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Elizabeth Stone: recommendation

19 Jul 2026 Lenny's Podcast Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO)

“And what are the bumpers or guardrails I should keep in mind? I think we need to encode that in our paved paths and our ways of working.”

— Elizabeth Stone

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Speaker
Elizabeth Stone
Attribution
Verified speaker
Claim type
recommendation
Recorded
19 Jul 2026
Publisher
Lenny's Podcast

Transcript context

…On the systems thinking piece, is the reason this is becoming more important, that people are moving so fast that you need to invest in platforms and frameworks and design language, and basically teach people to fish so they can not be blocked or are there other reasons? I think it's probably velocity. So, platforms do have a benefit of leverage. So, in general, that's an opportunity with or without AI, for a platform to get most teams 80% of the way there. And then they don't have to reinvent those building blocks. We have more bets that we're making across the business, more things we're trying to build. So, platform mindsets are good, and it's something that is relatively more recent for Netflix to think about that being a real critical enabler. There is also the sense of a scaffolding in a world of AI. So, not just the higher velocity, but you have more people doing more types of work that are different or new, like we were talking about. And there's risk that comes with, how do you think about access and identity in that situation? How do you think about security in that situation? How do you think about shipping high quality code and design and user experiences? And so, I don't think it scales well to have each person who's building something have to go figure out, could you remind me what good looks like here? And what are the bumpers or guardrails I should keep in mind? I think we need to encode that in our paved paths and our ways of working. And for a data science or analytical field to encode, here's the source of truth data, here's how to interpret it, here's how to access it, here's what to do with it or not to do with it and to be careful with certain types of data. An organization that has thousands of people can no longer rely on tribal knowledge or I'm going to find the one person who knows this. So, this was a challenge that was there before AI. It's probably a more urgent challenge with AI. And I like the idea of using AI or any new tech to motivate... We knew this is work we needed to do. No time like the present to invest in that more heavily across the team. I wonder if another reason for this becoming more valuable is because agents are now doing a lot of work. And giving them the context, giving them the scaffolding, giving them the design language, just speeds all that up?…

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