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Another indicator that AI is a bubble. Anthropic just released Claude Opus 4.7, and users are reporting significantly higher token burn rates (and therefore costs) for what appears to be a minor improvement over Opus 4.6. Discussion on Orange Reddit is here: https://news.ycombinator.com/item?id=47816960 and a tracker of the increased token burn rate is here: https://tokens.billchambers.me/leaderboard
The token tracker is based on user reporting, but has been fluctuating between 37% and 45%.
Even if AGI is actually possible with LLMs (or at all, but I'm not trying to start a discussion on metaphysics here), it looks like the capital needed to achieve it is drying up before it can be reached. Anthropic's move here (combined with them handicapping Opus 4.6 a few weeks ago) seems to clearly be an attempt to achieve profitability. The free/subsidized rate train for end users has pulled into the station, and now you have to pay more for the same (or worse) capabilities you were enjoying before.
I normally don't care much for the median Hacker News commenter (if me calling it Orange Reddit didn't already give that away), but I do find them to be a useful barometer for general sentiment in the tech industry. And a few months ago I would have said roughly 60% of HN users were AI believers/enthusiasts, 20% neutral or unsure, and 20% anti/negative. Anthropic's antics over the last few months (and Sam Altman's antics for his entire life) seem to have soured their views significantly, and I see this as a big sign of a sea change in sentiment about AI in the tech industry.
At least for me personally, I just hope this leads to less retarded mandates from my higher-ups about using AI X times a month etc. (we're literally tracked on usage and it can affect our raises/bonuses).
For everyone here, nut perhaps especially the AGI believers, have your feelings changed at all over the last few months?
I'm pretty convinced it isn't, based on a thought experiment I read about.
The argument goes basically like this:
Suppose you take the latest and greatest LLM and use it to generate a huge corpus of text and use that text to train a new LLM. And then repeat the process a number of times. Intuitively, it seems unlikely that the result will be any better than what you started with. And apparently both experiments and mathematics indicates that what happens is "model collapse," i.e. with each iteration the new model performs worse. Because you always lose a little with each iteration. Assuming that's all true, it follows that LLMs must be missing some essential attribute possessed by human brains. Because we apparently picked ourselves up by our bootstraps and created from scratch all the text which is used to create LLMs.
Anyway, it's just an argument I read and found to be persuasive. Feel free to correct me.
To me it's pretty obvious that AI is wildly over-hyped. But even so, the progress which has been made in the field is nothing short of astounding.
If nothing else, it's seems virtually certain to me that governments have realized the strategic implications of AI. Even without any private investment at all, the United States, China, and various other countries can throw quite a lot of resources at the problem.
Not really, I'm still pretty confident that (1) within the next 10 years or so, we (humanity) will get to AGI; and (2) regardless, there will be huge changes to the world economy.
Where did you hear that anyone is proposing to reach AGI via LLMs by training LLMs on their own generated output? That's clearly dumb and not what people propose. The model has to interact with something real, it has to "touch grass", for it to work. That's the external information. For example a coding LLM can get an informative learning signal by running its generated code through the compiler and running tests and seeing if the resulting program compiles, passes the tests, uses less RAM or is faster, etc. I'm not saying that leads to AGI, but there are clearly ways to obtain information from the outside world, and it's not just about sewing a pipe from the LLM's ass back into its mouth.
I presented the idea as a thought experiment, not as an actual proposal.
No, you presented it as a conceptual proof that LLMs will never get better. All it takes is one innovation that addresses your concern about recycled data to make it invalid. All arguments about intelligence are necessarily a bit wishy-washy, mind you, so I'm not saying your thought experiment is useless.
I think if you really want to argue that LLMs have an inherent cap on their capability, you should address their actual algorithm rather than how they're trained. However much we rejigger them with CoT thinking and non-text data sources, they're fundamentally not designed for anything more than next-token prediction. It should be a source of constant surprise that they do so well on such a wide variety of non-creative-writing tasks (look at early SSC posts about GPT3's output to see this surprise evolve in real time). You could argue that if LLMs end up hitting a soft or hard limit, that's really just the "surprise" petering out, that we really can't just take a glorified text completer and keep pumping neurons into it until it's a genius.
I don't personally believe this will happen, but hey, I don't think anyone really knows for sure.
Umm, no. In fact I totally think that LLMs will get better.
I presented it as a thought experiment to show that LLMs seem to be missing some essential attribute possessed by human brains.
Yes, of course. Well, perhaps more than one innovation. But yes, if LLMs are missing something important; and we create LLMs 2.0 which include that important thing (or those important things), then yeah, we'll have AGI.
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