Uber's $1,500/month AI limit is a useful signal for AI tool pricing
https://simonwillison.net/2026/Jun/3/uber-caps-usage/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.
I think the frontier labs will need to drop their high per-token prices at least for their low and mid-level models for the reason that several Chinese models (at least Qwen, DeepSeek, Kimi and GLM) are "close enough" that with the right harness they are cost effective alternatives.
They won't necessarily need to close the gap - at least not yet -, because these models won't necessarily compete at the same token counts. E.g. at least some of them need to do far more work to solve the same problems.
But, yeah, the prices will come down one way or the other.
At the same time, even the subscriptions for the cheap Chinese models are probably subsidised, and those subscriptions are likely to get less generous over time.
So you have on one end the token revenue trending down, on the other end the training cost going up for the next frontier models, and you need to pay back your 10y debt.
Not necessarily, the bond holders could simply take a massive hair cut and lose shitloads of money. On the topic of bubbles and exuberance, Jeff Bezos made the salient point that there was a massive over-invested biotech boom in the 1990s and tons of sophisticated investors ended up losing lots of money. But humanity still kept the medical advancements made by the boom. Stocks going down didn't un-research drugs, and it won't un-research new GPUs or un-build datacenters.
> „[AI vendors are] paying for a fixed cost with a depreciating commodity“
That's just a confusing way to say you don't think future models will be worth the development costs. Because if future models are significantly better, why would the price of tokens to access those models deprecate?
That would be half a trillion[1] redirected to regular people just from Google Ads.
[1] snatched my number from here: https://pixis.ai/blog/2025-google-advertising-benchmarks-for...
- most tasks do not require the latest frontier models, even if they are a magnitude more intelligent (we don’t actually know if that will be the case). Current Gemini flash is cheap, fast, and pretty capable with good guidance for most tasks
- now that companies pay API costs instead of a subscription they will be setting restrictions on token use to not have their budget explode (like Uber in this submission), that’s a strong incentive to NOT use expensive models, and limit their thinking budget
- there is competitive pressure from China and others who can offer very decent performances at a fraction of the token price
- the price of tokens for the frontier models is likely to go up, but the price to access older models is what depreciates! The overall price per token is going down now that we are in a new world where companies understand that token maxing is one of the stupidest concept ever created by humankind.
You can't consider it in vacuum. AI takes limited resources. So far it winded up cost on near every consumer electronics that runs an OS, and it winded up cost of energy that is used by the entire industry and every single customer
It's not just the cost of datacenters, it's cost of infrastructure (that given current direction of US govt will just be paid from people's fucking taxes and bills..) and cost of other industries turning outright unprofitable "thanks" to demands of AI
This is why I'm building role-model, a routing protocol and a router runtime: https://role-model.dev/
I think its only accounting depreciation.
I have been using my laptop for a decade, what is stopping datacenters from using the purchased GPU chips for a decade?
Model routers allow this to happen automatically without any more work by the user.
> a shittier model
A ton of tasks don't require the most expensive frontier models, etc.
> I’m not sure why anyone does it
1. Faster solutions from the LLM - also reduces employee costs of having the employee waiting on the LLM
2. Avoiding things like the half-billion dollar per month bill for a single company’s LLM use recently reported in Axios
Saves like $2-3 per session. Same quality code.
> Compounding the problem, labs in China often release dual-use capable models as open-weight. Once a model is open-weight, safeguards that do exist can be removed, making the model available to any state or non-state actor to use for malicious purposes, including the cyber and CBRN misuse those safeguards were built to prevent.
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.
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.
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.
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.
One organization, that is a software company
> which seems to be roughly inline with "normal" consumption for most full-time engineers
My peers are using $20/mo plans, only a handful are using more than $100/mo in tokens. We haven’t had any limits imposed yet.
Uber is not representative of any trend beyond big tech and VC over funded startups.
But this overlooks the other critical part of getting the most out of these things: the harness. I run an autonomous plan/design/code/build/test pipeline with agents using my own orchestrator. Different models are better at different stages, and I use LLMs to judge the output between them. Not everything needs Opus 4.8.
The harness provides both the scaffolding to get the right things into the model, and the right things out. But it also lets you dictate which model does which work.
It's the pipeline, not the model, that gets you quality at a given token budget.
Small models are fine for small coding tasks but I don't see why big ones can't be broken down most of the time.
> 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.
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.
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.
For a traditional software engineer? I retired last year after 3 decades and my salary was about the same as it was in the early 2000's at the last company I was at. Maybe I should have negotiated more but I thought only FAANG paid traditional pre-AI engineers more than $250K.
If 250k was the total comp (taking into account bonus/stocks/what have you) then yeah, you definitely should have negotiated.
If one uses AI minimally and is able to out perform peers who are maxing out AI spend, one might want to use that in salary negotiations.
This is not a good bellwether for the AI industry, including its adherents. Their growth assumed a level of indispensability that’s not being reflected in hard numbers and real costs, which lends credence to the notion that these IPOs being fast-tracked are meant to try and cash out before the bubble really pops in earnest. There’s no way consuming enterprises are going to pay such insane costs for such minimal uplift in the long run, and the AI companies can’t keep offering subsidized tokens via subscription plans at their current pricing.
Right now the AI LLM PRs we're seeing are just introducing more work for other people, while these so-called builders are looking good with their new dashboards and functionality they're demoing.
But you can't talk to them about the flow of the code. You can't ask them for their thinking as to why certain things are.
It's not built up from the ground with experience from x people taken into account. It's materialized from nothing, with no foundational separation, and barely any abstractions.
No one wants to touch it. The PRs are too large, and the 'authors' of the PRs aren't on call with us.
They get all the glory, but do none of the work.
It's kinda like designing a house and then sending it to an architect and engineer saying: make this work.
You can absolutely do this. It's even right most of the time.
You even have a fair chance of getting a response like that when there isn't anything wrong and the question wasn't rhetorical - which perfectly illustrates the level of the genuine understanding LLMs operate at.
I have never seen anywhere in the world people that hates so much the working class as people do in the USA.
In my country the average employee is competent, they do their work and create wealth for the nation.
Again, only in the USA people think that billionaires are the ones creating value. Total non-sense indoctrination.
There are plenty of valid criticisms or warnings about over-reliance on AI coding, but this is not one of them. Today, I am using a semi-autonomous agentic coding system which has an `interview` functionality built in - when it spits out the PR from the input, if you have questions about the motivation or context for a particular choice, you can start up a clone of the original agent in a sandbox to question it.
Now, you might claim that those responses aren't always reliable, accurate, or consistent, and that claim has a little more weight (though, in my experience, decreasingly so) - but it is _certainly_ not the case that you cannot interview an agent about choices made. I'm literally doing it every day.
I've never worked at a company that didn't have a technical backlog measured in years.
Literally nothing works, all the timers/time counters are different across the pages, constantly commands hardware to do stupid shit, breaks during critical moments/in front of clients.
Eventually mgmt had to institute change freezes for high profile events because the team was breaking too much shit all the time.
The average C suite dipshit doesn't realize that the performance drops off a cliff once your project is more than some fraction of the context window so they will make pretty dashboards all day long but once you need to cover all the edge cases of a real system it all explodes.
AI isn't trained on the type of software style we'll need to create systems using AI, it's trained on how we used to write software. It doesn't reuse code or elegantly structure annoying, it just adds more code until the thing builds and passes some fake tests, even if half of it is functionally dead/unused.
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.
Think of people who were very strict with variable names. People who pushed for multiple-levels deep of abstractions for a single API logic that’s not going to be reused. People who believed that coding is craft, rather than just a process to get to the end during work hours. This makes most of these people’s points more-or-less moot.
I was in some of those camps, but I’ve seen coding evolve in the last 15 years. So I understand that these priors need to be updated, as most arguments don’t apply to today’s world.
everyone making comparisons to the dotcom bubble seems misguided. this is clearly computing 2.0 imo
NFTs? My company had nothing to do with blockchain but I ended up working on NFT integration regardless.
Because there's not a single piece of evidence that this has improved the quality of the delivered software, or for that matter even the speed of features any of these companies produce, in fact if anything the opposite.
The point of software development, the hint is in the name, is to develop software, not consume tokens. If Uber was now full of 10x engineers the stock price of Uber would be up, not down on a yearly basis. Hilariously enough the only company whose stock price is up appears to be Antrophic
i.e. I am able to write about 1k lines of code of "acceptable" quality per week. Which means in 1 year, there will be about 5Ok LoC. I am pretty sure, that I would have to spent like 60-80% of time to maintain 1st year code and the rest to make new features in the second year so I would have to hire more people and spent time to onboard them to maintain velocity. All of that are rough estimates, probably overoptimistic and way worse in 3rd year. Good luck doing such estimates with code agents. Even worse if you already have huge amounts of legacy code.
As for why they got accepted so quickly 1) the industry's long running desperation to deskill computer programming 2) the addictive psychology baked into LLMs "That's an elegant solution! Shall I ... ?"
So there's a huge number of HN posters claiming that the price of tokens will go UP over time rather than down (that's how Moore's Law works, right???) or that code bases that AI contributes to will spontaneously combust, or something.
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?
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.
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.
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.
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.
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.
Unless they are iteratively replacing expensive vendors and optimizing other headcount costs?
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).
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?
I have still found the sweet spot for me is using LLMs but I am still in the drivers seat.
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/
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?
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.
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.
Anthropic: https://support.claude.com/en/articles/12883420-view-usage-a...
OpenAI: https://help.openai.com/en/articles/10875114-workspace-analy...
And the obvious question: what it's the cost of that revenue? Because it looks huge but ...
I just wanted to take their number at face value. It's not like it needs more real information to make AI a bubble.
https://openai.com/index/codex-for-every-role-tool-workflow/
However, that's an absurd scenario.
1. Why it's not a bold assumption: it's a bit shocking now. But in two years or so, many/most companies will realize this is the cost of doing business. Just like people are ok with using Outlook, or Office 365, or (in the case of Wall Street) Bloomberg terminals, people will realize that developers will need AI coding assistants.
2. Why the conclusion does not follow from the assumption: if the limit is set at $1500/developer/month, it does not mean all developers will use it. Companies will set incentives for people to not be very wasteful. It is more likely that on average developers will consume $100-200 worth of tokens per month, and there will be some outliers who will consume 10, 100, or 1000 times as much, but they'll be few.
An entreprise license for 0365 is something like $75 per person per month. Totally different order of magnitude.
And regarding Bloomberg terminals, Bloomberg only has 1 million users (semi random guess).
The reality will be that some places just won't pay for any licenses or will try to set up their own, local LLMs.
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.
If you are interested you can try it out at markbase.cloud (disclaimer and all that). I am not charging for it.
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.
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.
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 $1500 I have to let claude run unsupervised over night (and with the amount of mistakes it still makes and guidance it needs, I do not believe we are there yet.)
That's still in the ballpark. A modest change in your usage habits or workload could easily get you there.
Remember that utilization of these huge racks will not be 24h/7, and these are usually not GPU intensive shops that would train models on the spare compute. With prices of 100-200k USD and north with ~2 years lifetime, that would be hard to justify financially.
Self hosting could easily amount to ~1000 USD a month amortized across many developers. In rush hours - there will be hard rate limits.
Would that 1500-1000=500$ 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.
It costs money to maintain the hardware and hire experts to manage the services. For something as common as LLM models, there is absolutely no reason a company serves models on their own hardware unless they are maniac about sending bytes to AWS.
You tried that on a personal machine for yourself once. It's completely different calculation when serving a model to 3000 employees with ever evolving hardware and software requirements. You'll need dedicated hardware in data centers and experts to run them. A company will need to figure out how to manage acquisition, assets and expenses plus 1000 other things, in addition to its actual business. Guess who has figured out all of that already? AWS/Azure/OpenAI etc.
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.
> 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.
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.
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.
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.
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.
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.
One of my most expensive sessions cost me over $100 in token spend in a single evening. I'd just found out that the time tracking & invoicing SaaS I use is increasing their monthly pricing by 2.4x - so I assigned Claude Opus 4.8 to recreate the entire SaaS for myself, and load in 13 years of my historical data. I've only completed a full read-only implementation so far, with adding & editing of records still to come, but I do expect Claude will have fully recreated the entire SaaS for me at an API cost less than a single 1 year seat of continued subscription to their service. And since I'm actually on a Max plan, it didn't actually cost me $200 of tokens at all.
coff i would not buy the Bending Spoons IPO coff saaspocalypse
I could ramble on about where the other $1750 of usage goes, but I imagine it's similar for most heavy Claude / AI users. Interactive coding sessions, a daily personalized podcast, some automated overnight agentic "proactive" sessions, a daemon that wakes up if I send Claude an email or voicetext to check something when I'm out. I've also noticed that if Claude's tool-use goes haywire & Claude gets confused or lost, sometimes a single email reply session that would normally be just $1 of API might spiral to $12 of API while it bangs its head against trying to run a program that's in a different folder to the one it's currently in. Sometimes a simple 'pwd' would save you a lot of headache, Claude....
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.
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.
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
(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)
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.
But yeah, for a company at Uber’s scale, I can see why they would want real engineering discipline around it.
My $100 subscription is not cheap. At the same time our product burns orders of magnitude more tokens.
To the mooooon!
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?
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.
china will be major token exporter soon. mark my words.
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???
Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing
https://news.ycombinator.com/item?id=48268871
Uber torches 2026 AI budget on Claude Code in four months
https://news.ycombinator.com/item?id=47976415
Corporate America Is Starting to Ration AI as Cost Skyrockets
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.
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.
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.
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?
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.
You're $100/m plan is likely equivalent to thousands of dollars of API pricing. You are being subsidized by the companies using AI.
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.
Their wet dream was never automation. It was zero marginal cost labor. And that dream is starting to rot.
That's the most useful signal. Pre OpenAI mafia RAM pricing, that comes out to $250/month.
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.
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.