Inference is cash positive: it's research that takes up all the money. So, if you can get ahold of enough users, the volume eventually works in your favour.
This is VCs FOMOing as global-economy-threatening levels of leverage are being bet on an AI transformation that, by even the most optimistic estimates, cannot achieve a tiny portion of the required ROI in the required time.
IMHO, there's never been a better time to build your own product and learn to sell it. The effort that AI implementation requires is clearly exponential to complexity of the organization.
You can build faster now that you ever have: I am building faster than I have in 25 years of engineering. You have more capable support for all the unfamiliar processes of building a business imaginable.
And almost everyone larger than you is finding it harder to achieve similar productivity gains from implementing AI, if not outright struggling with it. This is a golden moment and won't last long.
I've claimed neither. I actually prefer restarting or rolling back quickly rather than trying to re-work suboptimal outputs - less chance of being rabbit holed. Just add what I've learned to the original ticket/prompt.
I think maybe there's another step too - breaking the design up into small enough peices that the LLM can follow it, and you can understand the output.
My point is that, if I can do it right, others can too. If someone's LLM is outputing slop, they are obviously doing something different: I'm using the same LLMs.
All the LLM hate here isn't observation, it's sour grapes. Complaining about slop and poor code quality outputs is confessing that you haven't taken the time to understand what is reasonable to ask for, aren't educating your junior engineers how to interact with LLMs.
9000-line PRs were never a good idea, have only been sufficiently plausible because we were forced to accept bad PR review practices. Coding was expensive and management beat us into LGTMing them into the codebase to keep the features churning.
Those days are gone. Coding is cheap. The same LLMs that enable people to submit 9000 line PRs of chaos can be used to quickly turn them into more sensible work. If they genuinely can't do a better job, rejecting the PR is still the right response. Just push back.
And if you are doing something fabulously unique, the LLM can still write all the code around it, likely help with many of the components, give you at least a first pass at tests, and enable rapid, meaningful refactors after each feature PR.
It's certainly an open question whether the providers can recoup the investments being made with growth alone, but it's not out of the question.