Build AI products at on-AI companies with Emily Glassberg Sands from Stripe

**Elad** (0:05)
Today, Sarah and I are joined by Emily Glassberg Sands, who's the Head of Information at Stripe, which includes Data Science, Growth, Machine Learning Infra, Business Applications, and Corporate Technology. Emily was previously the VP of Data Science at Coursera, where she led development of AI-powered products to have personalized learning, scalable teaching, skill measurement, and more. We're excited to talk with Emily today about Stripe, AI, FinTech, and Education.
Emily, welcome to No Priors.

**Emily Glassberg Sands** (0:32)
Thanks so much for having me.

**Elad** (0:34)
Yeah, thanks so much for joining.
So you now look at the information or get Stripe. Can you tell us a little bit more about what the organization does, how it's evolved under your tenure, and what are some of the span of responsibilities that you're focused on?

**Emily Glassberg Sands** (0:45)
Yeah. So I joined Stripe back in 2021
Originally actually to lead Data Science, and David Singleton, Stripe's CTO, reached out. I didn't know a ton about Stripe, but I knew millions of businesses were using it to collect payments, which had to mean really interesting data on those businesses and on a large swath of the economy. Stripe's clearly helping companies run more effectively and also in a position to learn from its data what kind of interventions significantly improve companies' long-term success, and in some cases to actually action those. Today, I wear two hats. So the first is I support a bunch of different teams that are together tasked with enabling the effective use of data across Stripe. And this includes from decision making internally to building data-powered products. We've been investing a bunch in foundations, which includes building out our ML infrastructure and better organizing our data, the really sexy stuff.
But also in applications like seeding a bunch of new gen AI bets and getting them out to our users. So that's kind of hat one. And then second, I'm accountable for our self-serve business. So a huge number of SMBs and startups come to Stripe directly to get started, they self-serve through the website. And we're really focused on understanding who those users are, getting them the right shape of integration efficiently, building product experiences that meet their needs, including as they grow and growing the portfolio of products they use. So for many of our users, it's not just payments, but invoicing or subscriptions or billing or tax or Revrec, depending on what their business model demands.

**Elad** (2:21)
And I guess Stripe for a long time has been doing different things. And ML in terms of traditional ML, I think fraud detection and the fraud detection API that you all have is one example of that. But you were actually quite early in terms of adopting LLMs and early generative AI models. Could you tell us a little bit more about how that came about, how the interest was sparked and how adoption really took off?

**Emily Glassberg Sands** (2:41)
I mean, I think it's fair to say that Stripe is first and foremost an AI company. As you know, Fintechs, including Stripe, have long used traditional ML in many contexts, including sort of fraud and risk. But first and foremost, we're building financial infrastructure for the Internet. So Stripe got started by enabling first, really digitally native startups to accept online payments. And then over time, millions of companies started relying on Stripe's financial infrastructure for a bunch of different needs, whether that's reducing fraud or managing money flows or unifying online or offline commerce, all the way to launching embedded financial offerings.
And so as not a kind of first and foremost AI company, we, probably like a lot of people listening to this podcast, had kind of our, like, hey, what the heck are we gonna do moment, a year or so ago when LLMs really broke through the zeitgeist. And we were looking at the technical breakthroughs on the product launches all over the ecosystem with awe, but also honestly, a little bit of overwhelm, the sense of, well, there's very clearly a real opportunity here to better serve our users, but what is it exactly? And how do we get it off the ground quickly and safely? So it starts with a story of three engineers who hacked together in three weeks an internal beta for an LLM Explorer.
And the basic idea of LLM Explorer was, hey, let's get a chat GPT-like interface in the hands of the 7,000 talented Stripe employees and really let them figure out how to apply it to their work. Our leaders all the way up to John and Patrick have intentionally crafted this strong culture of kind of bottoms up experimentation.
And we think a lot about sustaining it internally as we grow. And with LLMs, it was no different, right? And so where we started was, let's quickly unlock internal experimentation. Let's get LLMs safely in the hands of all employees at Stripe. The enthusiasm was palpable, you know, at Stripe as it was across industry. We knew the experimentation was going to happen. And so we really wanted to make sure that we enabled it to happen well and safely.