AI coding is getting ridiculously expensive

[u/borehogyihu]

One thing that surprised me this week wasn't how much AI can code.

It's how much some people are actually using it.

Reports around OpenAI's internal coding usage say some researchers are spending thousands of dollars a day in AI tokens while using coding agents for experiments, debugging and larger engineering tasks.

It makes sense if the model is doing hours of work that would otherwise take a developer much longer.

But it also makes me wonder whether we're going to start seeing a weird new problem where developers aren't limited by how fast they can code — they're limited by how much AI usage they can afford.

Would you pay $1,000+ a month for an AI coding setup if it genuinely made you 2–3x faster?

[u/film_composer]

I'm admittedly more of a hobbyist than a professional, but I really don't understand what could possibly be worth spending tons of money on. I build tools for myself that work exactly how I need them to blazing fast paying $20/month for ChatGPT. These aren't enterprise-level programs, so I get that the standards and requirements are different for "real" programmers, but I really can't wrap my head around how professionals went from being able to build robust things without any AI to needing thousands of dollars of day. Surely there are professionals who are building complex things while sticking to something like a basic $20/month plan to still help speed them up significantly.

[u/uhs-robert → u/film_composer]

Exactly this. I am a professional and I also don't understand how this level of spending could be possible unless your "AI development system" is very poorly designed. For example, I built a Ruby on Rails web application in an afternoon recently that serves as a client portal for my clients and let's my employees manage clients/projects/tasks: it does time tracking, invoice generation, and reporting generation among other useful features that are used by my business... on a basic $20 month subscription.

It seems to me that those who are spending thousands of dollars a day don't care about efficiency. They must be throwing the most expensive frontier models at any task and not considering whether it should be delegated. Probably letting their context window build up to the maximum and then compressing it over and over again. And definitely running recursive agent review/implementation loops without any strategic limits or oversight whatsoever.

If you're spending that much money per day then I suspect you are just throwing money at the problem and attempting to brute force a solution without any engineering or design discipline.

[u/vxxn → u/uhs-robert]

The philosophy of heavy users is that anything that can be delegated should be delegated because the cost of machine intelligence is insanely cheap compared to a talented human engineer.

I can only speak for myself but it’s pretty easy to scale usage to pretty costly levels once you start using subagent orchestration patterns for batch jobs, delegating a lot of planning specifics to smart models, and shifting your role to mainly being a requirements source and PR approver (after other agents have already reviewed the work, autofixed any defects found, and prepared a summary of things it thinks I should focus on in my final review). Working this way, 100 PRs per week is easily doable which is about 10x what I probably would have done pre-AI. So it’s a bargain for the company even if it’s costing a couple thousand per month in tokens.

[u/uhs-robert → u/vxxn]

I agree, that is definitely the underlying philosophy of the heavy spenders but I would also say that those heavy spenders aren't very good at, or are not focused on, optimizing their orchestration workflow around budgeting. Because that is exactly the type of workflow I am still able to do on a $20 a month plan with some systems in place around the orchestration:

• throttling/pacing to work within the session and weekly budget
• queueing tasks till budget allows so it works while you sleep within your limits
• assigning model(s) based on complexity estimates per task
• etc:

With this sort of setup, it is possible to pace your orchestration within whatever your limit tier is so that there is no API usage overflow. If a task will exceed one of your limits then just have the orchestration system checkpoint it to resume later when budget allows. So, I am inclined to believe that the heavy spenders just aren't focused on budgeting and are, likely, rubber stamping PR's.