Nathan Labenz: belief

Transcript context

…Yeah. That's what I'm guessing over time. Yeah. Yeah. Okay. Cool. One other big kind of question. question that I've asked a lot of people a lot of times, and I think you're the perfect person to touch on it. So you, of course, you know that Databricks acquired this company called Mosaic ML not too long ago, maybe two years ago now. And what Mosaic was doing, I thought was really interesting, which was starting with open source models, working with particular customers to do continued pre-training on data sets, which I assume were very often internal data sets, like the sort of data sets that might sit in a snowflake. I was really surprised. I spoke to Ali Godsey, the CEO of Databricks at a event not too long ago, and he said, we killed that product. So they basically turned Mosaic into a in-house research unit. But that product of offering this continued pre-training to try to create a model that like really knows your business inside and out, basically they don't offer it anymore. I was very surprised by this because I think, man, if I had a, if I'm GE or if I'm 3M or any number of a hundred year old, millions of employees over the generations, companies that have this incredible history and so much data that's accrued that nobody really understands at the company these days. If I could have a model that could have similar command of that information that doesn't, that only exists in my company and just nobody else outside has ever had access to as the the foundation models today generally have world knowledge, I would think that would be insanely valuable for a lot of enterprises. And yet we don't seem to be seeing, to my knowledge, many instances of whatever, 3M GPT or GE GPT, Pfizer GPT. Like, why don't we see that? Do you have a point of view? I do. So this is kind of similar to... how up until recently, when you'd ask a question on ChatGPT, you'll say, Hey, my information cutoff is whatever, a year ago, and I can only answer questions up to that point. And then web search as a tool came in, and now all of these platforms would use web search to give you the most up-to-date information so that their world information can be It is more about the intelligence to figure out when to use the tool to retrieve the information and then make sense of it and then give it back to you, versus having been trained, pre-trained with all that information upfront. To me, that pattern is exactly what's playing out, right? So in the enterprise world, you have a lot of information, and then your text to SQL and RAG solutions can bring that information in for the agent, for the platform to reason with and then give you information. The nice thing about that is it is substantially cheaper. The model keeps getting better as the underlying premier model keeps improving. And it's also, relatively easily tunable. You can update it, you can change things and so forth. So that means for me, majority of businesses would continue to benefit from this architecture rather than codifying all of that information in the weights of the model. They'll just use the information and then use tools to retrieve parts of the information that are relevant. The exception to that is what we discussed earlier, which is if there are certain tasks that require either high throughput, low cost, if you have a lot of data and in an area that the model has not seen before, then it might make sense to go create custom models for those specific tasks. So I do believe there is going to be increasingly a need for task-specific small models in large corporations or when we have that need. But still, the majority of the use cases will be more retrieval-oriented.…

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