Generative AI is booming
- Hundreds of new startups rush into the market to develop foundation models, build AI-native apps, and stand up infrastructure/tooling
- Models like Stable Diffusion and ChatGPT are setting historical records for user growth
- Side-by-side comparisons show AI models outperforming humans in some tasks by multiple orders of magnitude
- What we don’t know, and what has now become the critical question, is: Where in this market will value accrue?
High-Level Tech Stack: Infrastructure, Models, and Apps
- The stack can be divided into three layers: applications that integrate generative AI models into a user-facing product, either running their own model pipelines (“end-to-end apps”) or relying on a third-party API
- Models that power AI products, made available either as proprietary APIs or as open-source checkpoints
- Infrastructure vendors (i.e. cloud platforms and hardware manufacturers) that run training and inference workloads
Infrastructure vendors touch everything, and reap the rewards
- Nearly everything in generative AI passes through a cloud-hosted GPU (or TPU) at some point.
- Cloud providers collectively spend more than $100 billion per year in capex to ensure they have the most comprehensive, reliable, and cost-competitive platforms.
Where will value accrue?
- There don’t appear to be any systemic moats in generative AI.
- The potential size of this market is hard to grasp – somewhere between all software and all human endeavors – so we expect many, many players and healthy competition at all levels of the stack.
Generative AI apps struggle with retention and differentiation
- Growth alone is not enough to build durable software companies. Critically, growth must be profitable
- In the absence of strong technical differentiation, B2B and B2C apps drive long-term customer value through network effects, holding onto data, or building increasingly complex workflows.
- It’s not yet obvious that selling end-user apps is the only, or even the best, path to building a sustainable generative AI business.
Model providers invented generative AI, but haven’t reached large commercial scale
- Generative AI wouldn’t exist without the brilliant research and engineering work done at places like Google, OpenAI, and Stability
- Models released as open source can be hosted by anyone, including outside companies that don’t bear the costs associated with large-scale model training
- Commercialization is likely tied to hosting
- Demand for proprietary APIs is growing rapidly
- Hosting services for open-source models are emerging as useful hubs to easily share and integrate models
