alt.hn

6/3/2026 at 12:25:14 PM

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

 https://simonwillison.net/2026/Jun/3/uber-caps-usage/

by pdyc

6/3/2026 at 6:38:36 PM

> 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.

by ValentineC

6/3/2026 at 6:54:32 PM

id be amazed any american business will aend data to china

by cyanydeez

6/3/2026 at 6:48:34 PM

> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China?

I genuinely do not know how prices can get lower from the current major providers in NA without the whole market collapsing. Everyone is spending copious amounts of money to presumably make more money back.

by SecretDreams

6/3/2026 at 6:43:27 PM

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.

by testdelacc1

6/3/2026 at 6:50:07 PM

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...

by sevenzero

6/3/2026 at 6:58:28 PM

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.

by apsurd

6/3/2026 at 6:54:49 PM

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.

by hrpnk

6/3/2026 at 6:50:59 PM

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.

by pmontra

6/3/2026 at 6:53:30 PM

$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.

by hrpnk

6/3/2026 at 6:54:49 PM

Uber is likely on an enterprise plan - these charge tokens at API cost, which can be much more expensive than the $20 flat rate.

by idiliv

6/3/2026 at 1:32:30 PM

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.

by f311a

6/3/2026 at 6:45:53 PM

> 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.

by warmwaffles

6/3/2026 at 6:55:58 PM

He was proposing a solution. To use flash models and use them in a way that best amplifies your work.

by eikenberry

6/3/2026 at 6:07:39 PM

It's also a useful signal for AI value. Looks like it's a max value add of $18,000 per engineer per year.

by newobj

6/3/2026 at 6:54:37 PM

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".

by Anon1096

6/3/2026 at 6:28:16 PM

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.

by csallen

6/3/2026 at 6:30:20 PM

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.

by pqtyw

6/3/2026 at 6:37:49 PM

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

by alasano

6/3/2026 at 6:52:18 PM

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.

by tfehring

6/3/2026 at 6:26:11 PM

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.

by eqvinox

6/3/2026 at 6:18:16 PM

[dead]

by jdkdksksn

6/3/2026 at 6:29:37 PM

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

by galaxyLogic

6/3/2026 at 6:39:27 PM

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

by sva_

6/3/2026 at 6:45:44 PM

Some people also take weekends off.

by estomagordo

6/3/2026 at 1:18:38 PM

$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?

by CharlieDigital

6/3/2026 at 6:32:41 PM

How is tok/s not a bottleneck I? I assume most people still use ai agents interactively rather than leaving them to do their own thing during the night.

I find anything below 50 tps or so entirely unusable...

Regardless its Apples to oranges anyway, inference is quite cheap for open weight models its just that Claude and OpenAI can charge very high margins compared to e.g. DeepSeek or various provider on OpenRouter since open models are a commodity.

by pqtyw

6/3/2026 at 6:42:36 PM

I startup 4 or so projects then go do other things for 4 hours. I don’t have enough energy to steer overnight, but I’m at least “semi afk” for daytime steering. So throughput is king for me, tokens per hour. Not latency or actual tokens per second.

by brianwawok

6/3/2026 at 3:17:03 PM

I agree on the basic point, but running $1500/mo's worth of SOTA local AI is non-trivial already, and that's a figure for a single seat. That's equivalent to generating at least 20 tok/s on a 24/7 basis, in fact probably quite a bit more than that (because open-weight models are vastly cheaper than proprietary ones even when served from reputable Western providers - reaching the same spend would take around 100 tok/s or more, which is well within datacenter hardware territory).

You could probably reach the former figure on a prosumer platform but only for very special workloads. If you spend a lot of time on prefill (which is common for agentic workloads) the outlook is even worse since that's a significant constraint for any on-prem AI.

by zozbot234

6/3/2026 at 6:30:53 PM

I think companies will eventually just buy a local AI server.

Using local hardware is expensive when it's running a complicated software stack that can break in 10,000 different ways.

These eventual local AI servers will just talk some protocol for AI and sit in the corner and nobody will think about them.

I guess they still might need access to various systems, so idk. Eventually I think someone will offer "AI in a box" though, running the latest open model or whatever.

by Buttons840

6/3/2026 at 1:24:01 PM

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

by dkdcdev

6/3/2026 at 1:26:35 PM

> 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.

by embedding-shape

6/3/2026 at 1:29:44 PM

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.

by throwway120385

6/3/2026 at 1:36:32 PM

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

by idiotsecant

6/3/2026 at 1:39:20 PM

they also already do this…

Anthropic and OpenAI license to the public clouds. Google reportedly licenses to Apple. licensing to Fortune 100 companies running on their own infra is an obvious next step

it is a race to the bottom and I’m not sure the labs win that race. we’ll see!

by dkdcdev

6/3/2026 at 4:47:19 PM

The problem isn't really Uber, Microsoft or Nvidia, it's all the smaller none IT companies that also have developers on staff. They are screwed. $1500 per seat per month is just way to expensive, but they also can't afford to build and maintain their own on-premise solution. If Microsoft can't afford to run CoPilot for their own developer, what chance does any of their customers stand?

If the large, well founded IT companies in the world believes the current AI cost is to high, then Anthropic, OpenAI and CoPilot have no actual customer base. AI is then relegated to very profitable niche business, but that can't fund the R&D for the models.

by mrweasel

6/3/2026 at 6:18:11 PM

There's models for every price point. What was SOTA and stupid expensive to run a year ago is a cheap flash model today.

by treis

6/3/2026 at 6:14:04 PM

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.

by skybrian

6/3/2026 at 6:38:43 PM

$18k a year is a non starter in most companies. Ive seen companies balk at Intellij.

by ecshafer

6/3/2026 at 6:29:24 PM

Why are smaller non-IT companies "screwed" because they can't pay out the nose for their developers' AI usage? They're non-IT companies, developers are presumably not on their critical path, or not their bottleneck. Developers can keep on writing code the old way, or doing it with a more reasonable AI spend. I don't see how this "screws" any company.

by mvdtnz

6/3/2026 at 6:55:12 PM

That was badly worded on my part, my intend was to indicate that there was no way they can or will pay $1500 per month per seat.

by mrweasel

6/3/2026 at 1:28:58 PM

Right - the future of LLMs is like ol' windows XP+Dell. Commercialized "things" you run locally offline, co-designed with hardware, with a known productivity suite, and large businesses building the next generation thing and suite with 18mo release cycles (ish).

by jvanderbot

6/3/2026 at 6:12:13 PM

I don't see it. Leasing equipment and paying per seat license fees makes a lot of accounting and cash flow sense. Maybe when it gets to the point where you can run SOTA LLMs on consumer hardware. But that seems a solid decade and probably much more away.

Even then it makes more sense to rent the bigger GPU and get your answer faster.

by treis

6/3/2026 at 1:35:34 PM

XP? I can see the argument for enterprise support but in that case the latest windows OS is going to be virtually free and I dont know if MS and Dell etc. would even support an XP machine. Might even be required for hardware. If no enterprise support wouldnt Linux make a lot more sense?

I get that if it's offline the security downside of XP doesnt matter, and I assume XP is free, but being free doesnt really seem that valuable compared to alternatives (free linux and virtually free OS if buying wholesale).

by nonethewiser

6/3/2026 at 1:44:13 PM

"Windows XP+Dell" should have been in quotes. It's similar to the way enterprise productivity software was developed, packaged co-designed with hardware, and sold on an 18mo upgrade cycle assumption. It's not literally windows xp.

by jvanderbot

6/3/2026 at 5:14:06 PM

Oh gotcha. Yeah that's an interesting idea.

by nonethewiser

6/3/2026 at 1:28:10 PM

> it's WTF did Uber build with all of that spend?

You can ask the same for the median 330k salary in the US for Uber Engineering... and being a bit snarky, attending Uber engineers talks here and there at a few conferences, looks like. they love to (re)invent internal tooling/platforms. That's pretty expensive on its own.

EDIT: I'm not saying that Uber's engineers didn't add value to the company, they absolutely did and handling the scale up they had to handle is not an easy feat. But I do challenge the notion of "what features did they create with that (LLM) spending?" of GP.

by darkwater

6/3/2026 at 1:55:45 PM

> You can ask the same for the median 330k salary in the US for Uber Engineering

People DO.

It's well known that most tech companies are ran incompetently. As you say, it's not the engineers' fault.

But most projects and hiring in these companies exists to juice promotion criteria. And that, depending on perspective, these companies are either massively overstaffed or massively underproductive.

The comparison to AI spending being wasteful holds up pretty well, these are companies that readily piss away billions in pointless spending.

by SlinkyOnStairs

6/3/2026 at 6:23:51 PM

This is a very good answer but there's a flip side too.

The idea of "if you add intelligence you make more money" is contradicted by the fact companies don't just always hire more people. Wy doesn't google just hire everyone?

by FergusArgyll

6/3/2026 at 1:36:14 PM

This is what all "platform engineers" have to do once things are working nicely: you have to keep inventing work.

I don't know; I'm a Ron Popeil "set it and forget it" kind of guy. Make the dumbest, simplest thing that's going to work with some clear path for scaling. Then go do valuable things instead.

by CharlieDigital

6/3/2026 at 1:44:13 PM

But most Platform Engineering teams in smaller companies (and especially non-US) add a layer on top of existing technologies. A layer that usually maps to the specific culture and idiosyncrasies of that company; a bit like the deployment flow which is usually very specifically shaped on how a company is.

But in Uber's case, they tend to reinvent lower level pieces of platform/infra.

by darkwater

6/3/2026 at 1:37:41 PM

you don't get promotion for supporting existing things, but for "inventing" you can get promoted. also for large migrations

by throwaw12

6/3/2026 at 1:32:57 PM

Your last question is really important. What did they accomplish with all that spend?

I suspect there’s some mass delusion with respect to actual accomplishments as a result of LLM use. Sure, things are moving faster, but does it matter?

by ungreased0675

6/3/2026 at 1:47:07 PM

I am wondering more and more if this becomes true as these smaller models take off. I might be old fashioned but I have yet to crack the workflows some of the hype people spout like Claude codes Boris where he and others talk about running hundreds of agents overnight.

I have still found the sweet spot for me is using LLMs but I am still in the drivers seat.

by infecto

6/3/2026 at 5:47:14 PM

That's because for some of these folks, the cost of the tokens doesn't have to match the value of the output; the hype from the story is all they need.

Normal people have to produce something of value from that spend. So starting 100 agents and then waking up to something cool but useless just means you spent a few thousand dollars and created nothing of value............

by CharlieDigital

6/3/2026 at 3:04:20 PM

Running hundreds of agents overnight is almost certainly 99 percent waste.

by ofjcihen

6/3/2026 at 1:33:29 PM

If you believe a 128gb machine that is essentially DGX Spark in a laptop chassis can run models comparable to SOTA you either never ran open models on hard tasks, or you aren't scratching the surface of SOTA closed LLM capability in how you're using them.

by devttyeu

6/3/2026 at 1:45:45 PM

Can you show me an example of a hard task that can't be achieved using light models? When we don't want the model to work on autopilot without reviewing the code at all. Even SOTA models will produce garbage code, if you don't guide them all the time.

Hard tasks require a lot of guidance and code reviewing, unless you are creating another throw away project where correctness, maintainability and code understanding does not matter.

by f311a

6/3/2026 at 1:28:13 PM

$1.5kpm for SOTA. 128gb you run DSV4 Flash.

by sourcecodeplz

6/3/2026 at 6:38:49 PM

What's the point of running it locally though? Inference for open models is quite cheap already. They could just selfhost, anyway. The experience of running LLMs locally will be excruciatingly bad in comparison at least for the near future.

by pqtyw

6/3/2026 at 1:25:09 PM

> WTF did Uber build with all of that spend?

WTF did anyone build with all that spend? Despite all the feel-good anecdotes about how productive folks feel using ai coding tools there's a deafening silence when it comes to actual, demonstrated efficacy. How can we be this far entrenched in these workflows and still not know whether they actually do anything useful?

by jcgrillo

6/3/2026 at 1:28:42 PM

I can say at least for me at a small-ish company (~40 FTE) there has been a surge in internal productivity tools. Nothing to improve the end user product directly but a lot of tools to make processes easier and less error prone.

What would previously be janky internal dashboards or excel sheets are now actually nice to use tools. That said of course the maintenance cost of all that has yet to be discovered, and the ROI is questionable.

by awesan

6/3/2026 at 1:33:02 PM

About the same ~40 FTE team. We're doing the same thing. Smattering of internal tools, but no net gain in external revenue. Who knows which of those tools will have any value or ppl are just doing it because it's cool now to make fancy dashboards.

OK. I guess that's good, too.

by CharlieDigital

6/3/2026 at 1:37:32 PM

Yeah this seems to be a pretty widespread story, from what I've heard as well. The thing about those janky dashboards and spreadsheets though is that somebody understood them and built them with intent to solve a particular problem. Despite the rickety appearance, they're trustworthy tools. A polished single page app might look nicer but it's harder to debug than an excel sheet, and much less transparent in its internal workings--especially if nobody actually wrote it...

by jcgrillo

6/3/2026 at 6:15:51 PM

More importantly, it's questionable how much extra revenue improving a design of internal tool brings.

by izacus

6/3/2026 at 6:27:09 PM

~70 FTE Engineering team. We are shipping more features, especially features that previously would not have survived the cut to make it on the roadmap. Even though we are shipping more, our total amount of escaped bugs has not increased, so our escape rate has actually lowered. On top of that we are able to triage and fix escaped bugs more quickly now. And then of course there has been an uptick in internal tooling that makes the rest of the company more efficient, and we have been able to address tech debt at a higher rate than before.

I don't think this would have been possible without having solid engineering culture and processes in place before bringing in ai coding tools.

And I don't want to sugarcoat it, this hasn't been easy, requires continued discipline, and took well over a year to get good at. And we still have to continuously learn, experiment and adapt our training, tooling, and processes.

by ftkftk

6/3/2026 at 1:37:49 PM

The real answer?

Software engineer quality of life.

There can be an increase in productivity without a corresponding increase in total output. The gains could be captured by software engineers doing a days work in an hour then fucking off in a variety of ways.

by nonethewiser

6/3/2026 at 6:41:03 PM

> doing a days work in an hour then fucking off in a variety of ways

Until companies start hiring 5x less engineers than they did before and well.. we are clearly moving towards that direction

by pqtyw

6/3/2026 at 6:54:00 PM

Quite possibly. Doubftul it will happen all at once. If you can get 8 hours of work done in 1 they'd need to ramp up demand 8x. Would be interesting to see that happen over night. Happy monday. Here, take these 30 tickets.

by nonethewiser

6/3/2026 at 6:12:10 PM

Yeah I think this is probably most accurate.

by slopinthebag

6/3/2026 at 1:33:52 PM

Imo its pretty clear that anyone who is taking the issue at least somewhat seriously knows the amount of value they provide is not non-zero. However, the problems are manifold: firstly, toolchains vary wildly, from fancy autocomplete, to engineers chatting with codebases they're unfamiliar with, to people integrating them into devops and infra, to people doing spec driven development, with a thousand philosophies inbetween. Many people suspect that those above them in the ladder are on the cusp of massive failure due to losing track of the code, and many people higher on the ladder think those below them are overly cautious. I hate to be the guy saying "oh it must be somewhere in the middle", but I will say at the very least I like being able to use it to read docs for me, and to synthesize syntax and simple scripts (give me a join that works across these tables and gives me column x, y and z - give me a python script that parses a file like this example and extracts abc data - given this api spec figure out how I can get this data from this endpoint, go)

as for building actually complex software, the art of that is not in simply chaining together such scripts. Its the art of using architecture and testing to shape uncertainty, and developing requirements (and extrapolating sensibly from incomplete requirements). I don't think llms are great at this, but they arent terrible either. A lot of the more active users in the space are doing stuff where theyve realised they need more detailed specs, which like, yeah, we knew this already - better defined problems lead to better software.

by RugnirViking

6/3/2026 at 2:06:08 PM

I agree the most interesting use cases I've heard of are about increasing the rigor of software development practices, but there's definitely a lack of coherence in methodology.. I believe that some users and companies are successful in this effort, but the odd (and interesting!) thing is that so far we don't seem to know how to communicate how to do it successfully.

by jcgrillo

6/3/2026 at 1:34:05 PM

You can't get an edge using local models, these guys may have competitors that will spend on SOTA models. They won't likely ever consider local machines even for some offloading scenarios, the complexity and costs will be even higher.

by m3kw9

6/3/2026 at 1:39:50 PM

Consider rewiring your perspective: getting an edge doesn't really matter; the only thing that matters is will customers pay for this? Is this a useful, valuable problem to solve?

Coding faster doesn't really solve that.

Uber makes more money if people buy more rides, order more food, have some breakthrough in autonomous driving. They can save money if they can optimize some ops or spend somewhere. Is there any evidence that with the spend on AI that they achieved any of this? If they did, I'm sure we'd hear about it in some engineering blog.

by CharlieDigital

6/3/2026 at 2:17:10 PM

18k/yr? None of the LLMs generate anything like that in value!

by analognoise

6/3/2026 at 2:18:17 PM

I'm definitely getting that much value out of Claude Code and Copilot.

by simonw

6/3/2026 at 2:25:55 PM

You're a content creator; you define your revenue stream.

Uber engineers do not define their revenue stream; the product leadership team does.

$1500/mo of AI spend by engineers does not equate to revenue. They need to figure out revenue first before zeroing in on AI spend.

by CharlieDigital

6/3/2026 at 3:07:51 PM

Can you share some examples that you would say justify that price? Not a gotcha, I’m genuinely curious where you’re seeing a return at that level.

by ofjcihen

6/3/2026 at 4:04:45 PM

I've written tens of thousands of lines of tested, working code that I would not have written otherwise, and that code is useful to me.

I effectively get to operate at the rate of a small team of engineers - I know that because I've managed small teams of engineers in the past.

by simonw

6/3/2026 at 5:03:19 PM

> that I would not have written otherwise

I think this is the part I struggle with. The code I write makes me money or is a way of teaching me something, both of which are reasons that I would write the code regardless.

I don’t think I have any projects in mind that I’d be willing to spend half of a car on that I also wouldn’t have written myself.

Obviously just a personal take though. I’m glad you get the usage you want out of it.

by ofjcihen

6/3/2026 at 5:25:21 PM

My "job" is building open source software for data journalism (and anyone else who needs the tools data journalists need, which is pretty much everyone else). I can build more of those tools, and better, in exchange for a fraction of the cost it would take to hire a team to help.

by simonw

6/3/2026 at 1:20:39 PM

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.

by jkwang

6/3/2026 at 6:27:48 PM

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.

by etothet

6/3/2026 at 6:42:56 PM

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

by waffuldrop

6/3/2026 at 6:29:47 PM

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.

by rasbmn

6/3/2026 at 6:39:21 PM

Is this inside knowledge, or speculation?

by lazyasciiart

6/3/2026 at 1:16:04 PM

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.

by PessimalDecimal

6/3/2026 at 1:19:48 PM

There is no evidence that per-token inference prices (which is what Uber is setting a cap on) is subsidized.

by square_usual

6/3/2026 at 1:25:01 PM

AI companies have more expenses than inference.

by pier25

6/3/2026 at 1:55:06 PM

yes, and theres no evidence that they arent (or can't) use profitable inference to subsidise those other expenses. Some companies will keep spending massively to train better models, and some other companies will not, and offer good api prices. Which will end up being used? That depends on whether the spending turns into better value models

by RugnirViking

6/3/2026 at 6:47:04 PM

> theres no evidence that they arent (or can't) use profitable inference to subsidise those other expenses

as far as we know there's no evidence that they can produce any profits at all

by pier25

6/3/2026 at 1:23:38 PM

Is there any evidence that it's not?

by lelanthran

6/3/2026 at 6:46:14 PM

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...

by pqtyw

6/3/2026 at 1:35:17 PM

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.

by Topfi

6/3/2026 at 1:27:03 PM

Yes; they ban various uses of their subscriptions but say you can do whatever if you’re paying for the API without limits

by thejazzman

6/3/2026 at 6:47:13 PM

That's just market segmentation and them trying to maximize revenue it doesen't really say anything about their costs.

by pqtyw

6/3/2026 at 1:39:23 PM

This story isn't about those subscriptions - enterprise customers like Uber are paying the full API prices.

by simonw

6/3/2026 at 1:31:01 PM

That's not evidence. Very likely though, but the only evidence we get one way or another is when they IPO.

by lelanthran

6/3/2026 at 6:42:33 PM

The inference prices for very large open models would indicate that Antrophic's and OpenAI's margins are quite large.

by pqtyw

6/3/2026 at 1:18:56 PM

afaik, enterprise plans are not subsidized. its 20$/seat+api pricing. Unless you are saying api pricing itself is subsidized.

by pdyc

6/3/2026 at 1:21:01 PM

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.

by LurkandComment

6/3/2026 at 6:49:09 PM

Yeah, that's not going to work if you can get e.g. 80% of value by using 10-20x or more cheaper open models. At some point it would just make sense for large companies to rent compute and deploy their version of DeepSeek or whatever (if they don't trust Chinese providers)

by pqtyw

6/3/2026 at 1:24:45 PM

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)

by swiftcoder

6/3/2026 at 2:41:42 PM

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.

by LurkandComment

6/3/2026 at 6:50:00 PM

It's mostly R&D though, not inference. If LLM's effectively become a commodity then they are screwed anyway.

by pqtyw

6/3/2026 at 1:23:51 PM

None of what you said is true

by logancbrown

6/3/2026 at 1:28:46 PM

And you know this how?

by rimliu

6/3/2026 at 1:18:38 PM

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).

by boringg

6/3/2026 at 1:29:44 PM

I understand current Codex $20 sub is worth about $480 GPT5 api credits.

by sourcecodeplz

6/3/2026 at 1:39:39 PM

It's not. They recently forced enterprise customers onto API billing instead of the cheap consumer pricing. Now the pricing is brutal.

by MagicMoonlight

6/3/2026 at 1:28:49 PM

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

by epsteingpt

6/3/2026 at 6:48:55 PM

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

by wmf

6/3/2026 at 1:19:26 PM

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

by LurkandComment

6/3/2026 at 1:33:13 PM

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?

by jwpapi

6/3/2026 at 1:22:57 PM

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.

by ilia-a

6/3/2026 at 1:24:36 PM

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.

by iceman28

6/3/2026 at 1:22:07 PM

They are also beholden to enterprise pricing and can't use the subsidized consumer max plans.

by cloudking

6/3/2026 at 2:24:27 PM

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.

by sremani

6/3/2026 at 1:20:51 PM

A lot of things can be done with local models.

by jedisct1

6/3/2026 at 1:29:24 PM

Even more things can be done without any models just as well.

by rimliu

6/3/2026 at 1:38:50 PM

Single developers seeking local models.

by dude250711

6/3/2026 at 1:32:07 PM

[flagged]

by ashahin

6/3/2026 at 1:34:51 PM

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.

by onlyrealcuzzo

6/3/2026 at 6:10:48 PM

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.

by jbvlkt

6/3/2026 at 6:13:05 PM

LLM generated comments are against site rules btw.

by slopinthebag

6/3/2026 at 1:37:32 PM

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.

by hanzeweiasa