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I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms

by abuani 5mo ago

I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly. I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value. At a corporate level, I'd much rather hire a junior engineer who spends $100-$200/month and becomes productive then try and rationalize $100k/year in token spend.

ttariq 5mo ago
The token spend should really be seen against the output it created. I spent about 11k in tokens over a 40 day period (subsidized by the Max plan, of course -- I am not made of money), but the productivity in the period was insane. I was able to ship multiple fairly complex systems that are now working in production (document analysis system, resource management system, a complete re-architect of a healthcare system) plus loads and loads of experiements. Looking at token spend in isolation is measuring productivity by lines of code.
bdangubic 5mo ago
> I use llms daily this is your “problem” - you are missing the “nightly” part. on my box LLMs run 24/7 :)
pancsta 5mo ago
> in deep thinking mode You mean deep brute-force mode of search results parsing themselves…
CyberDildonics 5mo ago
They keep forgetting to put "make no mistakes", "think deeply" and "get it right the first time" in their prompts. When people have no ability to understand what they are doing, they will just rerun it endlessly hoping they get something passable. When that doesn't happen they burn money.
- dpark 5mo ago
  I doubt most of this is from rerunning the same prompts over and over. This token burn is more likely from people using swarms of agents and orchestrators for “efficiency”. “I’ve got 2 dozen agents churning through the backlog to build this feature that would take one agent an hour to implement.”
  - cyanydeez 5mo ago
    managers call meetings and agents call swarms.
entropicdrifter 5mo ago
I'm on the same page. Do people not analyze the problems themselves? Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works? I don't get it.
- swiftcoder 5mo ago
  > Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works? That is exactly what they are doing, yes
  - ravenstine 5mo ago
    And why wouldn't they? Companies are quite literally instructing them to do so. I work at such a company and have heard similar anecdotes from colleagues that work at other companies.
    - solenoid0937 5mo ago
      Why wouldn't you do this even if not instructed to do so? I can do so much more with my spare time now. I throw agents at problems and get way more done. $1k in tokens every day is easy to hit.
      - mkehrt 5mo ago
        What exactly are you “getting done”? I’m really curious what you’re doing with so many tokens.
  - Verdex 5mo ago
    That's my take as well. I've had my unPRed branches grabbed up and blindly merged by an agent twice now. The guy doing it was shocked both times that his PR had my change sets in it. Also one engineer is treating the code as assembly. I've asked some pointed questions about code in his PR and the response was "yeah, I don't know that's what the agent did". Edit: To everyone freaking out about the second guy. Yeah, I think being unable to answer questions about the code you're PRing is ill advised. But requirement gathering, codebase untangling, and acceptance testing are all nontrivial tasks that surround code gen. I'm a bit surprised that having random change sets slurped up into someone else's rubber stamped PR isnt the thing that people are put off by.
dpark 5mo ago
> responsibly There’s your problem. You’re trying to be responsible instead of trying to burn tokens so you can have your name on top of some leaderboard for most wasteful AI users.
- tcoff91 5mo ago
  The perverse incentives created by these AI leaderboards are crazy.
  - swader999 5mo ago
    But we need OKRs rocks and METRICS! Everyone must have their own one numberrrrr!
  - dpark 5mo ago
    The leaderboards are dumb, but I understand the point of telling people not to worry about tokens and just use it. They are trying to get people to try it, to discover new uses without asking “is this worth testing”. It’s basically early R&D budget. Eventually these companies will decide it’s time to transition into efficient usage.
    - tcoff91 5mo ago
      Yes I love that my employer says go wild with it. But I feel like the leaderboard is dumb.
gjulianm 5mo ago
Several options on how to burn that amount of money without being specifically looking to tokenmaxx - Agents that spawn other agents - Telling agents to go look at the entire codebase or at a lot of documents constantly - MCP/API use with a lot of noise - Loops where the agent is running unattended. I do think it's not really responsible use and a loop where the agent is trying to fix CI for one hour for something that would take you five minutes (for example) is absurd. But people do that.
- _alternator_ 5mo ago
  One of the new dynamics is a loop between a "code review" LLM and a "fix LLM". It's super annoying because the code review LLM often finds more bugs on a follow-up review that were there from the beginning, but at least I can loop both until check go green.
brokencode 5mo ago
Really depends on the repo you’re working in. If it’s very large, especially if the tool needs to refer to documentation for a lot of custom frameworks and APIs, you often end up needing very large context windows that burn through tokens faster. If it’s smaller or sticks with common frameworks that the model was trained on, it’s able to do a lot more with smaller context windows and token usage is way lower.
- some-guy 5mo ago
  I'm currently in repos where the context window required is so large that the output is almost always "wrong" for the problem at hand. Quite a few people at my company burn through tokens this way, and it certainly isn't providing value to the company.
  - AlotOfReading 5mo ago
    As always, improving accessibility for humans makes automation more effective. If the humans need to remember a PhD's worth of source code/documentation to contribute effectively, your codebase stinks.
    - bonesss 5mo ago
      I agree, in the general context of how I code. The LLM hype train has me reflecting on what a spoiled existence working in a ‘proper’ language provides though… React devs, JS devs, front-end devs working on large sites and frameworks might be triggering tens of files to be brought into context. What an OCaml dev can bring in through a 5 line union type can look very different in less token-efficient and terse languages.
    - ivirshup 5mo ago
      People at my company have started writing docs specifically for claude. They're quite useful for me too, but kinda disappointing they never wrote these docs for their colleagues.
      - 5mo ago
wolttam 5mo ago
It turns out writing good prompts helps to keep token usage down as the model wastes fewer tokens discovering context it needs that wasn't hinted at in the prompt. Whereas a good prompt will give solid leads to all the specifics needed to complete the task.
jp57 5mo ago
Claude is a mediocre programmer that can do great things with great supervision, but it can't make mediocre human programmers into good ones, because they can't provide great supervision. It will try and try and try, though.
- cyanydeez 5mo ago
  id bet its the LLM doom loop: vaguely ask it to do something, tab to news.ycombinator.com for 30 minutes, tab back, noticed it misunderstood the prompt. Restart with new improved prompt, tab back to HN. So yeah, probably the same thing people do anyway, just not compile time its now generating time.
  - th0raway 5mo ago
    We opened the Cloud Code floodgates all at once in my org. After a few months we looked at stats, and asked managers for impressions on performance changes. The API cost per engineer doesn't correlate with the apparent increases in performance, but it sure seems that the vast majority of people that used to have good reviews got a lot better, while the bottom third just didn't, even though they use the LLMs about as much. It makes the performance differences in teams look like an abyss. Someone appears stuck in a task, and we see what they've been prompting, and then one of the best seniors comes in, actually asks the questions well, and the LLM does all the debugging and all the fixing in 20 minutes. It's not that the best performers are magical prompt engineers providing detailed instructions: They ask better questions that the LLM knows how to try to answer, and provide the specific information that the LLM would take a while finding. It's as if some people just had no "theory of mind" of the LLM, and what it can know, and others just do. It's not a living thing or anything like that, but it's still so useful to predict it, put yourself in it's shoes, so to speak. Just like you'd do with a new hire, or a random junior.
    - anal_reactor 5mo ago
      This comment is buried deep but I think it's actually quite important. In 2005 you had the elderly googling "Can I have a recipe for an apple pie? Thank you." vs kids doing "apple pie recipe" and clicking the first result. Some (most?) people just weren't capable of conceptualizing the abstract idea of "internet search" so they talked to the machine like they'd talk to other humans. Until coronavirus virtually anyone regardless of mental skills could get a high-paying job as a coder, there was no filter at all. What we'll observe now is the split between those who can conceptualize the idea of an AI, and those who cannot. The latter group will be stuck talking to AI in a way that doesn't leverage how it actually works.
ajross 5mo ago
> I'd much rather hire a junior engineer who spends $100-$200/month I'd much rather hire a junior engineer at $1.20/hour too! Can you hook me up with your contract services provider? Obviously I know you're talking about AI costs only. But the idea of doing that analysis without looking at the salary of the person running the tool seems to be completely missing the point. Now, sure, there are legitimate arguments to be made about efficacy and efficiency and sustainability and best practices. But, no, $100k/year absolutely doesn't need to be "justified" if it works. That's cheaper than the alternative, and markedly so.
- hvb2 5mo ago
  > But, no, $100k/year absolutely doesn't need to be "justified" if it works. That's cheaper than the alternative, and markedly so. If you're trying to say that 100k is less than 200k, you're right. I don't see how any of that won't need to be justified. You can spend a lot of money and not get enough of a return...
  - ajross 5mo ago
    FWIW, you're nitpicking a strawman. I put "justified" in scare quotes for a reason, qualified it with "if it works" (which is, quite literally, the definition of a justification) and put it immediately after a sentence enumerating a list of legitimate questions for debate (all of which would be part of any justification analysis). You agree with me, basically. The core point is that these very large AI bills are not actually large in context, as the pre-existing scale of expenses for software engineering are larger still and this at least promises to reduce those markedly. To wit: argue about whether AI works[1] for software development, don't try to claim it's too expensive, it's clearly not. [1] "Is justified" in the vernacular.
boringg 5mo ago
Keep word doing A LOT of lifting “responsibly”
maxdo 5mo ago
In your fictional world you hire a junior who will write code manually, right? First , I interview people, Junior skills in manual coding dropped sharply this year. These are people who started they school manual and switched mid-course. In two years there will be no such people. well, that will never happened anymore in this world unless we will go back to caves, especially for juniors. Junior that writes good code is already a dying unicorn. The outcome will be ... you will hire a junior ... who will burn more tokens, and chances of mistakes with less expensive model, less tokens are even higher.
- krainboltgreene 5mo ago
  > well, that will never happened anymore in this world unless we will go back to caves The bubble is an echo chamber.
  - maxdo 5mo ago
    I'm interviewing juniors. Their manual skills drops sharply, and that's for people who went to school in manual age, and maybe last year it stopped to be manual. Lets see what will be in a year or two lol
    - sikozu 5mo ago
      At this point do you even need to hire any juniors at all? It seems like there's a heavy reliance on AI agents and LLM especially for juniors. Is hiring a warm body that sits on a chair and prompts at a computer a good use of money?
      - maxdo 5mo ago
        yes, and no. Everybody is trying to hunt junior unicorn, They exist , but the ratio is 1 out 30. For these people, AI is a real elevator of their career.
- AntiUSAbah 5mo ago
  Puh not good signs at all. I mean even the normal people we get in interviews have no clue, like 80% are just ignorant. I stoped an interview after 5 minutes: when i asked what ls -ahl is doing, he started telling me how he vibe/ai codes stuff and thats his workflow. Okay if you don't know the basics, guess what? everyone can replace you or at least i'm not hiring you (i only told him thats not what we are looking for and thanked him) we are doomed :D
maccard 5mo ago
There was a tool posted called codeburn that showed a breakdown of what activity your usage was spent on. Mine was almost all coding but other people in the thread said >50% of their usage was conversation. I’m inclined to agree with you that someone who is reasonable with their compute usage is likely to be thinking things through rather than just burning tokens to get an LLM to solve the problem
embedding-shape 5mo ago
> I just can't figure how _how_ to burn that much money a month responsibly. Same but in regards to quotas. I'm on the 200 EUR ChatGPT plan, so presumable have the highest quota, using the "most expensive" models, on highest reasoning, in fast-mode (1.5x quota usage) and after a full day of almost exclusively doing programming with agents, I still get nowhere close to hitting my quota. In fact, since I started using agents for coding, the only time I even got close, was when I was doing cross-platform development with the same as above, but on three computers at the same time, then I almost hit my weekly quota. But normally, I get down to ~20% of the quota but almost never below that. I don't see how I could either, I'm already doing lots of prompts and queries "for fun" basically.
- jackdoe 5mo ago
  I am running a bunch of autoresearch loops that optimize various compilers and its pretty easy to burn through as much money as you want if you have a measurable goal and good tests.
  - embedding-shape 5mo ago
    > have a measurable goal and good tests I have both of those, yet seemingly I guess I'm not setting my goal in such a way that it supports "endless inference" like that. My goals have eventually ends, and that's when I move on. Optimization sure sounds like something you can throw away a good amount of tokens/quotas on, so yeah.
  - gessha 5mo ago
    > if you have a measurable goal and good tests. you can burn through the money even easier if you don’t have them
- adi_kurian 5mo ago
  Codex quota is suspiciously high right now. Either way, the subscription plans are not sustainable, and perhaps less relevant to any discussion about corporate API use. The prosumer developer plans are an insane deal. It is a golden age right now and it will end. If you tried to use the APIs to achieve the same thing, you would be spending thousands upon thousands of dollars a month. My completely unfounded conjecture is that OpenAI is trying to grab developers back from Claude by burning $$$$.
_pdp_ 5mo ago
Do you run 20 claud code agent on max for 8 hours a day? :)
lumost 5mo ago
I spend 400-500 dollars per day during active development at this point. However with more aggressive task breakdowns I can spend ~5k per day. These spend rates are in part due to operating on a larger code base. Operating on a larger code base means more time searching and understanding the code, tests, test output. They are also due to going all-in on agentic coding. It can feel painfully slow to go back to coding by hand when for a dollar you can build the same functionality in a minute. Now do this with multiple sessions and you can see where the cost goes.
- steveBK123 5mo ago
  Your reply answers how you are able to spend money, not if it is returning sufficient dollar value per spend.. > I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value.
  - solenoid0937 5mo ago
    The problem with HN is that everyone here thinks like an engineer, not like a business owner. $10k a month on tokens is just not that much when you're already making $2M per engineer. If their productivity has increased even 10% then the spend was well worth it. Case in point, Meta made 33% more revenue this earnings report. Now you can nitpick and ask for attribution down to the dollar, but macro trends speak for themselves.
    - steveBK123 5mo ago
      Go look up a multi-year chart of their revenue and find the inflection point where the AI made it go up faster (there isn't). In fact revenue growth used to be higher pre-2023.
      - solenoid0937 5mo ago
        They were also a lot smaller pre-2023, 33% growth for a company of their size is simply insane. It is entirely likely that 33% simply wouldn't have happened without AI.
sailfast 5mo ago
In addition to what folks are saying here about larger code bases and multiple features at once, there’s also the time requirement to be efficient. It takes time to be more efficient with token usage and it may not be worth it for some of these companies so… burn away until we start to get more data and then we’ll check in.
gordonhart 5mo ago
On the OpenAI side, GPT-5.5 generates spend at a prolific rate that's even faster if you use it through an ACP connection in a tool like Zed. I used to never think about Codex rate limits and now I'm hitting mine every 5 hour block and spending ~$100/day on top of that in adhoc credit purchases.
bigbuppo 5mo ago
You're probably generating new code rather than analyzing old code for "improvement".
Salgat 5mo ago
Do lots of deep research and code reviews on large legacy codebases. I've created lots of documentation to reduce token consumption but it's still a lot of token consumption.
readitalready 5mo ago
I think companies are charged API prices vs individual prices. That alone is 10x for Anthropic. Not sure though.
benjiro3000 5mo ago
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xboxnolifes 5mo ago
I dont use automated agent workflows or anything, I just use clause as a pair programmer of sorts. A month or so ago I used claude Opus 4.6 for 2-4 hours on API pricing and racked up $20 in spend, which surprised me since that was much higher than my usual. I dont know about $10,000, but i can see hitting $1,000 pretty easily if you aren't looking at the costs.
rconti 5mo ago
Many companies actively hide the cost from their employees.
hadlock 5mo ago
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crystal_revenge 5mo ago
One thing that stands out it is it sounds like you're using LLMs for only one part of your process. You're having LLMs help you write code, but the code you're writing doesn't itself make use of LLMs. My current job basically involves trying to improve processes that themselves make heavy use of LLMs. Once you have multiple agents in parallel running multiple experiments on improving the performance of primarily LLM driven tools it's not that hard to get your token usage pretty high.
stronglikedan 5mo ago
I don't think it's about value. Tokenmaxxing is a thing now since that one CEO said he wants his $250k/yr devs to use $400-$500k/yr in tokens, so now it's all about how many agents can you have running concurrent tasks all day long.
o10449366 5mo ago
I spent $24,096.47 in "API" costs with my $200 Claude Code Max subscription in April. I'm building my own saas. I spent 6 months writing the code by hand before using Claude, and that was fine, but its much faster to give the exact specs to Claude and have 3-4 sessions working in parallel with me. When you validate changes with exact test specs there's much less correction you need to do. I always hit my weekly limit and it's far cheaper for me to use this than to hire someone and spend time onboarding them.
BeetleB 5mo ago
First: There's the obvious "If the company is letting me do it, I'll be wasteful." This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. People have already mentioned the size/complexity of the codebase. I'm new to my team and the codebase isn't huge, but it's large enough that there are plenty of parts I have little understanding about. When I'm given a task, then yes, I definitely go to Claude and ask it to find the relevant parts of code so I can understand the existing workflow before even attempting to change it. The downside is that I don't build expertise. But the reality is that with Claude, I can get the work done in 1 day that would take me 5 days of struggling, and if everyone is doing it, I can't be left behind. So I take the middle route - I get it done in 2-3 days instead of 1 so I can at least spend some time with the code. Especially with AI, the rate at which code changes in our codebase is insane. So I built a tool that takes a pull request, and tells the LLM to go deep and explain to me what that pull request does. (Note: I'm not the reviewer, I just want to keep tabs on the work that is going on in the team). And this is just the beginning. I haven't actually spent time to come up with more ways to use the LLM to help me. My usage is similar to yours, but if I were fairly experienced with the code base, I'd do a lot more. I haven't asked, but I suspect there are people in my team who go over $1K/month. As always, the bottleneck is proper testing and reviews. Edit: I'll also add that for not-so-important code used within the company, I suspect most people are going full-AI with it. For my personal (non-work) code, I just let the AI code it all - the risk is usually very low (and problems are caught quickly). If someone is using the "superpowers" skill, then even for basic features you can burn lots of tokens. I usually start with 20-40K tokens and end up with 80-90K tokens when it's finished. Which means that many of the requests prior to completion were sending in close to 80K tokens. Multiply that with the number of queries, etc. Wasteful, but if someone else is paying ...
- kurige 5mo ago
  > This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. I see this repeated by others, including coworkers. It completely ignores caching. Caching itself is complicated, but the "longer context window = more expensive" is not 100% true and you are hampering yourself if you're not taking full advantage of large context windows.
bs7280 5mo ago
I have ancedotal examples of claude code choosing a solution to a problem that is ridiculously token inefficient. One example - was giving several agents different sub problems to solve in a complex ML / forecasting problem. Each agent would write + run + read a jupyter notebook. This worked ok, the notebooks would be verbose but it was fine... until one of them wrote out hundreds of thousands of rows to a cell output, creating a 500MB ipynb file. Claude tried several times to read it and it used my entire context limit. The solution was to prescribe a better structure of doing the world (via CLI analysis scripts + folders to save research results to). But this required some planning, thought, and design work by me the operator. When I see people spending $10k a month in tokens, I can only assume they are taking lazy hands off approaches to solving problems with the expensive hammer that is claude code. EX: have claude read all your emails every day... the lazy solution is to simply do that, but a smarter solution is to first filter the email body HTML to remove the noise.
- 52-6F-62 5mo ago
  > have claude read all your emails every day But that is exactly what it is sold to people to do as a panacea: consume all the data, produce insights. Nobody is being instructed to be judicious. Everyone is being instructed to use it as much as possible for all problem areas.
  - hirako2000 5mo ago
    If you make 500k and aren't spending 250k in token, you should get fired.
    - quikoa 5mo ago
      I can't even tell whether this comment is serious or not.
      - ori_b 5mo ago
        https://www.businessinsider.com/jensen-huang-500k-engineers-250k-ai-tokens-nvidia-compute-2026-3 https://www.businessinsider.com/jensen-huang-500k-engineers-...
DeathArrow 5mo ago
It really depends on the way you use AI. If you just prompt it for a task and either accept or reject the output, you won't spend much. But if you are like me, you aggressively document and brainstorm before planning, you review that documentation with subagents, make modifications, you aggressively plan, you verify that plan with subagents,make modifications, have a large number of phases, planning again for each phase, writing tests to cover 100%, implement each phase, do intermediate and final code reviews with subagents, apply fixes, write final documentation and do all these in parallel, if you have multiple tabs in your terminal each running Claude Code for 10-12 hours a day, then $5000 per day is not much. If you use Anthropic or Open AI subscription and you spend $1000 per month, you are not using AI much.
- in_cahoots 5mo ago
  Are you bringing in at least $1.25M in additional yearly revenue to your company?
  - DeathArrow 5mo ago
    I think more than that.
- DeathArrow 5mo ago
  And also some of use run tens of rounds of gradually improving the projects. And that burns tokens like crazy.
hliyan 5mo ago
The answer may be agentic loops that keeps cycling through the same problem again and again until they land on a non-erroneous outcome. Some people boast having multiple such agents working in parallel on different problems, tending to one while another is processing, perhaps not unlike the movie mad scientist who runs around the lab throwing switches while laughing maniacally at the prospect of his impending success.
kansface 5mo ago
> I just can't figure how _how_ to burn that much money a month responsibly. I always have a few agents (2-5) doing research and working on plans in parallel. A plan is a thorough and unambiguous document describing the process to implement some feature. It contains goals, non-goals, data models, access patterns, explicit semantics, migrations, phasing, requirements, acceptance criteria, phased and final. Plans often require speculative work to formulate. Plans take hours to days to a couple of weeks to write. Humans may review the plans or derived RFCs. Chiefly AI reviews the code (multiple agents with differing prompts until a fixed point is reached between them). Tests and formal methods are meant to do heavy lifting. In my highest volume weeks, I ship low hundreds of thousands of lines of software not counting changes to deps. > At a corporate level, I'd much rather hire a junior engineer Any formulation of problem sufficient for a truly junior engineer to execute is better given to an agent. The solution is cheaper, faster, and likely better. If the later doesn't hold, 10 independent solutions are still cheaper and faster than a junior engineer. There is no longer any likely path to teaching a junior engineer the trade.
- ModernMech 5mo ago
  I dunno I've seen agents make boneheaded mistakes even a junior engineer wouldn't make. Treating them as strictly better than junior engineers is a problem, not just for that reason but because you're effectively killing the pipline for senior engineers. Then what?
  - kansface 5mo ago
    > I dunno I've seen agents make boneheaded mistakes even a junior engineer wouldn't make. Yes, of course. > you're effectively killing the pipline for senior engineers. Then what? I honestly don't know _what_. Its a prisoner's dilemma.
- codebolt 5mo ago
  Just out of curiosity, what type of systems are you working on? What type of features did you implement on your 100k LOC week?
paulsutter 5mo ago
I'm working on some serious data analysis + realtime async code, and I use 200-400 million tokens a day with Claude Code alone (via ccusage). The complexity of the code seems to have a big impact on the number of tokens used. On simpler projects I use many fewer tokens. My programming endurance is much greater now (2-3x focused hours per day), my productivity per hour is multiples higher, and I code seven days a week now because it's really exciting. All told, I would pay for these tools as much as I would pay for full-time human programmer(s).
Anon1096 5mo ago
The fully loaded cost of a senior engineer is already well past 400k. +5k a month is not that much if it helps them be XX% more productive. Personally at a different big tech I'm in the mid 4 digits AI spend per month and it helps me a lot, basically all coding has been trivialized and I work on an extremely large codebase. I'm spending more time on things closer to direct value generation like data analysis and experiment tweaking rather than spending time moving a variable across 10 layers of abstraction and making sure code compiles.
iLoveOncall 5mo ago
It's easily explained. People are losing their skill in real time and literally cannot develop anymore without AI. That's it.
wahnfrieden 5mo ago
You are probably guiding them step by step and reading the results. Maybe you also sit and wait for the results. Agents can iterate on a problem for hours if they can see their results and be given a higher level goal to evaluate their progress toward. When you have an agent working for minutes or hours, never wait on it. Use that time to spin up another agent. You can also spin up several agents in parallel to attempt the same item of work and compare their results to choose which to work off for next steps, instead of rolling the dice on a single option at a time and gambling that it's better to refine that first attempt instead of retrying from the start several more times. And if you are doing manual QA manually, you're missing out on having e.g. Codex's "Computer Use" or "Browser Use" automate your manual verification steps and collecting a report for you to review more quickly. Codex can control multiple virtual cursors simultaneously in the background without stealing focus, to parallelize this. If you want to use up more tokens to get more done (though more outside of your control and ability to review of course), that's how.
drfloyd51 5mo ago
Your estimates do not account for speed of delivery. If an AI can deliver 10x faster, the target is less than 10x a dev salary. But 10x faster also gets you to market sooner. Which has value.
- athrowaway3z 5mo ago
  I could argue in all the ways my personal experience disagree, but lets just Occam's razor: Most people agree big orgs regularly have dysfunctional incentives. We've seen it happen a thousand times. Your suggestion requires we also assume a 10x faster delivery time by people spending 200$ vs 1000$ - something I've yet to witness or hear a credible account of. So while that might be true in a small number of cases, in general its foolish to go with the "10x delivery speed" hypothesis.
fhn 5mo ago
a good way to prevent companies from adopting AI (and keeping your job) is to waste tokens making AI cost prohibitive
- munk-a 5mo ago
  That would be true in a sane world with investors who value profitability. But everything is now focused on DAU and the network effect. Overusing their services might actually make them look better to investors who shovel more money to them to light on fire.
munk-a 5mo ago
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barbazoo 5mo ago
In our org it's people that have too much stuff in their context, every mcp in the world installed, GTD, PAI, OpenClaw. I'm equally baffled how one can spend that much money during their day to day.
Sohcahtoa82 5mo ago
Yeah, I use Claude Code to do security reviews. For every CVE that Wiz flags, I have Claude Code check for reachability analysis. I typically consume about $200/month doing this. Most of our engineers are in the $200-400 range, with a few people around $1,000. But then there's one guy who's not only hitting $8,000, but supposedly has nearly 300,000 lines of code accepted (Note: This means he's accepted the lines of code from Claude, not that he's committed it). I can't figure out how.
canes123456 5mo ago
I use it as an ide. I am a security engineer but there a bunch of predictable things I need to write code for. Onboarding logs, writing detection rules, SOAR type stuff. It makes a diff and locally tests all the permutations I describe than I review the code.
raducu 5mo ago
> notice more and more are consumed $1k in tokens a month I've said it before: if you allow people to see how much others spent, they will try to climb up the "leaderboard". It takes just ONE little praise for using tokens or one perk gained, and the GAME IS ON among the developers!
Galanwe 5mo ago
> I just can't figure how _how_ to burn that much money a month responsibly. From my experience, this happens essentially by three means: - Level 0 (beginner users) long lived conversations: If you dont get in the habit of compressing, or otherwise manually forcing the model to summarize/checkpoint its work, you will often find people perpetually reusing the same conversation. This is especially true for _beginners_, which did not spend time curating their _base_ agent knowledge. They end up with a single meta conversation with huge context where they feel the agent is "educated", and feel like any new conversation with the agent is a loss of time because they have to re-educate it. - Level 1 (intermediate users) heavy explicit use of subagents: Once you discover the prompt pattern of "spawn 5 subagents to analyze your solution, each analyzing a different angle, summarize their findings", it can become addictive. It's not a bad habit per se, but if you're not careful it can drastically overspend your credits. Level 3 (expert users) extreme multitasking. Just genuinely having 10 worktrees perpetually in parallel and cycling between them in between agent responses. Again, not necessarily bad in itself, but can exponentially conse credits.
- hn111 5mo ago
  Where is level 2?
  - ori_b 5mo ago
    LLMs can't count well.
  - floren 5mo ago
    Still waiting for the output of that agent
  - tom_ 5mo ago
    There isn't one. Level 3 is just that much more advanced.
  - layer8 5mo ago
    It’s probably unary interpreted as binary, hence there is no level 2. Level 3 is followed by level 7. Level n is followed by level 2n + 1. Exponential growth. The singularity is near.
starkparker 5mo ago
At least your workplace doesn't frame raw usage as a leaderboard, with awards given out for topping it
hparadiz 5mo ago
Multimedia feedback can burn much more than that. If I'm sending frames of 3D engine's output. I mean I would like to send it a video if I could but that is too expensive but I'm sure there's orgs out there that really do want every frame in a prompt doing something. This can be exponential depending on the application. I recently wrote a Milkdrop visualization analyzer. I could have sent thousands of frames for each one. I didn't but well I wish I could haha.
ori_b 5mo ago
If Uber is like most other companies, there's a leaderboard for AI tokens consumed. If maximizing your token usage is going to get you to the top of the leaderboard, and therefore promoted for "productivity", people are going to find creative ways to be "productive".
- traderj0e 5mo ago
  The tokenmaxxing leaderboard where I work has a lot of new hires on it
  - ori_b 5mo ago
    Asking the LLM to spawn a subagent per file and look for bugs is a good way to waste a lot of tokens real fast for leaderboard success, and it's pretty defensible as useful work if someone tries to call you on it.
    - traderj0e 5mo ago
      Yeah and it's a natural extension of the typical busywork people used to spam pre-AI, "I split your ticket into 10 tickets and made a kanban for it"
MrDarcy 5mo ago
It turns into 50k to 100k or more of value for the employee the moment upper management made AI spend a personal performance target across most corporations.
neonstatic 5mo ago
My observation is - pasting long documents is a great way to burn tokens. Turn based conversation, even a very deep and technical one, consumes less tokens than "read these logs and tell me where the problem is". Ironically, the log reading example is a perfect use for a local LLM.
ChadMoran 5mo ago
Advanced agentic prompting.
maherbeg 5mo ago
I also don't think a lot of people know some of the more advanced context management tricks like /rewind /fork /tree to take advantage of prefix caching
dinobones 5mo ago
Don't underestimate corporate waste. If it's not someone's job to care for something, they really won't. Even before this AI wave, it was common for me to see spinning dev environments for like $3k/month that hadn't been used in months on AWS.
lispisok 5mo ago
>I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value At a lot of businesses $5-10k/mo of AI spend doesnt even translate into $5-10k/mo value. Churning out code was rarely the business value bottleneck. It was convenient for everybody else to blame developers not writing code fast enough for their failures. Now they have no excuse but I doubt will own up.
dvfjsdhgfv 5mo ago
> I just can't figure how _how_ to burn that much money a month responsibly. Well, if your bonus depends on spending it, you'll find a way.
traderj0e 5mo ago
Idk about Uber in particular, but aside from legit programmers using AI to help them do legit work faster, there are people spamming it for metrics. And the hiring pipeline has gotten screwed up somehow, like half the people who reached the onsite interview for a technical role lied about all their technical skills, or they didn't lie and manage to pass hiring but then only take the tasks that AI can solo. And if it can't, waste tokens until giving up.
bredren 5mo ago
1. Worktrees 2. Multiple simultaneous projects 3. Orchestration that includes handling of CI workflow 4. Active work to further improve or refine tooling 5. Experimentation producing muscle memory as experience versus code output
jb3689 5mo ago
$400 * 23 business days would be $9k. Sounds ballpark to me
malisper 5mo ago
I've been working on a project to build a new Postgres based database in Rust[0]. I'm four weeks in and have 93% of the Postgres test suite passing. I've found agents to have worked really well for this as I have an existing codebase that has good architecture that I can point my agents at. It's also easy to debug as I can diff what my agents are doing and what Postgres is doing. I've had to get multiple codex accounts, but there was a brief period of time where I tried API usage to see how expensive it would be. In about an hour I spent $650 of credits. I had codex estimate how much I would be spending if I was doing pure API usage and it estimated around $10k/week. For context Postgres is 1M lines of C code. It's looking like pgrust will come out as less lines of code than Postgres and at peak I was adding over 100k lines of code in a day. I would estimate it would take a team of 5 software engineers at least 3 years to get to where I got in a month with a couple Codex subscriptions. [0] https://github.com/malisper/pgrust https://github.com/malisper/pgrust
schainks 5mo ago
Try the Jira MCP server.
irlib 5mo ago
[dead]
insane_dreamer 5mo ago
I used CC frequently for development, Opus 4.7 with high thinking, with a $100 Max subscription, and haven't been rate limited yet. IMO a subscription is the way to go as it puts a ceiling on spending.
kulikalov 5mo ago
Slop architecture leads to compounding problems that people try to solve with more slop. If one wants to control the quality of the code then the throughput and multithreading is bottlenecked by how much code one can comprehend in a given period of time.
stevepotter 5mo ago
Upper management wants to say they use AI and spend is an easy indicator. That trickles down and ultimately the engineers spending a lot is seen as good. It’s just a lazy measurement, like cloud usage or number of microservices used to be.

by abuani 5mo ago

ttariq 5mo ago

The token spend should really be seen against the output it created. I spent about 11k in tokens over a 40 day period (subsidized by the Max plan, of course -- I am not made of money), but the productivity in the period was insane. I was able to ship multiple fairly complex systems that are now working in production (document analysis system, resource management system, a complete re-architect of a healthcare system) plus loads and loads of experiements. Looking at token spend in isolation is measuring productivity by lines of code.

bdangubic 5mo ago

> I use llms daily this is your “problem” - you are missing the “nightly” part. on my box LLMs run 24/7 :)

pancsta 5mo ago

> in deep thinking mode You mean deep brute-force mode of search results parsing themselves…

CyberDildonics 5mo ago

They keep forgetting to put "make no mistakes", "think deeply" and "get it right the first time" in their prompts. When people have no ability to understand what they are doing, they will just rerun it endlessly hoping they get something passable. When that doesn't happen they burn money.

dpark 5mo ago
I doubt most of this is from rerunning the same prompts over and over. This token burn is more likely from people using swarms of agents and orchestrators for “efficiency”. “I’ve got 2 dozen agents churning through the backlog to build this feature that would take one agent an hour to implement.”
- cyanydeez 5mo ago
  managers call meetings and agents call swarms.

entropicdrifter 5mo ago

I'm on the same page. Do people not analyze the problems themselves? Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works? I don't get it.

swiftcoder 5mo ago
> Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works? That is exactly what they are doing, yes
- ravenstine 5mo ago
  And why wouldn't they? Companies are quite literally instructing them to do so. I work at such a company and have heard similar anecdotes from colleagues that work at other companies.
  - solenoid0937 5mo ago
    Why wouldn't you do this even if not instructed to do so? I can do so much more with my spare time now. I throw agents at problems and get way more done. $1k in tokens every day is easy to hit.
    - mkehrt 5mo ago
      What exactly are you “getting done”? I’m really curious what you’re doing with so many tokens.
- Verdex 5mo ago
  That's my take as well. I've had my unPRed branches grabbed up and blindly merged by an agent twice now. The guy doing it was shocked both times that his PR had my change sets in it. Also one engineer is treating the code as assembly. I've asked some pointed questions about code in his PR and the response was "yeah, I don't know that's what the agent did". Edit: To everyone freaking out about the second guy. Yeah, I think being unable to answer questions about the code you're PRing is ill advised. But requirement gathering, codebase untangling, and acceptance testing are all nontrivial tasks that surround code gen. I'm a bit surprised that having random change sets slurped up into someone else's rubber stamped PR isnt the thing that people are put off by.

dpark 5mo ago

> responsibly There’s your problem. You’re trying to be responsible instead of trying to burn tokens so you can have your name on top of some leaderboard for most wasteful AI users.

tcoff91 5mo ago
The perverse incentives created by these AI leaderboards are crazy.
- swader999 5mo ago
  But we need OKRs rocks and METRICS! Everyone must have their own one numberrrrr!
- dpark 5mo ago
  The leaderboards are dumb, but I understand the point of telling people not to worry about tokens and just use it. They are trying to get people to try it, to discover new uses without asking “is this worth testing”. It’s basically early R&D budget. Eventually these companies will decide it’s time to transition into efficient usage.
  - tcoff91 5mo ago
    Yes I love that my employer says go wild with it. But I feel like the leaderboard is dumb.

gjulianm 5mo ago

Several options on how to burn that amount of money without being specifically looking to tokenmaxx - Agents that spawn other agents - Telling agents to go look at the entire codebase or at a lot of documents constantly - MCP/API use with a lot of noise - Loops where the agent is running unattended. I do think it's not really responsible use and a loop where the agent is trying to fix CI for one hour for something that would take you five minutes (for example) is absurd. But people do that.

_alternator_ 5mo ago
One of the new dynamics is a loop between a "code review" LLM and a "fix LLM". It's super annoying because the code review LLM often finds more bugs on a follow-up review that were there from the beginning, but at least I can loop both until check go green.

brokencode 5mo ago

Really depends on the repo you’re working in. If it’s very large, especially if the tool needs to refer to documentation for a lot of custom frameworks and APIs, you often end up needing very large context windows that burn through tokens faster. If it’s smaller or sticks with common frameworks that the model was trained on, it’s able to do a lot more with smaller context windows and token usage is way lower.

some-guy 5mo ago
I'm currently in repos where the context window required is so large that the output is almost always "wrong" for the problem at hand. Quite a few people at my company burn through tokens this way, and it certainly isn't providing value to the company.
- AlotOfReading 5mo ago
  As always, improving accessibility for humans makes automation more effective. If the humans need to remember a PhD's worth of source code/documentation to contribute effectively, your codebase stinks.
  - bonesss 5mo ago
    I agree, in the general context of how I code. The LLM hype train has me reflecting on what a spoiled existence working in a ‘proper’ language provides though… React devs, JS devs, front-end devs working on large sites and frameworks might be triggering tens of files to be brought into context. What an OCaml dev can bring in through a 5 line union type can look very different in less token-efficient and terse languages.
  - ivirshup 5mo ago
    People at my company have started writing docs specifically for claude. They're quite useful for me too, but kinda disappointing they never wrote these docs for their colleagues.
    - 5mo ago

wolttam 5mo ago

It turns out writing good prompts helps to keep token usage down as the model wastes fewer tokens discovering context it needs that wasn't hinted at in the prompt. Whereas a good prompt will give solid leads to all the specifics needed to complete the task.

jp57 5mo ago

Claude is a mediocre programmer that can do great things with great supervision, but it can't make mediocre human programmers into good ones, because they can't provide great supervision. It will try and try and try, though.

cyanydeez 5mo ago
id bet its the LLM doom loop: vaguely ask it to do something, tab to news.ycombinator.com for 30 minutes, tab back, noticed it misunderstood the prompt. Restart with new improved prompt, tab back to HN. So yeah, probably the same thing people do anyway, just not compile time its now generating time.
- th0raway 5mo ago
  We opened the Cloud Code floodgates all at once in my org. After a few months we looked at stats, and asked managers for impressions on performance changes. The API cost per engineer doesn't correlate with the apparent increases in performance, but it sure seems that the vast majority of people that used to have good reviews got a lot better, while the bottom third just didn't, even though they use the LLMs about as much. It makes the performance differences in teams look like an abyss. Someone appears stuck in a task, and we see what they've been prompting, and then one of the best seniors comes in, actually asks the questions well, and the LLM does all the debugging and all the fixing in 20 minutes. It's not that the best performers are magical prompt engineers providing detailed instructions: They ask better questions that the LLM knows how to try to answer, and provide the specific information that the LLM would take a while finding. It's as if some people just had no "theory of mind" of the LLM, and what it can know, and others just do. It's not a living thing or anything like that, but it's still so useful to predict it, put yourself in it's shoes, so to speak. Just like you'd do with a new hire, or a random junior.
  - anal_reactor 5mo ago
    This comment is buried deep but I think it's actually quite important. In 2005 you had the elderly googling "Can I have a recipe for an apple pie? Thank you." vs kids doing "apple pie recipe" and clicking the first result. Some (most?) people just weren't capable of conceptualizing the abstract idea of "internet search" so they talked to the machine like they'd talk to other humans. Until coronavirus virtually anyone regardless of mental skills could get a high-paying job as a coder, there was no filter at all. What we'll observe now is the split between those who can conceptualize the idea of an AI, and those who cannot. The latter group will be stuck talking to AI in a way that doesn't leverage how it actually works.

ajross 5mo ago

> I'd much rather hire a junior engineer who spends $100-$200/month I'd much rather hire a junior engineer at $1.20/hour too! Can you hook me up with your contract services provider? Obviously I know you're talking about AI costs only. But the idea of doing that analysis without looking at the salary of the person running the tool seems to be completely missing the point. Now, sure, there are legitimate arguments to be made about efficacy and efficiency and sustainability and best practices. But, no, $100k/year absolutely doesn't need to be "justified" if it works. That's cheaper than the alternative, and markedly so.

hvb2 5mo ago
> But, no, $100k/year absolutely doesn't need to be "justified" if it works. That's cheaper than the alternative, and markedly so. If you're trying to say that 100k is less than 200k, you're right. I don't see how any of that won't need to be justified. You can spend a lot of money and not get enough of a return...
- ajross 5mo ago
  FWIW, you're nitpicking a strawman. I put "justified" in scare quotes for a reason, qualified it with "if it works" (which is, quite literally, the definition of a justification) and put it immediately after a sentence enumerating a list of legitimate questions for debate (all of which would be part of any justification analysis). You agree with me, basically. The core point is that these very large AI bills are not actually large in context, as the pre-existing scale of expenses for software engineering are larger still and this at least promises to reduce those markedly. To wit: argue about whether AI works[1] for software development, don't try to claim it's too expensive, it's clearly not. [1] "Is justified" in the vernacular.

boringg 5mo ago

Keep word doing A LOT of lifting “responsibly”

maxdo 5mo ago

In your fictional world you hire a junior who will write code manually, right? First , I interview people, Junior skills in manual coding dropped sharply this year. These are people who started they school manual and switched mid-course. In two years there will be no such people. well, that will never happened anymore in this world unless we will go back to caves, especially for juniors. Junior that writes good code is already a dying unicorn. The outcome will be ... you will hire a junior ... who will burn more tokens, and chances of mistakes with less expensive model, less tokens are even higher.

krainboltgreene 5mo ago
> well, that will never happened anymore in this world unless we will go back to caves The bubble is an echo chamber.
- maxdo 5mo ago
  I'm interviewing juniors. Their manual skills drops sharply, and that's for people who went to school in manual age, and maybe last year it stopped to be manual. Lets see what will be in a year or two lol
  - sikozu 5mo ago
    At this point do you even need to hire any juniors at all? It seems like there's a heavy reliance on AI agents and LLM especially for juniors. Is hiring a warm body that sits on a chair and prompts at a computer a good use of money?
    - maxdo 5mo ago
      yes, and no. Everybody is trying to hunt junior unicorn, They exist , but the ratio is 1 out 30. For these people, AI is a real elevator of their career.
AntiUSAbah 5mo ago
Puh not good signs at all. I mean even the normal people we get in interviews have no clue, like 80% are just ignorant. I stoped an interview after 5 minutes: when i asked what ls -ahl is doing, he started telling me how he vibe/ai codes stuff and thats his workflow. Okay if you don't know the basics, guess what? everyone can replace you or at least i'm not hiring you (i only told him thats not what we are looking for and thanked him) we are doomed :D

maccard 5mo ago

There was a tool posted called codeburn that showed a breakdown of what activity your usage was spent on. Mine was almost all coding but other people in the thread said >50% of their usage was conversation. I’m inclined to agree with you that someone who is reasonable with their compute usage is likely to be thinking things through rather than just burning tokens to get an LLM to solve the problem

embedding-shape 5mo ago

> I just can't figure how _how_ to burn that much money a month responsibly. Same but in regards to quotas. I'm on the 200 EUR ChatGPT plan, so presumable have the highest quota, using the "most expensive" models, on highest reasoning, in fast-mode (1.5x quota usage) and after a full day of almost exclusively doing programming with agents, I still get nowhere close to hitting my quota. In fact, since I started using agents for coding, the only time I even got close, was when I was doing cross-platform development with the same as above, but on three computers at the same time, then I almost hit my weekly quota. But normally, I get down to ~20% of the quota but almost never below that. I don't see how I could either, I'm already doing lots of prompts and queries "for fun" basically.

jackdoe 5mo ago
I am running a bunch of autoresearch loops that optimize various compilers and its pretty easy to burn through as much money as you want if you have a measurable goal and good tests.
- embedding-shape 5mo ago
  > have a measurable goal and good tests I have both of those, yet seemingly I guess I'm not setting my goal in such a way that it supports "endless inference" like that. My goals have eventually ends, and that's when I move on. Optimization sure sounds like something you can throw away a good amount of tokens/quotas on, so yeah.
- gessha 5mo ago
  > if you have a measurable goal and good tests. you can burn through the money even easier if you don’t have them
adi_kurian 5mo ago
Codex quota is suspiciously high right now. Either way, the subscription plans are not sustainable, and perhaps less relevant to any discussion about corporate API use. The prosumer developer plans are an insane deal. It is a golden age right now and it will end. If you tried to use the APIs to achieve the same thing, you would be spending thousands upon thousands of dollars a month. My completely unfounded conjecture is that OpenAI is trying to grab developers back from Claude by burning $$$$.

_pdp_ 5mo ago

Do you run 20 claud code agent on max for 8 hours a day? :)

lumost 5mo ago

I spend 400-500 dollars per day during active development at this point. However with more aggressive task breakdowns I can spend ~5k per day. These spend rates are in part due to operating on a larger code base. Operating on a larger code base means more time searching and understanding the code, tests, test output. They are also due to going all-in on agentic coding. It can feel painfully slow to go back to coding by hand when for a dollar you can build the same functionality in a minute. Now do this with multiple sessions and you can see where the cost goes.

steveBK123 5mo ago
Your reply answers how you are able to spend money, not if it is returning sufficient dollar value per spend.. > I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value.
- solenoid0937 5mo ago
  The problem with HN is that everyone here thinks like an engineer, not like a business owner. $10k a month on tokens is just not that much when you're already making $2M per engineer. If their productivity has increased even 10% then the spend was well worth it. Case in point, Meta made 33% more revenue this earnings report. Now you can nitpick and ask for attribution down to the dollar, but macro trends speak for themselves.
  - steveBK123 5mo ago
    Go look up a multi-year chart of their revenue and find the inflection point where the AI made it go up faster (there isn't). In fact revenue growth used to be higher pre-2023.
    - solenoid0937 5mo ago
      They were also a lot smaller pre-2023, 33% growth for a company of their size is simply insane. It is entirely likely that 33% simply wouldn't have happened without AI.

sailfast 5mo ago

In addition to what folks are saying here about larger code bases and multiple features at once, there’s also the time requirement to be efficient. It takes time to be more efficient with token usage and it may not be worth it for some of these companies so… burn away until we start to get more data and then we’ll check in.

gordonhart 5mo ago

On the OpenAI side, GPT-5.5 generates spend at a prolific rate that's even faster if you use it through an ACP connection in a tool like Zed. I used to never think about Codex rate limits and now I'm hitting mine every 5 hour block and spending ~$100/day on top of that in adhoc credit purchases.

bigbuppo 5mo ago

You're probably generating new code rather than analyzing old code for "improvement".

Salgat 5mo ago

Do lots of deep research and code reviews on large legacy codebases. I've created lots of documentation to reduce token consumption but it's still a lot of token consumption.

readitalready 5mo ago

I think companies are charged API prices vs individual prices. That alone is 10x for Anthropic. Not sure though.

benjiro3000 5mo ago

[dead]

xboxnolifes 5mo ago

I dont use automated agent workflows or anything, I just use clause as a pair programmer of sorts. A month or so ago I used claude Opus 4.6 for 2-4 hours on API pricing and racked up $20 in spend, which surprised me since that was much higher than my usual. I dont know about $10,000, but i can see hitting $1,000 pretty easily if you aren't looking at the costs.

rconti 5mo ago

Many companies actively hide the cost from their employees.

hadlock 5mo ago

[dead]

crystal_revenge 5mo ago

One thing that stands out it is it sounds like you're using LLMs for only one part of your process. You're having LLMs help you write code, but the code you're writing doesn't itself make use of LLMs. My current job basically involves trying to improve processes that themselves make heavy use of LLMs. Once you have multiple agents in parallel running multiple experiments on improving the performance of primarily LLM driven tools it's not that hard to get your token usage pretty high.

stronglikedan 5mo ago

I don't think it's about value. Tokenmaxxing is a thing now since that one CEO said he wants his $250k/yr devs to use $400-$500k/yr in tokens, so now it's all about how many agents can you have running concurrent tasks all day long.

o10449366 5mo ago

I spent $24,096.47 in "API" costs with my $200 Claude Code Max subscription in April. I'm building my own saas. I spent 6 months writing the code by hand before using Claude, and that was fine, but its much faster to give the exact specs to Claude and have 3-4 sessions working in parallel with me. When you validate changes with exact test specs there's much less correction you need to do. I always hit my weekly limit and it's far cheaper for me to use this than to hire someone and spend time onboarding them.

BeetleB 5mo ago

First: There's the obvious "If the company is letting me do it, I'll be wasteful." This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. People have already mentioned the size/complexity of the codebase. I'm new to my team and the codebase isn't huge, but it's large enough that there are plenty of parts I have little understanding about. When I'm given a task, then yes, I definitely go to Claude and ask it to find the relevant parts of code so I can understand the existing workflow before even attempting to change it. The downside is that I don't build expertise. But the reality is that with Claude, I can get the work done in 1 day that would take me 5 days of struggling, and if everyone is doing it, I can't be left behind. So I take the middle route - I get it done in 2-3 days instead of 1 so I can at least spend some time with the code. Especially with AI, the rate at which code changes in our codebase is insane. So I built a tool that takes a pull request, and tells the LLM to go deep and explain to me what that pull request does. (Note: I'm not the reviewer, I just want to keep tabs on the work that is going on in the team). And this is just the beginning. I haven't actually spent time to come up with more ways to use the LLM to help me. My usage is similar to yours, but if I were fairly experienced with the code base, I'd do a lot more. I haven't asked, but I suspect there are people in my team who go over $1K/month. As always, the bottleneck is proper testing and reviews. Edit: I'll also add that for not-so-important code used within the company, I suspect most people are going full-AI with it. For my personal (non-work) code, I just let the AI code it all - the risk is usually very low (and problems are caught quickly). If someone is using the "superpowers" skill, then even for basic features you can burn lots of tokens. I usually start with 20-40K tokens and end up with 80-90K tokens when it's finished. Which means that many of the requests prior to completion were sending in close to 80K tokens. Multiply that with the number of queries, etc. Wasteful, but if someone else is paying ...

kurige 5mo ago
> This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. I see this repeated by others, including coworkers. It completely ignores caching. Caching itself is complicated, but the "longer context window = more expensive" is not 100% true and you are hampering yourself if you're not taking full advantage of large context windows.

bs7280 5mo ago

I have ancedotal examples of claude code choosing a solution to a problem that is ridiculously token inefficient. One example - was giving several agents different sub problems to solve in a complex ML / forecasting problem. Each agent would write + run + read a jupyter notebook. This worked ok, the notebooks would be verbose but it was fine... until one of them wrote out hundreds of thousands of rows to a cell output, creating a 500MB ipynb file. Claude tried several times to read it and it used my entire context limit. The solution was to prescribe a better structure of doing the world (via CLI analysis scripts + folders to save research results to). But this required some planning, thought, and design work by me the operator. When I see people spending $10k a month in tokens, I can only assume they are taking lazy hands off approaches to solving problems with the expensive hammer that is claude code. EX: have claude read all your emails every day... the lazy solution is to simply do that, but a smarter solution is to first filter the email body HTML to remove the noise.

52-6F-62 5mo ago
> have claude read all your emails every day But that is exactly what it is sold to people to do as a panacea: consume all the data, produce insights. Nobody is being instructed to be judicious. Everyone is being instructed to use it as much as possible for all problem areas.
- hirako2000 5mo ago
  If you make 500k and aren't spending 250k in token, you should get fired.
  - quikoa 5mo ago
    I can't even tell whether this comment is serious or not.
    - ori_b 5mo ago
      https://www.businessinsider.com/jensen-huang-500k-engineers-250k-ai-tokens-nvidia-compute-2026-3 https://www.businessinsider.com/jensen-huang-500k-engineers-...

DeathArrow 5mo ago

It really depends on the way you use AI. If you just prompt it for a task and either accept or reject the output, you won't spend much. But if you are like me, you aggressively document and brainstorm before planning, you review that documentation with subagents, make modifications, you aggressively plan, you verify that plan with subagents,make modifications, have a large number of phases, planning again for each phase, writing tests to cover 100%, implement each phase, do intermediate and final code reviews with subagents, apply fixes, write final documentation and do all these in parallel, if you have multiple tabs in your terminal each running Claude Code for 10-12 hours a day, then $5000 per day is not much. If you use Anthropic or Open AI subscription and you spend $1000 per month, you are not using AI much.

in_cahoots 5mo ago
Are you bringing in at least $1.25M in additional yearly revenue to your company?
- DeathArrow 5mo ago
  I think more than that.
DeathArrow 5mo ago
And also some of use run tens of rounds of gradually improving the projects. And that burns tokens like crazy.

hliyan 5mo ago

The answer may be agentic loops that keeps cycling through the same problem again and again until they land on a non-erroneous outcome. Some people boast having multiple such agents working in parallel on different problems, tending to one while another is processing, perhaps not unlike the movie mad scientist who runs around the lab throwing switches while laughing maniacally at the prospect of his impending success.

kansface 5mo ago

> I just can't figure how _how_ to burn that much money a month responsibly. I always have a few agents (2-5) doing research and working on plans in parallel. A plan is a thorough and unambiguous document describing the process to implement some feature. It contains goals, non-goals, data models, access patterns, explicit semantics, migrations, phasing, requirements, acceptance criteria, phased and final. Plans often require speculative work to formulate. Plans take hours to days to a couple of weeks to write. Humans may review the plans or derived RFCs. Chiefly AI reviews the code (multiple agents with differing prompts until a fixed point is reached between them). Tests and formal methods are meant to do heavy lifting. In my highest volume weeks, I ship low hundreds of thousands of lines of software not counting changes to deps. > At a corporate level, I'd much rather hire a junior engineer Any formulation of problem sufficient for a truly junior engineer to execute is better given to an agent. The solution is cheaper, faster, and likely better. If the later doesn't hold, 10 independent solutions are still cheaper and faster than a junior engineer. There is no longer any likely path to teaching a junior engineer the trade.

ModernMech 5mo ago
I dunno I've seen agents make boneheaded mistakes even a junior engineer wouldn't make. Treating them as strictly better than junior engineers is a problem, not just for that reason but because you're effectively killing the pipline for senior engineers. Then what?
- kansface 5mo ago
  > I dunno I've seen agents make boneheaded mistakes even a junior engineer wouldn't make. Yes, of course. > you're effectively killing the pipline for senior engineers. Then what? I honestly don't know _what_. Its a prisoner's dilemma.
codebolt 5mo ago
Just out of curiosity, what type of systems are you working on? What type of features did you implement on your 100k LOC week?

paulsutter 5mo ago

I'm working on some serious data analysis + realtime async code, and I use 200-400 million tokens a day with Claude Code alone (via ccusage). The complexity of the code seems to have a big impact on the number of tokens used. On simpler projects I use many fewer tokens. My programming endurance is much greater now (2-3x focused hours per day), my productivity per hour is multiples higher, and I code seven days a week now because it's really exciting. All told, I would pay for these tools as much as I would pay for full-time human programmer(s).

Anon1096 5mo ago

The fully loaded cost of a senior engineer is already well past 400k. +5k a month is not that much if it helps them be XX% more productive. Personally at a different big tech I'm in the mid 4 digits AI spend per month and it helps me a lot, basically all coding has been trivialized and I work on an extremely large codebase. I'm spending more time on things closer to direct value generation like data analysis and experiment tweaking rather than spending time moving a variable across 10 layers of abstraction and making sure code compiles.

iLoveOncall 5mo ago

It's easily explained. People are losing their skill in real time and literally cannot develop anymore without AI. That's it.

wahnfrieden 5mo ago

You are probably guiding them step by step and reading the results. Maybe you also sit and wait for the results. Agents can iterate on a problem for hours if they can see their results and be given a higher level goal to evaluate their progress toward. When you have an agent working for minutes or hours, never wait on it. Use that time to spin up another agent. You can also spin up several agents in parallel to attempt the same item of work and compare their results to choose which to work off for next steps, instead of rolling the dice on a single option at a time and gambling that it's better to refine that first attempt instead of retrying from the start several more times. And if you are doing manual QA manually, you're missing out on having e.g. Codex's "Computer Use" or "Browser Use" automate your manual verification steps and collecting a report for you to review more quickly. Codex can control multiple virtual cursors simultaneously in the background without stealing focus, to parallelize this. If you want to use up more tokens to get more done (though more outside of your control and ability to review of course), that's how.

drfloyd51 5mo ago

Your estimates do not account for speed of delivery. If an AI can deliver 10x faster, the target is less than 10x a dev salary. But 10x faster also gets you to market sooner. Which has value.

athrowaway3z 5mo ago
I could argue in all the ways my personal experience disagree, but lets just Occam's razor: Most people agree big orgs regularly have dysfunctional incentives. We've seen it happen a thousand times. Your suggestion requires we also assume a 10x faster delivery time by people spending 200$ vs 1000$ - something I've yet to witness or hear a credible account of. So while that might be true in a small number of cases, in general its foolish to go with the "10x delivery speed" hypothesis.

fhn 5mo ago

a good way to prevent companies from adopting AI (and keeping your job) is to waste tokens making AI cost prohibitive

munk-a 5mo ago
That would be true in a sane world with investors who value profitability. But everything is now focused on DAU and the network effect. Overusing their services might actually make them look better to investors who shovel more money to them to light on fire.

munk-a 5mo ago

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barbazoo 5mo ago

In our org it's people that have too much stuff in their context, every mcp in the world installed, GTD, PAI, OpenClaw. I'm equally baffled how one can spend that much money during their day to day.

Sohcahtoa82 5mo ago

Yeah, I use Claude Code to do security reviews. For every CVE that Wiz flags, I have Claude Code check for reachability analysis. I typically consume about $200/month doing this. Most of our engineers are in the $200-400 range, with a few people around $1,000. But then there's one guy who's not only hitting $8,000, but supposedly has nearly 300,000 lines of code accepted (Note: This means he's accepted the lines of code from Claude, not that he's committed it). I can't figure out how.

canes123456 5mo ago

I use it as an ide. I am a security engineer but there a bunch of predictable things I need to write code for. Onboarding logs, writing detection rules, SOAR type stuff. It makes a diff and locally tests all the permutations I describe than I review the code.

raducu 5mo ago

> notice more and more are consumed $1k in tokens a month I've said it before: if you allow people to see how much others spent, they will try to climb up the "leaderboard". It takes just ONE little praise for using tokens or one perk gained, and the GAME IS ON among the developers!

Galanwe 5mo ago

> I just can't figure how _how_ to burn that much money a month responsibly. From my experience, this happens essentially by three means: - Level 0 (beginner users) long lived conversations: If you dont get in the habit of compressing, or otherwise manually forcing the model to summarize/checkpoint its work, you will often find people perpetually reusing the same conversation. This is especially true for _beginners_, which did not spend time curating their _base_ agent knowledge. They end up with a single meta conversation with huge context where they feel the agent is "educated", and feel like any new conversation with the agent is a loss of time because they have to re-educate it. - Level 1 (intermediate users) heavy explicit use of subagents: Once you discover the prompt pattern of "spawn 5 subagents to analyze your solution, each analyzing a different angle, summarize their findings", it can become addictive. It's not a bad habit per se, but if you're not careful it can drastically overspend your credits. Level 3 (expert users) extreme multitasking. Just genuinely having 10 worktrees perpetually in parallel and cycling between them in between agent responses. Again, not necessarily bad in itself, but can exponentially conse credits.

hn111 5mo ago
Where is level 2?
- ori_b 5mo ago
  LLMs can't count well.
- floren 5mo ago
  Still waiting for the output of that agent
- tom_ 5mo ago
  There isn't one. Level 3 is just that much more advanced.
- layer8 5mo ago
  It’s probably unary interpreted as binary, hence there is no level 2. Level 3 is followed by level 7. Level n is followed by level 2n + 1. Exponential growth. The singularity is near.

starkparker 5mo ago

At least your workplace doesn't frame raw usage as a leaderboard, with awards given out for topping it

hparadiz 5mo ago

Multimedia feedback can burn much more than that. If I'm sending frames of 3D engine's output. I mean I would like to send it a video if I could but that is too expensive but I'm sure there's orgs out there that really do want every frame in a prompt doing something. This can be exponential depending on the application. I recently wrote a Milkdrop visualization analyzer. I could have sent thousands of frames for each one. I didn't but well I wish I could haha.

ori_b 5mo ago

If Uber is like most other companies, there's a leaderboard for AI tokens consumed. If maximizing your token usage is going to get you to the top of the leaderboard, and therefore promoted for "productivity", people are going to find creative ways to be "productive".

traderj0e 5mo ago
The tokenmaxxing leaderboard where I work has a lot of new hires on it
- ori_b 5mo ago
  Asking the LLM to spawn a subagent per file and look for bugs is a good way to waste a lot of tokens real fast for leaderboard success, and it's pretty defensible as useful work if someone tries to call you on it.
  - traderj0e 5mo ago
    Yeah and it's a natural extension of the typical busywork people used to spam pre-AI, "I split your ticket into 10 tickets and made a kanban for it"

MrDarcy 5mo ago

It turns into 50k to 100k or more of value for the employee the moment upper management made AI spend a personal performance target across most corporations.

neonstatic 5mo ago

My observation is - pasting long documents is a great way to burn tokens. Turn based conversation, even a very deep and technical one, consumes less tokens than "read these logs and tell me where the problem is". Ironically, the log reading example is a perfect use for a local LLM.

ChadMoran 5mo ago

Advanced agentic prompting.

maherbeg 5mo ago

I also don't think a lot of people know some of the more advanced context management tricks like /rewind /fork /tree to take advantage of prefix caching

dinobones 5mo ago

Don't underestimate corporate waste. If it's not someone's job to care for something, they really won't. Even before this AI wave, it was common for me to see spinning dev environments for like $3k/month that hadn't been used in months on AWS.

lispisok 5mo ago

>I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value At a lot of businesses $5-10k/mo of AI spend doesnt even translate into $5-10k/mo value. Churning out code was rarely the business value bottleneck. It was convenient for everybody else to blame developers not writing code fast enough for their failures. Now they have no excuse but I doubt will own up.

dvfjsdhgfv 5mo ago

> I just can't figure how _how_ to burn that much money a month responsibly. Well, if your bonus depends on spending it, you'll find a way.

traderj0e 5mo ago

Idk about Uber in particular, but aside from legit programmers using AI to help them do legit work faster, there are people spamming it for metrics. And the hiring pipeline has gotten screwed up somehow, like half the people who reached the onsite interview for a technical role lied about all their technical skills, or they didn't lie and manage to pass hiring but then only take the tasks that AI can solo. And if it can't, waste tokens until giving up.

bredren 5mo ago

1. Worktrees 2. Multiple simultaneous projects 3. Orchestration that includes handling of CI workflow 4. Active work to further improve or refine tooling 5. Experimentation producing muscle memory as experience versus code output

jb3689 5mo ago

$400 * 23 business days would be $9k. Sounds ballpark to me

malisper 5mo ago

I've been working on a project to build a new Postgres based database in Rust[0]. I'm four weeks in and have 93% of the Postgres test suite passing. I've found agents to have worked really well for this as I have an existing codebase that has good architecture that I can point my agents at. It's also easy to debug as I can diff what my agents are doing and what Postgres is doing. I've had to get multiple codex accounts, but there was a brief period of time where I tried API usage to see how expensive it would be. In about an hour I spent $650 of credits. I had codex estimate how much I would be spending if I was doing pure API usage and it estimated around $10k/week. For context Postgres is 1M lines of C code. It's looking like pgrust will come out as less lines of code than Postgres and at peak I was adding over 100k lines of code in a day. I would estimate it would take a team of 5 software engineers at least 3 years to get to where I got in a month with a couple Codex subscriptions. [0] https://github.com/malisper/pgrust https://github.com/malisper/pgrust

schainks 5mo ago

Try the Jira MCP server.

irlib 5mo ago

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insane_dreamer 5mo ago

I used CC frequently for development, Opus 4.7 with high thinking, with a $100 Max subscription, and haven't been rate limited yet. IMO a subscription is the way to go as it puts a ceiling on spending.

kulikalov 5mo ago

Slop architecture leads to compounding problems that people try to solve with more slop. If one wants to control the quality of the code then the throughput and multithreading is bottlenecked by how much code one can comprehend in a given period of time.

stevepotter 5mo ago

Upper management wants to say they use AI and spend is an easy indicator. That trickles down and ultimately the engineers spending a lot is seen as good. It’s just a lazy measurement, like cloud usage or number of microservices used to be.