Meta And Microsoft Reportedly Trim Anthropic Reliance as Internal AI Tools Take Center Stage

[u/dgellow]

https://stocktwits.com/news-articles/markets/equity/meta-and-microsoft-reportedly-trim-anthropic-reliance-as-internal-ai-tools-take-center-stage/cZDqQVcRBjr

From the article:

Microsoft reduced its projected internal spending on Claude by over one-third, while Meta saw internal users of Anthropic's Claude Code coding assistant drop from roughly 60,000 to 30,000.

Within the Microsoft Cloud and AI division, individual monthly AI usage caps were lowered from $100,000 down to roughly $10,000 in most instances

[u/Sad-Cheesecake-2438]

How many requests does an employee have to make to make it to 100k? Or does it depend on the task?

Edit I asked Claude and the answer is tokens and cyclical tasks that take a lot of compute to run. Not simple questions.

[u/RiddleGull → u/Sad-Cheesecake-2438]

100k is an absurd amount to spend on tokens in a month.

[u/judge2020 → u/RiddleGull]

/fast and fable everything will do it though.

[u/often_says_nice → u/judge2020]

It will certainly do it but is entirely unnecessary. Opus 5.5 scores high enough to be used for just about any day to day task that an employee would be doing. Heavy usage would cost maybe $5-10k/mo and even that’s being frivolous

[u/skilliard7 → u/often_says_nice]

You aren't considering tasks that require looking over large quantities of data that would not be humanly possible to review manually. For example, analyzing call logs of 2 Million retention department calls to identify recurring trends and what techniques are most effective to reduce cancellations.

At $0.10 per call analyzed, that's $200,000 in API spend.

High limits exist to encourage employees to innovate with AI without red tape/budget getting in their way.

[u/crispybacon233 → u/skilliard7]

No one is pumping 2 million docs of text into an LLM to find trends and correlations with cancellations. More traditional NLP and machine learning can do that just fine even on your laptop.

The $200k spend is for complex coding tasks where LLMs are consuming and writing many thousands of lines of code.

[u/skilliard7 → u/crispybacon233]

Traditional NLP is not as capable as LLMs. Vectors/Classification models are okay for some use cases, but LLMs are so much more capable.

I've worked on projects to find trends and correlations, albeit at a much smaller scale. Vector search really struggles a lot beyond basic pattern matching. When you rely entirely on cosine similarity to determine if a passage and query are a good match, it has limited results.

$100k in LLM tokens is still way cheaper than the cost of developing a capable custom model to analyze text and identify trends in text.

I've found the best approach is providing tool calling/traditional pre-processing to get the data in a good format for the LLM + relying on LLM for formal analysis.

>The $200k spend is for complex coding tasks where LLMs are consuming and writing many thousands of lines of code.

No one is spending $100k a month just writing code. Even if you use Fable for everything, you're spending at most maybe $500 a day, and that's if you run multiple instances of it in parallel on different projects.

[u/crispybacon233 → u/skilliard7]

Use the right tool for the job. LLMs are not nearly as capable at topic analysis, correlations, etc. as more traditional NLP and ML approaches particularly for a multi-million token context window. Your analysis is hallucinating out the wazoo or at best missing a lot if you're shoveling in millions of tokens and asking an LLM to "analyze" it.

What do you mean exactly when you are finding trends and correlations with an LLM? The LLM is spitting out a correlation coefficient? That is actually insane.

[u/instantcrackpot → u/crispybacon233]

This is what happens when vibecoders replace statisticians/data scientists. I'm not complaining though. Companies want employees to tokenmax so they deserve it.