Looking at the refutations of Zitrons predictions in TFA, it boils down to two categories:

1. Zitron claims model capability has peaked 2. Zitron claims AI lab growth (user and revenue) has stalled.

In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.

In the second case, numbers are given showing growth, directly refuting Zitron. However, I'm giving Zitron the benefit of the doubt, given the old saying - market can remain irrational far longer than you can remain solvent. As long as people can be convinced that the sky is falling, rational predictions rarely pan out.

> 1. Zitron claims model capability has peaked … Zitron is probably right in this regard …

Is there any objective measure that shows this?

Would we use examples such as his Feb 2024 claim "I believe we're reaching the upper limits about what generative AI can do"?

> 2. Zitron claims AI lab growth (user and revenue) has stalled.… I'm giving Zitron the benefit of the doubt …

Is there some date by which you'd say it'd be fair to evaluate whether Z’s claims are true (without the benefit of the doubt)?

You mentioned revenue - would we use claims such as his 2024 claim that the companies no longer knew how to grow? But that in 2024, 2025, and 2026 both the companies revenues and profits have grown at double-digit rates each period?

You also mentioned users - would we use claims that "Sundar Pichai wants Gemini to be 'used by 500 million people before the end of 2025, 'a number so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai", where Gemini then hit 750 M users?

Or by what measures should we evaluate whether Z's claims are true?

> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.

I am certain that plugging a circa 2023 model into a 2026 harness would be a pretty frustrating experience. Yes, you could code a bit with AI in 2023, but models are just much better at it than they used to be. And smaller open models are leaps and bounds better at it than they were three years ago.