> One thing I can hint at, but can't go into details, is that I personally know of at least one enterprise-grade project whose roadmap and scoping - and therefore, funding - is critically dependent on AI speeding up significant amount of development and devops tasks by at least 2-3x; that aspect is understood by both developers, managers, customers and investors, and not disputed.
Mm. I can now see why, in your other comment, you want to keep up with the SOTA.
It's actually unrelated. I try to keep up with the SOTA because if I'm not using the current-best model, then each time I have a hard time with it or get poor results, I keep wondering if I'm just wasting my time fighting with something a stronger model would do without problems. It's a personal thing; I've been like this ever since I got API access to GPT-4.
My use of LLMs isn't all that big, and I don't have any special early access or anything. It's just that the tokens are so cheap that, for casual personal and professional use, the pricing difference didn't matter. Switching to a stronger model meant that my average monthly bill went from $2 to $10 or something. These amounts were immaterial.
Use patterns and pricing changes, though, and recently this made some SOTA models (notably o3, gpt-4.5 and the most recent Opus model) too expensive for my use.
As for the project I referred to, let's put it this way: the reference point is what was SOTA ~2-3 months ago (Sonnet 3.7, Gemini 2.5 Pro). And the assumptions aren't just wishful thinking - they're based on actual experience with using these models (+ some tools) to speed up specific kind of work.
Mm. I can now see why, in your other comment, you want to keep up with the SOTA.