I know it's easy to forget, and InsideOutSanta kind of anchored the conversation on "revenue", but profit is ultimately what matters. Back when Silicon Valley was merely insane rather than bat-guano crazy insane, it was commonly observed that it's not hard to build a business around selling a dollar for 95 cents. The point being that it doesn't necessarily mean much when you have a business doing that, because of course the demand will be insane. It doesn't mean you have a viable business. You don't know you have a viable business until you transition to selling a dollar for $1.03. Many a VC-funded business that looked successful, even wildly successful, has run aground on that transition, or at least, suddenly stopped looking so wildly successful.

If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially.

The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less.

Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful.

As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use.

Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to.

There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger".

And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle.

[1]: https://jerf.org/iri/post/2026/programming_is_engineering/

I very much agree with this. Even the top tier models today, without the unit tests, without integration tests, and domain experts reviewing the code would flounder for 50% of the work they do. Sure they can write the unit tests and integration tests themselves, but at that point you aren't in need of a specific system being built, but rather an out of the box solution would probably fit your needs. It does speed up the grunt boilerplate work of development quite a bit, it does help with gnarly bugs and the like, but expertise is still needed. And we as engineers/programmers have systems in place that make using AI easier, we have the human context windows to be able to parse the technical jargon the AI spits out. Will AI for the masses be akin to slightly better automation?

> we are the field getting the most out of AI, and it's not even close.

Just emphasizing that as, due to spending far too much time online the past week, I've been seeing a fair bit of this. "AI is definitely gaining popularity because all the software companies I know are going all in on it."