Standalone model vendors face a pricing squeeze

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 2:19

   Do you think that serving enterprises with some type of model offering, but ultimately selling whatever the core things that enterprises mostly buy, do you think a company can build long-term value around that? Or do you think that will ultimately, whatever that is, resides within an AWS or a Microsoft Azure or a GCP or something like that? I think it would be hard to underwrite that alone as a massive business because you basically are dealing with this issue where all of the hyperscalers at a minimum and then a few other companies that aren't kind of natural hyperscalers, but they're adjacent enough to the hyperscalers.

2. 3:08

   They need to make sure their models get used at an unbelievable scale because they have to win an infrastructure war. They need the compute to come to them. They have other software they have to sell. So it is so existential to them to have their models get used at massive scale that they will constantly drive down the price of AI and the quality up in AI. that you would just get squeezed into such a difficult position that I'm not sure what your value proposition would be.

3. 3:43

   Like, we ran these tests on Gemma from two weeks ago and the new open source model from Google. Yeah, not to be confused with the severance character, Gemma. Yes, yes, exactly. Um, so, so we ran these tests and it actually performed only like a couple of points worse than Gemini on, on our evals for, for accurate, accurate data extraction, uh, from, from enterprise documents and data. And so the fact that you have an open weights model from Google that is almost at par with their commercial model that is only was already kind of state of the art two, three months prior as a set of breakthroughs means that like, you know, you're As an enterprise, you're going to have a choice between an open weights model, you're going to have a choice between a commercial model, they're going to all perform extremely well, and you're really only going to be making a choice of like, do I want all of the scaffolding managed for me or do I want to just run this in my own data center or GPUs or my own cloud?

4. 4:46

   But, like, imagine being a kind of a complete standalone company trying to do that when you have, you know, Alphabet and Meta and, you know, Microsoft and then OpenAI and Anthropic and X, like, everybody's doing that. I don't know how you would, I don't know how you'd sort of slip in there. So, you know, there's a case to be made for obviously an extreme, like, deep vertical, you know, use case or some, you know, kind of niche.

5. 5:16

   But I'm taking your question literally. I think you'd still eventually then need to build the software around it. You'd have to get really good at all of the technology that surrounds delivering that AI model to the customer in a way that lets them actually use it, as opposed to just selling tokens back and forth. I don't think there's a business model for kind of an independent player at scale to just own kind of monetizing tokens.