Capital Intense AI Bets
Some have described the bimodal distribution of GPU availability as “GPU-poor” and “GPU-rich” companies.
Will most returns to AI accrue to the companies with exclusive access to compute via GPUs and hardware, which are in short supply but necessary for large-scale training and inference?
What’s the risk and return for capital-intensive AI businesses? The rewards:
First-mover advantage in serving state-of-the-art models and quality
Aggregating demand in a blue ocean market with few actual incumbents
Capital-intensive businesses might get more capital-intensive in the future. Google only needed to crawl 26 million pages on the internet in 1998. In 2000, there were a billion pages. There are trillions of websites today (not all index, many spam).
Virtuous cycle between software usage and hardware design. Can vertically integrate both in specific ways.
The risks to a capital-intensive AI business:
Model architecture could be irrelevant when the model is trained, which could take months.
Disrupted up the stack: by middleware providers, products with distribution advantages, or vertical software.
Hardware ownership and deprecation. The premise of cloud computing is that companies don’t want to deal with managing data centers and real infrastructure.
Nobody knows what the most profitable use cases will be. Resource allocation depends heavily on this question (e.g., inference heavy? custom models? fine-tuning enough?)
First-mover advantage is sometimes overrated.
The risk and return for companies that don’t have direct access to GPUs and hardware. The risks:
Efficiency gains will likely be insignificant compared to hardware advances. Jevons Paradox and the old phrase about Intel and Microsoft: What Andy Giveth, Bill taketh away.
Capital-intensive businesses can reallocate their capital to copy, subsidize, or otherwise compete with you.
Return on investment. The companies that break through escape velocity, capital intensive or not, will have incredible outcomes.
Flexibility. Renting vs. owning is not always an obvious decision. It’s hard to fully utilize hardware, even for the best organizations.
The best distribution can’t be bought.