Basic common sense would tell you that starting with an assumption and working backwards to justify that assumption is called succumbing to confirmation bias.

I sourced my cost/flop estimate from here: https://aiimpacts.org/2019-recent-trends-in-gpu-price-per-fl...

I'll acknowledge that a fleet entirely made up of A100 GPUs running at full capacity 100% of the time with Azure's 3-year commitment pricing (likely very low margin) would make this 5 orders of magnitude cheaper. But the sequential nature of transformers makes it difficult to achieve perfect utilization, and there aren't enough A100 GPUs in the Azure fleet to serve ChatGPT's billion users, so they're certainly running on mostly lesser hardware. Also, my estimate was for GPT-3 not 4, and neglected to address any memory or bandwidth costs, not to mention the costs to train and iterate on the models in the first place, including legions of data curators and many researchers commanding million dollar salaries.

If ChatGPT plus is $20/mo and each user uses it ~10 times per day, then MS/OpenAI would break even if my estimate were 2 orders of magnitude too high. Within the year, we'll have a much better idea of the real cost of these models.

They are just giving away GPT-3 grade queries. In terms of revenue, that's NaN% gross margin! Thus, -10,000% gross margin is quite the improvement. While I personally don't think that they are spending quite that much on compute, burning investor capital to build a customer base is a time honored tradition. Which means we can't really deduce that because -10,000% is such a big number, that can't possibly be their compute costs.

“But just basic common sense should tell you that they are not operating at -10000% gross margins.”

I’ll be honest, I laughed out loud at this.

Pretty sure I read somewhere that Bing had around 1 billion searches per day BEFORE the hype of bingchat. At $7/query that makes MSFT’s $10 billion investment basically peanuts. Less than 2 days of Bings GPT usage payments. Lmao.