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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.
This is clearly proof that the Saurian Overlords of Agatha in the Hollow Earth taught us language.
More seriously, isn't there a lot of research going into using synthetic data safely? I thought that the current consensus was that you can avoid model collapse with synthetic data if it's properly labeled as such.
I have no idea, but intuitively it seems to me that training with synthetic data is something that can't possibly work. To be sure, I am neither a mathematician nor a computer scientist. But as I understand things, the basic operation of an LLM is to predict the most likely words to follow a string of words. Which is done by training a neural network on lots of text. It's difficult for me to see how an LLM could get better at predicting words if it's trained on its own output. It seems to me that if you created an LLM using a corpus of synthetic data, the best you could realistically hope to do would be to reverse-engineer the original LLM which had created the synthetic data in the first place.
Anyone who is a subject matter expert, feel free to correct me.
I agree with you intuitively. However large amounts of Serious People are spending large amounts of Serious Economic Resources to do literally just that. So they clearly see something there.
Mythos seems to be yet another OOM of compute and training and we can be sure a good % of that was synthetic at this point as they already ate the entire corpus of human writing a while ago
Part of me wonders whether this might be some kind of mass delusion and/or grift. It wouldn't be the first time something like that has happened. That being said, I am not a subject matter expert and haven't studied these issues carefully so I couldn't really say.
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