Asha Sharma: belief

Transcript context

…So when I hear this, what I'm thinking about is when I had Michael Truell in the podcast, the Cursor CEO, he talked a lot about how their big moat is the data that they capture from people using Cursor, accepting certain suggestions, not accepting other suggestions. Is that what you're talking about here? Just the proprietary data that companies gather from people using their product or is there something beyond that even? I think why we're seeing the rise of post-training happen is just that the models themselves are so powerful. As of this year, Nathan Lambert did this study that I thought was pretty interesting of all the top leader boards and it showed that once a model hits 30 billion parameters, the CapEx to actually train a model and put billions of tokens into a pre-run doesn't economically make sense and you can start to optimize on the loop. And so yeah, in many ways, I think using your own data is the best way to do that, but you can synthetically generate data. You have to come up with the rewards design, you have to actually roll it out, you have to A/B test it rigorously. You have to find the job to be done or the use case that it makes the most sense for. And then yes, that generates data that you can learn from. I haven't ever seen it be one loop for any product. I think it's multiple tracks running in parallel that are like assembly lines, if you will, and producing that. And so is this thesis that we're moving towards product as organism, is this basically for model companies or is this also true for, I don't know, SaaS businesses and tools and user tools?…

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