18 ms·

Uber's $1,500/month AI limit is a useful signal for AI tool pricing

• watershawl 4mo ago
  Do you think companies are gonna be like?: Wait a minute. We didn’t save money by adding AI. We just added an expense. Now we have to pay for employees AND AI.
• Ile09 4mo ago
  [dead]
• ChrisArchitect 4mo ago
  Related: Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing https://news.ycombinator.com/item?id=48268871 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 https://news.ycombinator.com/item?id=47976415 Corporate America Is Starting to Ration AI as Cost Skyrockets https://news.ycombinator.com/item?id=48335388 https://news.ycombinator.com/item?id=48335388
• PessimalDecimal 4mo ago
  These are still at currently subsidized prices. We'll see if they think they're getting $1500/month of value when that buys significantly fewer tokens.
  • boringg 4mo ago
    True but they will raise prices slowly so people will optimize their workflow so they aren't just throwing as much inference as fast as possible like the current state. Right now you should do everything you wanted to try out because it is cheap (as long as you don't become dependent ... the risk).
  • pdyc 4mo ago
    afaik, enterprise plans are not subsidized. its 20$/seat+api pricing. Unless you are saying api pricing itself is subsidized.
    • LurkandComment 4mo ago
      This is market introductory pricing that hasn't factored in cost recovery. Most of it has been run on early investment with the assumption they will recover costs in the long run. The prices are subsidized across the board and they will need to go up signficantly to recover them.
      • logancbrown 4mo ago
        None of what you said is true
        • rimliu 4mo ago
          And you know this how?
          • logancbrown 4mo ago
            Burden of proof is on you
      • swiftcoder 4mo ago
        Assuming this were accurate, then presumably the AI companies would be betting that inference costs come down before the bill is due - I don't see enterprises being willing to absorb another ~10x price increase for tokens (as they've just done going from subscription prices to per-token pricing)
        • LurkandComment 4mo ago
          For claude shops this was a huge hit. But lets back this up. There are some companies that haven't even built a break-even model at this price because they are funded by investment. As soon as those investors lose patience the first dominos will fall. For those who have somewhat of a business model, will it survive a price increase? The bigger question is do the base model providers have enough runway and have a way to keep going as they need to recover costs.
  • square_usual 4mo ago
    There is no evidence that per-token inference prices (which is what Uber is setting a cap on) is subsidized.
    • lelanthran 4mo ago
      Is there any evidence that it's not?
      • thejazzman 4mo ago
        Yes; they ban various uses of their subscriptions but say you can do whatever if you’re paying for the API without limits
        • lelanthran 4mo ago
          That's not evidence. Very likely though, but the only evidence we get one way or another is when they IPO.
        • simonw 4mo ago
          This story isn't about those subscriptions - enterprise customers like Uber are paying the full API prices.
        • pqtyw 4mo ago
          That's just market segmentation and them trying to maximize revenue it doesen't really say anything about their costs.
      • Topfi 4mo ago
        The fact that Anthropic models are offered at the same API pricing by not just themselves but AWS, Azure and Vertex despite Anthropic taking a major slice on licensing along with the cost an open weight 1T parameter model like K2.6 costs to run on any third-party provider, make it unlikely that API inference cost are subsidized by the labs.
      • pqtyw 4mo ago
        Openrouter? i.e. Even excluding Deep Seek inference for very large open models is way cheaper. Maybe these providers are not very profitable but its highly unlikely that they are losing $4 for every $1 they make since selling inference is their only product...
  • sourcecodeplz 4mo ago
    I understand current Codex $20 sub is worth about $480 GPT5 api credits.
    • esafak 4mo ago
      Way more. Track with https://github.com/junhoyeo/tokscale https://github.com/junhoyeo/tokscale
  • MagicMoonlight 4mo ago
    It's not. They recently forced enterprise customers onto API billing instead of the cheap consumer pricing. Now the pricing is brutal.
  • pqtyw 4mo ago
    The inference prices for very large open models would indicate that Antrophic's and OpenAI's margins are quite large.
• CharlieDigital 4mo ago
  $1500/mo is $18,000/seat/annum. Maybe Microsoft and Nvidia are on to something. 128 GB machines that can run local LLMs are a bargain even if priced $5-8k. Yes, tok/s is not quite there, but that's probably OK since the bottleneck really isn't the code; it's WTF did Uber build with all of that spend? How did it meaningfully impact their revenue in a positive direction?
  • dkdcdev 4mo ago
    at their scale they could also just run a large on-premise or rented (basically still cloud, but cheaper) GPU cluster and run through that. fixed costs, even license a SOTA model’s weights if you’d like
    • embedding-shape 4mo ago
      > even license a SOTA model’s weights if you’d like Yeah, I bet all labs releasing SOTA models are more than happy to remove the main way they make money and let you run it locally, especially if you're a big spender like Uber who seems very willing to throw money into the sea as an experiment.
      • throwway120385 4mo ago
        That's going to stop eventually, and I think at that point we're going to see business models more like the major CAD providers.
      • deleted 4mo ago
        [deleted]
      • idiotsecant 4mo ago
        I don't think they'll have a choice, open weights models are not far behind. At some point it's essentially a commodity game
        • dkdcdev 4mo ago
• LurkandComment 4mo ago
  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
• jkwang 4mo ago
  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.
  • simonw 4mo ago
    Both the Anthropic and OpenAI "Enterprise" plans include per-developer analytics: Anthropic: https://support.claude.com/en/articles/12883420-view-usage-analytics-for-team-and-enterprise-plans https://support.claude.com/en/articles/12883420-view-usage-a... OpenAI: https://help.openai.com/en/articles/10875114-workspace-analytics-for-chatgpt-enterprise-and-edu https://help.openai.com/en/articles/10875114-workspace-analy...
    • Igrom 4mo ago
      I believe you might be replying to a bot account.
      • lazyasciiart 4mo ago
        What makes it look like one? All their dead comments read pretty normal to me.
        • deleted 4mo ago
          [deleted]
        • deleted 4mo ago
          [deleted]
        • toraway 4mo ago
          That account is setup pretty well compared to many I see on here, no giant red flags in any individual comment and they’ve prompted/filtered out the repetitive structure decently so they don’t all look exactly the same. But exhibits number of common attributes/failure modes you notice when you spend a while reading through the history of obvious bot comments. Some of the signs in comment history: 1. Multiple uses of the notorious HN bot phrase “X is real” e.g “The tradeoff is real” Spam bot replies love that, at this point if I read that phrase on HN I immediately become suspicious. Also, ending a comment with an open ended question or random caveat/concern/qualifier too frequently. 2. Rapid fire commenting. Most new HN accounts run by actual people usually ease into commenting regularly after their first comment, spam bots go from 0-100 suddenly replying with detailed, uncannily on-topic comments 1-3x per day 3. Unnaturally on-topic but uncanny valley comments. Humans typically respond to an idiosyncratic idea of what the topic of a discussion is, but bots typically respond with a perfectly responsive summary and/or personal experience. As for uncanny valley, this is the big one that caught my eye: > Love this approach. SQLite's WAL mode + litestream backup handles most durability needs beautifully. The simplicity of keeping state in a single file you can query with SQL is underrated. Going to try this for our next side project. It’s just … bizarre haha. It’s replying with what is pretty much the most basic, fundamental description of what SQLite is, with zero actual opinion/experience/commentary. It’s like responding to an article about PepsiCo Q2 Earnings with “Great read - my family of four enjoys drinking Pepsi products, the carbonation and various flavor choices are a big plus compared to other brands”. If it wasn’t a bot it would have to be someone ineptly karma farming, and either is trash deserving of the flags it got. Plus, the initial comment in this thread while not enough evidence on its own is already suspicious because it’s confidently stating an anecdote about billing that just doesn’t really make sense as if it’s a super common way tokens are billed. It could be true in their unique case but it’s just weird and presented like it’s common knowledge so comes across either as a human pretending to have experience or a bot slightly off target. But it’s not enough without the full comment history, and even without a giant smoking gun having seen a lot of bot comment histories it fits the pattern closely enough in most ways to agree with the other commenter (and the flags/HN shadowbanning) that they are a LLM bot.
• jedisct1 4mo ago
  A lot of things can be done with local models.
  • rimliu 4mo ago
    Even more things can be done without any models just as well.
    • dude250711 4mo ago
      Single developers seeking local models.
      • fHr 4mo ago
        goated comment
  • deleted 4mo ago
    [deleted]
• cloudking 4mo ago
  They are also beholden to enterprise pricing and can't use the subsidized consumer max plans.
• ilia-a 4mo ago
  Seems odd limit, especially since it highly dependant on Token provider used, with Opus this is not much and could easily be burnt in a week or less, but with something like deepseek the 1500 can literarily be an annual budget. That being said, I do have to wonder why someone as bug as say Uber, simply not rollout OSS model in the cloud for their team, I'd imagine that would be cheapest & most flexible option, while also keeping all the data shared with LLM private.
  • iceman28 4mo ago
    It’s not just about the model but also setting up the system to create and share compute (GPUs) which is quite complicated on its own. Ubers primary business focus isn’t infrastructure.
  • deleted 4mo ago
    [deleted]
• epsteingpt 4mo ago
  Uber engineers reported that loading their workspace and pulling recent commits exhausted that AI limit for Claude Code (4.8 x-high) immediately.
  • wmf 4mo ago
    I don't think loading up a single context window costs $1,500. Which limit are you talking about?
• ashahin 4mo ago
  [flagged]
  • onlyrealcuzzo 4mo ago
    It's interesting to me how ineffective LLMs are at refactoring, but when you think closely about how they work, it makes sense. They are good at searching for things that have been done 10,000 times before, and slightly changing them. This is the majority of all "new" features. Almost nothing is "new"... Refactors are not this. If you can't just write a gsub to do the work, they need to essentially break it up into N problems to solve, each of them pretty slow and expensive. Sure, none of these problems individually are "new" - which is why they can do it. But they can't do it as effectively as you'd think.
    • jbvlkt 4mo ago
      Exactly my experience. I always refactor first myself then delegate boring tasks to AI. It saves me energy, time and also tokens. If code is not prepared for easy implementation agents always fail.
  • hanzeweiasa 4mo ago
    Good point about the unit of consumption shifting from prompts to agent loops. That makes pricing even trickier for vertical-specific AI tools. We see this firsthand building AI Workdeck (open-source AI workspace for legal teams). A single due diligence review might chain 20+ agent calls: OCR -> text extraction -> clause classification -> risk scoring -> evidence chain assembly. The user sees one action, but the backend burns through significant inference. The interesting thing about vertical tools is the pricing model can be fundamentally different. Horizontal tools charge per seat or per token. But in legal, the value is in the document, not the seat. A lawyer reviewing a 500-page M&A file gets way more value than one reviewing a 2-page NDA. Self-hosting changes the calculus too. Our users run on their own infra, so the AI cost is whatever their GPU costs. That makes $1,500/month caps less relevant and throughput optimization more important.
  • slopinthebag 4mo ago
    LLM generated comments are against site rules btw.
• f311a 4mo ago
  How many more months do we need to wait, until big companies realize that flash models work just fine if you: 1) Don't ask LLMs for big changes 2) Review everything and point them in the right direction Large models still suck at big changes, they produce questionable architecture and you still have to review the code, if your project is serious enough. The codebase quickly become a mess, if you don't pay enough attention. Does not matter which model. So why bother with big models, when flash models are 10x cheaper and much faster to iterate under guidance? Large models can be used for security and bug audits. Flash models work almost the same for changes under 300 LOC when you dictate how you want your code to look.
  • warmwaffles 4mo ago
    > Don't ask LLMs for big changes > Review everything and point them in the right direction Sorry upper management doesn't care. That's an engineering problem that you need to solve.
    • eikenberry 4mo ago
      They were proposing a solution.. To use flash models and use them in a way that best amplifies your work.
      • AgentMasterRace 4mo ago
        He was making a joke.
        • warmwaffles 4mo ago
          Indeed I was. But that's lost on people here.
          • eikenberry 4mo ago
            Maybe because it wasn't funny? ;)
• jwpapi 4mo ago
  If you estimate 10k salary per engineer that means the moment it’s cheaper for them to hire another engineer but that doesn’t mean it’s improving productivity 15% but if 15% is the moment it stopped being better than another human we can assume 7.5%? Probably even less because you would spend those 1500 extra per employee also if you just save 10% so 150 per employee that’s 1.5% on salary. This is imho one of the best ranges we can assume for now how much would that be on the whole swe market?
• sremani 4mo ago
  I have strong conviction that companies will now choose tech stack/programming languages based on 'tokenomics'. I am vibe coding using Clojure, a language I can read but cannot write and I never hit the usage limits even when using the latest model on Claude. I have similar experience with F#, which is a bit more verbose than clojure but absolutely beats every OOP language, Python, Typescript etc. The reason, I use F# & Clojure is they hit JVM and CLR, two popular enterprise stacks. In my not so humble opinion Lisp(Clojure) still remains the language of AI.
  • genericone 4mo ago
    Typescript is also hugely represented. My projects are TS in a big way, where I have no experience with it at all.
• newobj 4mo ago
  It's also a useful signal for AI value. Looks like it's a max value add of $18,000 per engineer per year.
  • jdkdksksn 4mo ago
    [dead]
  • eqvinox 4mo ago
    It's among a wave of fresh "non-insane" takes on AI in the enterprise. Maybe we can reel things in to a sustainable level before a giant bubble bursts.
  • csallen 4mo ago
    It's not so simple to determine and generalize how much value AI adds. It's going to be different on a per-company basis and a per-engineer basis. It's also affected by the competitive market place and how many other companies are using AI for their engineers. For example, what if you're a tiny startup and you're considering whether to hire an extra engineer or do all the coding yourself. I would estimate that AI is worth far more than $18,000 a year in that situation where you might reasonably decide to put off hiring an engineer.
  • pqtyw 4mo ago
    I find it really doubtful anyone has managed to quantify that in any meaningful way. Seems like mostly an arbitrary number. Also the article does claim that's its actual several times more than 18k if you are fine with using Codex, Cursor or etc. when you Claude tokens run out.
  • alasano 4mo ago
    Their initial budget for determining how much value AI adds is $18,000 per engineer.
  • tfehring 4mo ago
    Not really. There are clearly diminishing marginal returns, so it's likely that the first $2,400/engineer/year adds >>$2,400 of value, even if 18,001st $/engineer/year adds <$1 of value.
• etothet 4mo ago
  In my experience, this is far below the cost the average dev will incur per month so this seems very reasonable to me. And, no doubt there are exceptions for heavy users so they can get some extra token usage when they need it.
  • waffuldrop 4mo ago
    unless they changed something in the like 2 months (edit: besides implementing a cap for claude code specifically, since other tools already had caps) since ive left my job there im pretty sure 1500$ 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
• galaxyLogic 4mo ago
  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
  • sva_ 4mo ago
    Or be smarter about their usage. $50 on tokens per day can get you a long way.
    • estomagordo 4mo ago
      Some people also take weekends off.
  • aerhardt 4mo ago
    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.
• rasbmn 4mo ago
  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.
  • lazyasciiart 4mo ago
    Is this inside knowledge, or speculation?
• ValentineC 4mo ago
  > 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. Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China? Many lower-budget individuals are now moving to China open weight models like DeepSeek. I wonder if China's really subsidising the providers, or if inferencing costs are actually much lower, and Anthropic/OpenAI are just making sure no money's left on the table for their eventual IPOs.
  • testdelacc1 4mo ago
    Per token costs will fall, but the harnesses will get more token hungry. Instead of just centering the div it’ll spin up a battery of agents to architect, critique, advise, code, review, refactor and so on.
    • sevenzero 4mo ago
      I wish I could disable most of these. I already hate all the "oh you're actually right, let me fix that" nonsense. Then it proceeds to burn 50k tokens on the git history instead of copying logic A from a different part of the codebase to logic B, where I want that exact logic without having to write the boilerplate myself...
      • apsurd 4mo ago
        Makes me think of how my Claude.md files specifies to use the built in framework code-generators (rails). Those generators are deterministically right every time. I wonder how often the Agent actually follows the guidance. I do see them follow it when I look. But it doesn't seem so every time.
        • thefunnyman 4mo ago
          This is tricky since it can and will ignore your md directions. When possible I try to lean on tool call hooks or skills that invoke deterministic scripts. As much as you can remove the "choice" the better though still there's a lot of randomness in how reliably it invokes skills ime.
• pmontra 4mo ago
  I wonder what they are doing with $1500 per month. I'm on Claude Pro $20 plan and I'm doing well. That's 3 days per week. On the other 2 days I'm using a customer's Claude Max, I don't know if it's the $100 or the $200 plan, but I'm sharing it with some of its other developers.
  • hrpnk 4mo ago
    $1500/mth is token pricing. Your other plans are fixed price with rate limits where you get more tokens than the dollar equivalent you pay monthly. These plans are economical only if majority of users spend less tokens in $ than the plan's costs. This subsidizes the gap vs. power users who spend multiple k$ monthly in API tokens.
    • flyinglizard 4mo ago
      Yea, I’m sure the personal plans are subsidized. I have $200 Claude Max at home and straight API pricing at work and equivalent work would easily cost me 5x if not more on the API.
    • kingstnap 4mo ago
      Next to no one would be using less than the subscription price given how expensive Opus API is.
    • pmontra 4mo ago
      > Your other plans are fixed price with rate limits where you get more tokens than the dollar equivalent you pay monthly. Or the fixed cost plans reflect the real cost and the people paying API prices give them the profit. Anyway, none of my customers will let me bill them $1500 more (about $75 per day) because I'm using AI. And what for? I'm not working to move money from the pockets of my customers to the pockets of AI companies.
      • fontain 4mo ago
        No, we know from the financials of these companies that API prices are close to being at cost and the individual developer plans are heavily subsidized (because they are roughly 10% of API cost per token[1]). If plans were at cost and API pricing was marked up that would mean there’s a 90%+ profit margin on tokens and instead of raising money and talking about revenue, Anthropic and OpenAI would be talking about their obscene profits. [1] the caveat is that the average plan user probably doesn’t use all of their quota, I guess maybe 30% is the average across all users.
• hrpnk 4mo ago
  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.
• tuesdaynight 4mo ago
  Why there are so many people that still believe that AI coding is a fad? It's something that started less than two years ago and companies are already paying thousands per seat. I know one that gives you 5k per month. Which other tool went from nothing to this level of acceptance so quickly?
  • anthonypasq 4mo ago
    perhaps the personal computer? Companies were spending 3-5k (10-15k inflation adjusted) on every employee for just hardware. everyone making comparisons to the dotcom bubble seems misguided. this is clearly computing 2.0 imo
    • thewebguyd 4mo ago
      No disagreement on computing 2.0, but companies spending 3-5k per employee for hardware isn't generally a monthly cost. It's a at the time of hire, and then once every 3 to 5 years after that, for a monthly amortized cost of about $50/employee. I have my concerns with current inference pricing in that there's a non-zero possibility for a rug pull in the future for the subscription plans for organizations and individuals that can still use them. For now, its only companies larger than ~150 users that need to pay per token, but what if that wasn't the case? Not every company can afford over $1k/month/employee to give them access to AI tooling, further making it harder to compete against the behemoths. If we get to a point where an individual can no longer pay $100/month for nearly unlimited usage and instead must pay per token, that's going to be a problem. Personal computing eventually became an equalizer (until we started centralizing on mainframes again, aka the cloud) because it got cheap. My hope is that inference also gets just as, if not cheaper. I have high hopes for local AI and open weight models and we will continue the ethos of local, personal computing and not needing to offload everything to OpenAI/Anthropic/Google, etc. to get work done once the hardware and hardware availability catch up.
      • dghlsakjg 4mo ago
        Every employee doesn't need $1k in token spend per month, either. That kind of spend makes sense for technical workers in r+d. Most other workers are served fine by $20-30 worth of tokens on a budget model. You don't need Opus to help support write emails.
• throwaway613746 4mo ago
  [dead]
• cyanydeez 4mo ago
  no....the fact that you could buy a reasonably prices MAC or AMD395+ thats AI tool pricing; it loads a big enough model and spits out tokens just fast enough that you can read what it's doing and comprehend it instead of magic. That's the most useful signal. Pre OpenAI mafia RAM pricing, that comes out to $250/month.
• Ozzie-D 4mo ago
  [flagged]
• 5701652400 4mo ago
  eventually tokens will cost price of energy. and china is miles ahead. china will be major token exporter soon. mark my words.
  • dude250711 4mo ago
    Technically, tokens travel both ways.
    • 5701652400 4mo ago
      Technically, on both sides there is an intelligence producing them.
  • cmiles8 4mo ago
    Electricity actually is only a small part of the data center costs. There are challenges in getting enough electricity that create problems, but the cost of the electricity really isn’t an issue.
• dmaso191 4mo ago
  [flagged]
• cmiles8 4mo ago
  And $1500 a month is on the very high end of where most companies will land. When you run the numbers there isn’t a realistic path that connects the dots between likely market size and the claimed valuation of the AI companies. The math simply does not add up.
• szatkus 4mo ago
  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).
  • sothatsit 4mo ago
    You must not be using coding agents. You can sneeze and spend $1 on Opus in Claude Code.
• john01dav 4mo ago
  Why isn't self hosting (even just renting a GPU server, not necessarily on premise) at large companies or hosting via something like together AI to run the open weight models not more common? I've tried the open weight models and the premium models like Opus and Gemini Pro, and I find that the latter are a little better, but not nearly to the degree to justify the extreme price difference, since the differences largely don't matter for what I've tried them for, and I expect that many other users likely have similar use cases.
  • soleveloper 4mo ago
    If the premium models are just about 10% better - that could justify the price vs. self hosting a ~0.5-1T open weights model. Remember that utilization of these huge racks will not be 24h/7, and these are usually not GPU intensive shops that would train models on the spare compute. With prices of 100-200k USD and north with ~2 years lifetime, that would be hard to justify financially. Self hosting could easily amount to ~1000 USD a month amortized across many developers. In rush hours - there will be hard rate limits. Would that 1500-1000=500$ monthly USD justify the 10% decrease in "AI Productivity" ? I guess not. In most cases. For everyone that asks me around, I'd say that in short term, unless there's a really good reason to self host these coding assistant models, then the big 2/3 coding assistants providers are the better choice. No one got fired from licensing claude code.
  • datsci_est_2015 4mo ago
    There’s probably plenty of money to be made in LLMs as a service - but not enough time has passed for the commodification to occur. I’m with you in that when the dust settles I don’t think any of the frontier model providers will have a moat. Just like during the dotcom boom a catchy URL and a webpage that could accept payments wasn’t a moat, either.
  • malfist 4mo ago
    Where are you buying the GPUs to have enough compute to run a medium size buisness?
  • esikich 4mo ago
    Why do you think it would be more common? The pooling of GPUs to serve multiple users and connecting to docs/datalakes while respecting security controls, as a start, is non-trivial. You'd end up paying a team to manage that.
• siliconc0w 4mo ago
  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.
• geodel 4mo ago
  > 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.
  • iLoveOncall 4mo ago
    That's just Simon Willison since LLMs came out. It's glaringly obvious that he's a paid shill.
    • fontain 4mo ago
      oh come on, a paid shill? Simon is very fascinated by AI and at times he can be a little too optimistic but he is generally balanced and his perspective evolves over time which can be seen in his writing. Nerd who loves nerd things a little too much? Sure. Paid shill by Big LLM? Nah.
      • iLoveOncall 4mo ago
        Yes, a paid shill. You can find a clear point in time where he shifted from sceptic to 1000% fully onboard non-stop praise, with no reason.
        • HDThoreaun 4mo ago
          Maybe the reason is because he thought the tools became really powerful?
        • simonw 4mo ago
          I'd love to know when that point was myself!
• insane_dreamer 4mo ago
  I still have never hit a ceiling with my Claude Max $100 account, much less the Max $200 account. I'm not burning tokens needlessly, nor running it all day, but I do use CC almost daily. What are these devs doing that they are burning more than $1500 in tokens a month? Maybe it's just me, but I still find that I really have to "shepherd" the AI and work with it to get the results I want. And I read every line of code added and challenge the model's logic. So that limits my token burning. Maybe these people are just "vibe-coding" without really checking the results?
  • era-epoch 4mo ago
    I would not be surprised if they have engineers vibecoding 2-3 projects each simultaneously, nonstop, on largely un-moderated review-suggest-iterate-test feedback loops. All the code gets summarized and fed into their manager's agent contexts, probably duplicated several times across levels and departments, with some generated back-and-forth emails pinging around the org chart, eventually generating 2-3 long-winded reports that nobody will read chock full of generated visualizations that can all get consolidated into a generated slide deck that they'll show (maybe, at some point) to a handful of humans with more money than a human brain can conceptualize to demonstrate all of the innovation they're doing. I am increasingly convinced that many of these companies are dead trees whose only function is to burn money lest it fall into the hands of the peasantry.
  • HDBaseT 4mo ago
    You are paying account pricing. Uber is paying API pricing. You're $100/m plan is likely equivalent to thousands of dollars of API pricing. You are being subsidized by the companies using AI.
    • kshacker 4mo ago
      And this is why as the freeloader (includes me) volume goes up, they add more and more rules to constrain us.
    • insane_dreamer 4mo ago
      I wasn't aware the Max $100/user plan wasn't available to Enterprise; it used to be IIRC
• ipunchghosts 4mo ago
  Why aren't they using Claude code 20x for 200/month?
  • hazelnut 4mo ago
    if you have more than x seats, you have to use Enterprise pricing as far as I know which is pay as you go with a pool.
    • limagnolia 4mo ago
      At that point, why wouldn't they just give their employees a corporate debit card and say "go and buy the subscription you want."? I suppose the enterprise plans come with additional controls and features? But are they worth it?
• transitorykris 4mo ago
  Is anyone doing story point estimation in terms of tokens? If you have a token budget, does this change how you prioritize?
  • sanex 4mo ago
    I think there's too much variance between what model you're using and how much you turn your brain off. If I just paste a ticket number into 4.8xHigh its going to use a lot more tokens than if I read the ticket, tell Sonnet what it needs to do, make my commit, run unit tests myself, etc.
• colonelspace 4mo ago
  If a worker doesn't use their AI/LLM budget, can they get a raise?
  • asadm 4mo ago
    probably will get fired for lack of performance.
    • colonelspace 4mo ago
      Let's just say their performance (OKR, KPI, whatever "impact" metric you want) was indistinguishable from a peer that used the AI/LLM monthly allowance in full. Maybe a $10k raise would be nice?
      • HDThoreaun 4mo ago
        Theyd get a bad review for leaving performance on the table. When has finishing your work ever resulted in anything other than more work?
        • conartist6 4mo ago
          It's disturbingly anti-merotocratic. You're not allowed to prove that you're more useful without AI because they just assume that AI is a 10x multiplier on everyone.
  • cdavid 4mo ago
    no because it does not come from the same budget
    • colonelspace 4mo ago
      Money spent is money spent.
• cadamsdotcom 4mo ago
  Token costs rising because data center build costs must be paid down.. is not the whole picture. It is actually possible for token costs to fall despite the spending frenzy. Naively you’d expect to always keep paying more - but growth in token usage is what changes the equation. Amortizing debt over an exponentially growing amount of spend across a growing customer base (not per customer) lets the debt be paid off & costs covered even as each individual’s spend stays steady or even goes down - but it only works if there’s growth beyond some threshold that makes the whole thing hang together. No one on the outside knows how much growth that is, and everyone chases maximum growth. Jevons Paradox ends up being your friend as well as the friend of the inference providers as well as the friend of the inference financiers. If it’s a strong enough effect, it has potential to cancel out all the circular financing too, and let everyone ride out the bursting of the bubble.
• KnuthIsGod 4mo ago
  China will bring down the price per million tokens.
• suncemoje 4mo ago
  Lock-in / switching costs are increasingly concerning me. I am using Claude for a good year now and have been accumulating so much "knowledge" in there by now. If Claude became less favorable in terms of price/performance in the future, that would worry me. I've started to think about a distributed solution, where my storage is detached from the inference, but currently Claude is still the way to go for me. Wondering if anyone has similar concerns?
  • spicyusername 4mo ago
    Knowledge in there? Where is the knowledge stored? All of my knowledge typically gets stored in plans outside of the agent? And each agent window gets archived regularly, anyways.
  • sparrc 4mo ago
    My favorite solution to this is to use the Cline coding agent, which is open and allows you to easily switch between different providers and models.
  • dadoomer 4mo ago
    Isn't all the "knowledge" just text files? I've transitioned between services easily by simply copying the text files.
    • darkwizard42 4mo ago
      You can even just instruct the LLM to create a context file for you! They are surprisingly good at that as well.
      • iLoveOncall 4mo ago
        Studies show that LLM-generated context files have a negative impact on LLM performance: https://arxiv.org/abs/2602.11988 https://arxiv.org/abs/2602.11988
  • fg137 4mo ago
    What knowledge? Unless you work in some obscure domain, chances are that any general "knowledge" Claude has "learned" is already public data somewhere. If you don't believe me, launch Codex and immediately start working on the same project (s). You might discover that all the knowledge accumulated means almost nothing.
• walthamstow 4mo ago
  I think a lot of people are missing that this is $1500 _per tool_ which is still rather a lot of money.
  • spprashant 4mo ago
    Outside of coding what other tools expend that kind of tokens? People are not creating that many slide decks or videos are they?
    • deleted 4mo ago
      [deleted]
• nphardon 4mo ago
  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.
  • conartist6 4mo ago
    Poor people! Thinking takes calories
    • nphardon 4mo ago
      This is funny, i get it; but the idea that using LLM's precludes thinking is silly. We're doing some heavy lifting over here. There's a lot of noise around pie in the sky ai show n tell projects, but then there's quieter real work being done as well, with highly skilled engineers. 100x is a thing.
      • conartist6 4mo ago
        Sure 100x is a thing, but it's a thing without AI too. How AI brings about 100x is usually by generating 100x more output than was previously being generated, which is what some problems require. Humans go 100x by building communities, cultures, technologies, and relationships.
• agentbc9000 4mo ago
  [flagged]
• ewangzzz 4mo ago
  I'm curious how much of the usage comes from vibe coding vs using agents/harnesses in internal tooling
• thundergolfer 4mo ago
  > That means each employee's AI spending cap is ~11% of that median compensation package. Probably better to use the fully-loaded cost of the engineer, which is much higher than their compensation package. The fully-loaded cost is the total cost paid for the labor power of the engineer, and it includes big ticket items such as office space, food, equipment, insurance, payroll tax, fringe benefits, recruiting costs. If the median compensation package is $330k/year then the median fully loaded cost is probably around $450-500k.
  • munk-a 4mo ago
    My usual rule of thumb for the US is north of double the received compensation but something in that range sounds reasonable with such high compensation. It's actually really interesting and underappreciated how that fully-loaded cost varies from country to country. Canada (for most salary ranges) is about half again instead of double owing to the insurance portion coming out of income tax rather than being a hidden expense so Vancouver ends up being attractive for trading 160k USD for like 120k CAD in compensation and then also lowering overhead from 100k USD down to like 60k CAD. The savings can be extremely dramatic.
    • hansvm 4mo ago
      Why would double be a good rule of thumb for typical US SWEs? Most of the costs aren't proportional to salary, and the ones which are aren't anywhere approaching 50%, much less double.
      • jmalicki 4mo ago
        The costs to hire management and "support staff" like TPMs that scale with SWEs that help them meet goals is proportional to SWEs - often that is taken for the higher end fully loaded costs, depending on how you define it. Office space in downtown SF, Mountain View, or Palo Alto costs more than office space for back office workers in Nashville or Utah. Firms that hire SWEs often have fringe benefits like free food etc. and while they may apply to all workers, it tends to go along with hiring lots of SWEs. But yeah, double is insane. When I saw prices for COBRA from Facebook, it was $3300 a month, and that was god-tier insurance - the insurance benefits were so good they had a custom list of what was covered that was probably way better than anything available on the market (e.g. you want brand name drugs? no problem. You don't want to try both ambien and trazadone before taking a sleep medication doctors actually recommend? No problem - etc.) - but for my needs it was barely better than COBRA costing way less than half. $3300/mo, or even $1200/mo for an entry level ops worker is a lot of their salary, and probably where the double comes from. At SWE compensation most of it ceases to scale. The fully loaded costs including proportional management costs isn't relevant to the true marginal engineer, but estimates I've gotten from higher-ups definitely factor into engineering decisions about "should we spend engineering time to save money/make more money - how much will doing this thing cost the company" (opportunity costs are also relevant, but usually less grounded, since most projects don't have concrete benefits like "we will save $x/yr in infra costs")
• CSMastermind 4mo ago
  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.
  • seanlinehan 4mo ago
    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.
• nalekberov 4mo ago
  What is the point of allowing a developer to spend $18,000 a year on AI subscriptions? Can't they hire a decent developer who is capable of producing a quality solution faster? Clearly, these decisions are all made by high-level management team. I was recently talking to an HR person from a European company, and she goes: 'We are forcing our developers to use AI coding agents, but they are still kind of hesitant.' This person had never written a single line of code, nor did she know what software engineering is. For these people, using AI coding agents = faster delivery without breaking anything.
  • tmp10423288442 4mo ago
    It costs a lot more than $18,000 to hire a decent developer, pretty much anywhere in the world. Also using a model is better than another developer in some ways, because there aren't two independent minds trying to work with each other.
• era-epoch 4mo ago
  Reading the headline Oh that's actually really economical! I wonder if they're doing a lot on locally running models or managing a shared context or knowledge-base in some clever way, maybe just encouraging employees to be efficient and mindful. ... > each employee ... > per AI coding tool ... > I noted that my own token usage comes to about $1,000/month against each of Anthropic and OpenAI What on this godforsaken earth are all you rich idiots doing???
• marcosdumay 4mo ago
  Just to put this in context. If every company did this, all over the world, with that same limit, we are talking about something around $45B monthly in revenue for all AI companies to share.
  • patrickmcnamara 4mo ago
    45 billion / 1500 $ is 30 million workers. How did we arrive at 30 million?
    • tuvix 4mo ago
      I think maybe he meant specifically for software engineers?
  • FanaHOVA 4mo ago
    Are you saying there are only 30 million people employed in white collar jobs in the world?
    • marcosdumay 4mo ago
      About 30 million software developers. At least that's what a quick web search says.
      • piskov 4mo ago
        It is not only for devs https://openai.com/index/codex-for-every-role-tool-workflow/ https://openai.com/index/codex-for-every-role-tool-workflow/
        • marcosdumay 4mo ago
          So, are companies paying that amount for people at other roles to use it?
          • danny_codes 4mo ago
• blobbers 4mo ago
  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.
  • jatora 4mo ago
    I also randomly wrote some code in a bind yesterday, while I was on the toilet, and it felt so strange. That was the first I'd written in probably 6 months.
    • tcoff91 4mo ago
      You don't even make small tweaks by hand? There's so many things that are honestly faster to do by hand than wait for agents to do.
      • jatora 4mo ago
        Nope I'm a couple levels too far removed from the code at this point for that. Closest I get is during meta-management (modularizing, complexity reduction, etc) with agents
• noncoml 4mo ago
  They want to replace employees with AI, then replace paid AI with unpaid AI. Their wet dream was never automation. It was zero marginal cost labor. And that dream is starting to rot.
• andix 4mo ago
  It finally puts a number on productivity gain of engineers with AI. This is probably less than 10% of the cost of an average uber developer. So they don't assume much more productivity gain from AI than 10%. (Cost of an employee is much higher than their salary, it includes things like office space, supporting structures like HR/accounting, insurance, hardware/software, and much more)
  • al_borland 4mo ago
    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?
    • andix 4mo ago
      > But is it an accurate number No. There is no accurate number.
• LeicaLatte 4mo ago
  If china captures the market now, well deserved. Way cheaper compared to us providers.
• easygenes 4mo ago
  If I were paying API rates this year, I would have already burned through $20k in tokens. Looking forward to the costs of this level of capability coming down.
• linuxhansl 4mo ago
  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.)
  • fontain 4mo ago
    is this with a subscription or pure API billing?
  • root_axis 4mo ago
    > 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.
• dzonga 4mo ago
  > That means each employee's AI spending cap is ~11% of that median compensation package. when looking at costs - numbers make sense. however decisions as an org/company/solo founder - costs help you set prices, but to reach profitability you want to model around ROI. now the question is what's the ROI for a $36K/investment per engineer or $90M for the total org ? I bet the ROI is negative.
  • NichoPaolucci 4mo ago
    I'm in a similar boat - it's hard to measure, but let's say you pay an engineer 150K. Giving them a tool that costs 15K a year is effectively a 10% increase in that expense. If we were seeing 3X, 5X etc improvement from individual engineers, that 10% increase in expense would be a fantastic investment (even 3 engineers for the price of 1.1??!). I have a feeling they are just not seeing that much of an improvement.
• packspro 4mo ago
  [flagged]
  • AndrianV 4mo ago
    [dead]
• edg5000 4mo ago
  Why are people getting these high spending numbers? A 200 USD subscription for either Codex or Claude should give you plenty of usage. What am I missing? Are they just being dumb?
  • fontain 4mo ago
    The subscriptions are not available to enterprise users. Enterprise users must pay per-token. A $200 subscription gives you roughly the equivalent of $1500 in per-token billing.
    • edg5000 4mo ago
      What does enterprise mean in this context? Is it about privacy guarantees not offfered for the subscriptions? For sensitive data the only solution is local. But maybe companies do trust these agreements? I'm very confused.
• zftnb666 4mo ago
  [flagged]
• 827a 4mo ago
  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.
• zkmon 4mo ago
  The big question is, will the productivity gains be absorbed by the needs? Societies don't have a need for infinite amount of luxury and laziness offered by the productivity of the machines. At some point, you would shake off things, get up from the couch and start walking again, breathing afresh.
• sylwk 4mo ago
  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.
• Galanwe 4mo ago
  I think the logical follow up will be for Uber to lay off a bunch of people so that the remaining ones can token maxx. To the mooooon!
• thesumofall 4mo ago
  Plenty of comparisons here between salaries and token costs. All fair but very much assumes that salaries are rational. Why do we pay some engineers 10x as much for the same role just because they are in a different location? The WFH discussion surfaced some of that. If money is cheap, all sorts of funny things are happening. Is it worth to spend 1500 USD on AI? I don’t know. Is it worth paying engineers 300k USD instead of 30k? Honestly, I don’t know
  • palmotea 4mo ago
    > All fair but very much assumes that salaries are rational. Why do we pay some engineers 10x as much for the same role just because they are in a different location? Who's this "we" you're talking about? Are you a software engineer or a temporarily embarrassed billionaire? Do you think the rational thing is to pay the lowest regional salary worldwide?
    • inemesitaffia 4mo ago
      If your competitors do, you likely will
      • palmotea 4mo ago
        > If your competitors do, you likely will This kind of race-to-the-bottom logic needs to be rejected: by workers, business culture, and the government. Unfortunately business culture embraces races to the bottom (for everyone but owners and executives), and uses its lobbying might to push the government into tolerating or even supporting it. And there are a lot of deluded workers who (for some reason) seem to be feel smart when they parrot the ideas of people who want to screw them.
  • bjackman 4mo ago
    As well as rational vs irrational they are also just different types of spending. Hiring someone vs paying a vendor for a service: - different level of commitment - might tie your org to a physical location - different legal risks - shows investors a different picture (probably this would even influence a bank loan) - manager has to fight a different bureaucracy Not to mention that comparing the cost of a hire by looking at their salary is pretty dumb. ISTR hearing at Google that the overall estimated cost of employing a SWE is like 4X their compensation? Can't remember the exact figures though.
• meszmate 4mo ago
  It still probably produces better results than some junior engineers in a lot of cases. But yeah, for a company at Uber’s scale, I can see why they would want real engineering discipline around it.
• schnitzelstoat 4mo ago
  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.
  • Deathmax 4mo ago
    All new/renewing enterprise contracts with Claude Enterprise and ChatGPT Enterprise no longer offer usage-based subscriptions, but instead will charge API pricing for all tokens consumed, and as you've said, the subs are better deals than raw API pricing.
  • Marciplan 4mo ago
    BigCo's are not using the plans we are using, they can't.
• c7b 4mo ago
  1,5k. For two months of that spend you could buy a machine that can self-host decent models, plus a year's worth of electricity. It's not up there in terms of quality, but with a bit more effort it works pretty decently. I'm completely baffled that that's not way more common, is it really just the quality?
  • VBprogrammer 4mo ago
    I'd think for most companies the pace of change is too high at the moment. Give it a few years, a bit of a plateau in the improvements in frontier models and I can't see how many of these companies don't implode under the weight of competition on inference prices.
  • dmos62 4mo ago
    Decent vs best-money-can-buy. Further, a self-hosted LLM will be much slower.
    • VBprogrammer 4mo ago
      I think we're all past the "bet-money-can-buy" stage. The most expensive models are an order of magnitude more expensive than the middle ground ones, so you need to be selective about what you run where. And with a bit of careful routing - there isn't a lot stopping you sending the hard stuff to a cloud model and the average stuff to an on prem model.
      • dmos62 4mo ago
        Only people who do pay-per-use optimize this. Most heavy users have their use covered by an employer.
        • VBprogrammer 4mo ago
          I have my use covered by my employer but we also have budgets and limits.
  • reddec 4mo ago
• willyv3 4mo ago
  [dead]
• gck1 4mo ago
  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.
• gck1 4mo ago
  A lot of talk about cheaper models here. Just curios, is there any non-Anthropic model that can do UI well? GPT-5.5 is laughably bad, and I'm never restarting my Anthropic subscription after their 6-month sprint of gaslighting, even if opus was really good at UI.
• sameersri2004 4mo ago
  Its a lot when using Chinese models, less when using Opus 4.8
• deleted 4mo ago
  [deleted]
• eugeneonai 4mo ago
  [flagged]
• deviation 4mo ago
  $300/day at Apple, with an increase to $500 with manager approval.
• morpheos137 4mo ago
  the real interesting way to address the question of token effectiveness would be internal alpha vs beta testing and measuringing marginal revenue generated by similar teams using ai and at different usage levels. right now $1500 a month is not a meaningful signal of anything beyond current executive willingness to spend. in the long run executives will cut spending where it does not support income generation.
• shivyadavus 4mo ago
  [flagged]
• DarenWatson 4mo ago
  [flagged]
• maxothex 4mo ago
  [flagged]
• throw0606 4mo ago
  When blue-collars were loosing jobs they were told to learn to code and now engineers are vilifying AI for taking jobs
  • simbosambo 4mo ago
    [dead]
  • kixiQu 4mo ago
    Do you believe the same people were saying those things? (Were they really?) The idea that "different attitudes towards labor have been expressed by different people" doesn't feel too remarkable
• fourleafai 4mo ago
  [flagged]
• aitoukhrib 4mo ago
  [flagged]
• synergy20 4mo ago
  After a few months, now I could barely use up my $200 Claude Max unless I try really hard, I got it corporate API billing is more expensive, but, $1500 really should be enough for most work for me as a developer, am I missing something?
• nikolay 4mo ago
  Uber is a service company. Maybe there are tons of changes on the backend, but literally anything customer-facing barely ever changes. So, software development is more of a business optimization job than a product development one - you can't spend more than you're trying to optimize, so limits for them make sense. For a product development company facing fierce competition, setting limits limits your competitive advantage. As a person, I spend nearly half of that limit on non-essential stuff just for fun and learning, but $1,500 on non-subsidized enterprise plans is literally nothing!
• lanthissa 4mo ago
  yeah where i work has been at $150 a week with a pretty generous over ride if you ask. people self limit when there are caps. if you give people unlimited they wont even use sonnet easy things.
• themuskgpt2025 4mo ago
  [dead]
• lrvick 4mo ago
  This is madness. I bought my daily driver GPUs for $3k, and they run often 24/7 solving complex bugs and problems for me that would have previously taken months to fix. No rate limits, no censorship or refusal to work on security issues, and complete privacy. Also even when my internet is down they keep on working. Stop giving Anthropic money and figure out how to take the same money to buy some GPUs, and physically insert them into workstations. It is not that hard, I promise.
• throw1234567891 4mo ago
  Until SaaS providers run another price racket and there will be many mental exercises why the new price is also justified.
• benbct 4mo ago
  [dead]
• Avijit_Thawani 4mo ago
  Per provider is helpful distinction. Easier to enforce limit per provider (cursor/codex/claude) rather than a unified employee tracking. And it incentivizes power users to keep trying out all options rather than sticking to one out of habit.
• tmuhlestein 4mo ago
  [dead]