In a world where AI agents operate across multiple systems, organizations need more systems thinkers who build common infrastructure and 'paved paths,' rather than relying on locally-built, siloed solutions.
Stone explains Netflix is hiring more 'systems thinkers' who can build common infrastructure and paved paths, since AI agents operating across many systems need standardized, trustworthy source-of-truth data and guardrails. ✦ AI generated
Elizabeth Stone · Lenny's Podcast · 2026-07-19 · original ↗
starts at this moment · 14:32
“Are there functions that you are finding you are hiring more of? Like the pie chart pie expanding say for engineering or PM or design or something and then functions you're need less of with AI tool and LLMs rising.”
In a world of AI with agents operating across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work, common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important. So, we are hiring more people 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.
verbatim transcript · starts at 14:32
14:32across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work, common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important. So, we are hiring more people who can look across all the business domains and abstract that to here's the building
15:00blocks we're going to need in a world with AI. So, that's one of the lenses, but also just with a lens of what got Netflix here doesn't get Netflix there. And we're going to have to have a stronger set of infrastructure to move quickly in this future. So, that means that engineering profiles are more distributed systems, more infrastructure, more of that system thinking mindset than a a local business
15:22expertise. Though, of course, we still have people who are deep in personalization and advertising and content delivery. So, it's more something additive for us to have that core infrastructure and systems thinking. If I take another example, like design, it's extremely important that our experience design team is developing templates and again systems thinking for what does great user design look like at Netflix so that they can enable lots of
15:52people, including those who are not designers by training, to develop products that are coherent, that fit into the end-end member experience. I get really nervous about having different design languages or different types of user interactions and shipping Frankensteins, basically. So, designers need to then be the people we're hiring again for design systems thinking. How do we think about templates and expression of the brand and what a good
16:19user experience looks like and what is Netflix and like the Netflix differentiated special sauce. So, there's more people on our design team that have to think that way now than could I help to design a specific feature for a specific product. So, there's this stepping back to look at the big picture that I think is happening in every single function and that requires some, yeah, reorientation of skills among the
- ·AI agents will operate across multiple systems
- ·They need trustworthy, source-of-truth data
- ·Common infrastructure beats siloed solutions
- ·Hiring people who see across all business domains
- ·Building 'paved paths' with guardrails for AI work
- ·Solving problems once via core shared capabilities