It's pretty simple; organizations are willing to tolerate paying $1500/month/engineer, which seems to be roughly inline with "normal" consumption for most full-time engineers. If that number grows significantly, then I bet companies will start exploring flash models more, as you propose.

They are willing to tolerate it now, which is quite a switch up from the free for all we had a few weeks ago, and if they aren’t able to tie in this new ~$1500p/m cap to demonstrable productivity and revenue increases then that will be kneecapped even faster

There are plenty of expenses in this order of magnitude that are not tied to direct increases in productivity. I think it may become a serious hiring impediment for companies to be really skimpy on these budgets for example.

> organizations are willing to tolerate paying $1500/month/engineer One organization, that is a software company > which seems to be roughly inline with "normal" consumption for most full-time engineers My peers are using $20/mo plans, only a handful are using more than $100/mo in tokens. We haven’t had any limits imposed yet.

Is your argument that $1500 / mo is too much? Why would the engineering team not be more rigorous in their model selection given a constraint?

It is also possible that capping at $1500 will give you ~99% of the benefits. So even with gains that are much higher, a cap could be a rational decision. Also, most decisions, especially around AI aren't exactly rational, so I wouldn't read to much into this number.

This has happened to me, so I put this in my global CLAUDE.md, and it seems to help (I don't remember getting the response you mentioned for awhile now): **Lead with the answer when asked how/which/whether.** Name the command/mechanism first; a question seeking understanding isn't a go-ahead to execute. Answer, then offer to act.

I can see a corporate future where tokens are haggled over in department budgets just like any other line item. Some projects will get more of them, other projects will get less of them. "Use AI for everything" will become "use AI economically and build things that outlast our budget for it."

Neat fact, those kind of conversations are already happening at ${DAY_JOB}.

As a side note, I wonder when we'll hear the first reports about employees reselling (parts of) their token budget. Probably not worth it risking your job for a 200$/month good, but at 5K, I'm sure some folks will be tempted. Especially if companies do stupid things like token usage leaderboards.

So touche, but since it's usage per task it's kind of weird. This means that the average engineer is efficient at (say) identifying the first 10 tasks they should do but there are diminishing returns after that? That seems like a weird pattern. Wouldn't it be more likely that certain tasks have a ROI based on how efficient the task is generated? Like I'm trying to imagine in my head, if you think an engineer is more efficient with the tool, why deny them more tokens. I guess so they think to use them more efficiently? So, maybe I conclude that I think your conclusion that there must be $1500 per engineer is flawed. And even if it were true, I don't think the benefit would be evenly distributed. I suspect this is a first pass at figuring how to budget them and there will be a second pass. While it certainly reeks of motivated reasoning, Jensen Huang assertion that an expensive engineer should be using at least their salary in tokens feels more logically sound to me (assuming the average engineer is efficient at using tokens, I have a feeling it's a normal distribution)

Setting a cap motivates developers to invest their tokens wisely such as choosing the right models and not burning tokens for fun or side projects, same as any budget.. it’s not any deeper than that. At my company we can ask for temporary cap limits if it’s justified, which is fairly common.

"I suspect this is a first pass at figuring how to budget them and there will be a second pass." Completely agree with that.

There is a whole spectrum between "ai coding is a fad" and "unlimited tokens for every employees we don't even care if it actually ends up being a net positive financially"

> "unlimited tokens for every employees we don't even care if it actually ends up being a net positive financially" That was clearly a short-term trend that would obviously get fixed. Doesn't say much about AI coding as a business model.

Only people who do pay-per-use optimize this. Most heavy users have their use covered by an employer.

It's an extra 18k a year for developer tools when they're paying how much a year per developer? Having software developers at all isn't cheap. Also, I don't believe you need to spend $1500 a month on a coding agent if you optimize usage at all.

I use the $100/mo sub but my 30 day API cost is about $1700/mo. It really depends how you use it, if you're using prompts to generate detailed designs, breaking those into lists of tasks, and then feeding those to multiple agents - it's really easy to burn through many thousands. If you're being more deliberate and using a few agents at a time interactively, having it review PRs/resolve issues, automated clean-ups and performance optimization, etc it could be more like $1500. If you're just throwing it one-off questions like a better stack-overflow that is well under a $100. I've really gotten into /goal, if you can find something verifiable and leave it overnight - it's kinda like christmas morning to see where it landed.

I don't think it's a bold assumption, but I also don't think the assumption would lead to the conclusion. 1. Why it's not a bold assumption: it's a bit shocking now. But in two years or so, many/most companies will realize this is the cost of doing business. Just like people are ok with using Outlook, or Office 365, or (in the case of Wall Street) Bloomberg terminals, people will realize that developers will need AI coding assistants. 2. Why the conclusion does not follow from the assumption: if the limit is set at $1500/developer/month, it does not mean all developers will use it. Companies will set incentives for people to not be very wasteful. It is more likely that on average developers will consume $100-200 worth of tokens per month, and there will be some outliers who will consume 10, 100, or 1000 times as much, but they'll be few.

The $1500 number is less interesting than the fact that they hit a ceiling at all. Most engineering teams I've talked to have no idea what their AI spend is per developer because it's buried in a consolidated cloud bill. Having a hard cap forces two useful conversations: what workflows actually justify API calls vs local inference, and whether the output is being measured against any real productivity metric. Without that feedback loop it's just a race to see who can burn tokens fastest.

Both the Anthropic and OpenAI "Enterprise" plans include per-developer analytics: Anthropic: https://support.claude.com/en/articles/12883420-view-usage-a... OpenAI: https://help.openai.com/en/articles/10875114-workspace-analy...

I think the main thing companies should try to understand is avoiding the use of 'claude -p'. I definitely have written a goal file, and then just ran claude in a loop over the goal in order to 'token max'... why not? I'm doing research and have some clear KPIs where research into all kinds of techniques / tuning can improve the results. I can spend my budget on a "experiment with blah blah blah to improve blah blah" or give it a list of things to try that I know will take awhile. Its no problem hitting hundreds of $ of API spend while sitting at a computer with 3 monitors have 6 windows of useful claude code interactive sessions, while working on 2 or 3 projects and using worktrees, and it's a little weird when you hit your limit by 2 o'clock and have to wait for token budgets to reset; god forbid, I manually edit code... which I did do for the first time in months. You can also start to generate a lot of token spend if you do something like "hey make me a stylized slide deck using internal skill / agent XYZ based on commits A through C", which as an engineer, makes presentations building much less painful . This uber limit is not high compared to the big SV companies.

How are people using so many tokens? I'm on the $200/month enterprise plan for Claude Code (because it's a better deal than the API pricing) and I don't come close to the limits. If you use stuff like opusplan and /advisor so you use Sonnet for most of the work and only Opus for the really complex stuff then it's quite easy to keep costs low without affecting performance.

I use Claude every day. Often for multiple hours a day. Basically doing my job not worrying how many tokens I spend (as in too many or too few). This is a pretty complex code base (database optimizer and related). Just looked at spent for the past 30 day, didn't even come to $600. 95% of my tokens are from cache. If I were to reach even $1500 I have to let claude run unsupervised over night (and with the amount of mistakes it still makes and guidance it needs, I do not believe we are there yet.)

> didn't even come to $600. That's still in the ballpark. A modest change in your usage habits or workload could easily get you there.

> A $1,500 monthly limit per tool strikes me as a rational policy response to over-spending,... > I noted that my own token usage comes to about $1,000/month against each of Anthropic and OpenAI - which currently costs me just $100 per provider thanks to their generous subsidized plans for individual subscribers. This whole article seems to me like Multi level marketing "businesses" where 'Diamonds' have made their money by promoting MLM in seminars and telling hopefuls at bottom that "Buying AI subscription now is their one shot to be a winner in life" Perhaps there is something to MLM vs LLM to create a FOMO effect.

I just went through a similar discussion in my $WORK (traditional finance company on NYSE with average IT expertise) and I think the thought process is as such: it's one thing to just give your stellar dev/hacker a beefy GPU server and run whatever model they can run; it's another thing to maintain such platform for company wide. You would need human resource (likely way above normal software dev paygrade) to understand and maintain such models, maintain backend, availability etc. All these extra hassle make it just easier to pay a top tier external lab + slap a reasonable spending limit on everybody.

A blanket cap makes no sense to me. There's a power distribution of AI use in my company and I'd imagine it's the same at a much greater scale at Uber. I'd guess there should be a few people Uber is bascially allocating unlimited AI spending to and a large swath they're giving basically nothing.

I would assume that at least one of two things are true: 1. They're costs are so so out of control that they need to impose a blanket cap immediately. Figuring out an allocation mechanism that can be deployed company wide is time consuming and they need to staunch the bleeding immediately, despite it being obviously suboptimal. 2. The few people who should have unlimited tokens were given exactly that. No reason to introduce such nuance to a public PR move. The hard-cap limit is a great negotiating posture with token providers.

That's a lot. On my usual day I burn less than $1 on Opus. I could get beyond $10 only if I have a complex and well-defined problem, which is rare (the second part at least).

You must not be using coding agents. You can sneeze and spend $1 on Opus in Claude Code.

If a worker doesn't use their AI/LLM budget, can they get a raise?

no because it does not come from the same budget

Money spent is money spent.

This week an S&P 20 company with previously unlimited Claude limits also set a $250/mo/person limit; though its unclear to me how widely the limits are being enforced, may be the case that its just non-software engineers. Do with this info what you will.

True but they will raise prices slowly so people will optimize their workflow so they aren't just throwing as much inference as fast as possible like the current state. Right now you should do everything you wanted to try out because it is cheap (as long as you don't become dependent ... the risk).

In my experience, this is far below the cost the average dev will incur per month so this seems very reasonable to me. And, no doubt there are exceptions for heavy users so they can get some extra token usage when they need it.

unless they changed something in the like 2 months (edit: besides implementing a cap for claude code specifically, since other tools already had caps) since ive left my job there im pretty sure 1500$ is the very max you can use after maxing out free calls, initial budget, then 2 extensions individually reviewed by your manager higher ups pushed for these last 2 years to be AI focused so I don't think this restriction is a measure of "don't use too much AI" as much as it is a measure of "don't use only 'manual' AI tooling" since we had a dozen more specialized tools in-house running locally or otherwise that didn't count towards the budget

No, that's not what it means at all even if just doing it purely in math terms. Really it is just a reasonable amount to cap at to stop the long tail of super spenders (tokenmaxxers). You could also call it "the amount of AI spend after which Uber has decided there is diminishing returns for the average engineer".

I'm sure if a dev can show useful results at 1k they won't have trouble getting permission for a higher cap as well.

Their initial budget for determining how much value AI adds is $18,000 per engineer.

Uber engineers reported that loading their workspace and pulling recent commits exhausted that AI limit for Claude Code (4.8 x-high) immediately.

I don't think loading up a single context window costs $1,500. Which limit are you talking about?

1) This happened because they fundementally misunderstand how to use AI and how AI is priced 2) Most organizations are throwing everything in for analyses and not limiting the answer they want. You need to be specific of about what you analyze and what answers you want 3) People undervalue prompting or templated responses. I will have written. validated and sanity checked a prompt several times and run it across several models before I say its ready for use. But when it is, I know what it will give me and that the scope of its research and answer is as close to what I want as it can be. As little excess as I can. This all saves tokens

It's probabaly a good things that Uber-developers are now forced to do some coding on their own. Only use AI where it absolutely helps

Or be smarter about their usage. $50 on tokens per day can get you a long way.

Some people also take weekends off.

I don't think at $1,500 you're not forced to code on your own at all, in the sense of typing code. You're simply forced to not yolo-max twelve parallel agents at all times.

Due to recent Copilot price increase my friend was capped to $70 per month of usage. Not on a subscription… My $100 subscription is not cheap. At the same time our product burns orders of magnitude more tokens.

ccusage for codex tells me the medium feature I prompted in codex, with a $200 subscription, running for 72 hours and still not delivering full result would have cost ~ $2200 at API rates. I also misconfigured something in my agent's configuration and a simple web tool request (maybe 4 turns) through OR went to GPT-5.5 accidentally and that cost me ~$0.4. I have no idea how any business can afford API rates without having a mindset of casually setting money on fire.

But is it an accurate number? Does AI reach diminishing returns after $1,500/month, or is that all they are willing to risk/burn to stay in this game?

Uber is in the business of experimenting with robotaxis and automated food delivery. They can't say that $0 per employee is the appropriate amount for AI spending. So they capped it, perhaps in order to "send a signal" that is eagerly picked up by the AI boosters. There is no signal. Uber does not work any better since AI. They still want to promote AI, so they chose the highest number that doesn't bankrupt them so the press and AI promoters pick it up as the new price anchor. Probably they'll quietly reduce the number more soon.

Is anyone doing story point estimation in terms of tokens? If you have a token budget, does this change how you prioritize?

If budgeted at $1,500/month per user, power users still can get 5-10x of that allocation if the user pool is large enough.

Related: Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing https://news.ycombinator.com/item?id=48268871 Uber torches 2026 AI budget on Claude Code in four months https://news.ycombinator.com/item?id=47976415 Corporate America Is Starting to Ration AI as Cost Skyrockets https://news.ycombinator.com/item?id=48335388

It's wild; at my shop in Silicon Valley they dropped us from unlimited use to 60% prem budget on copilot. People are walking around like zombies.