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.
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?
>WTF did Uber build with all of that spend? How did it meaningfully impact their revenue in a positive direction?
Uber (and quite a few bay area companies and startups) can afford to spend that money. There is no expectation of profit, Uber lost ~62B and growing: https://uberlosses.com/
> How did it meaningfully impact their revenue in a positive direction?
It probably allowed them to avoid hiring as many people to build a certain amount of software. Even if it didn't increase revenue, it could have lowered human labor costs.
> 128 GB machines that can run local LLMs are a bargain even if priced $5-8k.
Don't forget the energy costs. Searching around, advanced models use an average of 25 Wh/1000Tok.
$1500/month gets you about 150M tokens.
At the aforementioned energy/token, that's 3750kWh.
What are your local office electricity rates/tariffs? (Hint: they are going up because of AI data centers). Even if my price and energy assumptions are wrong above, you probably aren't going to get the rates that the hyperscalers do.
Even at cheap (i.e Texas) retail electricity rates, that many tokens will probably cost you hundreds per month. In most other electricity markets, probably far more.
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
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.
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.
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.
I'm legit annoyed at opus 4.8 at any setting above 4.8.
I believe it can be great for vibe coding, but mundane day work? Hell no, I'd rather work with Haiku. It's too slow, checks too many things, it's annoying as hell.
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?
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.
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.
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.
> 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.
> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China?
Raise, they are going to raise the prices. We will spend more on AI infrastructure in 2026 and 2027 than the gross sales of the entire global software and services sector. Current pricing is at a major loss for current providers.
There's no way that all AI inference providers are colluding and/or all running at a massive loss, meaning the cheap Chinese model prices must be the real cost it takes to run frontier-class models PLUS their margin.
Look at Deepseek 4 Pro. https://openrouter.ai/deepseek/deepseek-v4-pro/providers
Deepseek and Baidu are subsidising prices but they probably train on inputs.
I have no model training and ZDR in OpenRouter enabled, and the first provider that shows up there is Deepinfra, significantly more expensive than Deepseek.
BUT much cheaper than Sonnet 4.6 and ChatGPT GPT-5.4.
Why would I even pay for deepseek? I get deepseek v4 flash for free with opencode. If I somehow run out of tokens for the day, I can just then on my vpn
I really don’t get it - why not put a Mac Studio with 128gb of ram on every engineers desk and be like “engineer, engineer your local LLM”. Makes no sense to be spending $20-30,000+ per year on cloud providers when Qwen et al are available. And even less sense to be sending all your company code and data to Anthropic and OpenAI when you can keep all that IP in the building.
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 $200 plan, but I'm sharing it with some of its other developers.
Why there are so many people that still believe that AI coding is a fad? It's something that started less than two years ago and companies are already paying thousands per seat. I know one that gives you 5k per month. Which other tool went from nothing to this level of acceptance so quickly?
As a side note, I wonder when we'll hear the first reports about employees reselling (parts of) their token budget.
Probably not worth it risking your job for a 200$/month good, but at 5K, I'm sure some folks will be tempted. Especially if companies do stupid things like token usage leaderboards.
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.
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.
123 comments
[ 3.0 ms ] story [ 84.4 ms ] threadUber’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
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?
Uber (and quite a few bay area companies and startups) can afford to spend that money. There is no expectation of profit, Uber lost ~62B and growing: https://uberlosses.com/
It probably allowed them to avoid hiring as many people to build a certain amount of software. Even if it didn't increase revenue, it could have lowered human labor costs.
> 128 GB machines that can run local LLMs are a bargain even if priced $5-8k.
Don't forget the energy costs. Searching around, advanced models use an average of 25 Wh/1000Tok.
$1500/month gets you about 150M tokens.
At the aforementioned energy/token, that's 3750kWh.
What are your local office electricity rates/tariffs? (Hint: they are going up because of AI data centers). Even if my price and energy assumptions are wrong above, you probably aren't going to get the rates that the hyperscalers do.
Even at cheap (i.e Texas) retail electricity rates, that many tokens will probably cost you hundreds per month. In most other electricity markets, probably far more.
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.
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.
I believe it can be great for vibe coding, but mundane day work? Hell no, I'd rather work with Haiku. It's too slow, checks too many things, it's annoying as hell.
You want to master your craft, develop "optimal" systems, understand where things are going by utilizing SOTA.
You can call it FOMO, but you get the point.
Chinese models prob work too, but idk since i cant use them at work
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?
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.
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.
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.
Raise, they are going to raise the prices. We will spend more on AI infrastructure in 2026 and 2027 than the gross sales of the entire global software and services sector. Current pricing is at a major loss for current providers.
https://martinalderson.com/posts/no-it-doesnt-cost-anthropic...
There's no way that all AI inference providers are colluding and/or all running at a massive loss, meaning the cheap Chinese model prices must be the real cost it takes to run frontier-class models PLUS their margin.
Look at Deepseek 4 Pro. https://openrouter.ai/deepseek/deepseek-v4-pro/providers Deepseek and Baidu are subsidising prices but they probably train on inputs. I have no model training and ZDR in OpenRouter enabled, and the first provider that shows up there is Deepinfra, significantly more expensive than Deepseek. BUT much cheaper than Sonnet 4.6 and ChatGPT GPT-5.4.
Are they even making money off them now ?
Let me ask you this: is any technology worth so much break-neck adoption without first seeing clear evidence of ROI? No. The adoption is irrational.
NFTs? My company had nothing to do with blockchain but I ended up working on NFT integration regardless.
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.
That's the most useful signal. Pre OpenAI mafia RAM pricing, that comes out to $250/month.
china will be major token exporter soon. mark my words.