Netflix CPTO on AI and the future of product and tech roles | Elizabeth Stone

**Lenny Rachitsky** (0:00)
Everyone can be everything now. PMs can ship code, designers can write PRDs, engineers can product, and there's this confusion and frustration of what is my job anymore.

**Elizabeth Stone** (0:08)
Anytime a new technology comes along, you go through a storming phase before you go through the forming phase of things. We are in the middle of that right now. I don't think that means we should put AI back into the box and say let's not use it.

**Lenny Rachitsky** (0:23)
If we all become builders, will we still need separate functions?

**Elizabeth Stone** (0:26)
I still see a craft excellence that's really important that I don't think is going away anytime soon. I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce.

**Lenny Rachitsky** (0:38)
If you look at the early culture deck of Netflix, high agency, autonomy, paying top of market, this is what I hear constantly now from how the top AI labs operate.

**Elizabeth Stone** (0:47)
Netflix's culture has always been excellence as an operating system. It's a resistance to do the thing that a lot of bigger companies would do and to feel comfortable in that discomfort very often.

**Lenny Rachitsky** (0:58)
What are the ingredients to make this happen?

**Elizabeth Stone** (1:00)
Talent density is the non-negotiable, being very comfortable with risk taking. In cases where things are not going well, not assume that process is going to fix it.

**Lenny Rachitsky** (1:09)
What have you added to the career ladders within this AI world?

**Elizabeth Stone** (1:13)
We need more systems thinkers, people who can look across all the business domains and abstract that to here's the building blocks we're going to need.

**Lenny Rachitsky** (1:22)
How do people learn this?

**Elizabeth Stone** (1:23)
Small trick, each problem you're trying to solve, step out one click to the what am I assuming is true about the broader space.

**Lenny Rachitsky** (1:34)
Today my guest is Elizabeth Stone, Product and Technology Officer at Netflix. This is Elizabeth's second visit to the podcast. Her first visit when she was just the CTO was, for the longest time, one of the most popular episodes of this podcast. You'll soon see why. This is such a killer conversation because when we got it two and a half years ago, AI was only starting to emerge. And as a long time head of engineering and product and data science, Elizabeth has such a unique perspective on where things are heading and what's worth paying attention to. Prior to Netflix, Elizabeth was VP of Science at Lyft, Chief Operating Officer at Nuna, an economist at the Analysis Group and a trader at Merrill Lynch. Before we get into it, don't forget to check out lennysproductpass.com for an entire year free of the hottest and best crafted AI products in the world, available exclusively to Lennys Newsletter subscribers. With that, I bring you Elizabeth Stone.
Elizabeth, thank you so much for being here. Welcome back to the podcast.

**Elizabeth Stone** (2:33)
Thank you. I'm honored to be here once and now twice.

**Lenny Rachitsky** (2:36)
That's right. That's a rare treat for me. I don't know if you know this, but your first visit to the podcast, your episode ended up being my second most popular episode. You're right behind Brian Chesky for the longest time.
Wow.

**Elizabeth Stone** (2:51)
I'm pleasantly surprised and also mildly competitive of how do I get to the first spot. But I'll set that aside for now.

**Lenny Rachitsky** (3:00)
This is her shot.

**Elizabeth Stone** (3:01)
Brian's amazing. So I'll let that one go.

**Lenny Rachitsky** (3:04)
Yeah. And then there's just like all these fancy AI people that are just coming in hot.
So it's been two and a half years at this point. A lot's changed.
Obviously, AI. Something AI is allowing people to do is everyone can kind of be everything now. This idea of PMs can ship code, designers can write PRDs and engineers can product and everyone's everything. There's a bunch of elements of this conversation. One is that I've heard from people that there's also this kind of confusion and frustration of like, what is my job anymore? Like, what am I responsible for as a PM, as a designer? Is that something you've experienced?

**Elizabeth Stone** (3:43)
I hear it within Netflix for sure.
I think anytime a new technology comes along, especially one that's as transformative as Gen. AI, you go through a storming phase before you go through the forming phase of things. And I think we are in the middle of that right now.
I don't think that means we should put AI back into the box and say, let's not use it, because this is complicating all of our preconceived notions about our roles. But I do think it means we have to be much more thoughtful about how do we get the benefits while reducing the costs. I think it's a great thing that people are experimenting with. How can I develop an idea faster, prototype an idea, put together an initial set of code that would allow us to test it? Do I believe that means anyone should be shipping code to production? That everyone should actually be doing everything? Probably not. But I think that it's good for people to be exploring what's possible. And then like I mentioned earlier, the benefit of having product and tech teams together is that if the business problem is clear, I think it's okay and it's healthy for there to be some fluidity in the roles that people play. Because instead of having to wait for the engineering team to be ready to be able to prototype something, product and design can move faster on it, but they should still work with their engineering partner to think through how should we productize this, how do we scale it, what are the guardrails for it. So I don't think it makes the functional expertise obsolete. I think it means that teams have to be more comfortable with maybe this helps us move faster in a certain direction. From an organizational perspective, things I think about to make this more coherent or less frustrating, are some of the things that have to be in place for us to get the benefits rather than the costs. So that includes clarity on source of truth data, guard rails on shipping code to production or testing before we make large changes, thinking about opportunities where we can trust the output of AI versus we should have a process or review that helps us check that we're getting high quality outcomes, and the importance of reiterating that humans are still responsible for what happens. So it can be that an agent wrote the code or I helped to do an analysis when that's not really my background, but it doesn't make people not have the responsibility that comes with what they've created. So I think investing in some of those core infrastructure and practices and reiterating the accountability and responsibility for the outcomes, helps to balance some of what's possible with what we should actually be doing.