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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.
I'm also convinced that LLM's aren't the path to AGI. They were grossly overrated from the very beginning. If you want real AI, you have to dump the snake oil. It's already empirically well adduced (1, 2) that LLM's can't get you there.
There's a 'lot' of things you need before you've actually got an intelligent machine that can think. It begins with constructing mental models. From there, you navigate those models in the imagination or perceptual space laid out to work out answers to questions and work out alternatives. Once you build those spaces (and in this case I mean building creative and entirely novel ones) and navigate them to accelerate anticipatory learning. Cats for example actually "learn" how to hunt by doing this. Once you've learned how to model spaces you can them move to modeling "systems;" and that's when you get to the point where it becomes possible to give AGI a theory of 'other' minds. And a "mind" in purely mechanistic and computational terms is simply another causal system; just like "spaces" and "systems" are particular causal systems.
Notice that's exactly what an LLM 'doesn't' do. If you take a look at Waymo's World Model for instance, this is exactly more along the lines for the correct pathway of approach that you need. When you're continuously inventing new models of imaginary environments, you begin to build the skillsets that slowly become applicable to the real world. When it can do that, that's more along the lines of where scaling becomes effective. Nothing like Sam Altman's idea of where it's relevant. When you've got to that stage, AI can then begin to model it's own causal system to think about it's own thinking such that it's capable of asking itself when it's wrong; or how to stack a particular sequence of events to achieve a desired end result.
Incidentally this is the exact pathway natural selection determined for human beings and I'm thoroughly convinced it's the 'only' way to get AGI. This is what human beings fundamentally are on the naturalist paradigm: models and model builders that also navigate and move about in those models. There's zero evidence that I've seen to indicate that the money flowing into AI at the present moment is traveling down that research pathway and make no mistake, eventually the supply of it is going to run out. But make no mistake. A 'lot' of rich people are stupid, so it's doubtful it'll ever go to the real thing. They'll throw it all at the next bullshit snake oil, get their fiscal bailout and blame it on immigrants on something. Presently I'm not left feeling very optimistic about the current state of the industry. It's incredibly destructive to the environment, it dumbs down the human intelligence, and it hasn't even been proven to even work. Why is the “world” so excited?
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