Riffing Off VC Charles Hudson’s Blog Post, Here’s What I’m Trying to Answer
If startup founders sometimes ‘Build in Public,’ is the analogou sventure capitalist motto to ‘Think in Public?’ Anyway, there’s no doubt that the story of the trailing months has been Artificial Intelligence. Over Homebrew’s first decade we’ve always been interested in what we’ve called ‘Applied AI’ (along with Applied CV, Applied ML)— opportunities where the technology itself was being extended and commercialized for a specific purpose (contrasted with core R&D or base model development). Companies such as Shield.ai, Kettle, and MasterfulAI, among many others, were Homebrew investments which fit this definition. But it’s also clear we’re at a new inflection point where our previous hypotheses needed to be updated. So like a stone in a polishing tumbler, ‘what are our principles here’ had been tossing around my head for a handful of quarters. And then I read Charles Hudson’s post, which prompted me [AI PUN] to just write this down.
In “Honest and Naive Questions from a Generalist Seed VC Grappling with the Generative AI Revolution,” Charles (whom I love) touches on similarish topics to what Satya and I have been chatting about.
I. Base Models
- Given team, data, and compute costs, will the ‘price of entry’ and ‘price of innovation’ on base models increase or decrease over time
- Will different data types produce/require their own base models, and under what conditions are these base models likely to be produced by different companies/sources vs under a single corporate umbrella
- How does one measure ‘quality’ and what characteristics will base model owners compete on besides ‘quality’ [price, latency, privacy, etc]
II. AI ‘Middleware’
- In a multi-base model world, won’t there be some value created by dynamically switching between models depending on the use case? Won’t most application owners who seek to integrate ‘AI’ be interested in “best results” more so than having to choose a model upfront
- Will this middleware layer have access to enough model attributes to even know when/how to manage between models
- Can these companies protect their margins or will they be subject to either (a) intense competition pushing margins down to ‘base model query price + a few basis points or (b) the base model companies behaving like the record labels and basically being very deliberate about taking the majority of revenue created by a service built on top of their IP
- Will middleware be able to augment the base models with new proprietary data in order to create a differentiated product
- Will middleware companies seek to aggregate proprietary data sources in order to improve base models in unique ways
III. AI ‘Native’ Applications
- What are the conditions by which the addition of AI catalyzes new product offerings built around this technology versus ‘AI’ being a feature that the market leading applications can build into their platforms. Will Zendesk be replaced by an AI Customer Support startup or does Zendesk integrate AI. Repeat this question for everything B2B.
- OpenAI is a for-profit, running a venture fund, etc — what types of ‘partnership risk’ is there in backing alternatives who are competing with OpenAI funded startups. Are all the base models doing to use their cash to try and develop their own ecosystems and implicitly/explicitly try to pick winning apps?
- What will the engineering teams at ‘non-native’ adopters need to look like in order to successfully integrate, manage and compete with the native apps
- Will businesses who believe they have proprietary data to may help improve base models be able to sell that data and/or ‘pay it in’ to the model in return for discounted usage? Will they seek to create improved layers atop the base models
If you have POVs here I’m always happy to hear from you [hunter at homebrew dot co]! Remember, we invest our personal capital (typically a $100k-$500k initial investment, although ability to go larger when appropriate) in your companies and then get to work supporting you.
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