When Tuhin Srivastava walked into the Greylock offices at 8:00 am in late 2019, he was only a few months into unemployment post-acquisition of his startup.
He declared, without any preamble, “I’m thinking about starting a new company in ML. We just really need to take the whole pipeline cost down, and improve the iteration cycle. Otherwise, most teams are never going to ship.”
I had known Tuhin for several years, having met him and Phil Howes early in his prior startup journey and loving their talent, entrepreneurialism, user centricity and product sense. Ever since, I’d been looking for an opportunity to work with them. As it turned out, this was the moment I had been waiting for.
We proceeded to talk for several hours about the idea that became Baseten. I found the diagram from my first meeting notes, which I’ve now seen Tuhin recreate some form of many times over the past two years.
We are at an extraordinary point of time in the arc of machine learning. Increased sophistication in collecting and managing data, advancements in model architectures (giant transformers!), open-source pre-trained model availability, and ever larger and cheaper compute all seem to come together to promise tremendous value for enterprises – and potentially even an economy of abundance. McKinsey famously estimated that industries could unlock $5T+ of value from integrating ML. And yet, real progress has fallen short.