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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?
Whether a random patched version is a significant upgrade is hardly strong evidence in any direction whether I is a bubble. Did you ever try my suggestions under your last fud post?
Came down with a cold, missed work for several days, and forgot. Sorry! I'll try to remember this week.
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It's not about profitability, it's that they got a giant wave of users but not enough compute to fill that demand. So, it's pretty obvious what must happen next, you do some mix of increased mandatory token efficiency (adaptive reasoning) + stricter limits (across the board, free and paid, but mostly targeting the super-user hogs who theoretically will pay for extra API usage after limits run out).
I will say though this probably bodes poorly for Claude in the near-medium term, because ChatGPT had the same thing more or less happen with their 5.0 launch (forced adaptive model selection for mandatory token efficiency) and it definitely took the wind out of their sails for at least 4-5 months.
At any rate, however, I strongly, strongly disagree about this empowering the skeptics (or being evidence of a shift against AI adoption). The fact that people are whining about problems with their tools is selection bias. It's kind of like the classic armoring spots on the airplane that didn't have holes (because they didn't survive to be examined), in that people wouldn't complain so vociferously if they weren't so needy for the tool in the first place. The complaints to me are evidence of a generalized latent enthusiasm, not pessimism. In the grand scheme of things, it's far, far better for a company to have complaints that users can't get enough of their product, than it is for the product to be simply ignored. In the near term, I expect a decent chunk of users to swing back toward the OpenAI offering, Codex (which is undergoing a PR blitz of sorts right now)
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I’ve found Opus 4.7 to generate better and more human-like text vs Opus 4.6 for my purposes, but I can’t indicate whether it’s any better at coding. I use a mix of LLMs for various things, and my feeling is that ChatGPT is more bland and LLM-y in its output, but much more generous with usage limits. In the limited coding I’ve done, I haven’t seen much of a difference between them. Its image generation model is also nice, as far as my amateur impression can tell.
But it’s a constant fight with the usage limits on Claude, whereas ChatGPT feels like it flows freely. My current pattern is to default to Chat for most informational and coding purposes and bring out Claude Opus for when I want a more thoughtful analysis of something. I don’t know how Sonnet compares to ChatGPT.
Gemini feels massively behind in both usability and tooling, and its integrations with third parties are only good for Google products.
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TracingWoodgrains has been a fan of Opus, and seems a little frustrated by 4.6. That said, it may depend on your use case.
I'm generally not that surprised if there are occasional stinkers. I've given specific caveats around other vendors : it's just too easy to benchmax or find a bad local maxima such that there's some minor revisions that either don't have any benefit, or only have backend benefit. Repeated problems or broader-scale issues would say more, but there's been a number of surprisingly good models from other vendors recently, including small-parameter and open-model approaches.
I'm skeptical that LLMs are themselves enough to go to AGI, but I'm also skeptical that they're going to stop at exactly last month's level of capability, and last month's capabilities included solving some Erdos problems. There's a lot of low-hanging fruit just in terms of UI and process tooling, nevermind areas where we haven't applied existing tools.
That said, I recognize that a lot of the major AI vendors have ranged from scumbags to scammers. Altman's ridiculous behaviors, especially in relation to RAM, have made the most enemies (maybe even more than Musk's more conventional culture war), but the best PR the whole faction has got has come from anti-AI people, so that's a whole big mess.
Somewhat an aside, but I consider that first link to be a first-degree chart-crime. First of all radar plots are inherently iffy, since we pay close attention to the "area" and the area is highly dependent on how the categories are organized (a "spiky" radar plot has much less total area than if you sort the axes to create a "lopsided" plot, despite showing the same information). This is a little bit defensible if the adjacency of the categories is obvious and inherent, but they frequently are not. For example, "Occupational: Writing Literature and Language" is NOT next to "Text: Creative Writing" for no good reason at all. And furthermore, what is the scale of the chart? It's "Arena rank"... which is NOT equally spaced. The chart implies that the difference between #1 and #2 is the same as (or even slightly bigger than, considering how the radar chart "expands") that between #3 and #4, but this is plainly not the case. They should be using some kind of actual score instead, perhaps a scaled one. Sure, it allows consistency across axes, but if we are comparing a model to its successor, the rating scale definitely shouldn't be implicitly including other models like it does now (in one spot it drops from rank 2 to rank 5, does this mean in that category some other model class does abnormally well, or that did Claude truly degrade?). Even worse, the center of the plot, usually a natural "zero", is not a zero at all - it's rank 6. There are, as you know, dozens and dozens of models in the rankings, so rank 6 being a zero score is totally nonsensical.
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I guess it depends whether you think this is a forced move due to running out of money or if they have run their internal numbers and think people are willing to pay the increased prices. VC money is a runway, it's not intended to be a permanent subsidy. If they reduce the amount of money they are burning on subsidized inference, that's money they can put into R&D, more GPUs, etc.
It's hard to speculate without knowing more about their internal metrics, but based on the complaints I have heard about Claude being slow, laggy, etc, it sounds like they are quite oversubscribed. If the demand exceeds the supply, increasing prices is the logical move.
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The way these Orange Reddit people use AI is revealing to me. I tried Opus 4.6 and got no benefit over Codex 5.3 but it made me run out of tokens very quickly. I use Codex 5.3 for my day job and several side projects. I think I got no benefit because I have expertise on what I'm doing, so I give pointed, well written prompts. These people must be completely out of their depths and therefore reliant on extremely costly extra layers of prompt refinement to get the same performance I can get with Codex 5.3.
Opus made you burn tokens quickly so you switched, but when these people also use Opus and burn tokens quick it's because they're using it wrong?
They're not using Opus wrong, but being reliant on Opus means they're bad at AI.
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You seem very eager to jump on any negative AI news out of some desire to prove the “AI bros” wrong. What’s your motivation? Annoyance at AI mandates from above? At insufferable people shoving AI slop in your face at every opportunity? Just disliking the concept in general?
I don’t know if I’m an “AI believer” (what do you mean exactly by that?), I dislike OpenAI and Anthropic for the shenanigans they keep pulling, and I’ll jump ship to whichever AI service provided the best value for money. The tech industry hype cycle goes on and on, at some point people went crazy over Java of all things, now it’s just a boring programming language and you don’t have to be a “Java believer” to use it.
All of the above, honestly? But the biggest would be annoyance at mandates from above, combined with a completely reversal in what people consider quality engineering in software that magically coincides with the rise in popularity of AI tools. See Lines of Code suddenly becoming a positive metric for a lot of people, versus the old Bill Gates quote "Measuring programming progress by lines of code is like measuring aircraft building progress by weight."
Sure, but despite Java's warts it's still used to this day to make a lot of the important software that keeps the modern world running. The AI hype bubble is much more reminiscent of the crypto bubble. No matter how many times you tried to make it clear that crypto is only useful where you need a distributed, immutable, trustless ledger (and even then it's questionable), crypto bros kept proposing uses in situations where trust was still required and other existing tools already did an infinitely better job for far less computing power. Similarly, I see retarded things like "I had AI generate a thing, and then I had another AI review it and tell me it looked great! What, review it myself? No, of course not, why would I do that?"
Except crypto was almost always purely in the realm of theory-applications.
With AI, right now, I can do things like generate custom flashcards for subjects I'm learning (job interview prep). I can get more in-detail answers about random questions without spending hours on Google piecing things together (just yesterday, asking for details about how stomachs process different macronutrient profiles). I can generate custom mini-apps for a wide variety of tasks (recently I made a custom task-selection spinner for my todo list that weights the important tasks more than smaller tasks, while occasionally mandating a break). It can make sure an email I send to a recruiter doesn't have obvious mistakes or commit a faux pas. I can get personal advice of at least middling quality without friction on a wide variety of topics. Obviously, it can code really well, and that touches my field very directly in a lot of ways. There are plenty of other use cases, too. These aren't "lines of code" type accomplishments, they are concrete deliverables of various scopes. Some of which were previously high-friction or even impossible.
Sure, some of these are gratuitous or busywork, but they are all real. Crypto stuff was like, "what if the government keeps track of property listings on the blockchain" which is a) something the government already does mostly just fine and b) obviously never happened and c) would have required very significant network effects. And currently, crypto is extremely useful for pretty much exactly two types of people: those who treat it like digital gold (it does OK at that) and criminals who can move money around that's difficult to track. Nothing else. So sure, in that sense it was real, but AI plainly can do more than two things and will continue to do more than two things even as hype dies out.
And sure, my IRL friend will give me better advice than Claude will, but there are some things that are so low-stakes that it would be disrespectful of their time to ask or discuss. Paradigms like that are all over, because of the speed and cost AI offers. In that sense, it's more like the Industrial Revolution, where speed and cost enable things to happen that previously were functionally impossible at scale. In fact most of the Industrial Revolution was about things that were already feasible to do, but were cost-prohibitive (or took too long). This in turn generated new industries that were previously only theory. Now, I don't think AI will have that level of impact on society, and I'm also not sold on it 'creating new industries' at all, but probably it's somewhere on the level of the impact similar to the invention of Google at least?
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I work at a dinosaur of a company, so I can't speak to this directly, but a friend of mine that I recently mentioned gave me an update the other day. They've gone from "you must burn as many tokens as possible to maximize your performance review" to "we must use our token budget wisely."
The timing is interesting. It happened right around the same time Anthropic started putting the screws on its customer base with increased token usage and tighter rate limits.
I really feel like the company that sits on a "good enough" model and aggressively cost cuts is going to win this particular war.
That would be OpenAI. Claude is a 2026 fad and will be over soon.
There are some Chinese contenders within striking distance as well. GLM-5.1 is open weight and seems to perform somewhere between Opus 4.5 and 4.6. It's pretty incredible that there is open weight competition that's less than six months behind frontier state of the art.
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The tracker is slop so I don't trust it one bit.
But in general for thinking models, more thinking effort = higher scores on pretty much all benchmarks. So they could easily have just tweaked a setting so 4.7 medium = 4.6 high. Voila, number goes up. Of course you're paying for those tokens anyways but the scale is fundamentally arbitrary - there's no real definition of what "low" or "high" thinking actually means.
I'd be more worried about the fact that the reception to 4.7 has been extremely lukewarm to say the least. Ain't nobody on twitter singing the praises of that model.
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I don't really see what this is supposed to prove one way or the other. You are still stuck in the timescale framing of the most fervent AI bros. Opus 4.6 came out in February, 2 months ago. So what if Opus 4.7 is not a revolutionary upgrade? If AI were truly stagnant, we won't really find out until someone posts in 2028 that Opus 6.7 is only a marginal upgrade over Opus 4.7.
I think you misunderstand my argument. I'm not arguing that AGI is impossible based on this (though I don't believe it's possible). I'm arguing that this is a strong sign that VC money is drying up before they could ever conceivably achieve AGI (even if it is possible).
Anthropic raised $30 billion two months ago, their problem isn’t lack of money. All the VC money in the world won’t solve a bad engineering culture.
Sure, but they're on track to burn $11 billion this year in expenses, and more in the future, so that's not going to last too long
...and $14 billion in revenue assuming zero growth. Or closer to $35B if their 10x/yr trajectory continues.
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If that were the end of the story it wouldn't be an issue. It's that it evidently uses significantly more computing power than the performance improvement would suggest, raising the spectre of rapidly diminishing returns.
It seems to me this also has financial implications. If you are paying per token, and the model's benchmark performance increases slightly, but its token cost to reach those higher benchmarks increases tremendously, suddenly you're paying a lot more to do, at best, slightly more.
If Anthropic is making margin on the token cost, then this is an improvement from their financial point of view, right?
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