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If there is a 3x productivity boost now, the correct assumption is that in the future the productivity boost, in places it carries over, will a lot more than 3x. Indeed, given how this scales, a better model might be in practice 10x or 100x or even 1,000x or more.
The revelation I've had working on Omarchy the last three months is that to get that magical 10X, 100X, in a few rare cases, 1000X productivity boost, you have to interact with the agents directly, and you cannot intermediate that bandwidth with another human because it's simply too slow.
Then there's another possibility which is that it actually already has worked but just the productivity boost isn't as big as would be obvious if people got 10% more productive. That would still be pretty impressive because it's hard to get a 10% across the board uplift.
If you start from a low baseline of performance, you might get some positive boost. It's quite small, but you might get it. But if you are actually quite a high performer, then you risk getting worse. with this is called the inverse U-shaped curve.
So, the lesson here is to bet on a system that's maximally learned and minimally constrained and leverage structure intentionally to boost performance and scaling laws both in training and in evaluation.
And the reason they came, I think, is absolutely because of the the better tooling. And I think we were totally right there that like adding a an erasable type system and then using that to enable great tooling is really where the pro where the programmer productivity boost is realized.
I expect AI to increase productivity of top mathematicians much more than that of top writers, because formalization is a kind of translation task, it doesn't require much judgement, and as long as it compiles noone needs to read the result.
My sense is that this hybrid is fairly common in practice; solvers aren't magical and if you can deduce additional structure using domain-specific analysis, it will often give the solver an important boost.
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