Most founders in their 20s and 30s have heard the same advice a thousand times. But when the Chief Product and Technology Officer of Netflix, Elizabeth Stone, tells you what skill her company is now hiring for in the AI era, it's worth listening. Forget "scrappy problem-solving." Stone says Netflix now needs "systems thinkers" to build its future.

What does a systems thinker look like? Someone who can “look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI,” Stone explained on Lenny's Podcast. This isn't just about scaling; it's about shifting from localized solutions to foundational infrastructure that makes every future innovation cheaper and faster.

Netflix, a company famous for empowered, autonomous teams, is intentionally pivoting its approach. While local teams once moved fast, AI demands common infrastructure and shared understanding. “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?” Stone said. Instead, the goal is to “encode that in our paved paths and our ways of working.”

Key Takeaways

  • Netflix is actively prioritizing and hiring for "systems thinkers" to build its AI-driven future, a shift from past localized autonomy.
  • A systems thinker can abstract business domains into foundational building blocks and common infrastructure, rather than just solving immediate, isolated problems.
  • This mindset extends beyond engineering: designers now need to apply "design systems thinking" to define brand expression and user experience guardrails.
  • The goal is for teams to contribute components that serve the broader organization, ensuring quality and scalability for colleagues and future innovation.
  • Elizabeth Stone's "Step Out One Click" method offers a practical, step-by-step approach to cultivate this crucial systems-thinking skill.

The Elizabeth Stone's 'Step Out One Click' Method for Systems Thinking

Step 1: Zoom out from the immediate problem: Each problem you're trying to solve, step out one click to the what am I assuming is true about the broader space in solving this problem?

Step 2: Consider broader implications: Do I think that the way I was planning to build this is going to make sense in a way that scales across multiple content types? Or it could be something that's a capability that then is contributed to a platform set of offerings for multiple areas.

Step 3: Connect to the larger organizational purpose: Is the consumer problem that I'm solving with this feature going to be one of the most important consumer problems that Netflix is going to need to solve as we have an expanding world of entertainment and we want to make it more personalized and immersive.

Step 4: Think about how to help others: How do I leave a better version of these systems? How do I think about the thing that's going to be high quality and scale for others? ...do the thing that is right for the broader organization instead of just what's right for you locally.

When This Works (and When It Doesn't)

This method shines when individuals at all levels need to develop a systems-thinking mindset, encouraging them to consider the wider context, scalability, and organizational impact of their work. It's about questioning assumptions and ensuring contributions are valuable beyond immediate tasks, building stronger systems for future innovation.

It works best when your organization is growing, expanding its product surface, or moving towards a platform strategy. For a very early-stage startup still searching for product-market fit with a single, focused offering, the overhead of this level of abstraction might slow down crucial validation cycles. However, even then, anticipating future needs can prevent costly re-architecting down the line.

What to Do With This

Imagine you're a founder building an AI assistant for sales teams, and your next feature is a new "cold email generator" module. Before you just build it, apply Stone's "Step Out One Click" method. First, Step 1: Zoom out from the immediate problem. Instead of assuming users just need any cold email, what's the broader assumption? Is it that they need personalized, high-conversion outbound copy for any channel? Next, Step 2: Consider broader implications. Will this email generator component scale to also generate LinkedIn messages, or even chat bot responses, using the same underlying personalization engine? Can it be a core "outbound messaging engine" capability for your platform? For Step 3: Connect to the larger organizational purpose, ask: Is optimizing outbound sales messaging with AI the most critical problem your company needs to solve to fulfill its mission of "automating revenue generation"? Finally, Step 4: Think about how to help others. How can you build this email generator so that future engineers can easily plug in new AI models for tone, add new CRM integrations, or expand to new messaging channels without a complete rewrite? Think about the APIs and data models you're leaving behind for your colleagues.