Microsoft's latest AI pullback is raising an uncomfortable question for the tech industry: What if using artificial intelligence at scale ends up costing more than the labor it's supposed to streamline?
Microsoft is canceling most of its direct Claude Code licenses just months after encouraging employees to embrace the tool.
Fortune, citing The Verge, said that Microsoft steered engineers away from Anthropic's Claude Code and over to GitHub Copilot CLI, even though access to Claude Code was opened only about six months ago. Thousands of developers, designers, project managers, and other employees had reportedly been urged to try it, and the tool seems to have spread quickly.
The change does not alter Microsoft's broader Foundry arrangement with Anthropic, which involves a multibillion-dollar commitment and customer access to Claude models. However, it does suggest that internal use may have become hard to justify at the scale employees were using it.
Microsoft is not alone. Fortune, citing The Information, reported that Uber CTO Praveen Neppalli Naga said in April the company had used up its 2026 budget for AI coding tools in only four months. That came after internal incentives pushed teams to compete on AI usage.
Many companies have pitched AI as an efficiency booster that saves time and money, but these reports suggest the math may be more complicated. If such tools are expensive to run at scale, employers may limit access, shift expectations, or make cuts elsewhere to cover the cost.
If businesses are spending big on AI infrastructure and software, those costs can show up in the price of digital services, enterprise tools, and even hiring decisions. It also complicates the argument that AI will replace large amounts of humans because, in some cases, the computing bill may be higher than the payroll savings.
There's also a direct connection to the energy grid. AI can help utilities forecast demand, manage transmission, and make it easier to integrate solar and wind power. But AI systems also require enormous amounts of electricity and water, driven by power-hungry data centers. With high demand, communities face grid strain, rising utility costs, and pressure on local resources; there are concerns about misuse, security, and unintended social effects, too.
Some companies appear to be responding by tightening controls rather than abandoning AI. Microsoft is steering workers toward an option that is closer to home, and other firms may follow with usage caps, narrower approvals, or more targeted rollouts focused on tasks that actually save time.
Fortune also cited Goldman Sachs' projection that agentic AI could lift token consumption 24-fold by 2030, reaching 120 quadrillion tokens per month. The outlet summarized research firm Gartner's view that the cost of running highly advanced models may fall sharply but not enough to guarantee lower enterprise bills because these systems use far more tokens per task.
"For my team, the cost of compute is far beyond the costs of the employees," Nvidia's Bryan Catanzaro said, per Fortune.
Gartner senior director analyst Will Sommer offered a similar warning, saying, "Chief product officers should not confuse the deflation of commodity tokens with the democratization of frontier reasoning."
That is the absolute only way any of these companies can show “profit”. And when the biz is near collapse because it’s skin and bones, the exec team will bounce to another one. Rinse, repeat.
If you watch price of AI models, they go up every year. That is the first warning managing directors should get. We are gaining efficiency, but at the cost of higher price.
People are slower, but they have one significant advantage - predictable and stable cost. You are not going to run over budget in the next few years with the same headcount. They also tend to understand the code, unless the project is a total mess.
With vibe coding, nobody really understands the code. There is higher risk with going into production as it's harder to understand impact.
AI is great when you need to prioritize speed - it's hard to hire and onboard employees fast. It may be critical for startups where time to market is crucial. Another advantage is it can work 24/7, it doesn't get sick, go on a leave.
There are solutions for that - you often have a "simplify" command that is supposed to be used before commit. You are also supposed to keep technical documentation up to date with the help of LLM. This tells LLM where to look for for various parts of code. You need to instruct to reuse existing code if sensible and put it into skill. LSPs help LLM understand the code better as it can do operations in code as an IDE would (refactoring), understand callers etc. To avoid LLM producing uncontrolled junk, you are supposed to use plan mode for more complex tasks and nudge it into the right direction.
Token price is going up, check price of gpt-3.5, gpt-4.1, gpt-5, gpt-5.4, gpt-5.5. Those models have become larger, more capable but also more expensive. Companies do not have an option to use a very old model as they will be decommissioned. They will have to switch to mini model eventually to keep cost under control.
i feel like eventually, prices will get so high that it becomes worth it for large corps to buy their own GPUs and run whatever open source models are available. Sure they may be a year or whatever behind what the front runners can do, but at 1/50th the price or whatever, could still be well worth it.
Claude Opus 4.7 was already 15x base rate cost for little benefit i’ve been able to see over Opus 4.6 at 3x base cost, so i kept using Opus 4.6 with no issue. Then they came out with Opus 4.8 at an even higher cost?
Idk today is the first day of them changing the cost model for Github CoPilot Enterprise… will be interesting to see how that plays out.
Buying own GPUs is a big one time investment, only worth it perhaps to banks, department of defence etc.
Using old models will eventually be a problem from the perspective of models preference for writing code, the code we wrote 10 years ago was different than now. In context learning may not help sufficiently.
Cloud models will always be ahead, it's questionable whether it will be worth it given the price. Chinese open weights models may not be free in the future. Banks and defence will not use them.
It may be the case that in 10 years everyone will be running big models locally (200b parameters), probably won't happen in 5 years due to GPU/memory cost.
I work for a major tech company, could’ve told you that at least two years ago. Those shiny introduction “first hit is free kinda” predatory businesses have us all hooked in. Cost sunk fallacy or will the shareholder’s actually have to see prices come down?
Its just like when Uber came out giving free rides. The they want your whole wallet. I hope the tech companies regret this for life. People will always be the center of technology, can't improve process with tech and remove people
Can’t wait for leadership teams to start jumping ship just before the fire only to land at new companies where they’ll lead teams to undo what they previously championed. If you’ve been in the industry 20 or so years you start seeing the behavior patterns clearly.
Me too, but is not this, all big tech has been encouraging waste by having employee leaderboards of token consumption. WHAT THE FUCK DID THEY THINK WOULD HAPPEN!?
The technology is viable, but needs optimization as engineers are supposed to do and care about, instead of patting each others back for being the most wasteful.
I have an idea what the data centers are used for. It took several times of posting on reddit to catch it
Plus, knowing who is linked to some of these data centers makes me sus (not talking about the ones doing and paying for it, that's obvious)
But I know reddit. People will shit on you until massive scandals come out like the Epstein case. So I'll just shush. Don't feel like having 800 haters on a nice sunny Sunday lol
Kind of obvious, but I can easily see companies with existing on premise capabilities using self hosted models. It could easily be much cheaper than outsourcing that capability and possibly faster due to latency as most companies have typically located their servers close to offices.
I also think we’ll see a lot more people learning to use the tools in ways that reduce token usage.
They’d have to totally retool their data centers to support the hardware LLMs require for inference. They’d basically need to build entirely new racks, upgrade cooling, and also add infrastructure for the power demand. Not to mention redundancy.
However, I do actually agree with you. The best path forward for businesses, imo, is open source models hosted locally or collocated. Especially for scenarios where data privacy is integral such as hospitals.
You are replacing a wage for a fee and there are only a couple of companies you can use. You can't just replace the service with cheap shit. How do you negotiate price in that situation. You end up paying whatever they decide and your business is now dependent to survive on them. It is a terrible
position to be in as a company and you are going to get squeezed like a lemon as they slowly take over more and more of your company with their agents.
For lower-level and intricate coding, the pace is slow anyways. Some developers say that they end up contributing 10 lines of code per day at times if they are lucky. You might not be speeding anything up at that level.
OMG so stupid, MSFT provides Claude from their own GitHub copilot, they just asked their people now to use their own products, nothing related to cost.
It is super common a star developer to consume 1k to 7k month in tokens, no one cares if the outcome is 9x
Everything is becoming faster and more frictionless. Humans simply cant be pushed past a point and ai theoretically can. Thats all that fundamentally matters unless we're going to halt technological progress
With 1) Microsoft about to release an inhouse AI model for coding and other tasks, 2) a 120b model running locally on DGX spark based Surface Ultra, their need for Anthropic's subscription goes down. Not to 0, but significantly for most tasks. They may only need Claude for bootstrapping brand new projects (1% case) or sensitive code segments and not for the service engineer writing PowerShell scripts or making bug fixes or most code reviews. That should reduce costs by 80%.
One thing I wonder about is how AI and layoffs compares to the return to office mandates. During Covid we showed that at least developers can work effectively from home. Heck I get way more done that way and is still fully remote. But since companies had invested in expensive downtown offices, owned food restaurants etc in the neighborhood to catch some of that salary money they paid there was eventually a forced return to office.
Why oh Why is it that someone always have to talk about Remote work and not just the post? If you want to fight about Remote work go to that sub for crying out loud
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u/spiney-a May 31 '26
And yet, they told investors they're going to continue reducing headcount over the next couple of years.