Asha Sharma: belief

Transcript context

…I want to make sure people truly understand what you're saying here because not everyone truly understands post-training, pre-training. What's the simplest way to understand the difference there and just why it's such a big deal that investment is moving to post-training? The way that I think about it is to create a foundation model, it requires a tremendous amount of compute, a tremendous amount of science. Expertise as we're seeing which the cost for scientists or the average value is raising dramatically and I think an expertise that we've seen isn't everywhere in the world right now. And so it's just a big CapEx investment to do that. With this explosion of models that we talked about in the beginning, there's a lot of good models to choose from for different domains. And so I think that you just get more leverage economically, you get more leverage from a taste perspective of how you actually want to steer a model if you're actually doing reinforcement learning or some sort of fine-tuning to actually start to optimize what's off the shelf for some outcome like price, performance, quality. If you think about that, that's not crazy, right? Ranking is an age-old optimization problem where you don't want to just take what's off the shelf because there's amazing frameworks and UI and components that the world is react components that are out there. You still want to tailor the experience to a set of use cases or a set of people. I think it's just the same industrial logic. So in practice, what this means is there's a GPT-5 model. You're saying there's a lot of opportunity and a much more efficient way to spend money, which is take something like that and then train that on additional custom data that you have, whether it's data or just reinforcement learning, maybe even with humans to align it with what you wanted to achieve?…

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