.
[1] https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
Software will never take off until we can show that it’s not just a bunch of simple instructions like load, store, add, and multiply.
> Just look at what they have done to computer science already.
I do. And it’s not good.
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I never understand the “only a next token predictor” argument. I do share the view that something fundamental about intelligence might be missing in LLMs, but I don’t see how this argument captures anything. If you can predict the exact next word Einstein is about to write, every time, you’re as intelligent as Einstein! It seems very clear that the LLM is also “thinking” about where the output is headed after this next token.
I do hear this position quite a lot though so I’d be interested to hear more about what I might be missing.