Please welcome to the stage co-hosts of the Acquired podcast, Ben Gilbert and David Rosenthal.
We like to say on the show, welcome to Acquired Live at Reinvent.
And we are your hosts. Well, this is fun.
This is a little bit different than my basement studio.
Yeah. In my backyard.
So, for folks who listen to Acquired, which I'm assuming if you're here is you, what we normally do is these 4-hour deep dives into hundred-year-old companies. You can listen in any podcast player. You put in your headphones, you basically don't see us. You go on your long run, you're doing your dishes, you're on your commute, and we're telling you a story over a hundred years of a company. And today we have the indulgence for David and I of talking about the present, of talking about the technology era that we are living in and getting to talk through that with the protagonists of some of the most important companies in the world here on this stage.
Yes. So, we have four really incredible guests here to join us today. We've got Max Neerchin, who is the global co-head of JP Morgan Payments. We have Greg Peters, the co-CEO of Netflix.
Heard of it. We have Arvin Shinovas, the co-founder and CEO of Perplexity, which I use all the time. And then, this is funny. I still haven't figured out the right way to introduce our final guest.
Our final guest host because he's both. Yes. Yes. Our guest and our host, Matt Garmin, the CEO of AWS.
It is going to be just an incredible morning and afternoon here today. Thank you all for joining us. And huge, huge thank you to Amazon, to Amazon Web Services, to JP Morgan. They have been incredible partners to us over the years at Acquired. I'm sure to all of you in the room planning today, getting these four incredible people here today to have great conversations with all of us. So, with that, let's get started. We'll throw it to a video with JP Morgan.
JP Morgan payments is known in the industry for innovation. There's so many key trends that we are not only participating in but driving. Every country has a rapidly developing ecosystem around payments. For us, it's critical to develop those linkages, to develop those products by country and deliver to our clients who may be very large multinationals operating in dozens of countries. Because in JP Morgan Payments, we ultimately want to help our clients run their business.
Please welcome to the stage the global co-head of JP Morgan Payments, Max Neerchin.
All right, so we have to pretend we don't know each other well and like we haven't been hanging out all the last few days at Reinvent and doing JP Morgan meetups and everything.
And what? Hanging out at Radio City, hanging out at Chase Center. We just get to see you in the most amazing venues around the world. Chase Center in San Francisco, Radio City in New York, this beautiful old Italian theater in Las Vegas.
It's a testament to our partnership. We've done already so many things together and you've been such amazing partners. So great to be here with you.
Thank you. So David and I know the JP Morgan payments story and I suspect most people in this room don't and they're kind of like, wait, there's this big enormous fintech company inside of the world's largest bank, America's largest bank, not the world's largest bank, large bank. How did this happen?
What is JP Morgan payments? So we ultimately work with companies of all sizes to manage their money and move their money. Think about all of us. We have a bank account. We have cash there and then we make payments out of it. We do this for companies of all sizes. And that means it's a bit more complicated because companies operate in many countries. They have different currencies. You have an entire finance department that needs access. So that is the service that we provide to our clients. And we do that at a massive scale. We process about 12 trillion dollars on a normal day, up to 15 trillion dollars on peak days. And that means that the world's GDP flows through our pipes basically every 10 days.
And we do that also at a massive precision. So our efficiency and precision rate is 99.9999%, which is basically the same level as space flight or nuclear power stations operate at. That is the service that we provide to our clients to make sure that their money is safe and that we can do all of this that they need. Do payroll, buy other companies, pay suppliers, and do all of this in more than 150 countries.
And accept payments from customers. I mean, principally.
And accept payments from customers. Great point. We just passed Black Friday. Cyber Monday was a huge event probably for all of us here, also for the payments industry. Everything went well. And if you bought something in the last week or so, likely we processed it and it's a really important part of our business.
So, how did this come to be part of JP Morgan though? I mean, this usually does not exist within a bank.
Payments companies are usually separate. This is the result of a whole bunch of strategic decisions over the years by Jamie, by the leadership team to build up this technology business within the bank. It is a business that really was built over decades and we have invested over a long period of time to build out the technology to deliver all these services because ultimately, exactly to your point, this is a technology business that needs to work very reliably and constantly renew and innovate. We also did a few acquisitions over the years. So for example, our merchant acquiring business was an acquisition probably 10 years ago and we also work with the ecosystem, the fintech ecosystem in particular, very actively. So we do not build everything just in house but we have a network of over 100 partners that help us deliver all these services and innovate for our clients.
So one of the themes that we're going to be talking with all the guests today about is investing in the future. You talked about the incredible precision and being a giant incumbent. I mean, this is an 18-plus billion dollar business on its own, just JP Morgan payments that exists within the bank. How do you think about allocating that next incremental dollar on innovation when you kind of have to protect this really durable important franchise?
Investments are absolutely key. One, it is a very competitive environment. So you need to invest to provide cutting edge solutions. But at the same time the payments world itself is also evolving so fast. It's scaling. So you need to make sure you keep the infrastructure fresh. And there we operate like any other technology company. We have a very rigid process around where we invest, how we invest. We absolutely continuously invest in good times and in bad times because you do need this continuous growth in the platform to make sure that you deliver solutions. And by the way, we also make mistakes. So you do want to continue to invest so that you have a cutting edge solution out there. And we think about investments for the future in three horizons. Most of our investment budget actually goes into Horizon 1, which is scaling the existing solutions, moving to the cloud. We have a fantastic partnership with AWS to make all of this work, enhancing the payment methods we offer. So that is probably the bulk of our investments. Then we have a second horizon. This is where we build new products, where we go to new countries, where we expand our network. Think about real-time payments. Think about opening up new countries in the Middle East. That is there. And then the third part which is probably 10-20% of the budget is reserved for real innovation because we are absolutely here to shape the future of payments. And this is where our innovation in blockchain technology, biometrics, AI, etc. fall. And all of this together continues the buildout of the platform.
It's funny. We wanted to ask you about blockchain which in some ways it feels like this is 2025. This is the wrong year to be talking about blockchain, but it's actually, despite everything that's happened in the crypto markets over the past few years, it's become a big part of fintech and JP Morgan payments. Tell us about what's happening with blockchain and why you guys have continued to invest in it over the past couple years.
Blockchain as you say is a very hot topic in payments at the moment. Actually we have been at it already for more than a decade. So we have seen all the ups and downs of the crypto space and the blockchain space. Most recently we launched our JP Morgan coin which is a tokenized deposit on the public chain. So think about it as a bank deposit where you can move money using the public chain.
It is similar to a stable coin that it leverages the blockchain to move money, but it is different given that it's a true bank deposit, which means it's insured.
Which means that you can pay interest and it also means that it's fully integrated into the JP Morgan infrastructure and you have the full backing of the JP Morgan balance sheet behind it.
And it's not a speculative asset, right?
It's not a speculative asset. It's literally the same as putting your cash into JP Morgan. What we like about the tokenized deposit is that it truly combines the benefits of both the banking world as well as the crypto digital world. It allows all the great characteristics of a traditional deposit, but then you can move money truly 365, 24/7. And you also have this feature of programmability where a CFO or a treasurer can say this is how we want to move money. You can put in some rules and it allows them to manage their entire treasury operation a lot more efficiently.
And does it reduce expense also of changing currencies versus traditional foreign exchange?
You still need to exchange money in the same way because ultimately most clients still want to move in and out of fiat currency at some point. The real interesting part is we see tremendous growth there. So we already process probably a few billion dollars every day through the tokenized deposits.
And the growth is truly exponential.
I mean this is, we interviewed Jamie over the summer at Radio City, Jamie Dimon, which was amazing. That JP Morgan is launching JP Morgan coin in 2025. If you had told that to Jamie when you took over the payments business a few years ago that JP Morgan would be launching JPM Coin, what would his reaction have been then?
Actually, I worked with Jamie now for a long time. He would have listened. He would have made me make the case, as we did. And I think there's a truly differentiated view between the blockchain technology that makes the payment system a lot more efficient and can really help create the benefits that we talked about and some of the cryptocurrencies where you can have different views in terms of how they adopt. And since this is truly focused on the blockchain in a very controlled manner, I think the whole company is behind it.
All right. I gotta ask one more question. What caused the institutional flip to stop saying, 'Hey, we hate Bitcoin and it's evil' to embracing it in the last year?
I think it was definitely becoming more nuanced in the messaging. Digital assets, digital currencies is not all Bitcoin and Bitcoin is not the same as digital assets and digital currencies. And I think they were often mashed together. But with the whole ecosystem maturing, I think everyone now sees the different aspects and the answers are just more nuanced now.
Makes sense. Let's flip to probably what I'm sure is on everybody's mind here and Reinvent certainly is on.
We're 12 minutes in and we haven't said AI yet.
Well, I think Max said AI, you know, it's one of the key three areas. We haven't said AI. We haven't said AI. Yes. Talk to us about AI. I mean you move 12 trillion dollars a day. How are you using? Is AI making an impact today on that?
AI is next to digital currency probably the second big mega trend in payment that is really the talk of the industry and in my view will actually have long-term a lot more profound impact and change the way we operate. And for us, having cutting edge AI solutions is a question of survival in the business over the long run. So it is front and center and I deal with it every day running the business. There are some use cases that we pursue for AI inside the company. For example, in our operations group we use AI to run investigations, sanction screening more efficiently, and it's more traditional AI, think machine learning, and we've seen really good impact already. So our volumes have increased double digits over the last five, six years and the headcount only 2-3%. So you get true operating leverage. We're excited about this. This will continue. Same engineering productivity. Our engineers use AI tools extensively and it has absolutely made them more productive, better output, less errors, and given how big our agenda is, that is really exciting. And then it's also helping our sales team become a lot more targeted. You get signals, you know, when to reach out to clients. So there's a lot of internal use cases that we pursue.
For client-facing, we also have a lot going on. I think fraud and protecting our clients' money is probably one of the key areas where we use AI. We have a large payments model that truly now moves from traditional AI to new AI. So we are no longer just using fraud labels and assisted learning to more predictive models unleashed on the 12 trillion worth of transactions and hundreds of millions of bank accounts that we see that truly creates superior fraud signals. So we have seen 30-40% improvement. I mean, if you can move the needle on fraud, that's massive impact to profitability for you and your customers, right?
And if I speak to our clients, fraud and keeping their money safe is probably the number one topic that they care about. They love all the discussion about AI, blockchain, they say please make sure the fraudsters don't come about our money.
And AI is making that harder than ever because the fraudsters are getting.
Correct. It's a true arms race. By the middle of the year we had already seen more fraud attempts on our clients' money than in all of last year. So you can definitely see how this is becoming a very prominent challenge. So the AI investment is really helping us safeguard our clients here. And then there's also solutions where we help our clients run their business better. For a CFO, it's a really hard task to forecast their cash flows, which ultimately tells them how much liquidity they need to hold. So AI can make this a much less manual task, much more precise, and ultimately help them save liquidity and working capital. So there's a lot basically in every aspect of payments that you can think about.
Yeah. I mean, your biggest customers are merchants. So predicting their cash, I mean we've studied so many merchants, retail companies on Acquired. The difference between a great enduring company and not is often cash flow management, working capital management, can you have a negative cash flow cycle.
And especially with more and more payments happening real time and in the moment, you need to take quick decisions in nanoseconds: is this fraud suspicious or not? So the bar is also raising from a consumer expectation standpoint which makes AI and the ability it offers even more important.
So, one thing that I have had a tough time doing in this AI era is reading beyond the headlines when there's a hot topic and trying to look under it and say, 'Okay, how much usage is there actually of that topic?' And one of the buzzy terms right now is agentic commerce. And I'm curious, between your clients and all the different companies that you power, what are examples where agentic commerce is really happening and there's a lot of activity there versus a lot of people showing cool mockups and saying agentic commerce is the future dot dot dot.
It's a great question and agentic commerce definitely meets the criteria of a lot of noise at the moment. Even a year ago, barely anybody was speaking about agentic commerce and today you see multiple announcements every day.
We're going to talk about it with Matt later.
Exactly. We look at agentic commerce as a transaction truly end to end from consumer identifying what they want to buy to the checkout to the post-purchase experience. And agents are already pretty prominent across the full value chain. They are often embedded in merchants or marketplaces as platforms. You have agents embedded in the various AI platforms. I mean, Perplexity is also coming. You have consumer agents coming up. So, there's a lot of activity in the space. Most of the impact has been so far on the search side of the customer experience where people use them to find what they want to buy. But more and more you see also checkout and actually customer experience and customer decisions taken over by agents. I do think there's still quite a lot of open questions. First of all, what are the use cases? Probably if you buy toilet paper, you're happy for an agent to do it. If you want to buy a nice pair of shoes or a nice dress, you probably still want to do it yourself. Then how do you deal again with fraud liability? What do you do if a truck with 20,000 rolls of toilet paper is outside your house ready for delivery? Who is now responsible?
Actually happened to me with bananas. I hit the wrong button.
It happened to you with bananas.
I thought I was ordering 10 bananas and I ordered 10 bushels last week. We have a lot of bananas at my house right now.
Yeah. You ordered 10 bushels of bananas on Amazon.
Were you on Amazon business? Like what? Okay. Okay. All right.
So there is open question. I think also the business model, a lot of commerce today is funded by advertising and commercials. How will this evolve going forward? So a lot going, but I do think it will mature and then have an impact. By the way, another area we are very passionate about is agentic treasury. This is where treasurers and CFOs just give a payment file and then they say, 'You optimize, dear agent, for speed, cost, global reach,' and then identify for every transaction what's the best way to do it. It's also a great use case.
This is also, I imagine, an area where JP Morgan kind of uniquely can create solutions for customers like, 'Hey, optimize my treasury.' There's everything you just said but there's also what portions of my treasury am I going to keep in what investments at what balance and match that with predict my cash flows and my cash flow needs.
Correct. And we do have also a very close partnership with our colleagues in the consumer bank to really look at the end to end journey from the consumer all the way to the merchants and everyone in between to embed our solutions there.
Well, Max, thank you so much for joining us today. That's a great place to leave it. Really appreciate you coming.
Thank you so much, Ben and David. And thanks also for the partnership that we have. Truly amazing.
It's truly amazing. Thank you, Max.
That was fun. For our next segment of the morning here, we have a company, this is appropriate on so many levels, has reinvented itself. Heyo.
Heyo. At least three times, arguably four or five kind of from whole cloth.
Ship me a lot of envelopes.
Not a lot of envelopes. In fact, zero envelopes these days.
And yet I consume more of their product than ever.
As do we all. As do we all.
So, before we announce our next guest, let's watch a little video about the company Netflix.
This is the battle for hearts and minds.
Building a real at-scale global streaming business is hard because you got to combine great tech product and great content.
We're going to be able to move more quickly than other streamers as we leverage pre-existing tech and data science assets and expertise.
This is furious action that broadens the Netflix offering.
I've been training for this all my life. We hope that you know that ultimately reinforces our value as the most important service for your entertainment needs.
How many times a day do you see that logo?
I mean once but like I do get the warm fuzzies seeing the rainbow Netflix. You're like so amped up for what's about to come next. Well, please welcome to the stage the co-CEO of Netflix, Greg Peters.
Thank you. Thank you. Well, we maybe inspired by some Netflix shows here.
I was going to say, is this an audition? Are we, should we talk later?
You're open. Okay. All right. Good. That's good to know.
I did see Nobody Wants This up there and I was wondering, did you throw that in because we're kind of doing a podcast here as an homage?
I was going to say, you know, it all fits together as a universe of entertainment that's connected, bouncing from one to the other.
It does. It does.
Well, this feels really poetic. I don't know how many people out there are like David and I and watched 12-year-old, 13-year-old Reinvent keynotes, but in the very first keynote, Andy Jassy first Reinvent interviewed Reed Hastings as a case study for the cloud for large companies moving to the cloud which at the time was complete heresy.
AWS was for startups, it was for Y Combinator companies, and here's Reed Hastings.
Well, and even then I think it was this idea that you would lose control over your infrastructure and your data and all these other things were out there, so it was definitely pushing the edge. But you know, we started that process in 2008, actually a couple years before that keynote. And just to step back and say over a decade and a half, what has happened both with Netflix and with AWS is pretty amazing. We were doing a million hours a month back then, a billion at the time of that 2012 Reinvent, and now we're doing 50 billion a quarterish every quarter. So growing up together has just been amazing.
Wow. That's incredible. Well, as we teed up in the intro, appropriate enough for Reinvent, we have to ask you, Netflix, unlike so many other companies, not just Reinvent, but you've killed what you were as a company in the past and gone to a whole different thing. There was the red envelopes to streaming. There was we will never have ad supported to actually we really are into ad supported. There was we won't have sports. We have NFL games on Christmas now. How do you make these decisions? When do you decide that we're not just going to do little big change?
Well, I think most of that is driven by the desire to grow the business and serve the customer more. So it's not probably different than what anybody else has experienced. And we really have felt that focus and being limited in what you did for a long period of time, especially as there's this big macro shift in how entertainment was going to be distributed was the way to win. And I think that served us quite well for a period of time, but then you also say, okay, now we need to think about what we're doing. It's interesting because you described them as big shifts and there were big shifts in strategy. That's definitely true. But I think even some of those there's good examples where we've taken an evolutionary or iterative approach towards how we entered those spaces because we generally come from the perspective that when we're going to do something new, we don't know what we're doing. We have not figured out how to be good at that thing. And I think it's funny because people say when you're doing something new, you just hire all the people that know how to go do that. And certainly you can do that and that's a useful approach to learning quickly. But I think it underestimates the need to essentially build institutional capability that understands how to go do something. You can hire a bunch of expertise in, but that expertise has to learn how to do it in the context of the business that we are currently operating.
Especially if you're doing something brand new. I mean, it's not like when you decided to do streaming through Silverlight to IE browsers launched.
I love the fact you're going way back to Silverlight. You were one of the very few people that.
I was very frustrated because I had a Mac. I remember it was so novel. It's not like there were a lot of people you could go hire that had that know-how.
So, there's multiple examples here. So you talk about doing something completely new. We were building from scratch. I remember even talking about how are we defining the standards that will be instituted for the streaming universe etc. So that's one example where you're building it as you go. I think ads is another really interesting one where it's not like this is a new space, right? People have been doing it and doing it well for a long period of time. And so we thought, okay, we're going to get into it. We'll do it in an iterative approach, partner to get an ad stack at the get-go and then build our own and learn through that process. But even this, I think, is a bit of a new version of ads because we're trying to bring essentially what has been amazing about digital advertising that folks like Google and Meta have been doing for a long time and are great at, great targeting, great relevance, all those things, measurement, but then also bring it into a premium TV-like ad experience. And so that's a whole new thing as well.
Yeah. Like we'd be terrified of programmatic ads on Acquired. We're so brand protective. I would never run a random commercial against it. Netflix is premium like that. I mean, you guys have to really be thoughtful about.
Yeah. And I think that's some of the differentiation that we have. Because when you think of brands, what can they get from Netflix that they can't get from maybe some of those other advertisers? It is that premium placement, the security that they have around brand. And increasingly what we're trying to do also is be able to do things that TV would never have been able to do, or not really to do at scale, which is bring brand-appropriate, brand-sensitive creative into title-sensitive creative and mash those two worlds up in a way that everyone feels super excited about. Which used to be quite an involved process and take a lot of time, but we're seeing you can actually accelerate that. And so that plays on that differentiation.
Or do it programmatically.
Well, I think we're going to walk towards that way. This is maybe the path that we see leveraging some of these new tools to get to, which is you start by taking what was an existing, call it 18-month process, heavy humans involved, and see if you can get that process to like three months. And then you start to really build more iterations and then more data and then you ask the question, okay, is there a certain set of situations or class of advertisers where you could actually make it truly automated? And so I think we're not there yet, but I think that would be the path we would seek to go.
So this is the story behind ads. Give us the story behind, hey, we're never going to do live. We're never going to do sports. And how do you walk your way into the NFL?
Yeah. I mean, so live, one of the big differentiators we had at the beginning was on demand. And so this whole idea that we are going to counterposition relative to traditional entertainment because we don't have the same constraints. And so when you're in that early stage, you really focus on the counterpositioning and live doesn't, you know, it obviously runs counter to that strategic. Now at some point you become the.
It's like Hamilton Helmer.
Hamilton, you love Hamilton. He's, shout out to Hamilton Helmer. If you don't know him, read his book. It's great. So when you are at a certain scale though, and you've become not so much where you're counterpositioning but you are for an increasing number of people the place that they show up to be entertained, then you get to start to say, okay, now we think we can layer on some of these more traditional things and compete in that way. So that's where we are. And live, maybe the other thing too on this is that I think about the entertainment ecosystem as increasingly moving to two poles. We get highly personalized, highly niche experiences that have tremendous value because you're speaking very specifically to what I want. But then that creates a space and a need for this other pole, which is how are we all having a shared experience? What are we all doing in a single moment where we know that there's millions, tens of millions, maybe hundreds of millions of people around the planet that are all doing the same thing so that we can talk about it and feel the power of that shared experience? And that's where I think live and some of the things we've been doing there come into play.
I was going to save this for later but did you have any idea that K-pop Demon Hunters was going to be one of those things?
We thought it was great. We thought it was an amazing movie. But I would say there's this extra layer that you get to where something magic happens where it enters into the zeitgeist and sort of builds on itself and becomes one of those events where now, whether it's social media or your kids' Halloween costumes, you cannot escape. I mean Ben Thompson is writing about K-pop Demon, which for him to write about an animated show like that is amazing.
Well, in that vein, one of the things we really wanted to ask you is if you look at the industry of Hollywood 10 years ago and you look at the industry of Hollywood today, the value chain, how it works, it's completely different.
Arguably completely different but.
Most folks in the audience are not from the entertainment industry. So, would you be willing to give us a brief primer on here's how you make a movie or a TV show 10 years ago and here's how it actually works today?
Yeah, I may sort of run super counter to your thesis here.
Because I would say I don't think it's actually that fundamentally different. And I know I don't want to understate the differences that are involved in changing company dynamics and things like that. So that's fair. If you step way back and take a long-term view, you'd say, what are the core functions or properties of this? One is you got to have a great story. You got to figure out a way to produce that story in a compelling way for audiences, leveraging the latest technology that exists out there. And then you have to find a way to connect that story with a bunch of people to watch it who are going to enjoy it. And you could have used the words that you used around several shifts, whether it's adding sound to movies, talkies, or television, remember television that was gigantic, and then VCRs and DVD and so on. So, do I think we have a very compelling, very effective distribution model in streaming? Now, we're able to build essentially a global entertainment network. And that means that creators when they have that story that's really compelling, we give them the tools to tell that story in an amazing way, sometimes in ways that they never could have before because we're upleveling their production capabilities. But then we also connect that story with the largest audience that that show could have ever possibly have seen. I mean, we think about whether it's Casa de Papel or Squid Game. These shows coming from countries and languages that have found audiences that are global audiences. That rarely happened before, if ever. And so in my mind, it's a quantitative difference if you will, but qualitatively the processes feels very similar to me.
I'll ask it at a sharper way. It used to be that the distributors didn't like to own all the risk and all the upside. And now it seems like Netflix and its peers want the risk and the upside. And you see less of these actors or studios getting big backends on things. Does that feel like a fair characterization of what happened?
Well, I think the reality is there's multiple models that are in play today. You still have all the way from folks who will literally produce something on their own. They will budget the whole thing and they will say then I want to go see who I can sell this to from a distributor, all the way to where folks like us and our peers get involved at the very beginning and say no, we will take the risk and we will support that. So there's a diversity of approaches out there which I think is very healthy for the ecosystem because when you have an idea and you're trying to get that idea out there, having many varieties and paths to yes is a very powerful thing to allow that to happen. So there are some shifts in that process but I think those shifts are also consistent with some of the value that we deliver and what we hope to offer, which is again about this: if we're excited about an idea and we feel really strong about the fact that if we can pull this off with the creative partners that we've got, we think we can find a really big audience for this thing. So then it's appropriate for us to say we're going to enable this to happen. We're going to lean in to make sure that that idea doesn't die because it can't find funding in some other way. And then of course we want to participate in the upside of that as well, and so then we get to these structures.
Right. I mean it is interesting, of course Hamilton Helmer has already come up once in this. For anyone who doesn't know, Netflix was the, Hamilton author of Seven Powers, the great strategy framework we use on every episode of Acquired. Netflix was like the test baby, like the perfect company for developing the seven powers.
And I don't know if you know this too, but we actually had Hamilton come in and he taught essentially a strategy course for all of our directors and above because we so believed in this. Because again, in our model we try and distribute the decision-making out, and so having this very strong strategic capability embedded throughout the company was important.
On this particular, let's talk about scale economies. This is like Netflix is the ultimate scale economies where the more the subscriber base grows, the more theoretically Netflix should be willing to pay for any given piece of content or to create any given piece of content because there's a wider base of people to amortize it over. And then if you play out that flywheel, it's like, well, you get the best content, you market it to the most people, you get more subscribers, and then you just can grow faster and pay more for content than anybody else. Where does that fall down? Because it sounds like utopian, like once you start that flywheel, you just run. What's hard about that for you guys?
Well, I think the maybe the one component that's important to layer on top of that is that not every title essentially has a global audience. And so that sort of global scaling effect and increasing leverage it doesn't really work across the entire population. We're at this point approaching close to a billion people who show up seeking to be entertained from us. They have a very wide range of tastes. And so increasingly what we have to do is think about how do we serve that wide range of tastes. When we produce a show, we think we're going to find a pretty big audience for it, and that's the goal obviously. But we also really are anchored in what we call local for local. You can't start the idea with, you're going to make a Squid Game that's amazing for Koreans but then it's going to be globally hugely popular. It just doesn't work that way. You get really inauthentic and sort of lame stories as a result. So you start with super local thinking about the local needs and then some of those work globally, but you're not really.
Is there like a point in the development process where you're like, this started local but we think it might be global?
Some of the creative execs have idea that this is going to go, but mostly you get surprised by how big those can get more than banking on it, I would say. But it just gets to this idea that you really are serving sections of audiences at the end.
Maybe now that you are at this giant scale that you're at, how do you decide when a regional investment is worth it versus something that you can amortize across all users?
It's a, I'm not surprising this question comes up all the time. So I think it's one of the trickiest things that we try and balance. Not surprisingly, we come from the perspective of there's great leverage in building global scaled approaches. But also what I am constantly reminded of is that actually serving that wide variety of people is a very local game. And so you try and do it at multiple layers. You're building core central stuff that you get great leverage off of. We do a single investment, almost some of the leverage scale benefits you talked about on content are the same for fixed cost investment on engineering and product advancements. So you get that same benefit there. Then there's some of that you're building called central systems that have an adaptation locally. So think about like payments rails. You do a central payments rail. You're using a lot of great logic there, but then you're actually interfacing with many different payments providers to actually make that work. But then ultimately, it's sort of individual personalization at the end of the day. You're bringing this vast library of content and we think about personalization from not only the title perspective, but how do we present that title in a way that we think is going to connect with the user? And increasingly now we're moving into personalization at the UI composition and module level. So that allows us to think about not only what's in the UI but the form and function of the UI and sort of say, hey, when you show up for us, what can we give you that specifically meets your needs? And that might be different time of day or day of week, right? So Thursday night versus Sunday.
And I've heard you say, so I'm sure everyone in the room is familiar with in the last year Netflix rolled out a new UI.
Hidden in that UI is the fact that you are not prefetching all the titles. You are watching my behavior on the UI and then in real time fetching the right content for me based on my behavior.
That's right. So we used to precompute overnight essentially in trough when you didn't have the operational systems at peak. We would precompute those recommendations but it doesn't allow us to do what exactly you're describing, which is react in session dynamically based on the signals that we're getting. So we had to rearchitect everything. We had to rearchitect the whole recommendation systems, how those are pipelined into the UI, how the UI gets composed, page construction, all those things. But it has a whole bunch of benefits because it allows the dynamic response but it now allows us to think about the construction of the UI based on need state or use case and we can use those signals to adapt to those things.
That is so cool. I'm now going to be very deliberate about where I'm sort of dwelling as I'm scrolling through, like how much of a trailer do I let play or.
You can do that or you can just let it fly. Here's an interesting twist on this too because it takes implicit signals which are valuable and stuff like that, but it also allows you to give very simple explicit queuing behaviors. I think you'll see more and more we'll give you because there's a vast universe of things that you could need, right? But ultimately.
We sort of know that that universe is centered around maybe four or five core use cases. So if we can find shorthand ways that you can understand where we say we know you're in one of these states, give us a hint about what you need from us right now. You can probably accelerate that process and give us a clue about exactly what you need.
Is this one of... we were talking a little bit downstairs before. In my opinion, at least, I'm curious, as a heavier user than me, if you would agree. You have the simplest UI of all the major streaming and entertainment platforms out there. You created the current streaming UI that everyone else copied. But I think even you look at, we have young children. We spent a lot of time on your competitor that has a mouse as its mascot. That is a very complicated UI.
Well, when they smash two streaming services together, it became complicated.
Yeah. Or even YouTube, it's actually quite busy when you open it up. Is this part of the ability to dynamically generate UI? Is this part of the philosophy of simplicity?
We've always been really anchored in a simple UI philosophy. We think there's a general trend in companies, because they just want to serve more people, right? There are more features you can add, and some product manager says, 'I know that would be somebody's going to use that.' But we generally think there's a tendency to overcomplicate and add. So we always try to have this very strong sense that every feature has to earn its weight. It's got to be delivering value commensurate to its pixel allocation, its engineering maintenance budget, whatever metric you think. We call this 'scraping the barnacles.' You inevitably build things where you think they're going to be big, and maybe they're not, or they're big at a time and then they change over a period of time.
At one point I star-rated thousands of movies on Netflix.
It was your repository, right? Well, actually, those aren't totally gone because we mapped them into the new model. We didn't lose that data. But we did find that a more simple model, believe it or not, even going from stars to just three thumb states, even that reduction changes how users engage and the amount of signal that you get. So we really try to be disciplined about pulling stuff back when it's not meeting that criteria anymore, so we can allocate those resources to something else that'll do better.
There's a cool... this is not on your iPad, so you will not see this. We were talking backstage. I think there's a really cool story that I had no idea about, about how deep Netflix integrates with the partner ecosystem and also a cool story of technology abstraction. Can you share with the audience how many Netflix clients there are, how many you need to build, and how that happens?
When you say clients, we're talking about the devices that you use to stream to, essentially endpoints.
Different kind of clients than JP Morgan.
Yeah, exactly. Different kind of clients. They're good clients too. We're on thousands and thousands of types of devices. We're at the point where we count in billions the number of actual physical instances of those things. We got to this because we were talking about how do you handle the diversity of this stuff, and the actual answer is we don't really have a lot of diversity. We have written an abstraction layer, essentially an SDK that we call the Netflix Ready Device Program, which we give to all of those device manufacturers. But the fun part is we do this at the chip level. When a new chip tapes out that's targeted at TVs or set-top boxes or adapter products, by the time that chip tapes out, Netflix is already integrated onto it and the latest version of NDP is there. So that just moves through the value chain. If you're a television manufacturer, the Netflix integration is already baked into the chip that you are buying from a MediaTek or a whatever.
That's right. So 10 years ago, 12 years ago, the Netflix pre-integration was a feature. When you were thinking about chip selection, you're like, 'Oh, I just get the Netflix piece. I don't have to worry about that anymore.' Now it's become ubiquitous. It's become a must-have feature when you're selling chips into the ecosystem, which is so cool. It's a great business model piece of leverage once you have it, because then those chip manufacturers need to do that in order to become valuable enough for their customers to buy them.
Yeah. Obviously what fuels it is the customer demand at the end of the day. That's the thing that matters. The whole thing works because there's enough demand where a TV manufacturer says that's pretty important for me to have that feature, and then that flows through the ecosystem that way.
We spoke about this a bit with Max, and it's going to be a recurring theme here. Maybe this is the right time to bring it up with you. Agents and AI. How are you guys thinking about this? Is there a future that you're thinking about where agents and AI services for consumers are the ones that are bringing them content to consume?
I think of this more as a software architecture question, first and foremost. If you think about where most of our systems have been architected before, there's hard-coded business logic that essentially is calling out to services to execute that logic. We're really pivoting more towards a model, an agentic model at the center of that. Think about this as a member model that understands a wide variety of data around how members behave and can think about how to optimize that for a certain set of goals. Now that model sits at the center, it calls out to all these services. Maybe it orchestrates other models to do other things for it to achieve a bunch of different goals. It provides a tremendous amount of flexibility and adaptability to how you then extend those systems in new spaces. For example, if you had a new content type and you want to do personalization for it, it used to be a many-month process to stand that up, get data in, tune the models, figure out what's working. Now we can do that very effectively. It also allows different use cases of how you do product development. Think about non-technical or less technical folks who might have an idea. Let's say after a football game, you mentioned live, how do we pivot folks off? There might be a specific need state to pivot people off of a football game to another show they might want to watch. Maybe it's not even a football game that we have, somebody else's football game, because we notice a surge of traffic after sporting events happen. So that product manager or merchandising person says, 'Okay, how do we fill the football-shaped hole in your heart right now?'
Well, I got to say, as a huge football fan, thank you for 'Quarterback' and 'Receiver' coming out during the offseason. It fills a lot.
That's a way to fill your... yeah, exactly. But doing that and flighting a promotional model before would take a lot of work. You think about, okay, what's the universe of titles? How do you filter that? Maybe you put that into a title rank or a traditional ML model to rank those in a personalized way. But now, you can have that person literally say, 'Okay, this is what I'm trying to do,' speak to that central model, and have it put together a bunch of stuff to try and solve that problem. Then these ideas live or die or get elevated in the UI based on their utility to any given user at the end of the day.
Netflix is famously on the cutting edge of machine learning. The Netflix algorithm, the Netflix Prize challenge. Have you deprecated a lot of the older models in this world of LLMs?
We're in this interesting spot. You talked about evolutionary versus revolutionary changes. This is a model where there are moments you really don't get the benefit of the new approach unless you really shift over. Cloud is another good example. When we were doing that process from 2008 or so to shift over, unless you're fully on the new model, you aren't reaping all the benefits. We call this the awkward period in the middle of Roman riding, where you have one foot on one horse and one foot on another. That's a place you don't want to be for too long. You want to minimize that. So we think about leapfrogging the existing approaches and trying to shift over. This is really hard to do because the existing approaches are highly refined, very sophisticated, hardened, all the corner cases have been worked out. Then you go to a new approach, you're super excited about what it could offer, but there's often a tough transition where you're betting on the future and you have to get through an awkward phase to get there.
When do you guys unleash the Chaos Monkey? Is that what it's called? At what point in the new technology onboarding process do you unleash the Chaos Monkey on it?
For us, that's a continual practice. That's how we ensure that we are resilient against a whole variety of things. We come from the philosophy that if you're not doing that constantly, you're just building in vulnerability and weakness. So that's an ongoing process.
And this is a process that runs within Netflix that is constantly just breaking stuff.
At least I read this 15 years ago. I don't know how true it is today.
No, it's we still very much believe in it. Whether it's Chaos Monkey, which is called service-level killing, or Chaos Kong, which takes out whole availability zones to see if you are resilient to availability zone failover. It's a pretty crucial part of how we think about our reliability schema. Our reliability metrics tend to demonstrate the value of that.
This feels like a funny question I almost had to ask you. There's two points of view. There was the streaming wars and they're over and Netflix won. And then there's this other perspective that there are a lot of streaming services out there. A lot of them have really matured. They consolidated into vertically integrated competitors. You've got YouTube. It is in some ways the most competitive media environment that has ever existed. How do you think about this?
I would say it is definitively the most competitive media environment that's ever existed. If you think about the range of choice available to any given person on the planet for what they can be entertained with, there's never been this range of options. They're increasingly completely friction-free. If you don't like what you're doing right now, you're a flip or a click away from doing something else. It's very simple. In my mind, it's incredibly competitive. We've expanded the types of entertainment available to people too. Games, social media, short form. There's so much going on. I think that's great for consumers. But it also requires us, our peers, everybody to be constantly reinventing themselves, pushing themselves to deliver more. If you stand still in this environment, you will have your lunch eaten for sure.
Well, that feels like a great place to leave it. Greg, this has been such a blast. Thank you so much.
My pleasure. Thanks for having me. Really appreciate it.
