No Priors Ep. 50 | With Stripe Head of Information Emily Glassberg Sands

Many companies that are building AI products for their users are not primarily AI companies. Today on No Priors, Sarah and Elad are joined by Emily Glassberg Sands who is the Head of Information at Stripe. They talk about how Stripe prioritizes AI projects and builds these tools from the inside out. Stripe was an early adopter of utilizing LLMs to help their end user. Emily talks about how they decided it was time to meaningfully invest in AI given the trajectory of the industry and the wealth of information Stripe has access to. The company’s goal with utilizing AI is to empower non-technical users to code using natural language and for technical users to be able to work much quicker and in this episode she talks about how their Radar Assistant and Sigma Assistant achieve those goals. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @emilygsands Show Notes: 0:00 Background 0:38 Emily’s role at Stripe 2:31 Adopting early gen AI models 4:44 Promoting internal usage of AI 8:17 Applied ML accelerator teams 10:36 Radar fraud assistant 13:30 Sigma assistant 14:32 How will AI affect Stripe in 3 years 17:00 Knowing when it’s time to invest more fully in AI 18:28 Deciding how to proliferate models 22:04 Whitespace for fintechs employing AI 25:41 Leveraging payments data for customers 27:51 Labor economics and data 30:10 Macro economic trends for strategic decisions 32:54 How will AI impact education 35:36 Unique needs of AI startups

Elad GilhostEmily Glassberg SandsguestSarah Guohost

Feb 8, 202439mWatch on YouTube ↗

EPISODE INFO

Released: February 8, 2024
Duration: 39m
Channel: No Priors
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Many companies that are building AI products for their users are not primarily AI companies. Today on No Priors, Sarah and Elad are joined by Emily Glassberg Sands who is the Head of Information at Stripe. They talk about how Stripe prioritizes AI projects and builds these tools from the inside out. Stripe was an early adopter of utilizing LLMs to help their end user. Emily talks about how they decided it was time to meaningfully invest in AI given the trajectory of the industry and the wealth of information Stripe has access to. The company’s goal with utilizing AI is to empower non-technical users to code using natural language and for technical users to be able to work much quicker and in this episode she talks about how their Radar Assistant and Sigma Assistant achieve those goals. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @emilygsands Show Notes: 0:00 Background 0:38 Emily’s role at Stripe 2:31 Adopting early gen AI models 4:44 Promoting internal usage of AI 8:17 Applied ML accelerator teams 10:36 Radar fraud assistant 13:30 Sigma assistant 14:32 How will AI affect Stripe in 3 years 17:00 Knowing when it’s time to invest more fully in AI 18:28 Deciding how to proliferate models 22:04 Whitespace for fintechs employing AI 25:41 Leveraging payments data for customers 27:51 Labor economics and data 30:10 Macro economic trends for strategic decisions 32:54 How will AI impact education 35:36 Unique needs of AI startups

SPEAKERS

Elad Gil
host
Emily Glassberg Sands
guest
Sarah Guo
host
Narrator
other

EPISODE SUMMARY

In this episode of No Priors, featuring Elad Gil and Emily Glassberg Sands, No Priors Ep. 50 | With Stripe Head of Information Emily Glassberg Sands explores stripe’s Data Chief on Generative AI, Fintech Infrastructure, and Education Emily Glassberg Sands, Head of Information at Stripe, explains how Stripe is using data science and generative AI to power both internal operations and user-facing financial products. She describes Stripe’s bottoms‑up approach to LLM adoption, starting with an internal LLM Explorer and accelerator teams that seed new AI bets. Sands details concrete applications like Radar Assistant and Sigma Assistant that translate natural language into code and analytics, democratizing fraud controls and business insights for non-technical users. She also connects Stripe’s macroeconomic vantage point and her labor economics background to broader opportunities in fintech, AI-native startups, and the future of education and skills.

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