> Every major tech company - the ones poised to get the first best rewards, have already gotten good incremental revenue from AI via ads ranking/recommendations (Google, Meta, etc.)
That's just software evolving. It happened before LLMs, it would happen without LLMs.
> good productivity increases due to scale of workforce and advanced in house tooling.
Exactly same case.
But I don’t really understand: the ask is for evidence AI is generating meaningful returns and it demonstrably is, even while we have integrated these tools only partially. “Just software evolving” um yes, I agree, just that now this happens faster and more efficiently. It is also more than that: models that power advertising and content recommendation at TikTok, Google, Facebook, Instagram, etc are not just “software evolving” it is meaningful improvements to models that are only possible with good AI.
Yes, it is meaningful improvements but AI changes working profitable software. It is more difficult to create exponential value from that compared to new platforms like the internet or mobile. Git and then github, for example, have a much bigger impact on increasing software development productivity than AI, with a fraction of the investment.
Are you saying AI is tackling the wrong bottlenecks? I’m not sure what you mean by “AI changes profitable software”. Maybe you mean: AI will not create something new, only do the existing things we do?
I agree the foundations: git, GitHub, compilers, etc. are arguably are “a fraction of the price” and today they have arguably more impact (though not sure by which measure). But literally since January we have been rolling out our replacements, I don’t really see how that wouldn’t be an earth shattering impact. You talk about GitHub and that’s fine but ignore the fact that huge swaths of the profession aren’t even directly using any of these tools anymore.
I’m not sure what you imagine the promise of AI to be, and without that I can’t really be specific in any refutation I would just say coding is only the beginning. It is the most powerful and also the easiest thing to solve first. Improved coding performance also improves generalization and performance on non-coding tasks, so that’s a nice bonus, and we’re maybe 5 years away from decent embodied systems which after an inflection point of consumer adoption will quickly get better via data flywheels and on policy learning. Basically there are very few bottlenecks that will not be touched.