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Comment by irthomasthomas | Hacker News Reader

If the goal was not to scoop them, why did openai put a massive team on this, working weekends, only after they heard rumors of the solution?

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Clearly The goal was to scoop Anthropic not a single researcher. OpenAI heard the rumor that Anthropic solved an open problem. So they went nuts pulling all plugs to scoop them.

Turns out it wasn’t actually Anthropic and just a researcher with a single Anthropic guy friend working on it .

Wild times

The researcher told them it was an independent effort, and they still pushed ahead with it.

I worked at OpenAI previously, but don't know any of the people involved in this.

My guess was it was probably this was more a nerd snipe than any action from OpenAI that was a "massive team" being put on it. Literally someone looking at this and asking "I wonder if our models are good enough yet".

It's easy to assume that having access to massive compute amounts means significant coordination, but this assumes that you're looking at the costs of this sort of thing from an external lens. Internally, tokens are often treated as free and infinite.

They said that a customer would have paid around 15 million for the required compute. I can't imagine that this was not a significant internal spending even with "free" tokens.

just because you can't imagine it doesn't mean it's not true

At Astra API prices that's 300B tokens (I saw 130B output tokens claimed elsewhere), large but not unheard of if you consider it across a few people doing random experiments with best-of-n type things. On my personal account, I've done a billion+ token days just on a normal pro 20x subscription. I know many others that wildly outpaced that by orders of magnitude. This was apparently 130B over 89 hours, so about 30x that rate. When things are free and you're expected to token max 30x seems fairly reasonable to me.

If you consider this as a cost to be compared against the question: "What does it take to be able to prove that you have a model that can solve the hardest problems that humans know about?", then spending a some amount of thousands/millions to know the boundaries of that seems not too important in comparison.

You've also got to consider this as compute that's allocated to pushing the frontier of what models can do, so while it's using GPUs that have been paid for etc., it's not like it's a cost that's supposed to be use less of this so that others can have capacity. If you made researchers afraid to use capacity like this, a lot of the things that improve would tend to do so significantly slower. (some may say that's a good thing ;)

A good way to think about this is when tokens are free, you get to choose whether you're optimizing for latency or intelligence rather than having to consider price.

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Publically, tibo (Codex owner) in Feb this year: https://x.com/thsottiaux/status/2024649339344445825

> OpenAI employees currently get unlimited inference. Usage is now peaking at > XX billion tokens per week for some of them.

Mathew Berman (AI Youtuber) in Jun: https://x.com/MatthewBerman/status/2067270730795134984

> I've used 25 billion tokens in the last 7 days.

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