Who Owns the Generative AI Platform?
We’re starting to see the very early stages of a tech stack emerge in generative artificial intelligence (AI). Hundreds of new startups are rushing into the market to develop foundation models, build AI-native apps, and stand up infrastructure/tooling.
Many hot technology trends get over-hyped far before the market catches up. But the generative AI boom has been accompanied by real gains in real markets, and real traction from real companies. Models like Stable Diffusion and ChatGPT are setting historical records for user growth, and several applications have reached $100 million of annualized revenue less than a year after launch. Side-by-side comparisons show AI models outperforming humans in some tasks by multiple orders of magnitude.
So, there is enough early data to suggest massive transformation is taking place. What we don’t know, and what has now become the critical question, is: Where in this market will value accrue?
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Navigating the High Cost of AI Compute
The generative AI boom is compute-bound. It has the unique property that adding more compute directly results in a better product. Usually, R&D investment is more directly tied to how valuable a product was, and that relationship is markedly sublinear. But this is not currently so with artificial intelligence and, as a result, a predominant factor driving the industry today is simply the cost of training and inference.
While we don’t know the true numbers, we’ve heard from reputable sources that the supply of compute is so constrained, demand outstrips it by a factor of 10(!) So we think it’s fair to say that, right now, access to compute resources — at the lowest total cost — has become a determining factor for the success of AI companies.
In fact, we’ve seen many companies spend more than 80% of their total capital raised on compute resources!
In this post, we try to break down the cost factors for an AI company. The absolute numbers will of course change over time, but we don’t see immediate relief from AI companies being bound by their access to compute resources. So, hopefully, this is a helpful framework for thinking through the landscape.
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