The title seems misleading, and reading the article explains the reason more clearly. There's nonsense OKR's and objectives at these companies to burn as many tokens as possible. It turns out that when you make a metric out of token usage, it unsurprisingly ends up becoming extremely expensive.

Inference is affordable, and you don't need a SOTA proprietary model to get a lot of use out of this technology. While you likely will still need a human engineer for quite a while longer, I don't agree that some number of humans + an LLM is going to be (or will ever remain) more expensive than just hiring more humans.

The premise of this article is incorrect - MS isn't cancelling Claude code internal usage because of AI costs too much, they're cancelling it because GitHub copilot is the compete product and they want their employees to use their product.

It's the same reason Teams got so much attention during lockdown.

The 'tokenmaxxing' trend is probably the more inane ideas emanating out of this whole AI wave. It goes in the opposite direction of efficiency and productivity maximization. Yet, it has wide acceptance.

Literally nowhere in the article does Microsoft report AI is more expensive than paying human employees.

The usage of AI has to be put in context for cost analysis.

A lot of people I see are using AI to beautify their documents, their slack conversations, emails, generating big enough documents with small prompts. Sending a slack message or email should not have required AI within the company. Its wastage of resources and time, just to make it sound better without changing much of the meaning.

Microsoft cancelled Claude because they need to dog food Copilot since it sucks. This was acknowledged internally and it’s not a secret.

It is probably more expensive for Microsoft now since the Anthropic tokens were subsidized.

http://archive.today/l3EEo

Burning tokens is as easy as throwing dollars in a furnace. Token usage is not a good measure of productivity. Problem is nobody has really been able to figure out how to gauge productive AI engagement. Are your developers maximizing productivity or are they burning tokens or resisting change.

reminds me of "The Feeling of Power" by Isaak Asimov

When you unleash it without a plan or discipline, they are correct.

With discipline, it’s an aggregator.

https://devarch.ai/

AI is not more expensive than paying a human employee. AI is not capable of replacing a human employee yet so the premise of the title is wrong.

Taalas: https://taalas.com/products/

They've made a hardware LLM that reaches over 14k TPS on Llama 3.1 8B, and you can try it here: https://chatjimmy.ai/

So clearly hardware LLMs are the future, and the cost will be drastically reduced. But I know that all the AI labs want to create a perception of high prices forever.

>> Microsoft has reportedly begun canceling most of its direct Claude Code licenses

Hearsay information and click bait.

Say AI is very expensive and costs a lot. What happens when no one can or is not willing to actually do the work manually?

What if companies both don't see a large return on investment, and at the same time can't reduce their AI spend?

For MSFT: Just download DeepSeek locally and use it.

Or train your own power efficient stack.

Somehow this 'ai companies will never be profitable' is believed by so many. It's often used by those who don't like AI. There is no doubt that ai is the most lucrative business currently out there. It will only get better. Faster hardware, better algorithms

AI isn't expensive.

Opus is expensive. And almost always unnecessary.

For now.

Is that why GitHub is always down?

Have you tried closing another strait?