What can happen though is that companies figure out how to prevent meaningful competition to preserve high margins. They're worth millions for the innovation but they get to billions through anti-competitive and extractive practices.

It really depends how you use it, if you're using prompts to generate detailed designs, breaking those into lists of tasks, and then feeding those to multiple agents - it's really easy to burn through many thousands.

If you're being more deliberate and using a few agents at a time interactively, having it review PRs/resolve issues, automated clean-ups and performance optimization, etc it could be more like $1500.

If you're just throwing it one-off questions like a better stack-overflow that is well under a $100.

I've really gotten into /goal, if you can find something verifiable and leave it overnight - it's kinda like christmas morning to see where it landed.

The problem with provisional employment is that it can take quite awhile for a new hire to be productive. In a complex FAANG environment I'd wager it's about six months before they're not a net drag on the team and maybe 1-2 years until they're close to fully productive. These are complex not just technologically but organizationally and there are tons of hidden rules and micro-decisions that are the difference between a project stalling vs moving forward.

I do recommend people get outside activities to balance things out - just walking my dog 1-2 miles a day is like therapy for me (and a good way to get unblocked and energized with a new idea).

* it cheats at verification. Even with specific instructions how to verify, it still cheats.

* generating UX(CLI tool) that is absolute garbage and inconsistent, even with specific instructions to minimize unnecessary flags, use convention over configuration ,etc.

* it absolutely will not go 'above and beyond' to solve problems - if task is hitting a permission or dependency barrier, it'll likely cheat or handwave the problem away. (gpt 5.5 xhigh)

There is maybe this hope/hubris that we can figure out just the right incantations or agent workflows to eliminate these issues - I was optimistic about this too but after trying for awhile and seeing them not only not go away but in some cases regress with newer models, I am less sure.

I thought initially this meant that the spec wasn't detailed enough but the problem is more agent adherence and laziness.

They have everything going for them - amazing technology and technologists, huge distribution and lock-in, and a giant compute advantage and the can pay for more out of cash flow rather than debt or equity. And yet it's still hard to see them not fumbling the ball.

This naturally involves a lot of tradeoffs and politics - senior engineers know to avoid adding 'weight' to their airframes and fight hard to avoid adding scope to the systems they're responsible for or divergence from their intended direction of travel. So compromises have to be struck or escalations to management to choose between priorities have to play out.

Maybe AI solves that as well but that is a lot more difficult lift.