Gergely Orosz

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And with this, let's get back to DAX and open code.

had a recent tweet about how inference is actually really really profitable like you were quoting someone who was saying like oh you know like these these uh ai model providers are are you know like having might be having financial difficulties can you explain to those of us software engineers who you know we don't we don't do inference i mean we use these models and we just assume that this this must be our our business how can it be profitable why is it profitable what are you seeing

It's interesting because when Brian Cantrell was on the podcast, he used to work at building a cloud service that would compete with AWS.

And he said that back then, this was the same thing.

AWS back then hid their financials, everyone thought, and they told everyone that cloud is a terrible business and it's like your red blood everywhere.

And then he started to do it and he said like, actually,

It's a really freaking profitable business to run a cloud, but it's kind of a welcome secret because why would Amazon or any other provider advertise the business that is printing money?

Yeah.

This being said, you also said something in public about GPUs.

I'm quoting you, there are just enough GPUs.

It's crazy that even a company our size is being bottlenecked by us.

What does that mean?

I want to talk about the hype of what productivity gains these AI tools, specifically AI agents are giving to engineering teams and the reality.

And you wrote a now very heavily quoted tweet, which was, I'll quote just some parts of it.

Everyone's talking about their teams, like they were at peak of efficiency and bottlenecked by the ability to produce code.

But the way things actually look like is, and you listed a few things like your team, your org really has good ideas.

People are not using AI to be 10x more productive.

They're using it to turn out their tasks with less energy to spend and so on.