Lenny's Podcast: Product | Career | Growth
00:31:16 · Elizabeth Stone
[00:31:16] S1: So, there's two that come to mind. So, the first is data analysis, distillation of information, modeling, which is using the tools to get our arms around all the insights we have, similar to what I mentioned before. What experiments have we run? What are the metrics that I should be looking at for a certain problem? What's the consumer research that we've done? And that is much higher velocity and much higher quality, contingent on you check that the results are valid, you work with your local data scientist on, am I using the source of truth data on this? But that's been a great one. And that's one personally that I would say I most use some of these tools for. So, that goes beyond prototyping and coding to general analytical thinking, and translating data to action and insight. The other one is on the content production creation part of the business, which has lots of applications. This was true before GenAI. So, ML and AI were deeply used in a lot of the production tools. We've used them to think about how to create promotional assets at scale, how to localize in subtitles and dubs. So, GenAI is a big step function in where the impact can be. In creative ideation, we call those things pre-visualization or basically bringing a creator's vision to life before you even get into the... You bring people to a set and start to actually go through the production itself. There's lots of use cases in post-production. So, we recently acquired a company, InterPositive, that was started by Ben Affleck, that built a set of models and capabilities. That allow you after you've shot something to relight, reframe, reshoot, change dialogue in ways that are very impactful to get higher quality content. Are still led by the filmmaker, creator saying, "You know what? I would like to try something else to bring this vision to life." But that impact is extremely promising and we're seeing lots of productions leverage different tools, some of them built in-house, some of them that we enable through other vendors for those content creation use cases. And then as we think about how content comes to the product, I mentioned localization, subtitles and dubs, but also how we create high quality trailers, images, artwork at scale that then we can use to help make sure that titles find their audiences around the world.
00:34:50 · Elizabeth Stone, Lenny Rachitsky
Those all are huge levers when we think about the AI impact. So, that again, goes well beyond prototyping or coding to some of the creative use cases. And you can imagine that, just like they work for studio productions for film and TV, they work for advertising, they work for marketing, off-service campaigns. And so, those are all areas that we're exploring. The Netflix prize. [00:35:26] S0: Yeah, the Netflix prize. Just show an example of how early you were to AI and ML. I think it was a million dollar prize to optimize the Netflix ranking algorithm a little bit, whoever could optimize it the most. And I think the winner optimized it by a few percentage points, something like that. And it was a huge deal. All these super smart people got around the world and it happened a few times, right?
00:35:49 · Elizabeth Stone
[00:35:49] S1: I mean, you said it on my behalf. Often, when there's questions about how is Netflix thinking about AI, it's great to remind people of exactly that point, that this is not new to us. That especially for personalization, it's been central to delivering a great experience to members. It's impossible to take the breadth of content that we have. There's ever more content, that's one of the challenges we face. And make discovery easier and easier and easier, which is one of the challenges that Netflix has. And using AI and ML has been a way to do that. You want to personalize right title for the right person at the right moment. That problem gets harder. The more exciting our catalog gets, the greater breadth of content we have, not just film and TV, but games and live and podcasts. Personalization becomes even more important and what that experience is. So, we can take a lot of that history and say, okay, well, now how do we solve this problem? Because the tech is even more powerful, but it gives us a running head start in being clear about the problem to solve, how important it is that Netflix solve that for our members. And then the same is true, as I was mentioning on the creative side of the house, AI and ML have been in things like visual effects or in localizing language for a long time. Now we say, what's the next era of that when the tech is more powerful? And in both cases, it ends up taking a strength that Netflix has, which is marrying entertainment, and technology, and making sure we stay ahead of the game to deliver things that are even better. So, I love that it's part of our history. It still continues to be a strength and it's going to have to be a strength, given the size of the challenges we're facing around the breadth of entertainment, while keeping a great experience.
00:37:33 · Lenny Rachitsky, Elizabeth Stone
[00:37:33] S0: Yeah. And I love that back then it was called machine learning and AI was like, no, no, it's not AI. AI is never going to happen. It's just machine learning. [00:37:41] S1: Well, then all of a sudden, we call everything AI and some of it's machine learning. [00:37:45] S0: That's right. That's right. [00:37:48] S1: It depends, the thing that is of the moment to describe. So, I think we bucket all of it as AI now. [00:37:54] S0: Yeah. And that's cool. [00:37:56] S1: And there's a lot of AI use cases that are not generative use cases. So, we could go down a deep dark hole of all the specific things. But in general, I don't think it would surprise anyone that Netflix is using a broad array with so much excitement about what's possible. The fun thing at Netflix for the people who work here is that if you're really passionate about the applications of tech for creative outlets, for consumer products, for infrastructure, we have all of those problems and AI is at the center of them. And it's good not to forget that that's true. Even if Netflix isn't branded as an AI company, AI is a tool that we're very comfortable using to get these great entertainment and technology outcomes.
00:37:56 · Elizabeth Stone, Lenny Rachitsky
[00:37:56] S1: And there's a lot of AI use cases that are not generative use cases. So, we could go down a deep dark hole of all the specific things. But in general, I don't think it would surprise anyone that Netflix is using a broad array with so much excitement about what's possible. The fun thing at Netflix for the people who work here is that if you're really passionate about the applications of tech for creative outlets, for consumer products, for infrastructure, we have all of those problems and AI is at the center of them. And it's good not to forget that that's true. Even if Netflix isn't branded as an AI company, AI is a tool that we're very comfortable using to get these great entertainment and technology outcomes. [00:38:37] S0: The other really interesting thing, just to keep complimenting Netflix here, if you look at the early culture deck of Netflix and also our conversation last time, things that emerge from that are things like high agency. This was something core to Netflix in the beginning. High agency, autonomy, high talent density, very bottoms-up thinking, super quick experiments and launching, paying top of market. This is what I hear constantly now from how the top AI labs operate. So, we're all ending here and this is where Netflix has been forever.
00:39:13 · Elizabeth Stone
[00:39:13] S1: Yeah. It's a little prescient in understanding what makes talent incredible. I've thought about all those aspects of the culture at Netflix as, this is going to sound a little bit nerdy, but excellence as an operating system. So, the goal of all those cultural elements wasn't the end bone themselves. It wasn't, let's just make sure people have as much responsibility as possible. We don't like process, so let's make sure that we don't have any of that. It was instead a very strongly held opinion that you get to excellence by giving people a lot of agency and accountability, by pushing decisions as deep in the organization as possible, hiring great people who can be trusted to have good judgment and make good decisions. And that ends up driving incredible outcomes, plus a lot more motivation and sense of responsibility. It means every person on the team can feel like I'm being given a lot of keys and a lot of accountability for what happens here. And I myself feel like when you know you're carrying that level of trust and accountability, you want to do your best work. And so, there's something that feels very intuitive about Netflix's culture has always been aiming at excellence. And when you have great talent and you give them the ability to do their best work without micromanaging it or drowning it in process, you actually get much better outcomes. And so, I do think that the newer era companies are picking up on something that is feeling very familiar to us. And it's not something that comes easily. So, culture's not a static thing. Culture needs to grow and evolve as the company gets bigger, the types of problems you're solving, change. But the notion that we're going for excellence and trusting that exceptional talent needs to be able to do their best work, that's unchanged and something that I think continues to be a special sauce for us.