If I may suggest people move away from harasses and start using Emacs. Gptel is amazing and the rest of Emacs plays incredibly well with llm development. Since the human is always in the loop token costs are also tiny compared to anything else.
I suspect this will turn out to be a super overblown issue: AI spend is literally the easiest spend to regulate in the entirety of businesses. No machinery is grinding to a halt over it, no asset that had to be bought and is now useless. You don't even have to employ or fire staff to give it a go (of course, you can still do both for other reasons). There are a lot of options that you can try out and substitute for each other, as new stuff comes up, because most things are compatible.
Sure, if you start at this point, where a good chunk of employees, who never had that ability, can now spend a lot of money at their discretion, that's probably going to be costly at first. Then people will learn from that and set direction adn guardrails.
yeah, it's easy to regulate, but the psychological impact isnt.
You spend 3 days making AI do something, and now what, you're suppose to spend 2 days trying to keep up with whatever it's done? That's like if your coworker's projects, every week, were handed to you on Thursday and you're told to finish them up.
No matter how smart you are, the bootstrap of new knowledge is impossible.
So sure, it's easy to turn the spigot off at arbitrary dollar values, but you can't just turn the worker's mindset off.
Workflow is valuable because of how in the flow it is.
I work at a very big company and I have unlimited access to Codex with no visibility on token usage. I don't know if this is the norm but seems kind of crazy to everyone in my team
In some sense, devs are now responsible for a P&L the way a business manager would be, but I suspect that nobody is paying enough attention to the P part of it. When you incentivize people to spend as much money as possible but don’t hold them accountable properly for what they actually produce, this is what happens.
I think a lot of ppl assume that usage here is simply for AI coding, which is easily governed. I suspect that the more tricky issue are those usage powering workflows and application logic that cannot be easily throttled down.
Yeah, and how we build—and what we surface operationally—matters as well.
I run several AI-based products, and I’ve moved almost entirely to agentic coding, so this is a huge concern for me. One thing that has helped in the coding realm is separating planning from execution: I use a stronger, more expensive model for architecture, research, and difficult decisions, then hand the implementation work to cheaper agents where the task is more constrained.
On the product side, I treat token usage and costs like any other production resource. We record usage by request, user, feature, and model, surface it in admin dashboards, and set budgets and hard cutoffs.
Alerts are useful, but automatic limits matter more—especially as a small business guy so a 2 AM loop doesn't bankrupt me :)
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[ 3.1 ms ] story [ 33.4 ms ] threadI frequently max out my weekly usage, and given this [1], hopefully Codex might give me more milage.
[1]: https://news.ycombinator.com/item?id=48126429
Sure, if you start at this point, where a good chunk of employees, who never had that ability, can now spend a lot of money at their discretion, that's probably going to be costly at first. Then people will learn from that and set direction adn guardrails.
Dare I say the GPU bought today won't be economical in 3-4 years time?
You spend 3 days making AI do something, and now what, you're suppose to spend 2 days trying to keep up with whatever it's done? That's like if your coworker's projects, every week, were handed to you on Thursday and you're told to finish them up.
No matter how smart you are, the bootstrap of new knowledge is impossible.
So sure, it's easy to turn the spigot off at arbitrary dollar values, but you can't just turn the worker's mindset off.
Workflow is valuable because of how in the flow it is.
Discussion then: https://news.ycombinator.com/item?id=48602571
I run several AI-based products, and I’ve moved almost entirely to agentic coding, so this is a huge concern for me. One thing that has helped in the coding realm is separating planning from execution: I use a stronger, more expensive model for architecture, research, and difficult decisions, then hand the implementation work to cheaper agents where the task is more constrained.
On the product side, I treat token usage and costs like any other production resource. We record usage by request, user, feature, and model, surface it in admin dashboards, and set budgets and hard cutoffs.
Alerts are useful, but automatic limits matter more—especially as a small business guy so a 2 AM loop doesn't bankrupt me :)
<gets bill>
No wait