Elizabeth Stone, Netflix’s chief product and technology officer, wants every employee to develop an “aspiration for AI fluency.” In an episode of Lenny’s Podcast released Sunday, Stone said Netflix is taking a company-wide approach, using an “overlay across all of the talent at Netflix,” rather than trying to spell out exactly how AI changes expectations at each job level.

The punchline here is simple: Stone says AI fluency is the new baseline expectation, including for senior leadership. She acknowledged it is “a tough thing to define,” but framed it as more than hands-on tooling. Netflix’s goal is to make employees comfortable enough with AI to understand where it is useful, exercise “good judgment,” and stay open to exploring and trying new things. And crucially, Stone said this non-negotiable applies even at the “senior-most levels” where leaders may not “write code as part of our day jobs,” but still need “deep fluency in AI.”

This is one of those moments where culture meets execution. Netflix is a company that treats product and technology as core to its competitive edge, not a back-office function. So when the chief product and technology officer talks about building an AI learning expectation across functions, it is implicitly about decision-making quality: who can evaluate AI output, who can spot misuse, and who can tell when a model is helpful versus when it is just confidently wrong.

Stone’s framing also does something executives will recognize from past technology shifts. Instead of role-specific mandates, she described encouraging a shared aspiration that can scale across the org. Expectations will vary depending on employee role and career stage, she said, but the target mindset stays the same. That matters because AI is uneven. Different teams experience it differently: some teams will experiment daily; others will use it indirectly through workflows, customer experiences, or internal decision support.

On the hiring side, Stone said Netflix has started discussing AI during interviews. The purpose is not simply to test whether candidates can use a tool, but to understand how job seekers think about the technology and how they use AI tools in their day-to-day lives. That shift has a second-order effect on the talent funnel. It changes what “signals” recruiting teams reward, moving the center of gravity from purely traditional domain experience toward demonstrated AI judgment and practical comfort.

There is also a labor-market sensitivity hiding in plain sight. As concerns grow that AI could reduce demand for entry-level workers across the job sector, Stone said junior talent remains a “critical part” of Netflix’s hiring strategy. She emphasized that Netflix is still hiring junior people and pointed to the company’s intern and new graduate programs. That is not a minor line. It is a direct attempt to prevent one of the most common narratives in AI adoption, the one that says companies will pause early-career hiring while they automate.

Stone argued that earlier career talent can bring a different kind of advantage. She said younger employees may be more open-minded, more comfortable with emerging AI technologies, and more attuned to how entertainment is changing. Then she added a personal and slightly pointed note to podcast host Lenny Rachitsky: “I can guarantee you that earlier career talent is going to be teaching older folks like me many new things, too.” The underlying logic is that AI fluency is not a one-time training session. It is a feedback loop, where newcomers bring fresh intuition about new tools and can reshape how the organization operates.

Zoom out and this becomes an AI governance conversation by stealth. Stone’s definition of AI fluency is built around judgment, exploration, and understanding where the technology is useful. That aligns with the real operational risks leaders worry about: hallucinations, compliance problems, data exposure, and overreliance on automated outputs. While Stone did not describe specific internal controls in the source, her emphasis on “good judgment” and “not about using the technology for the sake of using it” points to a philosophy boards and executives increasingly need. The question is less “Can employees use AI?” and more “Can employees make defensible decisions with AI in the loop?”

For executives watching this, the strategic stakes are straightforward. If Netflix is normalizing AI fluency as an expectation at all levels, competitors will feel pressure to do the same, either through training programs, interview changes, or performance expectations. At the same time, Netflix’s stance on junior talent suggests another takeaway: AI fluency does not have to mean freezing entry-level hiring. It can mean pairing early-career hires with a culture that upgrades everyone’s judgment, faster than training alone.

In other words, Netflix is not treating AI fluency like a developer-only skill. Stone described it as a shared capability that helps people work effectively as AI reshapes how work gets done. For leaders in product, engineering, ops, and HR, that is a useful benchmark: build an org-wide aspiration, validate it in interviews, and make sure the next generation remains part of the engine.