Elizabeth Stone, Netflix's Chief Product and Technology Officer, discusses how AI is transforming product and technology roles while maintaining the importance of specialized expertise. She emphasizes that while tools like generative AI enable product managers, designers, and engineers to work more fluidly across traditional functional boundaries—prototyping faster, analyzing data more efficiently, and writing initial code—this doesn't eliminate the need for specialized skills and craft excellence.

Stone identifies several key shifts at Netflix. Product managers, designers, and data scientists can now progress further in the product development lifecycle before engineering becomes a bottleneck, and business stakeholders across finance and content can generate hypotheses faster. However, she stresses that comparative advantages persist: data scientists excel at interpreting data accurately, product managers at framing problems correctly, and engineers at ensuring scalability and quality. Rather than eliminating functions, AI creates demand for new skill sets, particularly "systems thinkers" who can abstract across business domains to define building blocks and infrastructure. In design, this means hiring more people focused on design systems and templates that enable others to create coherent experiences at scale.

Stone introduces the concept of "excellence as an operating system" as Netflix's cultural foundation—an approach built on talent density, high agency, autonomy, and trust in decision-making. To maintain this culture with AI, Netflix emphasizes accountability, comfort with risk-taking and learning from failure, resistance to adding process as a solution to problems, and clear focus on business outcomes rather than personal success. The "keepers test"—regularly evaluating whether you would rehire someone—serves as a practical tool for ongoing feedback and tough conversations.

For junior talent, Stone acknowledges AI tools might reduce hands-on learning opportunities, but Netflix remains committed to intern and new grad programs. The company focuses on hiring people with curiosity and openness to change, and invests in mentorship around craft excellence and accountability for output quality. While code-writing tools may become more capable, understanding how systems work—critical for debugging, quality assessment, and determining if something is actually working—remains essential.

Regarding entertainment's future, Stone envisions a shift beyond traditional film and TV toward diverse, personalized, interactive formats including games, live content, podcasts, and shorts. Netflix's role is enabling creators to use whatever tools align with their vision rather than prescribing a single approach. While AI will play a material role in production, Stone believes compelling entertainment requires humanity at its core—storytelling is fundamentally human, and audiences connect with human performers and emotion.

Throughout, Stone emphasizes that technology success depends on solving real consumer problems and delivering products people love, not on capability for its own sake.