Posted by samizdis 8 hours ago
Even if you show them benchmarks that show another model equally as good if not better, they refuse to use it. My suspicion is they've convinced themselves that Opus must be the best, because of reputation and price. They might've used a different model and didn't have a good experience, making them double down.
I hope a research institution will perform an experiment. My hypothesis is that if you swapped out a couple similar state-of-the-art models, even changing the "class" of model (Sonnet <-> Opus, GPT 5.4 <-> Sonnet), the user won't be able to tell which is which. This would show that the experience is subjective, and that bias is informing their decision, rather than rationality.
It's like wine tasting experiments. People rate a $100 bottle of wine higher than a $10 bottle. But if they actually taste the same, you should be buying the $10 bottle. But people don't, because they believe the $100 bottle is better. In the AI case, the problem is people won't stop buying the expensive bottle, because they've convinced themselves they must use the more expensive bottle.
https://old.reddit.com/r/ClaudeCode/comments/1s7zg7h/investi...
You've hit your limit · resets 2am (America/Los_Angeles)
I waited until the next day to ask it to do it again, and then:
You've hit your limit · resets 1pm (America/Los_Angeles)
At which point I just gave up
Just a shockingly constrained service tier right now.
I'm sure it's more complex, but why not improve internal implicit caching and pass the savings on? Presumably Anthropic already benefits from caching repeated prompt prefixes internally — just do that better, extend the TTL window, and let users benefit. Explicit caching stays for production use cases with semi-static prompts where you want control.
The current 5-min default TTL + 2x penalty for 1-hour cache feels punitive for an interactive coding tool.
As the tooling matures I think we'll see better support for mixing models — local and cloud, picking the right one for the task. Run the cheap stuff locally, use the expensive cloud models only when you actually need them. That would go a long way toward managing costs.
There's also the dependency risk people aren't talking about enough. These providers can change pricing whenever they want. A tool you've built your entire workflow around can become inaccessible overnight just because the economics shifted. It's the vendor lock-in problem all over again but with less predictability.
There's no other way that these companies can compete against the likes of Google, and Facebook unless they sell themselves to these companies. With AWS and GCP spending hundreds of billions of dollars per year, there's no way that Anthropic or OpenAI can continue competing unless they make an absurd amount of money and throw that at resources like their own datacenters, etc and they can't do that at $20/month.
Without heavy collusion or outright legislative fiat (banning open models) I don’t see how Anthropic/OpenAI justify their (alleged) market caps
I routinely match or beat Claude with regards to speed, I often race it to the solution because Claude just takes so long to produce a usable result.
Staying competitive doesn't mean only paying an AI for slop that often takes longer to produce. AI is a convenience, it is not the only way to produce code or even the most cost effective or fastest way. AI code also comes with more risk, and more cognitive load if you actually read and understand everything it wrote. And if you don't then you're a bit foolish to trust it blindly. Many developers are waking up to the reality of using AI, and it's not really living up to the hype.
* Models will manage tokens more efficiently
* Agents will manage models more efficiently
* Users will manage agents more efficiently
Why are we acting like technology is on pause?