Does Generative AI Need a New Business Model?

Generative AI is expensive. Training a large language model costs tens or hundreds of millions of dollars. OpenAI is said to be spending $12 billion on Nvidia chips next year—equal to roughly 40% of Apple’s entire R&D spend. Big Tech’s investment in generative AI startups is somewhere around $20 billion. Clearly, those “in-the-know,” think there is a massive opportunity to get a return on these kinds of capital expenditures.

Here’s the issue: the current business model doesn’t make sense because increasing usage conflicts with profits. 

There are three major costs involved in generative AI: 

1. Infrastructure: data centers, servers, GPU chips, etc
2. Training: operating costs to train models
3. Inference: operating costs to generate content for users

The first two costs—infrastructure and training—are knowable by the companies building generative AI. They can predict the capital investment and operating expenses to train a model based on the size of the model being trained. There’s clearly some margin of error because training isn’t an error-free process, but it’s logical that estimates are within an acceptable margin of error. If these were the only costs, one could create a pricing model that would provide a decent return on capital.