Netflix aims for ‘AI fluency’ among all its employees, yet expertise in the field remains limited.

      When Elizabeth Stone discusses the future of work at Netflix, she repeatedly refers to a concept that is simple to articulate but difficult to define. During her appearance on Lenny’s Podcast in mid-July, the company’s chief product and technology officer described “AI fluency” as a goal for every employee—something she aims to integrate throughout the entire organization instead of limiting it to a few specialized positions.

      By fluency, she does not mean using technology for its own sake. She explained it as encompassing three aspects: a mindset geared towards experimentation, the ability to discern where AI is genuinely beneficial versus where it is not, and a proven skill in effectively utilizing the tools. She implied that this expectation is nearing a point of being non-negotiable for various roles, coinciding with the trend of AI-native startups hiring fewer junior staff and graduates leveraging AI during interviews to secure positions.

      Stone is a credible advocate for this approach. She became Netflix’s first chief technology officer in 2023 and was subsequently promoted in February to a newly established role that combines product and engineering responsibilities.

      Instead of revising its career ladders step by step, Netflix has chosen to treat fluency as an overarching expectation that applies across all job categories. The intention is that a marketer, data scientist, and staff engineer should all possess fluency, even if its expression differs in each role. This strategy offers a neat solution to the complex issue of modifying job descriptions for rapidly evolving technology.

      This perspective is most clearly reflected in the way the company conducts technical interviews. Candidates are permitted to utilize AI tools while coding, based on the rationale that these tools are increasingly relevant to daily work. However, Netflix asserts that it still maintains a strong focus on core principles, such as code quality, testing, and system design understanding.

      Stone contends that these foundational skills are becoming more difficult to find, rather than easier. “I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce,” she noted, suggesting that expertise has become more valuable rather than rendered obsolete by technology.

      There exists a real tension in this viewpoint. If entry-level engineers rely on AI from the outset, one might question whether they will achieve the profound understanding that Stone values, and she does not provide a clear solution to this concern. This apprehension reflects broader worries about early-career employment, a sentiment not unique to Netflix.

      Significantly, she did not mention any hiring freeze. Nothing in her conversation indicated that Netflix was closing the door on junior talent, and the company continues to post new graduate engineering positions while raising expectations for these candidates.

      Her remarks come as Netflix intensifies its use of technology in various areas, having already applied AI across numerous titles and utilized generative tools to assist subscribers in finding something to watch.

      For a company that spent years reducing friction through automation, the more intriguing challenge now is a human one. Stone’s perspective is less dire than some of the sector's more alarmist projections and can arguably be seen as more demanding. She is not suggesting that AI will perform the work for individuals; instead, she posits that familiarity with AI is becoming a prerequisite for entry, all while employers everywhere reconsider the criteria for a first job.

      Whether this “aspiration” ultimately evolves into a formal requirement is a key development to observe. For now, it serves as a signal from one of the most scrutinized engineering cultures in the tech industry regarding what it will take to be hired there and to remain valuable once employed.