The reason I suspect there might be a dot-com bust style in the AI space (though it's a much smaller section of the tech market right now, so I hope it won't be a dot-com bust size correction of the market) is that I see a lot of what was the attitude towards the internet in the late 90s. People assumed that the interet was going to do all this magical things, in magical ways, with a lot of handwaving, we'd just say "computer I want a pizza" and the pizza would just "appear" on my table, no explanation of the logistics of getting it there. When you asked "how do you expect to be able to make a grocery delivery system be cheap enough that people would use it" the answer would be "man you don't understand, it's the internet: see that exponential growth? It's just going to keep going baby"; as if you downloaded the groceries.

Same thing with AI. There's things where I really think it'll be revolutionary, but way to many people are making claims that are ridiculous, leave a lot of unanswered hard and important questions, which are just handwaved away showing us how AI is getting exponentially better by some arbitrary metric (even though the hard problem that is being asked about wouldn't be solved by AI in their model).

Something that I think will be a revolution in AI: Janitorial work. There's a lot of work you need to do to keep a codebase at large companies in a good state, paying down tech debt and keeping the work going. When you do a breaking change, or deprecate some functionality in a library in exchage for a better way to do it, ideally you want that code base to be updated. Turns out the easiest way (when it's entirely within a company) is if the people upstream just go and change things. Smart engineers will try to make these changes something that is awk friendly, so they run a script over all the repos (one of the advantage of monorepos) and their files and then create the very large amount of PRs to handle this (which can be automated). With ML agents, you can just document how to handle this, and let AI agents sourge over the whole codebase and do the updates. They should be small and specific, and engineers can look at this. Open Source projects may push documentation on "best practices" or migration guides when they release a v2, that are written in such a clear and simple thing (which is a good thing either way) that an AI agent could just use it. They just make a pointer for the AI-agent to follow the docs, and then you get an "auto-updater" or "good-practices-code-fixer" for free with your docs. This frees engineers to do bigger, more aggresive changes foundational changes when needed, without wasting time on making them actually be used, letting an AI agent handle that instead.

Something that won't be the revolution: vibe-coders replacing engineers. To be fair the main reason this won't work is something that most engineers do not know, or would rather not know: their job is barely related to writing code; their job is related to translating ambiguous problems and solutions into concrete and mathematically rigurous and specific solutions (so clear that a machine could follow). This is done through a series of interactions, sharing, and iterations. AI agents aren't really any better than humans are, and AI agents aren't really cheaper than humans are, once you put all the cost in: you still need the person (prompt-engineer) that translates ambiguous problems into prompts that AI agents could solve, and then understand the code that the agent generates well enough that they can catch issues. This skill is just as rare and hard to get as software engineering, so you end up with exactly the same amount of employees and the same cost. And this is assuming that AI agents become better than really solid engineers. Though again, that is more about the abilities to work on meetings, delegate tasks, and work with others, not quite "just coding", that's the difference between a junior and a mid. So if you need the same amount of engineers as before, and they are just as hard to get, what are the gains to offset the costs of paying for those very expensive machines?