How does Netflix CPTO rebuild talent in the midst of AI turmoil?

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With the emergence of generative AI technology, the productivity paradigm in the tech industry is undergoing an unprecedented shift. Product managers can directly generate prototypes, designers can write PRDs, and engineers begin to take on more product decision-making responsibilities. This "everyone can be everything" phenomenon, while increasing efficiency, has also caused confusion and anxiety among many professionals about their own positions.

As the mastermind behind Netflix's global product and technology architecture, Elizabeth Stone shared in a podcast her insights into talent development opportunities, role rebuilding and development mechanisms under the wave of AI, as well as Netflix's practices. She believes that the talent structure is tilting from "narrow expert" to "cross-border talent", and the functional boundaries under AI tools are blurring, but "Craft Excellence" in all areas is still very rare and vital today.

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01. How to understand that products, engineering and design talent will be more valuable in the age of AI?

What is the meaning of Netflix's "residence test"?

How do we understand the value of products, engineering and design talent in the age of AI?

Elizabeth Stone, Netflix's chief product and technology officer (CPTO) recently gave an in-depth analysis of the job boundaries, professional values and talent standards of the AI era, in an interview on the topic of AI impacting jobs.

1 Stone is the Chief Product and Technical Officer (CPTO) of Netflix, responsible for engineering, products, and design. Prior to joining Netflix, she was the Vice President of Science at Lyft, Chief Operating Officer at Nuna, an economist at Analysis Group, and a trader at Merlin Securities.

2 Elizabeth Stone emphasizes that companies are currently going through a "storm phase" under the influence of disruptive technology (AI) and that any organization will be in chaos, but that doesn't mean the demise of functional expertise, and that people shouldn't "put it back in the box" to renew AI out of fear of the obscurity of roles.

1 Stone emphasizes that "Craft Excellence" in all fields is still very scarce and vital in this day and age. Product managers are still defining "what to do"; engineers are in control of "how to do"; and designers are still responsible for the consistency and brand expression of the overall user experience.

2 In real-world business, product managers, designers, and data scientists can now (on a clear business plan) go even further in the early stages of R&D. Ideas that used to have to wait for the engineering team to line up for advancement can now be quickly built into testable drafts or code prototypes using AI tools.

3 When the non-engineering role completes the initial prototype, it is still necessary to work closely with engineering partners to solve the problems of product engineering, scalability and safety defense together. The "relative competitive advantage" of each function has not disappeared, only that the connecting nodes of the collaboration have become more agile and seamless.

3 In the age of AI, another impact of big language models and the application of smart entities within the enterprise is "the evaporation and refinement of knowledge", where knowledge and experience are no longer difficult to obtain, but Stone believes that "humans must take 100% responsibility for the final product".

1 Stone takes Netflix as an example, where the company has accumulated huge amounts of experimental data, user insights, and cross-sectoral business feedback over the past few decades. In the past, obtaining this knowledge was dependent on listening to or cramming with old employees; AI is now able to instantly extract and refine core conclusions, allowing teams to quickly form new assumptions.

2 Increasing efficiency is not the same as shifting responsibility, and Stone emphasizes that whether a piece of code is written by an AI Agent or a piece of data analysis is done by non-professionals through AI tools, the quality of the results, the risks and the business consequences are ultimately implemented on specifically responsible human employees.

4 Stone took Netflix as an example, pointing out that "Systems Thinkers" are becoming the most frequent demand in the era of AI. She said Netflix is increasingly investing in central engineering and platform architecture to bring in and train system-thinking talent.

1 With the overall increase in the speed of R&D, the demand for "system thinkers" in organizations has exploded. In the age of AI, if everyone used Agent to build products quickly independently, it could lead to system fragmentation, security vulnerabilities and user experience cracking (i.e. "Frankenstein" products).

2 Systemic thinking is the talent that Netflix desperately needs. At the engineering level, enterprises need someone who can abstract across all areas of their business, build universal paved paths, unified authentication, security patches, and infrastructure underlying modules.

3 Systemic thinking at the design level means that designers need to move from "design a specific function" to "design systems and templates" to develop a unified language of expression that empowers non-designers to create products with Netflix features and experiences that are consistent.

Systematic talent at the data level is responsible for establishing a unified source of authoritative data (Source of Truth) and clear access and interpretation standards.

5 Under the wave of AI, the structure of talent is shifting from "narrow, deep expertise" to "cross-border talent". According to Stone, Netflix specifically designed its current talent assessment system to address the abstract expectations of "AI Fluency" and eliminate AI indicators written in black boards at each level of the career ladder.

1 "AI Flow Concentration" refers to the agile mindset needed for continuous exploration and experimentation, a clear understanding of where AI can generate value, and the ability to embrace ambiguity and remain open and excited about the rapid evolution of work patterns.

2 In a Netflix engineering interview, the examiners will allow candidates to complete code testing using AI tools, focusing on how candidates use AI to solve problems in their daily work, and their adaptability and exploratory spirit in the face of change.

What is Netflix's "Excellence as an Operating System"?

1 Elizabeth Stone, under the theme of Corporate Culture and Practice, refines the cultural essence of Netflix as the core concept of "Excellence as an Operating System" and outlines the company's practices in this culture.

1 Stone notes that Silicon Valley's top AI labs -- such as OpenAI, Anthropic, and others -- show remarkable commonality in organizational governance. These teams are highly autonomous, highly level playing field, pay top-notch salaries, pursue extremely high talent density, and encourage rapid downward transformation.

2 The absolute premise of "operating system excellence" is enough "talent density" to ensure decentralized decision-making and to encourage risk-taking not to turn into disaster. Furthermore, business managers must firmly resist the temptation of "processing", control the desire to control micro-management, and have selflessness as a first priority for the benefit of Netflix and its global members.

3 In Stone's view, the elimination of cultural elements such as processes, decentralization, high-wage robbery is not the goal itself, but the means to drive extreme business output...