If you were wondering the same thing I am - it's not about skills loss, quality, and less about money spent. It's more about frontier AI shops dogfooding their own models.
I think if you talk to LLMs and give feedback or openly say what works and what doesn't, you are essentially solving a captcha and produce accurate training data, while you pay for the token spend. I'd be a bit nervous with this lol.
Just one unsanitized input and you leak info. Or one hidden character and code may or may not belong to you anymore. Its very odd on many levels
If it’s anything like AWS there’s hundreds of people making bespoke software factory setups, enhanced interfaces for ai tools, spinning up 10 parallel review agents with the best model available, etc because the budget is basically unlimited.
My company took away my Claude because it’s too expensive. I feel like there is a reckoning coming. The accountants are finally realising the cost of token maxing.
That's pretty stupid. Most people who are incurring significant costs are just tokenmaxxing rather than being efficient with usage. You can get 99% of jobs and work done with Haiku/Luna in a collaberating working enviroment.
I feel like people who are later to the AI game just like to "oneshot" and sink a bunch of usage into generating garbage
There really is a skill to using it effectively. I've tried coaching some of the devs on my team. Some get it, some don't.
Our company has been tracking token usage and models used vs output (tickets, story points, PRs, deploys, etc...). A dev got chewed out, even after I warned him, because he spent over $2k in a single month almost exclusively on Opus while his actual productivity in terms of what he delivered was abysmal.
I get it but it goes against the grain for me. Isn't it ironic that we have to waste our precious and expensive human brain cycles to think about how to use AI cheaply so that it is not more expensive than us?
In other words I want to spend 100% of my mental capacity in the problem domain for the things AI cannot do for me, like steering, grounding, verification and not for things AI could do.
Claude, vibe code me an entire startup, the actual product doesn't matter, but it should all be based on the incredible pun "turn 'sorry' points into story points."
/goal get accepted into Y Combinator, you have an unlimited token budget, be bold.
Attempting a serious but not-a-certified-whatever answer: "Points" do have meaning when properly used as a kind of moving-average tool for forecasting within a particular context.
Problems arise when people try to perma-peg them to particular tasks, or (worse) man-hours or (much worse) man-hours across teams. Even just encouraging the humans to answer in terms of hours/days taints the accuracy of the forecast by introducing a kind of bias.
So, what you do is you recognize every ticket has a somewhat variable “actual effort”; and, if you’ve been honest in approximate effort pointing, you’ll know your team (or your own) velocity.
From there you can run Monte Carlo simulations - say a few hundred thousand, and get a pretty good estimate of actual time spent.
there's a manifold to what "effective" means. The problem is once you get into the vibe flow, it's really difficult to eject yourself into the other realms of vscode or IDE or whatever it is you normal do because the vibing provides no anchor to what you're doing.
Even if these models are smart enough to reorient themselves, they get entirely stuck in a desert and now you're asking someone to just pull up stakes and digg them out even thought they only watched them get there and the UI provides so much speed that no human can comprehend how they got there in the first place.
It's like asking a pilot to take over in an emergency situation when they're not tasked with any of the every day requirements of the job. The orgs are relying on borrowed time of experienced professionals, and that's going to erode away and what replaces it is mostly people who understand how to navigate context but not use any of the _classic_ tools.
It's a real conundrum and won't be easily surfaced but for a decade.
I’m trying really hard to keep my skills up but it doesn’t feel productive when I’m using it to write code. It feels like I’m slowing down the AI to the point that it’s not as effective as just letting it go. But I don’t get all the learning that comes from that time along the way.
Have you found ways to stay sharp while using it? Or are you relying on other projects outside of work to keep your skills fresh?
I feel like it's only within the past few months that opus got to the point where guiding the model is faster than doing things myself. I tried out sonnet recently and it was not a net positive to my work. I feel like anything that I'd trust haiku to handle isn't worth doing in the first place.
For context, I'm doing a range of tasks, everything from one-shotting adhoc scripts to having 4 hour 10M+ token conversations debugging things.
Or does a "collaborating work environment" mean that everything is basically spoonfed to them? Or do you only ever use ghost suggestions?
I genuinely cannot even fathom. Just how do you even get into a state where tasks are so clear and cookie cutter? These things are abhorrent. Not only are they not useful, it's an outright form of psychological torture to try and use them. They almost fight you.
Luna doesn't even respond to steers properly! You try steering it and it immediately gets distracted and then just stops.
I can imagine coercing Sonnet into doing some of my tasks okay, but Haiku? Especially 4.5? Really?
I think you might be overestimating the sort of projects most of us have worked on throughout our careers -- we haven't been doing much groundbreaking work. LLMs can easily and successfully write most code.
It's possible it's my role distorting my perception, cause technically I don't write software, I work an SRE role. None of my items come pre-chewed or paced, it's all good luck and god bless.
I'm desperately trying to classify and standardize my work items and delegate them to less capable models, because my usage is clearly unsustainable and this same sentiment as above keeps being pushed on me too. But all my tasks are genuinely fairly arbitrary, so there's no real way around the agent actually being able to reason about business and technical context proper. It's not even that they're hard, it's just that they're dynamic.
I can get Luna to do things like walk our observability stack and perform a healthcheck, then defer to a stronger model if anything looks super off, but if I'm being entirely honest, this could basically be just a script. Which Opus 5.5 will immediately write for itself if it doesn't yet exist, run that, and then off it goes depending. But Luna will never actually do an investigation proper. Heck, it can't even read our dashboards most of the time, tripping up on Grafana minutia.
It feels like that surgeon vs surgeon comparison, where you're made to decide based on their surgery success rate, and the better succeeding surgeon simply reward hacks the number by only operating on less dicey cases. Except there's no objective way to make this classification here, so jackasses like the above get to play with my insecurities with full obnoxious confidence, while I'm left desperately trying to slim my usage and failing to do so between two moments of crippling self doubt and blockers.
I use Opus 5.5 heavily but only spend around $800/week at API rates. I mean, I say "only"... That's a lot in absolute terms, but trivial compared to my salary and EASY worth it.
The reckoning started years ago when we did the equivalent to token maxxing hiring coders for everything to crank LOC
Software is inherently a physics problem not all the job titles and specializations made up the last 20 years as dev job salaries kept attracting people
That was all illusory social construct to prop up jobs
Still a whole lot of that in tech but it's all at the top of the org now. Leadership sensory experience and thus innate habit to forecast future been programmed by years of yes men they refuse to accept the jig is up for them too
Sensory memory of being a useless figurehead fosters a lot of existential dread in priests, politicians, and the like. Completely aware their day to day effort is insufficient to sustain them they know how co-dependent they are. They'll dig in harder.
See Chris Matthews flame out shrieking about socialist execution squads. Dude seriously thought everyone wants to hang him from a lamp post. The reality is people just want a sense of control back and not have their perception dragged along by Chris Matthews.
which is quite sad because opus 5.5 is really good. i say this as an anthropic hater. i wish I could move away to other models like 6.1 sol or deepseek or whatever, but they just all lack something. i _trust_ opus 5.5
i hope other labs catch up, especially chinese labs.
I mean, we’re not far from a situation where instead of how many story points you completed per sprint the metric to optimize is going to be what was your efficiency? How many story points did you complete while minimizing your token usage. In fact, that’s a pretty good idea. I’m going try and implement it at work with some sort of complexity normalization function
AFAIK there is no such explicit, company-wide effort at meta. The article seems to try to slip Meta in there with whatever reporting they are doing on Microsoft, despite there being no such evidence for meta.
To also add an important context to the drop in Claude code users reported for meta - this elides that we are absolutely still using the Anthropic models full steam ahead, but are moving towards internal interfaces and harnesses.
So I would question if that 50% drop in CC users is more of an interface change than anything else.
I for one have stopped explicitly using codex or CC entirely. But the interfaces I am using still use those harnesses under the hood. I wonder how that is counted.
Considering that’s a healthy portion of a salary for an additional employee per person, the fact they’re slashing spending sure makes it look like AI wasn’t even a 2x multiplier at minimum.
We're sitting on a year of more or less capable coding models and I have not exactly seen a revolution in new software being released. AI is likely a force multiplier for specific subsets of individuals and workflows. Coding is not a bottleneck for a lot of systems.
I suspect that SaaS is silently being eaten alive as an industry.
It’s mostly from people using their personal accounts to run LLM services that serve a larger team or organization. At least at Microsoft, it’s still impressively hard to get access to an LLM for service usage with high enough rate limits to be useful, making running services on dev boxes much more appealing (despite the countless drawbacks that few people seem to care about around security, compliance, reliability, etc).
Right! it seems obvious why: Both these companies want to dogfood their own coding models and stop paying competition.
You can also read this as diminishing returns / AI isn't good enough, etc, but the simplest explanation is that they don't want to send money to Anthropic.
This, and in addition to dogfooding, incentivizing employees to be more effective with the cheaper models. A lot of problems don't need anything fancy, but it takes more brain power and engineering effort to make that work. By default humans will take the path of least resistance if it's available.
But Microsoft and Meta are not blocking competitor tools for internal use, they're merely trying to reduce costs and divert a fraction of use to their own technologies. Microsoft and Meta are both still spending nine figures a year on Claude, and the article does not state or imply they're even considering a complete halt.
If true, it is a huge blow to Anthropic’s revenue stream. IIRC it was reported that the quarter of their revenue comes from just two clients and as the ex-Meta guy who left this July, I am convinced that Meta must be one of the two.
> The company's financial trajectory already shows how quickly that equation is changing. Anthropic's revenue run rate was about $9 billion at the end of 2025, according to the company, before rising to more than $47 billion by May. Anthropic has projected revenue of at least $10.9 billion for the second quarter of 2026, more than double the previous quarter, on track for its first quarterly operating profit of $559 million.
> The company has told a small group of shareholders that its adjusted operating income will be positive for the second consecutive quarter, according to multiple people with knowledge of the matter.
These are leaked and self-reported numbers, but no matter how much skepticism you pile on them it looks likely that Anthropic in 2026 have had some of the fastest revenue growth of any company in history.
I feel like "good enough" was reached around Opus 4.6 - 4.8. All I wanted after that is improved speed, continued tweaks to the tooling to get the most out of it and quality of life features added.
At Microsoft, the recommendation is to use cheaper models for tasks that don't need frontier models. I personally use Lua and it's more than capable for complex problems.
I assume that at shops that both employ engineers and are developing an AI product, internal usage is not about improving productivity, it is about improving the offering. Of course they want employees to use internal tools.
Maybe I am slow here and everyone is using Claude with credits at max use. But isn't Claude Teams like $25/month per developer for ordinary use? What the heck of these guys doing that makes it get that phenomenally expensive for their use cases? These are presumably well capable engineers who started to use this as an aid right not just throw Fable at everything and loop to the max?
There are people out there building AI building orchestrators for orchestrators for orchestrators for agents. The author of that blog post later claimed to be spending the equivalent of $122k/month on tokens (by rotating their usage between 21 accounts).
As far as I can tell, the only thing that this level of spend has produced so far is an indie 2D RPG video game.
> Gas Town was intended to be reusable, but I only ever wound up using it to build itself. Gas Town fell apart at the seams with Opus 4.7. Up through 4.6 it was working brilliantly. With 4.7 we saw the introduction of the "just two more things" tic, which prevented Opus from ever converging on being ready to do real work—it always wanted to fiddle with Gas Town itself. The Opus tic never went away, so Gas Town effectively burned down. It had other problems, too, but 4.7 was the final straw
I hadn't seen the $122K figure mentioned previously. $87K for API-style pricing was mentioned in the above post, and ~$2,800/mo for multiple Claude Max accounts:
> My solution has been to create a token tap on $200 Max accounts, which for me work out to ~30x the list-price equivalent. So in reality I'm only spending about $2800/month out of pocket for my $87k "worth" of tokens. Though that number keeps growing alarmingly.
(He never seemed to provide numbers for Gas Town initially, whether what he paid, or what the API-style pricing would have charged, so it was interesting to get some actual figures. Sounds like a lot to me, but if he's genuinely getting multiple people's-worth of work out of it, then...)
(Also in the article: a little morsel of Emacs content, which was nice to see.)
It doesn't say they're cutting back on OpenAI usage.
To me this seems mostly related to the way Anthropic showed the level at which they monitor sessions, plus wanting to limit how much training data they're directly feeding into a company that competes with their own products/investments.
I should probably know more about Claude's TOS, but it is probably a mistake for these companies not to leverage the plausible deniability of their usage and turn it into a massive distillation resource for their own models.
The cybersecuritynews.com news one simply republishes details of a story published by The Information. At least they have the decency to LINK to that Information story:
It may bei cost efficient, but is it wise? We use not only the big US models, but also Chinese ones. This way we can compare who makes the difference. Simplified: Knowledge comes before economic aspects.
CEO's nephew showed him how good the Chinese models are?
I am only half joking, I heard something like "my son or nephew did this cool thing with $X so we'll take $this_radical_step because of it" enough times over my career.
There is a perceived opportunity cost from someone using a lower-tier model on their task. What if the better model did a "better" job? what if my trials and tribulations are due to model quality?
If you are used to talking to opus5.5 medium, going to GPT6.1 luna low will feel like a step down. Why would any employee take the (personal) risk?
You're opening yourself up to data right and privacy risks with that. My company demands that I use their enterprise account because they can claim full ownership of all produced output and have full logs of every interaction.
I think that gets legally murky, if the employee is the one who pays for the tool.
If you were wondering the same thing I am - it's not about skills loss, quality, and less about money spent. It's more about frontier AI shops dogfooding their own models.
I think if you talk to LLMs and give feedback or openly say what works and what doesn't, you are essentially solving a captcha and produce accurate training data, while you pay for the token spend. I'd be a bit nervous with this lol.
Just one unsanitized input and you leak info. Or one hidden character and code may or may not belong to you anymore. Its very odd on many levels
Accurate training data?
At best you produce some noisy signals that are going to have a tiny impact if even that.
And that's on a personal plan where you didn't opt out of sharing usage data.
Business plans offer zero data retention. This is a non-issue.
If it’s anything like AWS there’s hundreds of people making bespoke software factory setups, enhanced interfaces for ai tools, spinning up 10 parallel review agents with the best model available, etc because the budget is basically unlimited.
Meanwhile https://www.reddit.com/r/GeminiAI/comments/1wh0qxq/google_fi...
This made me laugh https://www.reddit.com/r/GeminiAI/comments/1wh0qxq/comment/p...
They give claude usage on antigravity and aren't them a investor on antrhopic?
What in the world is happening?
Ask Claude
Dogfooding their own model ls and not letting their competitors use their data to train their.
I wonder why they allow it at all.
Like its a no brainer to force your employees to use your own models, then RL train them to be better.
Or let them use Claude, track everything, and use that data to train your own models.
Microsoft?
Copilot
That's not a model.
True, but its not pure OpenAI GPT. If the point is dogfooding, then they'd use Copilot instead of using OpenAI's models directly
They do.
If you ask Microsoft, it is a lifestyle.
My company took away my Claude because it’s too expensive. I feel like there is a reckoning coming. The accountants are finally realising the cost of token maxing.
That's pretty stupid. Most people who are incurring significant costs are just tokenmaxxing rather than being efficient with usage. You can get 99% of jobs and work done with Haiku/Luna in a collaberating working enviroment.
I feel like people who are later to the AI game just like to "oneshot" and sink a bunch of usage into generating garbage
There really is a skill to using it effectively. I've tried coaching some of the devs on my team. Some get it, some don't.
Our company has been tracking token usage and models used vs output (tickets, story points, PRs, deploys, etc...). A dev got chewed out, even after I warned him, because he spent over $2k in a single month almost exclusively on Opus while his actual productivity in terms of what he delivered was abysmal.
I get it but it goes against the grain for me. Isn't it ironic that we have to waste our precious and expensive human brain cycles to think about how to use AI cheaply so that it is not more expensive than us?
In other words I want to spend 100% of my mental capacity in the problem domain for the things AI cannot do for me, like steering, grounding, verification and not for things AI could do.
I dunno, using tools and resources effectively is arguably the essence of good engineering.
So you are going to blame this one that dev?
Define productivity, and while at it, quality, maintainability , modularity and so forth.
What do sorry points mean anymore.
I love the typo.
Claude, vibe code me an entire startup, the actual product doesn't matter, but it should all be based on the incredible pun "turn 'sorry' points into story points."
/goal get accepted into Y Combinator, you have an unlimited token budget, be bold.
Did they ever have meaning? It's always been a nebulous feels term
Attempting a serious but not-a-certified-whatever answer: "Points" do have meaning when properly used as a kind of moving-average tool for forecasting within a particular context.
Problems arise when people try to perma-peg them to particular tasks, or (worse) man-hours or (much worse) man-hours across teams. Even just encouraging the humans to answer in terms of hours/days taints the accuracy of the forecast by introducing a kind of bias.
For forecasting what, if not man-hours?
Effort. Which is a very nebulous term, I agree.
So, what you do is you recognize every ticket has a somewhat variable “actual effort”; and, if you’ve been honest in approximate effort pointing, you’ll know your team (or your own) velocity.
From there you can run Monte Carlo simulations - say a few hundred thousand, and get a pretty good estimate of actual time spent.
I’ve seen it work before with shocking accuracy.
there's a manifold to what "effective" means. The problem is once you get into the vibe flow, it's really difficult to eject yourself into the other realms of vscode or IDE or whatever it is you normal do because the vibing provides no anchor to what you're doing.
Even if these models are smart enough to reorient themselves, they get entirely stuck in a desert and now you're asking someone to just pull up stakes and digg them out even thought they only watched them get there and the UI provides so much speed that no human can comprehend how they got there in the first place.
It's like asking a pilot to take over in an emergency situation when they're not tasked with any of the every day requirements of the job. The orgs are relying on borrowed time of experienced professionals, and that's going to erode away and what replaces it is mostly people who understand how to navigate context but not use any of the _classic_ tools.
It's a real conundrum and won't be easily surfaced but for a decade.
I’m trying really hard to keep my skills up but it doesn’t feel productive when I’m using it to write code. It feels like I’m slowing down the AI to the point that it’s not as effective as just letting it go. But I don’t get all the learning that comes from that time along the way.
Have you found ways to stay sharp while using it? Or are you relying on other projects outside of work to keep your skills fresh?
I feel like it's only within the past few months that opus got to the point where guiding the model is faster than doing things myself. I tried out sonnet recently and it was not a net positive to my work. I feel like anything that I'd trust haiku to handle isn't worth doing in the first place.
For context, I'm doing a range of tasks, everything from one-shotting adhoc scripts to having 4 hour 10M+ token conversations debugging things.
> You can get 99% of jobs and work done with Haiku/Luna in a collaberating working enviroment.
Optimally? Opus will pay for itself if you save just 10% of your time
True. I always Opus to pay for itself if it wants to get used by me.
the poster did mention "if it saves 10% of your time".
So be less snarky?
Sorry, but that is nonsense. Compared to opus haiku doesn't cut it most of the time.
What on earth do you even do with these models?
Or does a "collaborating work environment" mean that everything is basically spoonfed to them? Or do you only ever use ghost suggestions?
I genuinely cannot even fathom. Just how do you even get into a state where tasks are so clear and cookie cutter? These things are abhorrent. Not only are they not useful, it's an outright form of psychological torture to try and use them. They almost fight you.
Luna doesn't even respond to steers properly! You try steering it and it immediately gets distracted and then just stops.
I can imagine coercing Sonnet into doing some of my tasks okay, but Haiku? Especially 4.5? Really?
I think you might be overestimating the sort of projects most of us have worked on throughout our careers -- we haven't been doing much groundbreaking work. LLMs can easily and successfully write most code.
It's possible it's my role distorting my perception, cause technically I don't write software, I work an SRE role. None of my items come pre-chewed or paced, it's all good luck and god bless.
I'm desperately trying to classify and standardize my work items and delegate them to less capable models, because my usage is clearly unsustainable and this same sentiment as above keeps being pushed on me too. But all my tasks are genuinely fairly arbitrary, so there's no real way around the agent actually being able to reason about business and technical context proper. It's not even that they're hard, it's just that they're dynamic.
I can get Luna to do things like walk our observability stack and perform a healthcheck, then defer to a stronger model if anything looks super off, but if I'm being entirely honest, this could basically be just a script. Which Opus 5.5 will immediately write for itself if it doesn't yet exist, run that, and then off it goes depending. But Luna will never actually do an investigation proper. Heck, it can't even read our dashboards most of the time, tripping up on Grafana minutia.
It feels like that surgeon vs surgeon comparison, where you're made to decide based on their surgery success rate, and the better succeeding surgeon simply reward hacks the number by only operating on less dicey cases. Except there's no objective way to make this classification here, so jackasses like the above get to play with my insecurities with full obnoxious confidence, while I'm left desperately trying to slim my usage and failing to do so between two moments of crippling self doubt and blockers.
Our ai basic analysis for SRE / k8s based platform is haiku and its surprisngly good.
I wouldn't even tried it, i would still just go with even Opus (we don't have that many alerts) but it really surpsied me.
When i ran into usage limits a few days ago i switched most to Sonnet and again was surprised how good it is now.
You realise the subject here is Meta, which is all in on this stuff? Of course they are going to use Muse Spark over Claude.
>Great Depression style collapse and all the current AI companies go bankrupt.
Oh this is just a 33 day old doomer account.
And yours is a 4 year old hype account?
Nah, I just respond to a few things here and there.
$200 a month is too expensive yet they employ human developers?
Besides that the article states quite high numbers, budget is budget in these companies.
You had budget for your normal salaries, for externals and now suddenly you have a few millions additional.
What do you do? You compensate.
Business people doing business things.
It's unclear how much OP's company was spending. The article gives a figure of $100k/month per employee.
But even $200/month is worth shaving if it doesn't generate value.
Corporations pay API rates.
I use Opus 5.5 heavily but only spend around $800/week at API rates. I mean, I say "only"... That's a lot in absolute terms, but trivial compared to my salary and EASY worth it.
So what, if they're cutting it, it has propagated to the last bean counter that the roi isn't there.
This takes some doing and now is the time where it's dawning on the finance departments.
The reckoning started years ago when we did the equivalent to token maxxing hiring coders for everything to crank LOC
Software is inherently a physics problem not all the job titles and specializations made up the last 20 years as dev job salaries kept attracting people
That was all illusory social construct to prop up jobs
Still a whole lot of that in tech but it's all at the top of the org now. Leadership sensory experience and thus innate habit to forecast future been programmed by years of yes men they refuse to accept the jig is up for them too
Sensory memory of being a useless figurehead fosters a lot of existential dread in priests, politicians, and the like. Completely aware their day to day effort is insufficient to sustain them they know how co-dependent they are. They'll dig in harder.
See Chris Matthews flame out shrieking about socialist execution squads. Dude seriously thought everyone wants to hang him from a lamp post. The reality is people just want a sense of control back and not have their perception dragged along by Chris Matthews.
The reckoning will be throwing out all external help, then reducing team sizes.
We're just getting put on a budget.
Our velocity is twice as high as it was before Claude, so I doubt that we'll ever go back, but I could see efficiency being a priority.
My thoughts were the reckoning would come when Infra teams started offloading AWS usage to LLMs and ended up token maxing and deploy maxing.
which is quite sad because opus 5.5 is really good. i say this as an anthropic hater. i wish I could move away to other models like 6.1 sol or deepseek or whatever, but they just all lack something. i _trust_ opus 5.5
i hope other labs catch up, especially chinese labs.
Not sure it's even possible to catch up by distilling.
I mean, we’re not far from a situation where instead of how many story points you completed per sprint the metric to optimize is going to be what was your efficiency? How many story points did you complete while minimizing your token usage. In fact, that’s a pretty good idea. I’m going try and implement it at work with some sort of complexity normalization function
You have to use Windows, Teams and Copilot; how many people did I just lose?
Gamedevs are still around.
AFAIK there is no such explicit, company-wide effort at meta. The article seems to try to slip Meta in there with whatever reporting they are doing on Microsoft, despite there being no such evidence for meta.
To also add an important context to the drop in Claude code users reported for meta - this elides that we are absolutely still using the Anthropic models full steam ahead, but are moving towards internal interfaces and harnesses.
So I would question if that 50% drop in CC users is more of an interface change than anything else.
I for one have stopped explicitly using codex or CC entirely. But the interfaces I am using still use those harnesses under the hood. I wonder how that is counted.
> monthly AI spending limits have reportedly been slashed from $100,000 per employee...
what. I can see a team of 20 costing 100k per month (but rare), but per person?
Considering that’s a healthy portion of a salary for an additional employee per person, the fact they’re slashing spending sure makes it look like AI wasn’t even a 2x multiplier at minimum.
We're sitting on a year of more or less capable coding models and I have not exactly seen a revolution in new software being released. AI is likely a force multiplier for specific subsets of individuals and workflows. Coding is not a bottleneck for a lot of systems.
I suspect that SaaS is silently being eaten alive as an industry.
Very likely a negative multiplier.
It’s mostly from people using their personal accounts to run LLM services that serve a larger team or organization. At least at Microsoft, it’s still impressively hard to get access to an LLM for service usage with high enough rate limits to be useful, making running services on dev boxes much more appealing (despite the countless drawbacks that few people seem to care about around security, compliance, reliability, etc).
This is not surprising. Every big lab blocks competitor tools for internal use; it is a data governance thing, not a quality statement.
Right! it seems obvious why: Both these companies want to dogfood their own coding models and stop paying competition.
You can also read this as diminishing returns / AI isn't good enough, etc, but the simplest explanation is that they don't want to send money to Anthropic.
This, and in addition to dogfooding, incentivizing employees to be more effective with the cheaper models. A lot of problems don't need anything fancy, but it takes more brain power and engineering effort to make that work. By default humans will take the path of least resistance if it's available.
This is likely the future as well. Down the road, every company will have their own internal coding models.
Does MS have coding models?
But Microsoft and Meta are not blocking competitor tools for internal use, they're merely trying to reduce costs and divert a fraction of use to their own technologies. Microsoft and Meta are both still spending nine figures a year on Claude, and the article does not state or imply they're even considering a complete halt.
So nobody except AI labs is allowed to do "data governance"?
If true, it is a huge blow to Anthropic’s revenue stream. IIRC it was reported that the quarter of their revenue comes from just two clients and as the ex-Meta guy who left this July, I am convinced that Meta must be one of the two.
That was 2025. In 2025 a quarter of their revenue came from two customers, and those customers were GitHub Copilot and Cursor.
In 2026 their revenue has gone up by a factor of more than 10x, and they no longer have just two whale customers.
I heard a rumor recently that customers spending less than $100m/year aren't even considered their "top tier" now.
We know the 2025 revenue numbers from Anthropic's leaked prospectus.
Where are you getting the 2026 figures from? The Bloomberg article that was "on track to generate" and just prediction?
The 2026 figures have been coming out all year. I collected some of them here: https://simonwillison.net/2026/May/29/anthropic/ and https://simonwillison.net/2026/Aug/23/anthropics-best-ai-mod...
The most recent reporting from Reuters themselves (somehow not included in their more recent article about the IPO stuff): https://www.reuters.com/business/anthropic-ipo-valuation-hin...
> The company's financial trajectory already shows how quickly that equation is changing. Anthropic's revenue run rate was about $9 billion at the end of 2025, according to the company, before rising to more than $47 billion by May. Anthropic has projected revenue of at least $10.9 billion for the second quarter of 2026, more than double the previous quarter, on track for its first quarterly operating profit of $559 million.
Here's the FT: https://www.ft.com/content/4564e6a5-69e9-40a6-bf0f-a888f2f4f... - "Anthropic tells investors it will be profitable for second straight quarter"
> The company has told a small group of shareholders that its adjusted operating income will be positive for the second consecutive quarter, according to multiple people with knowledge of the matter.
These are leaked and self-reported numbers, but no matter how much skepticism you pile on them it looks likely that Anthropic in 2026 have had some of the fastest revenue growth of any company in history.
Meta was #1 by far
Well that explains the limitation.
We've reached the era of "good enough" ai, it seems. The truth is you don't need the best model in most cases.
I feel like "good enough" was reached around Opus 4.6 - 4.8. All I wanted after that is improved speed, continued tweaks to the tooling to get the most out of it and quality of life features added.
That's where I am too. Opus 5.5 but increasingly faster is a future I'm hoping for.
Muse Spark 1.3 Max is quite good for almost all of my usecases and its cheap.
isn't it cheap till if you allow them to use your data?
You mean the contributor tier? I think it turns out cheaper even without that.
That’s fine if you are working on Meta source.
At Microsoft, the recommendation is to use cheaper models for tasks that don't need frontier models. I personally use Lua and it's more than capable for complex problems.
That's a good start. Now they just need to limit use of CoPilot, OpenAI and MetaAI and they'll really be getting somewhere.
I assume that at shops that both employ engineers and are developing an AI product, internal usage is not about improving productivity, it is about improving the offering. Of course they want employees to use internal tools.
Large companies will take the same route as Meta and Microsoft. Small players will go for local LLMs with the right hardware.
Maybe I am slow here and everyone is using Claude with credits at max use. But isn't Claude Teams like $25/month per developer for ordinary use? What the heck of these guys doing that makes it get that phenomenally expensive for their use cases? These are presumably well capable engineers who started to use this as an aid right not just throw Fable at everything and loop to the max?
You should read Gas Town: https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d...
There are people out there building AI building orchestrators for orchestrators for orchestrators for agents. The author of that blog post later claimed to be spending the equivalent of $122k/month on tokens (by rotating their usage between 21 accounts).
As far as I can tell, the only thing that this level of spend has produced so far is an indie 2D RPG video game.
The RPG long predates Gas Town.
Regarding Gas Town, see also https://yegge.ai/essays/the-shape-of-things-to-come/ :
> Gas Town was intended to be reusable, but I only ever wound up using it to build itself. Gas Town fell apart at the seams with Opus 4.7. Up through 4.6 it was working brilliantly. With 4.7 we saw the introduction of the "just two more things" tic, which prevented Opus from ever converging on being ready to do real work—it always wanted to fiddle with Gas Town itself. The Opus tic never went away, so Gas Town effectively burned down. It had other problems, too, but 4.7 was the final straw
I hadn't seen the $122K figure mentioned previously. $87K for API-style pricing was mentioned in the above post, and ~$2,800/mo for multiple Claude Max accounts:
> My solution has been to create a token tap on $200 Max accounts, which for me work out to ~30x the list-price equivalent. So in reality I'm only spending about $2800/month out of pocket for my $87k "worth" of tokens. Though that number keeps growing alarmingly.
(He never seemed to provide numbers for Gas Town initially, whether what he paid, or what the API-style pricing would have charged, so it was interesting to get some actual figures. Sounds like a lot to me, but if he's genuinely getting multiple people's-worth of work out of it, then...)
(Also in the article: a little morsel of Emacs content, which was nice to see.)
Is the game even good?
Correction: maintenance of an indie 2D RPG game that had existed for a few decades already.
If you’re a large company you gotta pay API rates basically. Team plans exclude a lot of governance/IdP stuff and have a cap on seats too.
Team plan has a seat limit.
Limited to $10,000/employee/month lol. This is just to cut off a few people doing absurd things with low ROI. Don't read too much into it.
OTOH "$105 million to Claude Code over a 28-day timeframe"
$1.4B/year is not a small number, even at Meta's scale, when it's money going to a competitor
That's what you got from 200$ claude sub.
... in favour of their own AI tooling.
I assume this is being pounced on by "I told you so" AI skeptics. Sorry but it's not what you were looking for.
That's a shame. Microsoft's W11 quality disaster might have actually been improved with a frontier model.
Or maybe not, but the bar was set pretty low that going all-in on AI might have been worth it.
It doesn't say they're cutting back on OpenAI usage.
To me this seems mostly related to the way Anthropic showed the level at which they monitor sessions, plus wanting to limit how much training data they're directly feeding into a company that competes with their own products/investments.
I should probably know more about Claude's TOS, but it is probably a mistake for these companies not to leverage the plausible deniability of their usage and turn it into a massive distillation resource for their own models.
GLHF
This story is re-published from https://cybersecuritynews.com/meta-microsoft-claude-ai/ (it credits that source at the bottom) but with the internal links removed.
The cybersecuritynews.com news one simply republishes details of a story published by The Information. At least they have the decency to LINK to that Information story:
https://www.theinformation.com/articles/meta-microsoft-work-...
... and of course the Information story is behind a paywall.
In other news, the CIA is limiting it's employees from submitting top secret information on KGB owned and operated websites.
Wait, what? We pretended this tech would save the world and it won’t? Oh man.
So they're going to fall even further behind? Buying puts on Meta and Microsoft, or even better might give it to Fable to handle it for me
Microsoft is like $100bn deep into OpenAI.
I hope leadership isn’t susceptible to falling for the sunk cost fallacy.
Who's ahead, again? Do they have a moat, or just a nice field?
Maybe a "ha-ha".
Microsoft is only limiting employees to $10k a month, down from $100k a month. :)
Absurd
It may bei cost efficient, but is it wise? We use not only the big US models, but also Chinese ones. This way we can compare who makes the difference. Simplified: Knowledge comes before economic aspects.
CEO's nephew showed him how good the Chinese models are?
I am only half joking, I heard something like "my son or nephew did this cool thing with $X so we'll take $this_radical_step because of it" enough times over my career.
In the video realm, it was everyone's nephew with a 5D could shoot this for $500 when getting a $20k+ quote to shoot something
There is a perceived opportunity cost from someone using a lower-tier model on their task. What if the better model did a "better" job? what if my trials and tribulations are due to model quality?
If you are used to talking to opus5.5 medium, going to GPT6.1 luna low will feel like a step down. Why would any employee take the (personal) risk?
This may be seen as radical. But I think AI tools should be paid by the employee. After all, you should know how to do your work without AI.
Should they pay for their pipelines too?
After all, they should know how to compile their software. Any automation of that process is cheating their employer.
Sure. Should I also pay rent for my desk at the office?
You're opening yourself up to data right and privacy risks with that. My company demands that I use their enterprise account because they can claim full ownership of all produced output and have full logs of every interaction.
I think that gets legally murky, if the employee is the one who pays for the tool.
The forklift should just be paid for by the employee. After all they should be strong enough to do work without one.
If my employer is not forcing me to use a tool and gives me freedom, I'll be perfectly OK to use my own tools which I bought with my own money.
If my employer is putting scoreboards to see and champion who uses a tool which costs money to use, they shall pay for the tool.
Sorry, I'm not a ladder climber, yet I'm not mindless enough to bankrupt myself.
I could still do my job by typing all code into notepad, but companies don't charge employees for their IDE usage for a reason.
Nah. Work provides the tools.
An LLM is not too dissimilar to a Work Laptop or an IDE license.
I don't understand your logic, why would an employee pay for a tool their employer wants them to use?
Yeah that only makes sense if they are a freelancer/contractor.
I am fine with that if I can keep the time saved for my personal use.
In California this would mean the employee would own the IP -- or at least it would be murky -- and companies and lawyers don't like murky.
Employer pays tools used for work. Whether they are used to speed up work or to make it possible.
"employees should foot the bill for tools that directly benefit their billion dollar employers"
I’d love it if I could bring my own computer to do my work, rather than be stuck with garbage hardware because of an enterprise agreement.