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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 not convinced it’s a bubble. It might be, but gaging that from random commentary on HN isn’t a good way to figure it out. There are all kinds of reasons that sentiment might be going south, a lot of it being that people are expecting it to come much faster than it actually can. Early LLMs fed this in my view because at the start minor changes were big improvements. Going from an AI that could barely understand a simply question to one that can write an essay on a topic was quick, maybe 3-4 releases. If it takes 6-10 to get AI to get you a publication worthy book on the topic of the query, I don’t think that’s a problem for AI — which will eventually get there — though it probably means a much harder time getting funding to work on the next projects.
I'm firmly convinced it'll pop late 2026 (this year) or 2027. Could be wrong, could entirely be on point.
Suppose the current state of the industry sustains itself at equilibrium. I still think when you factor in all the costs AI entails, it can't license the claim that it's good for almost anything. AI makes so many mistakes that it actually reduces productivity because for every mistake it makes, it costs even more in time and resources to go back and fix; which is often greater than the associated costs of just doing it yourself. Humans are more productive than AI (incidentally this was proved by an analysis that was meant to refute that claim).
With LLM's it's error rate is always going to be the same no matter how much data it gets or at what scale. If you want AGI, you have to abandon LLM's because it's a straight up, dead end technology. It's use cases are small, narrow and mostly consist of merely baseline automation of tasks (hence, it's just a fancy autocomplete). They're unreliable and can be exploited. They don't think. They don't comprehend what they're doing. In fact, they're actually stupid. And worst of all, it can't be fixed. It just doesn't help things. Like, at all. Everyone is always saying forthcoming iterations will eventually solve all these issues but really, they won't. And there's no evidence of that.
The notion as well that AI is going to cut the labor market down is also false due to a basic rule in economics that's been understand since Keynes' heyday: if you double the productivity of your workers, the 'general' tendency isn't to fire half of your staff, it's to sell twice as much stuff. The fact that a lot of AI is also being sold way below the cost just to get market is an indication that it may not be cheaper even if and when they turn out to work. It isn't sustainable.
Shit's fucked up and it's going to be bad.
I’m not sure. Again the entire field is in its infancy. You’re probably right that LLMs are not by themselves going to be AGI. But creating a system with multiple systems run by an agent might be able to go farther in that direction than just LLM with agent.
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A lot of those sources are written last summer, or last fall (in which case they'd likely be building on older observations). Anecdata: my company encouraged use of LLMs then. I found them totally useless in our not so easy codebase, shelved the thing and went on the manual way. At the time I'd probably have agreed with the vibe of your post. Then reading some hype about Gemini 3 in the winter I gave it another shot; models turned out to have got over some hump; and now they look like genuinely useful productivity tools.
I can believe LLMs will have a way harder time cracking law or medicine or mechanical engineering or whatever, but with coding you can come up with endless tasks that are sort of real-world difficult that you can beat the model against on giant server farms without zero interaction with the real world, the same formula that worked for AlphaGo, so stands to reason that they'd git gud there faster.
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An entirely ai slop analysis.... proves nothing in my eyes
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