Sequoia partner Pat Grady traced his firm’s AI journey to 2016-2018, when it was becoming clear that cloud and mobile were late in the cycle and partners began “dedicating more of our time to scanning the horizon.”
Digging deep on data and machine learning, he said, led the firm to Hugging Face and some of the application companies that have led the current wave.
He likened foundation models to databases, and suggested they wouldn’t be that profitable in and of themselves, citing the example of MongoDB, a very good database company that was worth mere billions, as opposed to many multiples of that. OpenAI, he allowed, was in a different category with ChatGPT as a consumer product, but mostly AI was a “technical enabler, not a consumer front-end.”
He argued that foundation models were advanced enough now for founders to build market-defining application companies: “I think the capabilities today are enough to build just trillions of dollars worth of new businesses.”
He also took the contrarian view that big new improvements in the foundation models weren’t necessarily a good thing just now. Customers were saying “please give us a stable model where we don’t have to re-do our prompts every time,” Grady said. “A little stability for the model layer would actually be good.”
What Sequoia LPs mostly wanted to know about AI, Grady said, was whether generative AI would ruin their existing software portfolio-and he is confident it will not.
“I think the thing to be done today is very different. It’s not, ‘find the software company and build the AI native version of it,’ it's ‘find the services industry and AI-enable it,’” he declared.
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