> ... but it seems like Anthropic is going for the Tinder/casino intermittent reinforcement strategy: optimized to keep you spending tokens instead of achieving results.

This part of the above comment strikes me as uncharitable and overconfident. And, to be blunt, presumptuous. To claim to know a company's strategy as an outsider is messy stuff.

My prior: it is 10X to 20X more likely Anthropic has done something other than shift to a short-term squeeze their customers strategy (which I think is only around ~5%)

What do I mean by "something other"? (1) One possibility is they are having capacity and/or infrastructure problems so the model performance is degraded. (2) Another possibility is that they are not as tuned to to what customers want relative to what their engineers want. (3) It is also possible they have slowed down their models down due to safety concerns. To be more specific, they are erring on the side of caution (which would be consistent with their press releases about safety concerns of Mythos). Also, the above three possibilities are not mutually exclusive.

I don't expect us (readers here) to agree on the probabilities down to the ±5% level, but I would think a large chunk of informed and reasonable people can probably converge to something close to ±20%. At the very least, can we agree all of these factors are strong contenders: each covers maybe at least 10% to 30% of the probability space?

How short-sighted, dumb, or back-against-the-wall would Anthropic have to be to shift to a "let's make our new models intentionally _worse_ than our previous ones?" strategy? Think on this. I'm not necessarily "pro" Anthropic. They could lose standing with me over time, for sure. I'm willing to think it through. What would the world have to look like for this to be the case.

There are other factors that push back against claims of a "short-term greedy strategy" argument. Most importantly, they aren't stupid; they know customers care about quality. They are playing a longer game than that.

Yes, I understand that Opus 4.7 is not impressing people or worse. I feel similarly based on my "feels", but I also know I haven't run benchmarks nor have I used it very long.

I think most people viewed Opus 4.6 as a big step forward. People are somewhat conditioned to expect a newer model to be better, and Opus 4.7 doesn't match that expectation. I also know that I've been asking Claude to help me with Bayesian probabilistic modeling techniques that are well outside what I was doing a few weeks ago (detailed research and systems / software development), so it is just as likely that I'm pushing it outside its expertise.

Anonymous request-token comparisons from Opus 4.6 and Opus 4.7