> I cannot even understand what I do stands for in the context of your question That he had a higher budget than he knew what to do with. When I worked at Goog

> I cannot even understand what I do stands for in the context of your question That he had a higher budget than he knew what to do with. When I worked at Google I could bring up thousands of workers doing big tasks for hours without issue whenever I wanted, for me that was the same as being infinite since I never needed more, and that team didn't even have a particularly large budget. I can see a top ML team having enough compute budget to run a task on the entire Google scrape index dataset every day to test things, you don't need that much to do that, I wasn't that far from that. At that point the issue is no longer budget but time for these projects to run and return a result. Of course that was before LLMs, the models before then weren't that expensive.