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.
> 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.
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.
> 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.
You still pay for cache hits and refreshes, but the cost is lower.
The default Claude cache expires in 5 minutes. If you take a short break to review the code, talk to someone, or do anything other than continuously interact with the session it's going to get evicted and start over.
Also anecdotally, caching has just been broken at times for me. I've had active conversations where turns less than 5 minutes apart were consuming so much quota that I doubt anything was being billed at the cache rate.
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.
>> Nobody is being instructed to be judicious. Everyone is being instructed to use it as much as possible for all problem areas.
Do you think this is because the LLM owners have such a massive ROI they're trying to cover so they're actively encouraging teams not to be judicious so then you get into this vicious cycle where both the LLMs and companies are both burning through cast like crazy?
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.
The codebase and the topic you're working on are huge variables.
I don't use LLMs to write code (other than simple refactors and throwaway stuff) but I do use them heavily to crawl through big codebases and identify which files and functions I need to understand.
Some of the codebases I explore will burn through tokens at a rapid rate because there is so much complex code to get through. If I use the $20 Claude plan and Opus I can go through my entire 5-hour allocation in a single prompt exploring the codebase some times, and it's justified.
Other times I'm working on simple topics, even in a large codebase, and it will sip tokens because it only needs to walk a couple files to get to what it needs to answer my questions.
Yes, in a reasonable microservice land where the places you need to connect to are all documented in very concise places, you have have extremely productive $10 days. In the giant monorepo with everything custom, you can't just rely on built in knowledge of 80% of you libraries, so it's a very different world.
A place like Google has to be so much better off just training library concepts in, given how much of the things the LLM will "instinctively" reach for are unlikely to be available. Not unlike the acclimation period what happens when someone comes in or out of a company like that, and suddenly every library and infra tool you were used to are just not available. We need a lot more searching when that happens to us, and the LLM suffers from the same context issue. The human just has all of that trained in after a 6 months, but the LLM doesn't.
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.
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.
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.
I recently saw this with the logseq api - the published api was an auto-generated stub. So I tried to grep the source code for the function and found detailed documentation written for claude. So I guess one benefit of all of this is that it's making people actually document things and maybe plan a little bit before implementing.
As someone who has written many docs, it's because 99% won't read it (rightfully so if it's verbose). You can turn that doc into a skill in a repo and Claude will read it everytime it's needed.
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.
Begs the question if we should move on to minimal microservices so that whole project lives in context of llm. I hardly have to do anything when I'm working with small project with llm.
Why not take it a step further? Make each function in the codebase its own project. Then the codebase can fit into the context window easily. All you have to do is debug issues between functions calling each other.
I don't think it's a joke about left-pad, but the idea that the complexity increases tremendously when you take a cloud of "small" things all communicating with each other. You've just pushed the complexity elsewhere. Claude can easily crunch the small microservice, but you're pushing the complexity to communications issues, race conditions, etc.
Like I said, if your work is already contained neatly inside one microservice then it doesn't matter.
The same would be true in a monolith: The context to understand what's happening would be contained to a few files.
When the work starts crossing through domains and potentially requiring insight into how other pieces work, fail, scale, etc. then the microservice model blows up complexity faster than anything, even if you have the API documented.
Ironically this is accidentally begging the question - that breaking them up into LLM context windows would be good because it would be to fit them in LLM context windows.
Maybe you're right but I'm aghast at how much of engineering over the last 15 years has been breaking up working monoliths to fit better within the budget of an external provider (first it was AWS). Those prices can change.
There are good reasons to use microservices but so often they're used for the wrong reasons.
Generally speaking no. Treat your IP (the code that runs your business, makes your business competitive or special) as precious and don't make it subservient to infra. It should be in the format (code, architecture, structure) that best serves it.
I've done the opposite, moving multiple tightly coupled repos into a single monorepo. Saves the step of the llm realizing there's a bigger context, finding the repo, then also scanning/searching it. Especially for fixes that are simply one line each in two repos.
I'm a fan of the monorepo in general, even before LLMs. If using git it leverages git's best feature IMO, the commit as a snapshot of the entire repo. I've worked on so many projects where tightly coupled things are split across repos because it's thought of as a best practice, and it just makes it more difficult to figure out what code you are running.
Unquestionably good. They want a product that provides value anywhere it's tried so as to establish the reputation as a magic human replacement. Gaming consumption based pricing at this point would be quitting before the race is over. They can always tweak the pricing knobs later once the industry is fully hooked.
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.
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.
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?
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.
My friend is a CTO at a non-tech company and he's now dealing with code from non-SWEs trying to self serve with LLMs.
But it's like a kid running a lemonade stand. Total DIY weekend project quality stuff that they are demanding go live. Hardcoded credentials, no concept of dev/qa/prod environments, no logging, no tests, no source control.
I'm not really sure teaching basic SWE practices / SDLC / system design to people whose day job is like.. accounting makes sense compared to just accelerating developer productivity.
It’s the same dilemma as old: it’s easier to teach a doctor UML than a coder Doctoring. But, critically, that’s about making doctor-facing IT systems not performing their skilled jobs.
Bringing code does not help, but a validated user story with flow diagrams, a UI suggestion, and a valid ticket could. That’s the bridge to gap.
Were I that CTO I’d explain that code carries liability, SWEs can end up in jail for malfeasance, fines, penalties, and lawsuits are what awaits us for eff-ups. “Coders” get fired if their code doesn’t work. Same speech to the devs, do exactly as much unsolicited Accounting as you wanna get fired for. Talk fences, good neighbours.
The ROI on teaching UML to a doctor is pretty low though right?
Non-technical people are not writing tickets, they are just slinging slop.
Another anecdote of things I've seen - a non technical person setting up some web scraping monstrosity with 200k lines of code. They beat their chest about how they didn't need the IT org. 1 month goes by and of course it breaks as soon as anything on the website changes and now they have a gun to ITs head to "fix it" and take it over.
This outcome for a DIY brittle web scraper is obvious to anyone that's ever written code, but shocking to someone who thinks LLMs are magic.
No, you should have forward deployed engineers sitting and working right beside these traditional non SW roles if you need to fully integrate AI into their mix.
Right, unfortunately a lot of orgs are quickly letting loose some combination of non-tech self-serve AI coding and tech org staffing reductions rather than ADDING forward deployed engineers.
Because he's paid to deliver code that works. Letting an AI agent do everything would be fine if it didn't make any mistakes, but that's far from reality.
Do typesetters inexplicably change the meaning of the book or document being typeset? Do compilers alter the behavior intended by the programmer, sometimes in ways that are not immediately obvious? Did the invention of typesetters lead to investments so massive, that the investors had to herald the end of handwriting (no equivalent analogy for compilers)?
It reminds me of the guy who replaced his static blog deployment scripts with asking chatgpt to generate the html from his text based on a template, and said that he isn't sure that the llm isn't changing his writing but hopes it isn't
Resistance to technological change has been a thing since farming was invented. Socrates thought that writing will ruin everyone's memory, and that people who just rely on written word will appear knowledgeable while actually knowing nothing.
The only difference is that this is happening to us.
I take the prompts to the AI so the manager doesn't have to! I have prompting skills!!
I just can't make the joke work. There really are people that think they can get paid to press the agent's on button. How long before their checks stop clearing and it "just works itself out naturally"?
That's literally how some Meta AI jobs looked a few years back - set up a few parameters, push a button, wait until training and evals are finished; repeat if. needed. $500k+/year.
It's bizarre to me that people being paid to use their brains with a job title including the word "engineer", which essentially means "clever thought thinker" in Latin, just offloading all of their thinking to a bot instead of just using it as a way to ensure clean execution and faster understanding of the structures of underdocumented projects.
There's some people who are offloading all of their thinking to a bot, and I agree with you that I don't really understand this. But the good version of it is to offload some of your thinking to a bot so you can focus your own thinking on the parts that matter. My time is much better spent on "ah there is a scalability tradeoff here" than "I guess I have to initialize the FooBarProviderServiceProvider in a different spot so that I can pass a mock to the FooBarProvisionConsumer unit tests".
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.
To be fair, taking an average SWE at $160k/y, and spending $1k/m, and offloading mechanical ticket work from their working set sounds like a bargain to me. They could be spending the time on design and planning and working on new things, figuring out how to save costs in optimizations. In fact for every soul sucking mechanical task you offload, the better of you are overall.
It’s not like AI is the first time this happened. CI/CD and extensive preflight and integration and canary testing is also a way of saving engineer time and improving throughput at the cost of latency and compute resources. This is just moving up the semantic stack.
Obviously as engineers we say “awesome more features and products!” but management says “awesome fewer engineers!” either way pasting the ticket in and letting a machine do the work for a fraction of the cost was the right choice. There’s no John Henry award.
> pasting the ticket in and letting a machine do the work for a fraction of the cost was the right choice
If it were producing equivalent outcomes, sure. So far I haven't personally seeing strong evidence for that. LLMs do write code pretty competently at this point, but actually solving the correct problem, and without introducing unintended consequences, is a different matter entirely
This. LLMs are terrible at planning/architecture and maintaining clarity of vision across a project. There are lots of tools that mitigate these issues but they're going to keep coming up regardless because of the fundamental nature of LLMs.
If you're not doing the design of the solutions for problems as an engineer or at least making the decisions and owning the maintenance of that architecture/design, what even is your job at that point?
> and offloading mechanical ticket work from their working set sounds like a bargain to me
Unfortunately the people who offload the work of understanding and interacting with tickets just end up offloading the consequences to everyone else who has to do extra work to make sure their LLM understands the task, review the work to make sure they built the right thing, and on and on.
The same thing happens when people start sending AI bots to attend meetings: The person freed up their own time, but now everyone else has to work hard to make sure their AI bot gets the right message to them and follow up to make sure what was supposed to happen in the meeting gets to them.
If someone sends a bot to a meeting, warn them the first time. Fire them the second, for exactly the reason that you said in your last paragraph: They're pushing their work onto other people.
Not what I do. I'll reformulate the ticket description so that the purpose and as many details as possible about the solution are made clear from the start. Then I tell Opus to go and research the relevant parts of the codebase and what needs to be done, and write its findings to a research.md file. Then I'll review that file, bring answers to any open questions and hash out more details if any parts seem fuzzy. When the research is sound I'll ask Opus to produce a plan.md document that lists all the changes that need to be made as actionable steps (possibly broken into phases). Then I'll let Sonnet execute the steps one by one and quickly review the changes as we go along.
> Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works?
"Their ticket" = that was AI generated.
After which they will wait their AI generated PR be checked by an automated AI QA that will validate against the AI generated spec.
It feels like important metric of "corporate AI adoption" should be how effective the human in steering the AI.
IF THE HUMAN ISN'T EFFECTIVE, THE HUMAN NEEDS TO GO.
If it manages to solve the working solutions - then it's great! why would you waste your time on it?
It it fails - then it's great! you find your value by solving the ticket, which can be a great example where human can still prevail to the AI (joke: AI companies might be interested to buy such examples)
(All assuming that your time cost is pricier than token spending. Totally different story if your wage is less than token cost)
> Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works?
There's also the pattern of creating an army of agents to solve problems. Human write a plan. One agent elaborates on it. Another reviews it and makes changes. Another splits it up into tasks and delegates out to multiple agents who make changes. Yet another agent reviews the changes, and on and on. All working around the clock.
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.
> 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.
I don't know about the GP, but my workflow is similar to theirs, but I aim to ship low thousands of lines per week. The fewer the better. I even tell the agent to only write high SNR tests, otherwise it just adds useless "make sure this function returns this thing we hardcoded".
I usually succeed, BTW. I spend a lot of time planning, but usually each PR is a few hundred lines, and fairly easily reviewable.
I mostly work with Python backends, though these days it might be any language (Ruby, Go, TS).
I am sorry, I am probably just very dumb, but this sounds extremely wasteful. If this is a reflection of how software was made before AI I wonder how anything was ever made.
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?
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.
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.
> 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.
There are tools that let you extract out what the API price would be for a subscription plan use. I typically have monthly runs that are on the order of $2k - $4k at API prices, despite paying a mere $200/mo to Anthropic.
Edit: Just checked with ccusage and I've been doing about $450/day for the last week. A bit more than usual, but I still haven't come close to weekly limits and never hit the 5hr rate limit.
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 $$$$.
> If you tried to use the APIs to achieve the same thing, you would be spending thousands upon thousands of dollars a month.
Yeah, obviously, not sure why anyone would be using APIs at this point, seems bananas to spend more than 10 EUR per day when these "almost-endless" subscriptions exists.
> My completely unfounded conjecture is that OpenAI is trying to grab developers back from Claude by burning $$$$.
Unlikely, since codex TUI was launched OpenAI pretty much had every developers pocket already as the agent is miles and leagues ahead of Claude Code, pretty much from inception. No other provider comes close to ChatGPT's Pro Mode either, I don't even think it's a quota/pricing thing, have the best models and people will flock by themselves.
> miles and leagues ahead of Claude Code, pretty much from inception.
Can codex run background tasks yet? CC's ability to run a process in the background and monitor its output for errors while another process access that first process, is probably what got cc so popular for web development over codex to start with.
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.
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.
> Same but in regards to quotas. I'm on the 200 EUR ChatGPT plan,
The API rates and monthly plan rates are not the same.
If you're using enough to justify the 200EUR plan (instead of the 100EUR plan), your use might actually be as high as some of the API bills discussed above.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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).
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.
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.
> 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.
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.
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.
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.”
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.
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)
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
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?
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.
> If it was actually productive, then the revenue would increase and affordability wouldn't be a question.
Revenue has increased. Have you seen Meta's latest earnings? +33% revenue - in this economy.
Affordability is not a question. There is a reason companies like Meta have no issue with their engineers spending $1k/day on tokens. It's just not that much compared to how much they make per employee.
That means absolutely nothing in the context of this conversation. It says right in their release ad impressions are up almost 20% and cost per add is up 12%. Those two metrics alone account for most of the increase in their revenue. Absolutely no conclusion can be drawn regarding the impact of AI on those numbers one way or the other.
It's not like they used AI to crank out some new revenue generating piece of software, or massively reduce operating costs. In fact their operating costs rose by 35%.
Yes, my thoughts exactly. Productivity by definition creates things, hopefully valuable things. Is all the extra burn on chatbots worth the cost? Has Uber somehow gotten dramatically more efficient and effective due to this massive budget overrun? Or have they just given people shiny and expensive ways to push the same work around?
I'd argue it's often the contrary -- since it's easy to ship features and fixes, people often ship things without questioning if it makes business sense to support a use case, or if the design is solid. Now you have exactly the same revenge but more things to maintain
Depending if the site has a direct competitor and non-sticky customers, you can often get accurate loss estimates from outages. For example, friends of mine at Doordash would know when UberEats was down by the corresponding spike in traffic to their app. The competitor captures all the lost traffic.
Most enterprises will have a harder time quantifying losses, as some percentage of customers will come back later. To understand that, you need to look for a drop in completed purchase rates compared to site visits.
For a SaaS, it's even more difficult, as customers are often held captive by long contracts and might tolerate SLA breaches up to a certain point. A reasonable, though fictional, proxy would be the revenue for the contract pro-rated against the uptime during that period.
This is my thought too. The eggheads in accounting set budgets, and we produce products within that budget. I could be twice as productive with twice as many people, and maybe 50% more productive with good AI, but if it's not budgeted for it's an issue (especially short-term before the product is released).
That is not true at all. No matter how "productive" a company is means nothing if people aren't buying your product. And using LLMs to be more productive will not convince anyone to buy your product. Human creativity and intuition to make a product that people want to use is what sells. Productivity for productivity's sake doesn't really move the needle at all, and can make things worse.
It's actually incredible the extent to which non devs imposing KPIs on devs underestimate how badly this will get gamed, whether it's AIs, PR/line counting or whatever.
Exactly. At Cerebras I know of several people who burn tokens on completely USELESS tasks (randomly changing pixels in an image) just to keep them high up on the token leaderboard.
I suspect the other tokenboard leaders are doing the same. They made the metric "token usage" (which is just a proxy for LOC) so that's what they're gonna get.
Gaming is one thing, fundamentally not understanding how engineering works will lead to shittier outcomes and cost the company in ways the management will never understand.
Management in the age of AI is falling for the doorman fallacy wrt engineering. If lines of code were the most valuable aspect of software engineering, my front end JavaScript intern would’ve been the most valuable person in the company. https://www.jaakkoj.com/concepts/doorman-fallacy
I actually do this, but that's mostly because our team reviewed all the existing autoformatters for the relatively obscure language we use, and either really hated the formatting or found that they actually introduced errors!
I don't understand this critique.
(1) Did you previously think you weren't getting paid for doing what a company wants you to do, aka what THEY thought was productive?
(2) Do you think all this AI generated code is useless?
I think the point was that, when you make a metric goal of "you must use AI this much", then people will use AI even in ways that isn't adding to productivity.
To answer your second question: Yes, much of it is worse than useless. The tools need guidance to produce useful output. If you use it poorly, you will get garbage output that may do more harm than good.
And your response does not address the point being made in the comment you replied to: Many people are being evaluated by how many tokens they burn, which is about as good a metric as lines of code written.
I think parent is saying "% of code being generated by AI" is not a generally good, direct metric for business value. It's akin to the "we are pushing SO MUCH CODE" phase of early ai marketing.
If we're trying to measure the value of adopting tool, it's probably better to measure the ROI of that tool rather than the usage % of that tool, especially when usage is basically mandated.
To directly answer your questions:
1. You're being paid to create value for the business, which "doing what they think is productive" is a proxy for. You're not being paid to use a tool a high % of the time.
2. I doesn't seem like parent even commented on the quality of the code generated. I think anyone that uses it regularly can agree that: a) the code is not useless and
b) all generated code is not immediately production ready c
) AI generation of code is an accelerant for software development
1) I think if the company I work for spends too much effort on things that aren't going to make money, they won't be able to pay me anymore, no matter what they "think" is productive. That's not how executives at companies like this make decisions, though.
Goodhart's Law isn't a problem immediately. If you want more code to be written, and the only feasible way to write it to goals is to heavily use AI, then you might run into the problems of AI-generated code, and an infrastructure that's poorly architected and much less understood than it would've been ten years ago.
1. At my level, the company is not just paying me to do a task the way they want it done, they are paying for my experience to orchestrate the best way to do it. They want an outcome, and I'm responsible for figuring out how to get to that outcome with the right balance of cost, correctness, etc. But yes, the most dystopian reality is what you said.
2. It's not useless, but the AI generated code is absolutely lower quality than what I would have written myself, but there is no desire to clean it up. Companies have always had a disastrously bad understanding of technical debt and they finally have tool they can shove down developers throats that trades even more velocity for even less quality. They're going to take that trade every single time.
> (1) ...getting paid for doing what a company wants you to do...?
At my previous company, when the thing they thought they wanted me to do (which was not the thing they actually wanted... but whatever) diverged from my values I quit. You can just do things.
> (2) Do you think all this AI generated code is useless?
Almost universally, yes. Especially in organizations that historically haven't been particularly careful about hiring and have a huge number of young, inexperienced people. There are exceptions but they're rare enough that throwing that particular baby out with the bathwater isn't a big loss.
you're missing their point; LLM use is often a part of your evaluation at some of these larger companies and they expect you to use them heavily or you will get a lashing
GP just saying that any metric will be gamed and if you have some costs that is associated to that, it will grow. Let’s say you set some metric that says the most productive dev are the ones that has the most files changes, you can soon expect every function and structure to be its own file. Same if you say that sales commision are based on how much time you spend calling, expect the phone bills to grow a lot.
> figuring out if the company can afford this level of productivity at scale
This is the thing that boggles my mind. They spent their budget. They have 4 months of data. What do they have to show for it?
I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it.
But are you saying that Uber made this tool available, urged everybody to use it, and is confused about what happens when it worked? It's one thing if they decide AI isn't productive enough to be worth the cost.
Are they out of ideas on what to build next, or something?
The personal max and teams plan actually are an amazing bargain compared to the API PAYG cost you get with Enterprise. I guess they really need their Enterprise features though, otherwise they could just tell users to expense a $200 max sub. Enterprises gonna Enterprise.
> I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it.
I'm glad to see we've reached the point of AI discourse at which anything that might be construed as criticism must be prefixed by "I'm also part of the cult, I'm not a non-believer, but" to avoid being dismissed as a heretic.
I think as it becomes more common for executives to think we can replace software engineering with agents, I wonder if they might be basing their decisions off of unrealistic perceptions of the average software engineer. I guess I'm mulling two somewhat contradictory senses:
1. You get out of it what you put into it. A savvy CTO might be incredibly excited by everything they can do with agents, and improperly think that all the software engineers can do the same thing, when in reality your org's average software engineers might not have the creativity to even think of many cases where it could save them work. So by mandating agent usage, you might find that productivity hasn't improved while AI costs have increased.
2. When using AI, there are two gaps that become more obvious. First is the gap of: who tells the agent what to do? In many orgs, product isn't technically savvy enough to come up with a detailed spec/plan that LLM can use. And many cog-in-machine developers aren't positioned to come up with the spec, they just want to implement it. By expecting work to be implemented by agent-using developers, you might instead find a lot of idle workers waiting for work to show up. Second is the qa/review cycle. You've introduced a big change to the org but are you really saving cost or shifting it?
I'm all for introducing LLM as optional to help existing developers increase velocity and quality, but I think the "let's restructure the org" movement is really dicey, especially for mid-size or smaller employers.
Beyond that, it's a force multiplier and it doesn't care if the force is positive or negative. Someone with poor software engineering principals can use AI to make an absolute mess quickly.
There is a major disconnect in that people think token usage is exclusively tied to human typing rates...it isn't true. When software developers evolve to using self-managing CLI tools (like Claude Code - the source article mentions this), they are not merely chatting; they are unleashing loops of agency.
When you enter one single inquiry of "find and fix the memory leak in the billing service" you are not submitting just one single inquiry. The tool is searching through an entire code repository for relevant code, pulling 15 related files into context (easily 200k+ tokens) proposing a fix, running the test suite and failing, taking an entire stack trace of errors into context and looping to keep iterating towards the solution.. In that process you can loop multiple times (10+) in a very short period of times (within 5 minutes). While you grab a cup of coffee you will have consumed $20 in token usage. At the enterprise level (like with Uber) when you multiply that out by thousands of software developers using it as a personal shell tool your budget disappears very very quickly.
And on your point about the junior developer: Comparing $100,000/year in tokens to hiring a junior developer is such a ridiculous false equivalency that even makes you question whether they even understand how to make such a comparison.
The cost to a business of one junior engineer with a $100,000 salary is not just the $100,000 in salary but also an additional $40,000+ in benefits and taxes, as well as in hardware.
Also, you are disregarding another cost of hiring junior engineers that is their mentorship cost. Each week, your senior and staff engineers spend hours mentoring junior engineers by reviewing their code, pairing with them, and unblocking their progress. Mentoring requires a substantial amount of time and will be expensive to your business.
The return on investment (ROI) for the $10,000 monthly expenditure on tokens is not so much about replacing the junior engineer with the AI. Instead, the ROI is that your senior engineers can use the huge amount of compute power to create boilerplate and tests, and refactor their code 3x quicker than if they had to mentor junior engineers. In addition, LLMs do not sleep, require one-on-ones, or leave for another company for 20% more pay in 18 months, when the value to the code base made them an asset to your business.
Lastly, the main reason that Uber has problems with their AI business is that due to the UX of these agentic tools, developers think of the API calls made to the AI as free and as a result, treat them like a basic grep command.
According to [1], there are about 5500 people in Engineering at Uber. Using $1250 as the mid-point of the $ spend range, that comes to about $6.8 Million in engineering AI spend, ballpark, with the range being $2.75 Million - $12 Million. The article lists $3.4 Billion as the R&D spend.
The AI spend does not appear to be a significant chunk of R&D spending (0.3% in 4 months or 1% annualized). If they didn't plan for it, sure, it's not peanuts in the budget, but in context not that much.
The real question is, what did they get for that amount? The article claims that 70% of the code commit is now AI-generated, so presumably the code passed review and tests. Did it accelerate the feature count? did it reduce quality problems? Did it lead to other benefits?
Sadly the article is silent on the outcomes, besides the higher spend.
Maybe 4 months is too soon to assess the benefits. On the other hand, in an agile world ...
The actual source https://www.theinformation.com/newsletters/applied-ai/uber-c... says "about 11% of real, live updates to the code in its backend systems are being written by AI agents built primarily with Claude Code, up from just a fraction of a percent three months ago" and "He wouldn’t disclose exact figures of the company’s software budget or what it spends on AI coding tools."
Have we reached a point yet where companies are spending millions a year on software licenses, cloud and AI to the point where the return isn't worth it?
Years ago I did work for a company that was spending over a million on Oracle product licenses and I was part of the consultant team they hired to rip it all out and just go for simple maintainable code based on open source products. Not only did it transform into a codebase that the average newly hired developer could maintain, you also had the savings of not paying Oracle a significant portion of your revenue.
I feel like that will repeat itself in a few years time with the current cloud and AI train everyone is on.
I haven't been in a professional setting for a while, I just code for fun nowadays so perhaps I'm somewhat out of the loop.
It's very easy to blow through hundreds of dollars a session using API tokens especially with the 1m context if you aren't careful about clearing old context.
At the same time the subscription will allow the same usage for hundreds of dollars a month.
Either Anthropic is absolutely hosing API users, massively subsidizing subscriptions, or a little bit of both.
"Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute"
Really curious how many people actually get close to that level of usage? Their general business plan only offers the $100 version, with pay-as-you-go above that.
If 95% of people are using $100 of value a month, the whales may not be hurting them that badly.
Anthropic has a very "interesting" business model where you get subscription pricing as long as you are under 150 employees. When you hit 151, you have to start paying API prices overnight for everyone, and your total bill instantly multiplies.
They are getting you hooked on cheaper tokens, then raking you in when you get scale. I'm sure Uber gets a break on list price, but I doubt they are anywhere near <150 employee subscription pricing.
Yeah, it's basically the opposite of how "product-led growth" SaaS works. Generally pay-as-you-go pricing is expensive at scale, but attractive initially. So you start on a pay-as-you-go plan, but as you scale you end up transitioning off pay-as-you-go to a negotiated commit. I.e. you call sales and sign a contract. Anthropic basically flips that around backwards.
I evaluated the pricing and could not justify the jump to Enterprise from Team. You lose the monthly subscription entirely when you jump to enterprise so you lose your ability to control costs.
You can cap per user, but not having the rolling cap are you really just going to tell a member of your team “No AI for the rest of the month”
I love how these articles drop, and all of a sudden HN is filled with people who think engineering productivity is simple to measure.
Yes, productivity implies revenue (or cost reduction), and revenue is measurable.
However:
1. You spend money today to build features that drive revenue in the future, so when expenses go up rapidly today, you don’t yet have the revenue to measure.
2. It’s inherently a counterfactual consideration: you have these features completed today, using AI. You’re profitable/unprofitable. So AI is productive/unproductive, right? No. You have to estimate what you would’ve gotten done without AI, and how much revenue you would’ve had then.
3. Business is often a Red Queen’s race. If you don’t make improvements, it’s often the case that you’ll lose revenue, as competitors take advantage.
4. Most likely, AI use is a mixture of working on things that matter and people throwing shit against the wall “because it’s easy now.” Actually measuring the potential productivity improvements means figuring out how to keep the first category and avoid the second.
This isn’t me arguing for or against AI. It’s just me telling you not to be lazy and say “if it were productive you’d be able to measure it.”
> HN is filled with people who think engineering productivity is simple to measure.
I think the prevailing (correct) consensus is that developer productivity is actually very hard to measure, and every time it is attempted the measure is immediately made a target making the whole thing pointless even if it had been a solid measurement- which it wasn't.
IDK where you're getting the idea here that measuring productivity of anyone who isn't a factory worker is easy.
I mean, the option is not zero productivity or some productivity: it could be negative.
We doubt the productivity because we have enough experience with Claude Code to know that flooding your organization with that many tokens isn't just unproductive, it's actively harmful.
Minor shifts in productivity are hard to measure. Major jumps in productivity would be obvious. I think it’s clear that, if AI is affecting productivity, it’s to a minor degree at best.
If it were 10x productive you'd be able to measure it indirectly, you'd be unable to avoid measuring it. So the initial claims were clearly lies. The research question is:
Is it >1.0x productive?
I agree that's very hard to measure. But given what this shit costs, it had better be answerable, and the multiple had better justify the cost.
I didn't see a bit where they said how this transformed into more productivity and more profit? What is the point in using AI to make developers more productive if you don't either have more features coded making more money, or fewer developers saving cost?
Not surprising, hit my 5h limit on Claude Code Max Plan, had some credits so switched to extended (api). 40 minutes later $30 credits gone... so yeah, I can see how this can happen.
While this is a fundamentally stupid story to begin with, it was at least reported somewhat better in other venues. The original report came from The Information, and at least this Yahoo Finance[0] writeup mentioned that. This article has very little content and no sourcing.
> Monthly API costs per engineer ranged from $500 to $2,000 as adoption skyrocketed across the company.
That's...not exactly a lot per engineer. It sounds like they just didn't budget correctly. Especially if the net of that work is more features that would have otherwise required hiring more engineers, which would cost a lot more than $500 to $2000 a month.
No, it's really not a lot at all, especially if you've got a mandate to maximize your AI usage, which many engineering orgs have right now. I burned $216 USD using Claude Code in March just doing some casual development on the side and certainly not as a part of any professional workplace mandate.
If they burned through their ML budget in four months while using heavily subsidized models, we're going to see companies burn through their ML budgets in less than a week once those subsidies are no longer in place and they have to pay per tokens used.....
I think the tech industry in general is taking advantage of the fact that software productivity is hard to quantify to say whatever they want about their AI productivity gains. Apparently we are past the point of having to justify anything and can just equivocate increased AI spend with success.
AI coding tools probably need the same boring governance as cloud spend: budgets, alerts, team-level visibility, and a way to spot runaway usage before finance notices.
this is pointless without knowing what they are measuring. you could genuinely moving faster or you could be optimizing for engineers in a rat race to push more code because all their peers are now doing it because those are the metrics you are measuring for "ai productivity".
Most people don't have the team and time to do heavy token efficiency engineering. But that's all we do. marketplace.neurometric.ai has a bunch of task specific small models, and we charge flat monthly fees. We bear the token risk.
we run an agentic pipeline in a different domain (data sourcing) and the only
way the math works is to be ruthless about which stages actually need which model.
As a founder, the question I always have is "what is the marginal value per token relative to engineer-hours saved." More of a gut feel at the moment, but would be great to calculate.
It's obvious that the word productivity has been used in this discussion to mean something other than the plain meaning of the word. If AI was productive, there would be no question about whether it could be afforded. If you're asking whether you can afford it then it isn't productive by definition.
They are using it to mean a mechanism that produces prodigious amounts of toxic waste. That does not conform to the historical understanding of the word.
It's funny how Paul is recommending people use PR firms, while in more recent videos michael seibel and others have strongly recommended against using them. It's interesting how things shift in ~20 years
This continues to boggle my mind so hopefully somebody can explain how this is happening.
I’ve been using all these tools since they started popping out around 2021 personally and professionally. I probably built four or five products at this point with assistance, not to mention the thousands and thousands of back-and-forth conversations for research or search or rubber ducking or whatever.
I have never spent more than whatever the professional max plan is that is consistently $20 a month.
I asked a friend of mine who spent a couple hundred dollars in like an few hours how they did it. The answer was they basically getting these agent groups of agents stuck in a loop and they’re constantly just generating verbose bullshit that is not even interrogated and doesn’t come out with any artifact that is inspectable no matter how expert you are.
The couple of stories I have heard of these massive crazy spends are people literally just assuming these things can complete an entire human task in one shot, so they continue to hit the “spin the wheel” button until they get something closer to what they want
But I’ve yet to see that actually work
and it actually flies in the face of every instruction guide or documentation or prompt engineering process that has been described over the last almost 5 years
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.
> 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.
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 ...
> 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.
You still pay for cache hits and refreshes, but the cost is lower.
The default Claude cache expires in 5 minutes. If you take a short break to review the code, talk to someone, or do anything other than continuously interact with the session it's going to get evicted and start over.
You can opt in to a 1-hour cache at a higher rate https://platform.claude.com/docs/en/build-with-claude/prompt...
Also anecdotally, caching has just been broken at times for me. I've had active conversations where turns less than 5 minutes apart were consuming so much quota that I doubt anything was being billed at the cache rate.
It’s crazy that people don’t understand cached tokens despite them being priced separately on the cost pages of every single provider.
Its crazy that people think caching is such a silver bullet, despite the cost of long context windows still being ridiculously high even with caching. https://blog.exe.dev/expensively-quadratic https://news.ycombinator.com/item?id=47000034
> It’s crazy that people don’t understand cached tokens despite them being priced separately on the cost pages of every single provider.
Depends on your subscription type. Some are just a flat monthly fee.
Those aren't the people worrying about token counts, then.
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.
> 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.
If you make 500k and aren't spending 250k in token, you should get fired.
I can't even tell whether this comment is serious or not.
>> Nobody is being instructed to be judicious. Everyone is being instructed to use it as much as possible for all problem areas.
Do you think this is because the LLM owners have such a massive ROI they're trying to cover so they're actively encouraging teams not to be judicious so then you get into this vicious cycle where both the LLMs and companies are both burning through cast like crazy?
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.
The codebase and the topic you're working on are huge variables.
I don't use LLMs to write code (other than simple refactors and throwaway stuff) but I do use them heavily to crawl through big codebases and identify which files and functions I need to understand.
Some of the codebases I explore will burn through tokens at a rapid rate because there is so much complex code to get through. If I use the $20 Claude plan and Opus I can go through my entire 5-hour allocation in a single prompt exploring the codebase some times, and it's justified.
Other times I'm working on simple topics, even in a large codebase, and it will sip tokens because it only needs to walk a couple files to get to what it needs to answer my questions.
Yes, in a reasonable microservice land where the places you need to connect to are all documented in very concise places, you have have extremely productive $10 days. In the giant monorepo with everything custom, you can't just rely on built in knowledge of 80% of you libraries, so it's a very different world.
A place like Google has to be so much better off just training library concepts in, given how much of the things the LLM will "instinctively" reach for are unlikely to be available. Not unlike the acclimation period what happens when someone comes in or out of a company like that, and suddenly every library and infra tool you were used to are just not available. We need a lot more searching when that happens to us, and the LLM suffers from the same context issue. The human just has all of that trained in after a 6 months, but the LLM doesn't.
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.
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.
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.
I recently saw this with the logseq api - the published api was an auto-generated stub. So I tried to grep the source code for the function and found detailed documentation written for claude. So I guess one benefit of all of this is that it's making people actually document things and maybe plan a little bit before implementing.
As someone who has written many docs, it's because 99% won't read it (rightfully so if it's verbose). You can turn that doc into a skill in a repo and Claude will read it everytime it's needed.
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.
Begs the question if we should move on to minimal microservices so that whole project lives in context of llm. I hardly have to do anything when I'm working with small project with llm.
Why not take it a step further? Make each function in the codebase its own project. Then the codebase can fit into the context window easily. All you have to do is debug issues between functions calling each other.
Wait, is this a joke about Lambda?
I don't think it's a joke about left-pad, but the idea that the complexity increases tremendously when you take a cloud of "small" things all communicating with each other. You've just pushed the complexity elsewhere. Claude can easily crunch the small microservice, but you're pushing the complexity to communications issues, race conditions, etc.
left-pad
In my experience, the result is just more crawling across the separate microservices and additional reasoning to confirm how it all fits together.
The monolithic codebases are easier to crawl for any problem that can't be conveniently isolated to a single microservice.
A good API should be documented, and AI should not have to read the internal code to understand how to use it.
Like I said, if your work is already contained neatly inside one microservice then it doesn't matter.
The same would be true in a monolith: The context to understand what's happening would be contained to a few files.
When the work starts crossing through domains and potentially requiring insight into how other pieces work, fail, scale, etc. then the microservice model blows up complexity faster than anything, even if you have the API documented.
Sounds like tight coupling issue, not services per se
Ironically this is accidentally begging the question - that breaking them up into LLM context windows would be good because it would be to fit them in LLM context windows.
Maybe you're right but I'm aghast at how much of engineering over the last 15 years has been breaking up working monoliths to fit better within the budget of an external provider (first it was AWS). Those prices can change.
There are good reasons to use microservices but so often they're used for the wrong reasons.
Generally speaking no. Treat your IP (the code that runs your business, makes your business competitive or special) as precious and don't make it subservient to infra. It should be in the format (code, architecture, structure) that best serves it.
And yet so many companies spent the last decade doing it to fit into AWS pricing models
I've done the opposite, moving multiple tightly coupled repos into a single monorepo. Saves the step of the llm realizing there's a bigger context, finding the repo, then also scanning/searching it. Especially for fixes that are simply one line each in two repos.
I'm a fan of the monorepo in general, even before LLMs. If using git it leverages git's best feature IMO, the commit as a snapshot of the entire repo. I've worked on so many projects where tightly coupled things are split across repos because it's thought of as a best practice, and it just makes it more difficult to figure out what code you are running.
Orchestration between those services and the integration testing for any reasonably complex change can still be quite large.
The whole service might fit in a context window but the details of the system around it will still be relevant.
On larger repos it spends a lot of time just finding the one line of code that needs to change. (I have the same problem, as a human!)
Will this result in people moving away from large monorepos to per-unit, quasi-micro repositories to save in token use?
So if the AI could do the same work on huge codebases with far fewer tokens, would it be good or bad for the AI companies do you think?
Unquestionably good. They want a product that provides value anywhere it's tried so as to establish the reputation as a magic human replacement. Gaming consumption based pricing at this point would be quitting before the race is over. They can always tweak the pricing knobs later once the industry is fully hooked.
Right but what if the thing that made fewer tokens necessary also kneecapped the idea of making humans dependent on AI to write software.
It would be good for the first AI company offering this.
Or an anti-ai company of course too; one whose goal was to level the playing field between humans and AIs again
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.
At least your workplace doesn't frame raw usage as a leaderboard, with awards given out for topping it
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.
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.
> 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
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.
My friend is a CTO at a non-tech company and he's now dealing with code from non-SWEs trying to self serve with LLMs.
But it's like a kid running a lemonade stand. Total DIY weekend project quality stuff that they are demanding go live. Hardcoded credentials, no concept of dev/qa/prod environments, no logging, no tests, no source control.
I'm not really sure teaching basic SWE practices / SDLC / system design to people whose day job is like.. accounting makes sense compared to just accelerating developer productivity.
It’s the same dilemma as old: it’s easier to teach a doctor UML than a coder Doctoring. But, critically, that’s about making doctor-facing IT systems not performing their skilled jobs.
Bringing code does not help, but a validated user story with flow diagrams, a UI suggestion, and a valid ticket could. That’s the bridge to gap.
Were I that CTO I’d explain that code carries liability, SWEs can end up in jail for malfeasance, fines, penalties, and lawsuits are what awaits us for eff-ups. “Coders” get fired if their code doesn’t work. Same speech to the devs, do exactly as much unsolicited Accounting as you wanna get fired for. Talk fences, good neighbours.
The ROI on teaching UML to a doctor is pretty low though right?
Non-technical people are not writing tickets, they are just slinging slop.
Another anecdote of things I've seen - a non technical person setting up some web scraping monstrosity with 200k lines of code. They beat their chest about how they didn't need the IT org. 1 month goes by and of course it breaks as soon as anything on the website changes and now they have a gun to ITs head to "fix it" and take it over.
This outcome for a DIY brittle web scraper is obvious to anyone that's ever written code, but shocking to someone who thinks LLMs are magic.
No, you should have forward deployed engineers sitting and working right beside these traditional non SW roles if you need to fully integrate AI into their mix.
Right, unfortunately a lot of orgs are quickly letting loose some combination of non-tech self-serve AI coding and tech org staffing reductions rather than ADDING forward deployed engineers.
So he's being paid and is sitting there letting an AI tool do his work for him? Insanity.
We didn’t mind when typesetting was automated. Or when compilers were invented. Why is this different?
Because he's paid to deliver code that works. Letting an AI agent do everything would be fine if it didn't make any mistakes, but that's far from reality.
Do typesetters inexplicably change the meaning of the book or document being typeset? Do compilers alter the behavior intended by the programmer, sometimes in ways that are not immediately obvious? Did the invention of typesetters lead to investments so massive, that the investors had to herald the end of handwriting (no equivalent analogy for compilers)?
It reminds me of the guy who replaced his static blog deployment scripts with asking chatgpt to generate the html from his text based on a template, and said that he isn't sure that the llm isn't changing his writing but hopes it isn't
So I take we can soon replace coders entirely. Just fire all of them. And let some intern under VP prompt the whole thing?
Resistance to technological change has been a thing since farming was invented. Socrates thought that writing will ruin everyone's memory, and that people who just rely on written word will appear knowledgeable while actually knowing nothing.
The only difference is that this is happening to us.
Do typesetters or compilers write the code for you? Or are you perhaps using a disingenuous analogy?
To that last guy, as the manager I would say "What is it that you do here??"
That's just a straight-shooter with "upper management" written all over him.
He signs the TPS reports.
“I’m the prompter.”
I take the prompts to the AI so the manager doesn't have to! I have prompting skills!!
I just can't make the joke work. There really are people that think they can get paid to press the agent's on button. How long before their checks stop clearing and it "just works itself out naturally"?
That's literally how some Meta AI jobs looked a few years back - set up a few parameters, push a button, wait until training and evals are finished; repeat if. needed. $500k+/year.
What color is your stapler?
"I write the prompts"
It's bizarre to me that people being paid to use their brains with a job title including the word "engineer", which essentially means "clever thought thinker" in Latin, just offloading all of their thinking to a bot instead of just using it as a way to ensure clean execution and faster understanding of the structures of underdocumented projects.
There's some people who are offloading all of their thinking to a bot, and I agree with you that I don't really understand this. But the good version of it is to offload some of your thinking to a bot so you can focus your own thinking on the parts that matter. My time is much better spent on "ah there is a scalability tradeoff here" than "I guess I have to initialize the FooBarProviderServiceProvider in a different spot so that I can pass a mock to the FooBarProvisionConsumer unit tests".
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.
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.
What exactly are you “getting done”? I’m really curious what you’re doing with so many tokens.
To be fair, taking an average SWE at $160k/y, and spending $1k/m, and offloading mechanical ticket work from their working set sounds like a bargain to me. They could be spending the time on design and planning and working on new things, figuring out how to save costs in optimizations. In fact for every soul sucking mechanical task you offload, the better of you are overall.
It’s not like AI is the first time this happened. CI/CD and extensive preflight and integration and canary testing is also a way of saving engineer time and improving throughput at the cost of latency and compute resources. This is just moving up the semantic stack.
Obviously as engineers we say “awesome more features and products!” but management says “awesome fewer engineers!” either way pasting the ticket in and letting a machine do the work for a fraction of the cost was the right choice. There’s no John Henry award.
> pasting the ticket in and letting a machine do the work for a fraction of the cost was the right choice
If it were producing equivalent outcomes, sure. So far I haven't personally seeing strong evidence for that. LLMs do write code pretty competently at this point, but actually solving the correct problem, and without introducing unintended consequences, is a different matter entirely
This. LLMs are terrible at planning/architecture and maintaining clarity of vision across a project. There are lots of tools that mitigate these issues but they're going to keep coming up regardless because of the fundamental nature of LLMs.
If you're not doing the design of the solutions for problems as an engineer or at least making the decisions and owning the maintenance of that architecture/design, what even is your job at that point?
> and offloading mechanical ticket work from their working set sounds like a bargain to me
Unfortunately the people who offload the work of understanding and interacting with tickets just end up offloading the consequences to everyone else who has to do extra work to make sure their LLM understands the task, review the work to make sure they built the right thing, and on and on.
The same thing happens when people start sending AI bots to attend meetings: The person freed up their own time, but now everyone else has to work hard to make sure their AI bot gets the right message to them and follow up to make sure what was supposed to happen in the meeting gets to them.
If someone sends a bot to a meeting, warn them the first time. Fire them the second, for exactly the reason that you said in your last paragraph: They're pushing their work onto other people.
Not what I do. I'll reformulate the ticket description so that the purpose and as many details as possible about the solution are made clear from the start. Then I tell Opus to go and research the relevant parts of the codebase and what needs to be done, and write its findings to a research.md file. Then I'll review that file, bring answers to any open questions and hash out more details if any parts seem fuzzy. When the research is sound I'll ask Opus to produce a plan.md document that lists all the changes that need to be made as actionable steps (possibly broken into phases). Then I'll let Sonnet execute the steps one by one and quickly review the changes as we go along.
> Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works?
"Their ticket" = that was AI generated. After which they will wait their AI generated PR be checked by an automated AI QA that will validate against the AI generated spec.
It feels like important metric of "corporate AI adoption" should be how effective the human in steering the AI.
IF THE HUMAN ISN'T EFFECTIVE, THE HUMAN NEEDS TO GO.
You should.
If it manages to solve the working solutions - then it's great! why would you waste your time on it?
It it fails - then it's great! you find your value by solving the ticket, which can be a great example where human can still prevail to the AI (joke: AI companies might be interested to buy such examples)
(All assuming that your time cost is pricier than token spending. Totally different story if your wage is less than token cost)
> Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works?
There's also the pattern of creating an army of agents to solve problems. Human write a plan. One agent elaborates on it. Another reviews it and makes changes. Another splits it up into tasks and delegates out to multiple agents who make changes. Yet another agent reviews the changes, and on and on. All working around the clock.
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.
> 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.
Just out of curiosity, what type of systems are you working on? What type of features did you implement on your 100k LOC week?
I don't know about the GP, but my workflow is similar to theirs, but I aim to ship low thousands of lines per week. The fewer the better. I even tell the agent to only write high SNR tests, otherwise it just adds useless "make sure this function returns this thing we hardcoded".
I usually succeed, BTW. I spend a lot of time planning, but usually each PR is a few hundred lines, and fairly easily reviewable.
I mostly work with Python backends, though these days it might be any language (Ruby, Go, TS).
I am sorry, I am probably just very dumb, but this sounds extremely wasteful. If this is a reflection of how software was made before AI I wonder how anything was ever made.
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?
> 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.
The perverse incentives created by these AI leaderboards are crazy.
But we need OKRs rocks and METRICS! Everyone must have their own one numberrrrr!
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.
Yes I love that my employer says go wild with it. But I feel like the leaderboard is dumb.
> 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.
There are tools that let you extract out what the API price would be for a subscription plan use. I typically have monthly runs that are on the order of $2k - $4k at API prices, despite paying a mere $200/mo to Anthropic.
Edit: Just checked with ccusage and I've been doing about $450/day for the last week. A bit more than usual, but I still haven't come close to weekly limits and never hit the 5hr rate limit.
I have to churn to get to my ChatGPT Plus $20 plan limits with gpt-5.5 xhigh. Starts to feel like I'm doing something wrong.
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 $$$$.
> If you tried to use the APIs to achieve the same thing, you would be spending thousands upon thousands of dollars a month.
Yeah, obviously, not sure why anyone would be using APIs at this point, seems bananas to spend more than 10 EUR per day when these "almost-endless" subscriptions exists.
> My completely unfounded conjecture is that OpenAI is trying to grab developers back from Claude by burning $$$$.
Unlikely, since codex TUI was launched OpenAI pretty much had every developers pocket already as the agent is miles and leagues ahead of Claude Code, pretty much from inception. No other provider comes close to ChatGPT's Pro Mode either, I don't even think it's a quota/pricing thing, have the best models and people will flock by themselves.
> miles and leagues ahead of Claude Code, pretty much from inception.
Can codex run background tasks yet? CC's ability to run a process in the background and monitor its output for errors while another process access that first process, is probably what got cc so popular for web development over codex to start with.
Codex quota is/was 2x its normal amount for some promotion or something. I thought it ran out today but can't check right now
Every time I read about codex it's someone saying the extra limits ended that day. I'm not saying you're wrong in this instance
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.
> 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.
> Same but in regards to quotas. I'm on the 200 EUR ChatGPT plan,
The API rates and monthly plan rates are not the same.
If you're using enough to justify the 200EUR plan (instead of the 100EUR plan), your use might actually be as high as some of the API bills discussed above.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
> 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!
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.
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.
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.
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.
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.
I think companies are charged API prices vs individual prices. That alone is 10x for Anthropic. Not sure though.
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.
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.
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
a good way to prevent companies from adopting AI (and keeping your job) is to waste tokens making AI cost prohibitive
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.
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.
You're probably generating new code rather than analyzing old code for "improvement".
Many companies actively hide the cost from their employees.
Keep word doing A LOT of lifting “responsibly”
> I use llms daily
this is your “problem” - you are missing the “nightly” part. on my box LLMs run 24/7 :)
Do you run 20 claud code agent on max for 8 hours a day? :)
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.
It's easily explained. People are losing their skill in real time and literally cannot develop anymore without AI. That's it.
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).
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.
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.
> 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.
> 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...
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.
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.
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.”
managers call meetings and agents call swarms.
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.
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
> well, that will never happened anymore in this world unless we will go back to caves
The bubble is an echo chamber.
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
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?
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.
I know I'm responding to AI right now, but
> which means figuring out if the company can afford this level of productivity at scale.
If it was actually productive, then the revenue would increase and affordability wouldn't be a question.
> If it was actually productive, then the revenue would increase and affordability wouldn't be a question.
Revenue has increased. Have you seen Meta's latest earnings? +33% revenue - in this economy.
Affordability is not a question. There is a reason companies like Meta have no issue with their engineers spending $1k/day on tokens. It's just not that much compared to how much they make per employee.
How can that be attributed to any code an LLM wrote?
>$8 billion of net income was the result of a tax benefit the company realized in the first quarter of the year.
So exactly how much of their revenue is because of any code LLMs wrote vs. just structural tail winds?
You can always say "it's not because of LLMs", that's nearly unfalsifiable.
But if all of your peers are saying LLMs are more productive, if you're building things faster than ever before, the macro picture speaks for itself.
This article is about Uber, not Meta
That means absolutely nothing in the context of this conversation. It says right in their release ad impressions are up almost 20% and cost per add is up 12%. Those two metrics alone account for most of the increase in their revenue. Absolutely no conclusion can be drawn regarding the impact of AI on those numbers one way or the other.
It's not like they used AI to crank out some new revenue generating piece of software, or massively reduce operating costs. In fact their operating costs rose by 35%.
Yes, my thoughts exactly. Productivity by definition creates things, hopefully valuable things. Is all the extra burn on chatbots worth the cost? Has Uber somehow gotten dramatically more efficient and effective due to this massive budget overrun? Or have they just given people shiny and expensive ways to push the same work around?
Not every change a developer makes increases revenue, and the changes that do often have a lag time.
I'd argue it's often the contrary -- since it's easy to ship features and fixes, people often ship things without questioning if it makes business sense to support a use case, or if the design is solid. Now you have exactly the same revenge but more things to maintain
What if you're the SRE and the code fixes mean the site goes from 99.0% uptime to 99.9% up? How do you measure the revenue from that?
Depending if the site has a direct competitor and non-sticky customers, you can often get accurate loss estimates from outages. For example, friends of mine at Doordash would know when UberEats was down by the corresponding spike in traffic to their app. The competitor captures all the lost traffic.
Most enterprises will have a harder time quantifying losses, as some percentage of customers will come back later. To understand that, you need to look for a drop in completed purchase rates compared to site visits.
For a SaaS, it's even more difficult, as customers are often held captive by long contracts and might tolerate SLA breaches up to a certain point. A reasonable, though fictional, proxy would be the revenue for the contract pro-rated against the uptime during that period.
This is my thought too. The eggheads in accounting set budgets, and we produce products within that budget. I could be twice as productive with twice as many people, and maybe 50% more productive with good AI, but if it's not budgeted for it's an issue (especially short-term before the product is released).
That is not true at all. No matter how "productive" a company is means nothing if people aren't buying your product. And using LLMs to be more productive will not convince anyone to buy your product. Human creativity and intuition to make a product that people want to use is what sells. Productivity for productivity's sake doesn't really move the needle at all, and can make things worse.
> 95% of Uber engineers now use AI tools monthly with 70% of committed code originating from AI.
Well, that’s to be expected when using AI tools becomes relevant in your performance evaluation.
It's actually incredible the extent to which non devs imposing KPIs on devs underestimate how badly this will get gamed, whether it's AIs, PR/line counting or whatever.
Exactly. At Cerebras I know of several people who burn tokens on completely USELESS tasks (randomly changing pixels in an image) just to keep them high up on the token leaderboard.
I suspect the other tokenboard leaders are doing the same. They made the metric "token usage" (which is just a proxy for LOC) so that's what they're gonna get.
Gaming is one thing, fundamentally not understanding how engineering works will lead to shittier outcomes and cost the company in ways the management will never understand.
Management in the age of AI is falling for the doorman fallacy wrt engineering. If lines of code were the most valuable aspect of software engineering, my front end JavaScript intern would’ve been the most valuable person in the company. https://www.jaakkoj.com/concepts/doorman-fallacy
I think PRs is pretty good, IF
1. you sample a few to see that they are actually meaningful,
2. they go to prod and are validated without having to roll back.
Still needs to be managed. But it should be much easier for a manager to catch an engineer gaming PRs than something like AI use or lines of code.
It’s very easy to split changes in more PRs than needed to boost the number.
Someone at my job uses AI tools to reformat his code...
I actually do this, but that's mostly because our team reviewed all the existing autoformatters for the relatively obscure language we use, and either really hated the formatting or found that they actually introduced errors!
My coworker said he does that too. Also have coworkers using AI to run git commands. Nothing fancy either- just pull, push, merge etc
Easily fixable with another KPI to measure the gaming itself :P
yeah and once the KPI is "how much AI did you use" instead of "what did you ship," the budget blowout writes itself. people will game the number.
When managers and VPs all say, you must use AI or else you will not work here, then yes, people will use it.
I don't understand this critique. (1) Did you previously think you weren't getting paid for doing what a company wants you to do, aka what THEY thought was productive? (2) Do you think all this AI generated code is useless?
Edit: y'all are some whiney folk, ain't ya?
I think the point was that, when you make a metric goal of "you must use AI this much", then people will use AI even in ways that isn't adding to productivity.
To answer your second question: Yes, much of it is worse than useless. The tools need guidance to produce useful output. If you use it poorly, you will get garbage output that may do more harm than good.
And your response does not address the point being made in the comment you replied to: Many people are being evaluated by how many tokens they burn, which is about as good a metric as lines of code written.
I think parent is saying "% of code being generated by AI" is not a generally good, direct metric for business value. It's akin to the "we are pushing SO MUCH CODE" phase of early ai marketing.
If we're trying to measure the value of adopting tool, it's probably better to measure the ROI of that tool rather than the usage % of that tool, especially when usage is basically mandated.
To directly answer your questions:
1. You're being paid to create value for the business, which "doing what they think is productive" is a proxy for. You're not being paid to use a tool a high % of the time.
2. I doesn't seem like parent even commented on the quality of the code generated. I think anyone that uses it regularly can agree that: a) the code is not useless and b) all generated code is not immediately production ready c ) AI generation of code is an accelerant for software development
1) I think if the company I work for spends too much effort on things that aren't going to make money, they won't be able to pay me anymore, no matter what they "think" is productive. That's not how executives at companies like this make decisions, though.
2) Mostly, yes.
Goodhart's Law isn't a problem immediately. If you want more code to be written, and the only feasible way to write it to goals is to heavily use AI, then you might run into the problems of AI-generated code, and an infrastructure that's poorly architected and much less understood than it would've been ten years ago.
Not OP, but:
1. At my level, the company is not just paying me to do a task the way they want it done, they are paying for my experience to orchestrate the best way to do it. They want an outcome, and I'm responsible for figuring out how to get to that outcome with the right balance of cost, correctness, etc. But yes, the most dystopian reality is what you said.
2. It's not useless, but the AI generated code is absolutely lower quality than what I would have written myself, but there is no desire to clean it up. Companies have always had a disastrously bad understanding of technical debt and they finally have tool they can shove down developers throats that trades even more velocity for even less quality. They're going to take that trade every single time.
> (1) ...getting paid for doing what a company wants you to do...?
At my previous company, when the thing they thought they wanted me to do (which was not the thing they actually wanted... but whatever) diverged from my values I quit. You can just do things.
> (2) Do you think all this AI generated code is useless?
Almost universally, yes. Especially in organizations that historically haven't been particularly careful about hiring and have a huge number of young, inexperienced people. There are exceptions but they're rare enough that throwing that particular baby out with the bathwater isn't a big loss.
you're missing their point; LLM use is often a part of your evaluation at some of these larger companies and they expect you to use them heavily or you will get a lashing
GP just saying that any metric will be gamed and if you have some costs that is associated to that, it will grow. Let’s say you set some metric that says the most productive dev are the ones that has the most files changes, you can soon expect every function and structure to be its own file. Same if you say that sales commision are based on how much time you spend calling, expect the phone bills to grow a lot.
It's wild that the article frames this as
> what started as an experiment in productivity became a runaway success
and
> figuring out if the company can afford this level of productivity at scale
It seems like they're equating "developers are spending a ton of money on this" with "this is creating a ton of value".
I'm not saying that AI tools aren't valuable, but the article doesn't question this equivalence at all.
> figuring out if the company can afford this level of productivity at scale
This is the thing that boggles my mind. They spent their budget. They have 4 months of data. What do they have to show for it?
I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it.
But are you saying that Uber made this tool available, urged everybody to use it, and is confused about what happens when it worked? It's one thing if they decide AI isn't productive enough to be worth the cost.
Are they out of ideas on what to build next, or something?
The personal max and teams plan actually are an amazing bargain compared to the API PAYG cost you get with Enterprise. I guess they really need their Enterprise features though, otherwise they could just tell users to expense a $200 max sub. Enterprises gonna Enterprise.
Entreprise gets you the written agreement that the data you send to Claude will never be used for model training
It also gives the plan admins the ability to surveil in automated fashion what the company employees are prompting.
If I explicitly turn this off in Claude’s settings isn’t it the same thing?
> I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it.
I'm glad to see we've reached the point of AI discourse at which anything that might be construed as criticism must be prefixed by "I'm also part of the cult, I'm not a non-believer, but" to avoid being dismissed as a heretic.
Since AI has become a partisan political football it makes sense
I wonder how much of this AI budget was spent on their LLM-heavy CI/CD pipeline: https://www.uber.com/us/en/blog/ureview/
I'm considering rolling out something similar but am not sure if it would exceed the expenses of Claude Code Review at an estimated $20 per PR.
I think as it becomes more common for executives to think we can replace software engineering with agents, I wonder if they might be basing their decisions off of unrealistic perceptions of the average software engineer. I guess I'm mulling two somewhat contradictory senses:
1. You get out of it what you put into it. A savvy CTO might be incredibly excited by everything they can do with agents, and improperly think that all the software engineers can do the same thing, when in reality your org's average software engineers might not have the creativity to even think of many cases where it could save them work. So by mandating agent usage, you might find that productivity hasn't improved while AI costs have increased.
2. When using AI, there are two gaps that become more obvious. First is the gap of: who tells the agent what to do? In many orgs, product isn't technically savvy enough to come up with a detailed spec/plan that LLM can use. And many cog-in-machine developers aren't positioned to come up with the spec, they just want to implement it. By expecting work to be implemented by agent-using developers, you might instead find a lot of idle workers waiting for work to show up. Second is the qa/review cycle. You've introduced a big change to the org but are you really saving cost or shifting it?
I'm all for introducing LLM as optional to help existing developers increase velocity and quality, but I think the "let's restructure the org" movement is really dicey, especially for mid-size or smaller employers.
> You get out of it what you put into it.
Beyond that, it's a force multiplier and it doesn't care if the force is positive or negative. Someone with poor software engineering principals can use AI to make an absolute mess quickly.
There is a major disconnect in that people think token usage is exclusively tied to human typing rates...it isn't true. When software developers evolve to using self-managing CLI tools (like Claude Code - the source article mentions this), they are not merely chatting; they are unleashing loops of agency.
When you enter one single inquiry of "find and fix the memory leak in the billing service" you are not submitting just one single inquiry. The tool is searching through an entire code repository for relevant code, pulling 15 related files into context (easily 200k+ tokens) proposing a fix, running the test suite and failing, taking an entire stack trace of errors into context and looping to keep iterating towards the solution.. In that process you can loop multiple times (10+) in a very short period of times (within 5 minutes). While you grab a cup of coffee you will have consumed $20 in token usage. At the enterprise level (like with Uber) when you multiply that out by thousands of software developers using it as a personal shell tool your budget disappears very very quickly.
And on your point about the junior developer: Comparing $100,000/year in tokens to hiring a junior developer is such a ridiculous false equivalency that even makes you question whether they even understand how to make such a comparison.
The cost to a business of one junior engineer with a $100,000 salary is not just the $100,000 in salary but also an additional $40,000+ in benefits and taxes, as well as in hardware.
Also, you are disregarding another cost of hiring junior engineers that is their mentorship cost. Each week, your senior and staff engineers spend hours mentoring junior engineers by reviewing their code, pairing with them, and unblocking their progress. Mentoring requires a substantial amount of time and will be expensive to your business.
The return on investment (ROI) for the $10,000 monthly expenditure on tokens is not so much about replacing the junior engineer with the AI. Instead, the ROI is that your senior engineers can use the huge amount of compute power to create boilerplate and tests, and refactor their code 3x quicker than if they had to mentor junior engineers. In addition, LLMs do not sleep, require one-on-ones, or leave for another company for 20% more pay in 18 months, when the value to the code base made them an asset to your business.
Lastly, the main reason that Uber has problems with their AI business is that due to the UX of these agentic tools, developers think of the API calls made to the AI as free and as a result, treat them like a basic grep command.
According to [1], there are about 5500 people in Engineering at Uber. Using $1250 as the mid-point of the $ spend range, that comes to about $6.8 Million in engineering AI spend, ballpark, with the range being $2.75 Million - $12 Million. The article lists $3.4 Billion as the R&D spend.
The AI spend does not appear to be a significant chunk of R&D spending (0.3% in 4 months or 1% annualized). If they didn't plan for it, sure, it's not peanuts in the budget, but in context not that much.
The real question is, what did they get for that amount? The article claims that 70% of the code commit is now AI-generated, so presumably the code passed review and tests. Did it accelerate the feature count? did it reduce quality problems? Did it lead to other benefits?
Sadly the article is silent on the outcomes, besides the higher spend.
Maybe 4 months is too soon to assess the benefits. On the other hand, in an agile world ...
[1] https://www.unifygtm.com/insights-headcount/uber
Everything in this article is purely fake. The numbers don't add up, don't match any reported info, and are just fiction.
The actual source https://www.theinformation.com/newsletters/applied-ai/uber-c... says "about 11% of real, live updates to the code in its backend systems are being written by AI agents built primarily with Claude Code, up from just a fraction of a percent three months ago" and "He wouldn’t disclose exact figures of the company’s software budget or what it spends on AI coding tools."
> Uber's unexpected budget burn matters because it signals how valuable AI tools have become to engineering productivity.
This infers value from spend, which makes no sense. Burning the budget tells us engineers like the tool, not that it's producing value.
Show me how to make two dollars whilst spending one, and budget isn't a problem.
Have we reached a point yet where companies are spending millions a year on software licenses, cloud and AI to the point where the return isn't worth it?
Years ago I did work for a company that was spending over a million on Oracle product licenses and I was part of the consultant team they hired to rip it all out and just go for simple maintainable code based on open source products. Not only did it transform into a codebase that the average newly hired developer could maintain, you also had the savings of not paying Oracle a significant portion of your revenue.
I feel like that will repeat itself in a few years time with the current cloud and AI train everyone is on.
I haven't been in a professional setting for a while, I just code for fun nowadays so perhaps I'm somewhat out of the loop.
It's very easy to blow through hundreds of dollars a session using API tokens especially with the 1m context if you aren't careful about clearing old context.
At the same time the subscription will allow the same usage for hundreds of dollars a month.
Either Anthropic is absolutely hosing API users, massively subsidizing subscriptions, or a little bit of both.
https://www.forbes.com/sites/annatong/2026/03/05/cursor-goes...
"Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute"
Really curious how many people actually get close to that level of usage? Their general business plan only offers the $100 version, with pay-as-you-go above that.
If 95% of people are using $100 of value a month, the whales may not be hurting them that badly.
Anthropic has a very "interesting" business model where you get subscription pricing as long as you are under 150 employees. When you hit 151, you have to start paying API prices overnight for everyone, and your total bill instantly multiplies.
They are getting you hooked on cheaper tokens, then raking you in when you get scale. I'm sure Uber gets a break on list price, but I doubt they are anywhere near <150 employee subscription pricing.
Strange pricing model for a company selling the idea of having fewer employees.
Don't the incentives align? If you have fewer employees, then you pay less...
Yeah, it's basically the opposite of how "product-led growth" SaaS works. Generally pay-as-you-go pricing is expensive at scale, but attractive initially. So you start on a pay-as-you-go plan, but as you scale you end up transitioning off pay-as-you-go to a negotiated commit. I.e. you call sales and sign a contract. Anthropic basically flips that around backwards.
I evaluated the pricing and could not justify the jump to Enterprise from Team. You lose the monthly subscription entirely when you jump to enterprise so you lose your ability to control costs.
You can cap per user, but not having the rolling cap are you really just going to tell a member of your team “No AI for the rest of the month”
It’s a risky deal as it sets up now IMO.
I love how these articles drop, and all of a sudden HN is filled with people who think engineering productivity is simple to measure.
Yes, productivity implies revenue (or cost reduction), and revenue is measurable.
However:
1. You spend money today to build features that drive revenue in the future, so when expenses go up rapidly today, you don’t yet have the revenue to measure.
2. It’s inherently a counterfactual consideration: you have these features completed today, using AI. You’re profitable/unprofitable. So AI is productive/unproductive, right? No. You have to estimate what you would’ve gotten done without AI, and how much revenue you would’ve had then.
3. Business is often a Red Queen’s race. If you don’t make improvements, it’s often the case that you’ll lose revenue, as competitors take advantage.
4. Most likely, AI use is a mixture of working on things that matter and people throwing shit against the wall “because it’s easy now.” Actually measuring the potential productivity improvements means figuring out how to keep the first category and avoid the second.
This isn’t me arguing for or against AI. It’s just me telling you not to be lazy and say “if it were productive you’d be able to measure it.”
> HN is filled with people who think engineering productivity is simple to measure.
I think the prevailing (correct) consensus is that developer productivity is actually very hard to measure, and every time it is attempted the measure is immediately made a target making the whole thing pointless even if it had been a solid measurement- which it wasn't.
IDK where you're getting the idea here that measuring productivity of anyone who isn't a factory worker is easy.
I do not think it is easy, like I said. I am saying other people are acting like it’s easy.
See the second comment on this article. https://news.ycombinator.com/item?id=47976781
See @emp17344 responding to me.
That second comment isn't making that statement though.
It's saying that: cost vs revenue is something we can see.
If I buy a plow for $2,500 and it enables growth of $5000, then arguing "the plow was expensive" is a moot point.
It doesn't make any argument about measured productivity, only investment vs return.
> You spend money today to build features that drive revenue in the future
Totally but new features in their app or better software are not going to increase Uber's revenue/profit significantly.
I mean, the option is not zero productivity or some productivity: it could be negative.
We doubt the productivity because we have enough experience with Claude Code to know that flooding your organization with that many tokens isn't just unproductive, it's actively harmful.
Minor shifts in productivity are hard to measure. Major jumps in productivity would be obvious. I think it’s clear that, if AI is affecting productivity, it’s to a minor degree at best.
If it were 10x productive you'd be able to measure it indirectly, you'd be unable to avoid measuring it. So the initial claims were clearly lies. The research question is:
I agree that's very hard to measure. But given what this shit costs, it had better be answerable, and the multiple had better justify the cost.I didn't see a bit where they said how this transformed into more productivity and more profit? What is the point in using AI to make developers more productive if you don't either have more features coded making more money, or fewer developers saving cost?
Can these AI-generated articles not be prompted to at least cite the primary sources? How do I know any of this is true?
Here's a much better article: https://aimagazine.com/news/why-uber-has-already-burned-thro...
The OP isn't a good article, but this one is about an entirely different subject?
Ah sorry, it's one of those annoying websites that automatically load another article when you scroll down too far. Updated the link.
Not surprising, hit my 5h limit on Claude Code Max Plan, had some credits so switched to extended (api). 40 minutes later $30 credits gone... so yeah, I can see how this can happen.
While this is a fundamentally stupid story to begin with, it was at least reported somewhat better in other venues. The original report came from The Information, and at least this Yahoo Finance[0] writeup mentioned that. This article has very little content and no sourcing.
[0]: https://finance.yahoo.com/sectors/technology/articles/ubers-...
What is Uber developing? They're an app and a car allocator back end. Both work OK. Why are they spending so much?
They gave up on self-driving, so that's not it.
> Both work OK
If only. The optimizations they do on their matching algorithm has made the UX so terrible, I regularly use Lyft instead now.
this is the most tired hn comment ever
"X is just Y - why is it so complicated?"
its lazy and boring to read these on every thread about a disliked big company
The more I use Claude Code the harder it is for me to believe this behavior is a byproduct of the model. Behavior = ridiculously token inefficiencies
> Monthly API costs per engineer ranged from $500 to $2,000 as adoption skyrocketed across the company.
That's...not exactly a lot per engineer. It sounds like they just didn't budget correctly. Especially if the net of that work is more features that would have otherwise required hiring more engineers, which would cost a lot more than $500 to $2000 a month.
Its a lot. Its a lot for being able to generate that many tokens.
And i'm not talking about some genies 10x developer who is working with multiply git worktrees on x tasks in parallel in high quality
No, it's really not a lot at all, especially if you've got a mandate to maximize your AI usage, which many engineering orgs have right now. I burned $216 USD using Claude Code in March just doing some casual development on the side and certainly not as a part of any professional workplace mandate.
If they burned through their ML budget in four months while using heavily subsidized models, we're going to see companies burn through their ML budgets in less than a week once those subsidies are no longer in place and they have to pay per tokens used.....
> Uber's unexpected budget burn matters because it signals how valuable AI tools have become to engineering productivity
That's a bit of a logical leap with no demonstrable increase in productivity.
All this shows is that they're spending a lot more on AI than they budgeted for. Nothing else.
I think the tech industry in general is taking advantage of the fact that software productivity is hard to quantify to say whatever they want about their AI productivity gains. Apparently we are past the point of having to justify anything and can just equivocate increased AI spend with success.
Could be negative! All it shows is that Uber is probably incentivizing token usage just like so many other companies are.
You get what you measure.
Wonderful, so when will I see novel features in my Uber app?
if you mean novel bugs than probably at the next app update
Hahaha.. good one :D
You can now reportedly book a hotel from the Uber app...which is totally a useful feature that I'm sure everyone will start to use /s
https://investor.uber.com/news-events/news/press-release-det...
I didn't know this. There's a term for this--which everyone of us now know--enshitification.
> the AI coding tools represent a meaningful chunk that nobody expected would require this much capital so quickly
Surprised Pikachu moment.
And it's going to become even more expensive when AI companies start charging to actually make a profit.
AI coding tools probably need the same boring governance as cloud spend: budgets, alerts, team-level visibility, and a way to spot runaway usage before finance notices.
It's GPT 5.5 and it still can't do exactly the same thing I want. So, I think companies should call AI a lost cause.
There's a line where the unfettered spending is just wasteful, we are well past the line
I am confused - what did they ship based on this spending? - it is totally alright to spend that money if it made significant progress in some area.
or did the engineers just chill and let claude take over daily duties? (this is also a benefit for employees in my opinion)
Wonder how many tokens would be saved if everyone just put “be brief” in their prompts.
Also wonder if there is some perverse incentive for models to be verbose to juice tokens.
this is pointless without knowing what they are measuring. you could genuinely moving faster or you could be optimizing for engineers in a rat race to push more code because all their peers are now doing it because those are the metrics you are measuring for "ai productivity".
Most people don't have the team and time to do heavy token efficiency engineering. But that's all we do. marketplace.neurometric.ai has a bunch of task specific small models, and we charge flat monthly fees. We bear the token risk.
Interesting. Some companies have rolled it out to every department with a small budget.
I wonder how this will end as AI becomes more expensive to use. If you can't quantify ROI then I guess you're cooked.
> what started as an experiment in productivity became a runaway success
Successfully burning through cash and tokens, alright, but what have they gotten out of it?
But did it make them more productive?
What are the sources for the “facts” presented in this post?
There are no sources or references.
we run an agentic pipeline in a different domain (data sourcing) and the only way the math works is to be ruthless about which stages actually need which model.
As a founder, the question I always have is "what is the marginal value per token relative to engineer-hours saved." More of a gut feel at the moment, but would be great to calculate.
Might as well get while the getting is good and Anthropic is subsidizing the cost of compute
Honest question, does Uber need that much R&D? And do they expect the ROI to be positive?
i assume this also includes their self driving vehicle research and trucking, not just their consumer mobile app dev
No mention of if it actually improved outcomes.
That's not the point silly goose.
It's obvious that the word productivity has been used in this discussion to mean something other than the plain meaning of the word. If AI was productive, there would be no question about whether it could be afforded. If you're asking whether you can afford it then it isn't productive by definition.
They are using it to mean a mechanism that produces prodigious amounts of toxic waste. That does not conform to the historical understanding of the word.
This terrible unsourced article seems to be citing this information piece: https://www.theinformation.com/newsletters/applied-ai/uber-c...
... but the key fact about "$500-$2000" per engineer does not appear there, and seems to be fabricated.
Thank you for the link.
> 70% of committed code originating from AI.
How are they calculating that? They could be using my tool, Buildermark, but I do t think they are: https://buildermark.dev
Is this a submarine? https://paulgraham.com/submarine.html
It's funny how Paul is recommending people use PR firms, while in more recent videos michael seibel and others have strongly recommended against using them. It's interesting how things shift in ~20 years
This continues to boggle my mind so hopefully somebody can explain how this is happening.
I’ve been using all these tools since they started popping out around 2021 personally and professionally. I probably built four or five products at this point with assistance, not to mention the thousands and thousands of back-and-forth conversations for research or search or rubber ducking or whatever.
I have never spent more than whatever the professional max plan is that is consistently $20 a month.
I asked a friend of mine who spent a couple hundred dollars in like an few hours how they did it. The answer was they basically getting these agent groups of agents stuck in a loop and they’re constantly just generating verbose bullshit that is not even interrogated and doesn’t come out with any artifact that is inspectable no matter how expert you are.
The couple of stories I have heard of these massive crazy spends are people literally just assuming these things can complete an entire human task in one shot, so they continue to hit the “spin the wheel” button until they get something closer to what they want
But I’ve yet to see that actually work
and it actually flies in the face of every instruction guide or documentation or prompt engineering process that has been described over the last almost 5 years
i bet someone mentioned openclaw one too many times
AI token austerity when
Now AI slop factories make the HN front page?