One very popular area of venture investing right now is startups building the “picks and shovels” for AI. These companies range from model fine-tuning, to observability, to AI “abstraction” (e.g. AI inference as a service). The bet investors are making is that as startups and enterprises add AI to their product offerings, they might be unwilling or unable to build these capabilities in-house, and would prefer a buy vs. build approach. The following post is a deep dive on AaaS (AI-as-a-Service) startups, specifically focusing on AI inference startups, covering the following: • Why there’s even a need for AI inference abstraction • How the convergence of developer experience, performance, and price among inference abstraction platforms implies rapid commoditization • The brutal competitive dynamics and the fact that the current available TAM is actually highly constrained (likely well under $1B) • What an investor needs to believe to invest in AI inference companies, focusing on the need for massive TAM expansion, product expansion, and potential M&A opportunities. I also argue that only megafunds can “play” at this layer • Startups using composable building blocks, like AI inference abstraction platforms, will benefit in the short-term but suffer in the longer term Check it out here https://open.substack.com/pub/eastwind/p/a-deep-dive-on-ai-inference-startups