No idea why you’d say they have critically underinvested in product when Claude Code dominates and they’ve also released popular tools like Cowork and integrations for Microsoft products at an incredibly rapid pace.

Cost is becoming more of a factor, and no doubt they’ll work on that. There’s no reason to think they won’t be able to release cheaper models if they optimize for that rather than improving performance.

It feels like the only way to push the limits of newer models is with really long context questions that require reasoning. Any short request will naturally just be within the distribution of all the recent models so there isn't a performance difference there.

I think the near future is looking like a bunch of business-critical tasks that scale infinitely with better reasoning, all being done on whatever the most advanced model is at a high cost. Trading stocks, running a business, looking for tax dodges, writing high-performance code. These are all things where there's a tangible return on each jump in reasoning.

The model improvements being beyond human comprehension is one of the more ridiculous statements I’ve heard in the last couple of days about AI. We could reason about Higgs bosons and gravitational waves but have no ability to quantify or reason about the difference between Opus 4.7 vs 4.8.

I keep trying to switch to something else but I keep coming back. (Typically after a few days of giving a new model an honest go, and finding myself constantly asking Sonnet to fix its output... Yes, even Sonnet wins on this front! They really do have some kind of special sauce.)

I'm not where most of their money comes from though, and I don't know how universal my experience is.

Because you seem to be saying that Anthropic not changing the price of Opus is bad, but then two of your positive examples are Gemini 3.5 Flash (which tripled the 3.1 Flash token prices) and GPT-5.5 (which doubled the GPT-5.4 price, and is slightly more expensive per token than Opus).

Is your argument actually that price hikes are good? That doesn't seem to fit with the general tenor of the message.

Yeah nah, the models' flaws are pretty obvious when you use them. And as a user, you can absolutely know when a flaw disappears or barrier is cleared.

This is lack of imagination. If you use these models heavily enough, pretty soon you'll hit the edges of their capabilities. The smarter among us are collecting these problems into a personal benchmark and use that to judge model capability. I think this is the right approach, and dare I say, even better than generic benchmarks. To me, it matters less what the benchmark says, and more what my particular problems are.

You realize gpt-5.5 is also double the price of gpt-5.4, which itself was a price increase too, right?

Labs are divorcing pricing from inference costs.