What was the motivation behind starting Pinecone and how did the company come to be?

At the end of the day, it's very simple. What Pinecone does—vector databases—has been a foundational capability in big companies for a very long time. 

From image search, to semantic search in text, to anomaly detection, to security and fraud detection—and I was personally involved in building these things at Yahoo and at AWS. I have friends and colleagues who work on the same things at Facebook and Google and so on. I myself work in this field, I've been a scientist and engineer working on these kinds of problems for a very long time. 

So it's very natural to me, and it’s been something I’ve wanted to do for a long time, but it never quite reached the tipping point in terms of mindshare and developing an awareness of it, that the average developer knew what it was. It was obvious to me that the value for tens of thousands of amazing applications is there—and by the way, these are not small applications—feed ranking at Facebook, text search at Google, or shopping recommendation at Amazon, they're all based on vector search. They're all based on something like Pinecone. These are not small applications, these are the cash cows at the biggest companies, driven by AI with this kind of information.

Nevertheless, the mindshare in the market wasn't there yet. And then, with foundational models, with language models, with vision models, creating these vector embeddings, creating these numeric representations of text and images and so on, suddenly that really sprung into the shared mindset of pretty much every developer. Now, of course, with everything that's happening with OpenAI and all that stuff, however much we thought we were already on steroids, now we're on double dosage.