AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
At Stripe, they believe in finding the correct set of early users to co-create their products with. An example is Stripe billing, where they worked closely with existing users like Figma and Slack to understand their needs and gather feedback. Only when this alpha group was satisfied, did they consider a broader release.
Stripe emphasizes building a product-oriented engineering team. Even before hiring their first product manager, every engineer at Stripe exercises many skills typically found in product managers. They collaborate closely with users, focus on user needs, and prioritize product strategy.
Meticulous craftsmanship is a core value at Stripe. They operationalize this principle by implementing friction logging, where team members analyze the user experience, identify areas of friction, and address them with meticulous attention to detail. This practice helps prioritize user-centric improvements and drive better product experiences.
David Singleton, CTO at Stripe, believes in getting deeply involved in the product development process. He practices engineering occasions, where he joins a team, completes a small task, and meticulously documents the experience. This hands-on approach helps him maintain a deep understanding of the challenges and achievements within the team.
The speaker highlights the importance of understanding users through direct communication and surveys to prioritize roadmap development. They emphasize the significance of measuring user experiences and using the feedback to invest in areas that enhance productivity.
The podcast discusses how Stripe operates in a constantly changing environment due to evolving user needs. Despite the challenges, Stripe prioritizes reliability and uptime due to the critical nature of their services. They employ automated testing, staging environments, and gradual deployment to ensure high availability and minimize issues.
Stripe leverages machine learning and AI techniques in areas like payment fraud detection (Radar) and offering language models for generating code and SQL queries. They prioritize serving AI startups by providing cost-effective AI tools and enabling monetization models. Stripe actively applies AI to improve internal efficiency, user support, and development processes.
Brought to you by Mixpanel—Product analytics that everyone can trust, use, and afford | Eppo—Run reliable, impactful experiments | Braintrust—For when you needed talent, yesterday
—
David Singleton is Chief Technology Officer at Stripe, where he oversees engineering and design teams. Since joining Stripe, David has helped grow the technology org across the U.S. and developed new engineering hubs in Singapore and Dublin as well as Stripe’s fifth hub, remote engineering, across the globe. Before Stripe, he spent 11 years at Google, where he was VP of Engineering, leading product development and coordinating more than 15 different hardware partnerships. In today’s episode, we cover:
• Hiring secrets that set Stripe employees apart
• How to build a product-minded engineering team
• How to operationalize meticulousness
• Strategies for maintaining developer productivity at scale
• The process of “friction logging” used to make better products
• How AI is changing the way engineers work
• Insights for planning and prioritizing at scale
—
Find the full transcript at: https://www.lennysnewsletter.com/p/building-a-culture-of-excellence
—
Where to find David Singleton:
• Twitter: https://twitter.com/dps
• LinkedIn: https://www.linkedin.com/in/davidpsingleton/
• Website: https://blog.singleton.io/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) David’s background
(04:22) How Stripe’s unique hiring process has helped them build an incredible team
(12:27) An example of a relentlessly curious and passionate employee
(14:11) Structured hiring loops at Stripe
(16:39) How Stripe built a product-minded engineering culture
(21:56) Stripe’s operating principles
(25:39) How Stripe uses “friction logging” to build a meticulous product culture
(32:22) How to operationalize friction logging
(35:02) How to set PMs up for success
(36:53) Stripe’s collaborative approach to product evaluation
(41:17) Advice for presenting to CTOs
(42:58) How to get better at building products
(45:28) Stripe’s “engineerications” and the importance of getting into the weeds as a leader
(52:03) Auto-testing and other strategies to improve shipping speeds
(59:29) Improving developer productivity
(1:00:54) How AI has impacted the way Stripe builds product
(1:07:03) Why David is excited about Copilot
(1:09:24) Lessons from managing people
(1:14:30) Planning and prioritization based on first-principles thinking
(1:18:23) Lenny’s feedback from using Stripe
(1:19:14) What’s next for Stripe
(1:22:10) Lightning round
—
Referenced:
• Stripe: https://stripe.com/
• Jeff Weinstein: https://www.linkedin.com/in/jeffwweinstein/
• How we use friction logs to improve products at Stripe: https://dev.to/stripe/how-we-use-friction-logs-to-improve-products-at-stripe-i6p
• GitHub Copilot: https://github.com/features/copilot
• High Output Management by Andrew Grove: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884
• Build by Tony Fadell: https://www.amazon.com/Build/dp/1787634116/ref=tmm_pap_swatch_0
• Scaling People: Tactics for Management and Company Building by Claire Hughes Johnson: https://www.amazon.com/Scaling-People-Tactics-Management-Building/dp/1953953212/
• Andrej Karpathy on YouTube: https://www.youtube.com/@AndrejKarpathy
• Midjourney: https://www.midjourney.com/home/
• Emily Sands: https://www.linkedin.com/in/egsands/
• Michelle Bu: https://www.linkedin.com/in/michellebu/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Lenny may be an investor in the companies discussed.
Listen to all your favourite podcasts with AI-powered features
Listen to the best highlights from the podcasts you love and dive into the full episode
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
Listen to all your favourite podcasts with AI-powered features
Listen to the best highlights from the podcasts you love and dive into the full episode