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Comment by rapnie | Hacker News Reader

Nice saying. If in the prediction "given [intricate analysis] the whole [shebang] goes [bust] at [date]", only the [date] turns out to be wrong, I'd say it is still a valuable prediction however, though it was wrong.

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Problem is even his “intricate analysis” was wrong: for example he was saying the models wouldn’t keep improving… in 2024.

Have they improved? Is there evidence of that? Got a task you were doing in 2024 and 2026 and the results of each?

There is plenty of evidence that they have improved in all benchmarks and also in my private experience. But have they improved in the things they still fail at? No, they still fail at them. You need only one example of failure to prove that it still fails. They still fail a lot on many real world tasks.

So, depending on what you ask, they may have not improved even a tiny bit.

I am a very light user, so this is my feeling from reading about other people's experience; I wouldn't say that they plateau'd but up to 4.5/4.8 the gains in the models felt exponential while since then they feel more linear and the big improvements are coming less from the models and more from everything around it (harnesses, agentic development, skills...).

So, while I don't feel like there has not been improvement, it really feels like there is a limit that will be reached sooner than later (and for sure before any AGI).

Oh my goodness. Is this really a good-faith question?

Since almost everything eventually goes bust, I wouldn't agree that this was particularly valuable, unless the analysis proved out in other ways. It's like the saying that some economists have predicted 10 of the last 4 recessions. The analysis that leads to that prediction may or may not be valuable, but is only valuable if it predicts something, because otherwise how can you know it has any accuracy at all?

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