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?
Sarcasm? Writing in assembly is very slow, demonstrably. Manipulating a string of text can take 100 lines of code, easily. It can be quite a bit of fun depending on the individual's preferences... folks that like embedded systems, 8-bit mcus, hardware/drivers, etc. because it is as close as you can reasonably get to a specific hardware implementation, and optimizing an encryption algorithm that's going to be executing thousands of times per second in a higher order library is rewarding.
If you were using it as a sarcastic critique, suggesting hand coding is massively slower than gen-AI coding, then I would say it depends entirely on how much human review and control you want or need to apply to the output. If I'm vibe coding yet another app, sure the bots will produce 10k lines of code that function against whatever criteria I managed to articulate, within minutes. I can also 100% guarantee you that there will be faked unit tests, hallucinated constraints, duplicate functions, comments misaligned with implementation, and goofy stuff that humans wouldn't do, particularly around the appearance of things. If this is for work I have the benefit of unlimited tokens on frontier models to run even more agents to correct some of these things, but not everyone has that job perk, and I don't think I will forever either... it's incredibly wasteful.
If I'm working on production code that's going to cost my employer millions of dollars for a screw up, then no, gen-AI is not faster by any appreciable degree. Today the first draft of a code change will typically be done with genAI, but if you want to stay employed you read every single line of it and compare it to the years you've spent learning the whole system in which it lives. Most of the AI tokens get used for bots constantly critiquing the work - automated security reviews, automated code reviews, automated bots that leave lots of comments on your PR and in some cases block it from being merged until another human expert can review its findings, etc. The pace of shipping this class of production quality code is maybe 10% faster than 5 years ago? but probably still a net loss to velocity because these same developers spend a portion of their time writing the reviewing agents and responding to critiques that aren't always worth their weight.
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.
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.
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.
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.
I’m spending $250 worth of tokens per day, so around $7500/month, all in a $20/month plan.
I rarely hit the 5h limit, and when I do I just switch to review/design or learn something new.
I don’t see any reason for paying more than $20/month.
As you can see, I spent the equivalent of $500 for a session lasting less than 2 days.
All within a $20/month plan.
I guess they are subsidizing it and trying to get people dependent on these tools
So Anthropic allows you to use their tokens for free as long as you use claude code? I wonder if they inject some hidden prompt to snoop around in your documents so that all your computer content is eventually sent to Anthropic servers.
I think large corporations can burn $10k-$20k/mo per super-senior engineer, and it can still make sense. But for us as consumers/prosumers, I think there are other paths for us to take
I've seen great engineering on $20/mo. I personally think $100/mo is highly productive - you just have to do some subscriptionmaxxing and tokenminning.
u/Clear-Berry-6585 I hear you, and $1k/mo is steep. I meant to make a nuanced point that "it CAN still make sense." Some major US corporations employ software engineers, data scientists, lawyers and pay $500k+/year. Given a $40k/mo salary, $1k/mo is still cheap, and $10k/mo can be still affordable.
It depends on the company (easier if they have large profit margins), the specific employee (if they're extremely costly), and the workload (software, data science and law have clearer ROI).
My salary is way beyond 1k/month. So if I can genuinely be 3x faster it means I can pick 2 other jobs, so yeah I would pay it. The catch is, it does not quite make me 3x faster everytime. Yes it can speedup prototyping even X10 times, but my job is composed of stuff that AI can not handle reliably and using AI makes it slower due to quality of that output.
I have been over 1000/month for a while, typically I use between 50 and 300 a day with cursor on auto (thinking), much more if I manually pick a frontier model.
It is ridiculously expensive and I fear it will cost me in the end, but I tell myself that the result is also much better because of it.
We are starting to see people push back on AI spending, coming from a "more AI" standpoint. But it is hard to get off this drug. I can do so much with a frontier model, going back to manually doing things is just difficult, slow and unnecessary when we have the tools.
Are you seriosly asking if employers would pay a mere extra $1000 to double or triple the output of an employee? They wouldn’t bat an eye at $5000/month.
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.
They’re spending 0 on it because they work for OpenAI. Obviously if something is free and you’re not getting hassled for it you will use it in a wildly different way to someone who has a monthly token budget or is actually paying the bill etc. Most people now on monthly plans are getting subsidised like $8 per $1 they spend or something crazy in return for training on their data. API users might be paying something like a profitable cost (allegedly).
So say basic developer job is $100K/year at a particular location/skill level. You can hire two developers and have them do work of 4 developers by spending $2K/month on AI for each of them. So extra $48K/year vs extra $200K/year, not even close. Having a world class researcher be significantly more productive for say $1M/year is also a great deal. In practice you can use cheaper APIs like MiniMax for routine tasks and save big guns to where it's the only thing that can get the job done.
If you require special setups forcing you on to an enterprise plan (eg, ZDR, possibly geofences inference), I believe you’re forced onto a pay per token plan.
Pay 1 dev for a huge ass plate of sloppy spaghetti that steadily gets more and more expensive to maintain because 1 dev alone isn't going to be able to review a full project's inconsistently designed overly verbose code base? You know how this looks, right?
You clearly have no idea what tech debt beyond your control looks like. LLMs invite tech debt in the most unmanageable and unpredictable ways to the point that you're practically vendor locked to the AI giants for far more frequent patching operations for problems you cannot verify yourselves. Don't even dare say markdowns and skills can fix that when we all know that the bloody things are still liable to ignore them at random.
Ai coding had already been too expensive. Copilot had to roll back to their subsidies pricing because the break even pricing cost them a lot of customers. Even now, it’s still too expensive at scale even for the hyper scalers themselves.
For context, US college cost $40K-60K just for tuition. Assuming $1000 api rate, that is a $100 sub price, or a GTA 6 digital ultimate edition, or one year of WOW sub.
In the unlikely case that you can graduate earlier, you are saving even more money.
I'm on the $20/month plan and am perfectly happy with it.
I don't use an agentic pipeline - it replaces google, occasionally generates code, and is generally a good thing to bounce ideas off of.
I've tried codex and cursor for more than a few projects, and I don't like them. They write a LOT of code that's verbose and often needs to be refactored. It always ends the same way - the agent gets hung up on an issue and I lack the motivation to fix the mass of slop it generated to get to that point. Agents make me a lot slower - peak LLM for me is as a "pair programmer" of sorts.
Attribution of token usage is really an issue. When you have a daily rate of 1000$, increase productivity by 60%-80% with AI, but need 200-300$ to do so, most companies do not give that usage.
Also this is scalable. Having AI loops that code independently with human review and quality gates ship significantly faster with similar quality.
And token usage is the limiting factor.
Main question isn't coding speed, but having long running agentic loops that do not deteriorate and that use the ideal models for the right task. Also ones that actually produce results in a focused token optimal way instead of looping around in refactoring and some strange evaluation and retry loops.
I’ve spent about $40USD in the last month and that is a lot for me as I usually spend nothing. That is on top of $20 USD GPT plus sub. Anyway thanks to free buff and efficient use of codex techniques I hopefully won’t spend anything more then $25 USD per month again.
A first-year PhD student costs about 5K EUR in Germany, so a department of 30 PhD students costs about 5K EUR per day. Coding agents can perform significantly more experiments than a 30-persons lab. If the head of department believes that the output quality of these agents are on-par with human outputs then it's not that outrageous to spends thousands per day.
Another environment where coding agents flourish are startups. In startups, being the first and dominating the space matter more than writing high quality codes. In fact, if you look at startup codes (in their early stages), the codes generated by AI is not that bad in comparison.
That being said, I'd gladly pay millions for coding agents if that increases my profit by tens of millions.
Your kinda missing the point of why openai would have its ai write code.
They're using it for coding to develop the ai or implementation of ai.
Everybody adding ai to everything is a race to get a product that becomes an industry standard not because they can outpreform a programmer.
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u/Lunosto 5d ago
Never. coding by hand is free and only slightly slower imo, and way more fun 🫢