[docmars]

This is correct, but part of the issue is that it significantly increases token usage costs. Some companies are doing:

- PRD and spec fulfillment review

- code review + correction loops

- security review + corrections

- addl. test coverage and tidying

- addl. type checks and tidying

- addl. lint checks and tidying

- maybe more I haven't listed

And these are run after each commit, so you can only imagine the costs per engineer doing this 10, 20, 50+ times per day depending on how much work they're knocking out.

[vidarh]

Sure, it adds tokens. I've burnt 200 million tokens today on a single project.

The question is what your time is worth for the company, and which tasks costs less to have an agent automate than having you do.

[docmars]

I think if you were to scale that kind of usage across a reasonable team size, costs would start to add up fast — and possibly beyond the cost of paying another engineer every year, especially if a lot of your teammates are new to AI, or aren't using it efficiently. Of course, it all depends on the appetite of the company.

The other constraint is, for those who are being laid off (maybe because of cost reduction to support an AI budget for a smaller team to use), engineers wanting to expand their skill set and practice these levels of usage + efficiency are effectively unable to with their own funding, making it more difficult to find employment as expectations heighten.

Prior to AI entering the fray, software development was largely free for everyone, allowing anyone with enough time and motivation to build the skills towards gainful employment. As AI becomes more prevalent and expectations around how it's used become higher, fewer and fewer applicants will be able to claim they have the experience necessary because it was out of reach due to costs.