Elizabeth Stone

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598 appearances 1 recordings 1 series first heard Jul 2026 last heard 19 Jul

Elizabeth Stone’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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

So that includes clarity on source of truth data, guardrails on shipping code to production or testing before we make large changes,

Thinking about opportunities where we can trust the output of AI versus we should have a process or review that helps us check that we're getting high quality outcomes and the importance of reiterating that humans are still responsible for what happens.

So it can be that an agent wrote the code or I helped to do an analysis when that's not really my background, but it doesn't make, it doesn't make people not have the responsibility that comes with what they've created.

So I think investing in some of those core infrastructure and practices and reiterating the accountability and responsibility for the outcomes helps to balance some of like what's possible with what we should actually be doing.

So you've mentioned some of the things, so I'll reiterate them and then maybe build.

So I have found that PMs, designers, data scientists are able to get farther in the product development lifecycle before engineering really needs to be front of the line in unlocking things than was true a couple years ago.

I say that with some caution because, like we were talking about, I don't think it's great to all of a sudden have thousands of prototypes if they're not aimed at, this is an important problem to solve for the business.

And the engineering partners are aware that we're solving that problem and that designers and product managers are going to take the lead in starting to shape the idea.

But it's not working in a vacuum and it's not throwing a bunch of spaghetti at the wall to see what sticks.

But when it's the right problem approached in a thoughtful way with some alignment on that, I've seen product design data science move faster in the direction of let's get to something that's testable on this hypothesis.

So that's prototyping, that's writing code.

The other thing I've seen as being very valuable is we have a lot of information running around in the virtual walls of Netflix.

We have experiments we've run over decades.

We have insights from consumers.

We have input from stakeholders across the business.

And that was a problem that really presented a challenge of like, how do we get the most out of that long history of knowledge and learnings to say, let's apply that to the problem we've got now to move faster in, this is a promising path, or this is something that we've learned something about and we could leverage here.

And AI is very powerful at distilling information

looking across a broad set of things, doing an analysis around it, getting to the core of here's some insights to start with.

I would hesitate to rely on that exclusively, but I think it's a head start.

And I find even in my own work day to day, instead of

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