Inside how The New York Times leverages AI

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I come from a family where the intersection of journalism and tech really kind of runs in our blood. And one of the people who I just admire the most is actually my great-grandmother. She founded one of the very first TV stations in the Midwest in the nineteen fifties. She was definitely the only woman in any of these rooms and in any of these contexts. And I remember her describing to me that one of the first things she had to do to really bring TV to South Dakota and to North Dakota was to erect a chain of microwave transmitters.

It was one of the longest chains of transmitters at the time that existed in the country. And so I think of her as in many ways the original technologist and kind of media mind. And she just had this sense of of building and making to help figure out how to help support the business of journalism. And I remember thinking like, that is what I want to do when I grow up.

So that's exactly what Alex Hardiman did. She's now chief product officer at the New York Times, continuing the legacy of her grandmother. It's probably no surprise that with Alex's interest in technology and journalism, AI is a big part of her role. And while we've witnessed the rise of AI-generated articles on some platforms, that's not Alex's MO.

We really do feel like AI is an assistive technology. We are not using AI to replace journalists and to create journalism on its own.

So how is the trusted newsroom of the New York Times actually implementing AI on a day-to-day basis? On this episode, we're answering that question and many more. We'll get into how the Times is using AI to build audiences, the future of journalism, and why Wordle is still as relevant as ever. I'm Rana El-Khalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Hi Alex, welcome to the show.

Thank you so much for having me, Rana. A big fan and admirer of you and the uh AI conversations that you're helping to unlock for so many of us.

Well, we are excited to have you on, and we're gonna talk about your role as chief product officer at the New York Times. But I just wanted to let you know we are avid subscribers and big fans of the Times. Um, my kids, who are twenty two and sixteen, we have a WhatsApp group and we share articles and debate them on WhatsApp very passionately. And my daughter loves the cooking. I'm I'm not a good cook, but my daughter loves the cooking section and she draws a lot of uh recipes from there.

So we're we're big fans.

Uh, thank you so much for saying that. I mean, we, you know, we try to make something for everyone, um, in the course of their daily life. So I that means a lot. Thank you for sharing.

And then I have to ask you this, cause at some point a couple of years ago at my company, Afectiva, we had a Slack channel for for Wordle. We were all doing Wordle every single day. Do but then I I don't know. I I don't do that anymore. Do people still play the game?

Oh my goodness, yes. I mean, Wordle has had it just has such cultural relevance. I kind of think of Wordle almost as, um, one of our Taylor Swift strategies. Because in the same way that Taylor Swift can bring together eight-year-olds and eighty-eight-year-olds from across generations, different ideologies, I really find that Wordle and the fact that people can come together to play the same game every single day and then share what was hard, their scores, what they're proud of, if they got Wordle in one.

It just it is such a bridging and bonding experience. And so it is, uh, still a very, very big part of our game's portfolio and a really big part of what makes The Times just a very community-oriented and culturally relevant, uh, product for so many people.

Yeah, I love that. I remember having tons of conversations with people around their Wordle approach, right? Do you use the same set of letters every time, or do you change it up? And it's very cool.

what's your starter word? Do you have one?

I do not have a starter word. I feel like that would be so boring. I don't know. Do you have a starter word?

I I used to. And then, um, Tracy Bennett, our editor, actually said, you know, just if you actually wanna win, the best way is not to use the same starter word. Is to just get inspired by any word and then build a strategy off of that. So I I definitely try to get inspired by whatever whatever the thing is in my mind in the moment.

I love it. So you were the chief product officer at the Times, and of course, the role of chief product officer is very common at tech organizations, but how common is it in a news organization? And what does the role entail?

Um, it's it's a really great question. So I am technically the first chief product officer. But when you think about one of the best products that the New York Times has ever made, it's the newspaper. So we didn't have a chief product officer, but our history of 175 years goes back to us knowing how to create this incredible corpus of diverse journalism to meet a range of news and life needs. And to wrap that journalism in an extraordinary user experience that people can use to understand and act on the world around them.

And so, day to day, what that just means is I've got a portfolio of products that I help oversee in news, in games, in cooking, in sports with the athletic, in shopping with Wirecutter. And we really we're in these giant spaces where we believe that we can be truly the essential subscription to help people, you know, understand and engage with the world around them. And it's a it's a privilege to be doing this job.

Yeah, that's amazing. So I I imagine your team consists of a a of a number of product managers. Do they need to have a background in software, a background in journalism, both? Like, how do you bridge these two worlds?

So one of the things I actually really love, um, about I'll, I'll get to the the talent point in a moment, because something that's really unique about the New York Times. And I, I was at the New York Times, by the way, for ten years. Then I left and I went to Meta for several years, then went to the Atlantic and then came back to the New York Times. And what I'll just say, though, is there's something so unique about how we build product at the New York Times.

We own the journalism, we own the software that helps to distribute that journalism, and we own well over 150 direct relationships with our subscribers and future subscribers. And so in many ways, when we think about building products, we kind of own the full stack. And that's very different than technology companies. And so the way that we think about building products is pulling together a product manager, an editor, a data scientist, a designer, a product marketer, a team of engineers.

And it's the blend of art and science where we really have our editorial teams and our product teams truly embedded. So we can, um, build products with a lot of creativity and magic and art, in addition to the traditional science that you see from a purely d data-driven, uh, product company, more a la a la meta. And so I'll say that product managers can come from so many different backgrounds. We see product managers who, you know, are former engineers, former designers, former journalists, um, former artists.

And it's something I'm actually quite proud of, uh, for our team at the New York Times.

Yeah, you talked about how, like, the Times is kind of a full stack organization and you own the relationship with your subscribers. What does that mean?

yeah, so so let me let me tell you a little bit about the the business transformation of our story, because this is a very, very central part of, um, of our our current strategy. So if you go back about, uh, let's see, about ten years ago, when we were looking at our business, we were print first, we were advertising first, and to be honest, we didn't have a really clear path for growth. We had about a million digital subscribers at the time.

Um, our newsroom was shrinking, and we were, to be honest, we were playing defense just to try to stay afloat. We weren't going on offense. And today, we're in a totally different place. Um, and it's been a hard one where we are a digital-first, subscription-first business. We're now closing in on about 12 million subscribers. We've more than doubled our newsroom now to over 2,600 journalists. We have a business that is growing, um, you know, it's 2.5 billion dollar business.

But that kind of transformation, it did not happen by accident. If you go back ten years ago, the media industry was largely we were un people were unbundling their content and their services, and the idea was to go chase traffic and ad dollars on the big tech platforms. And at that moment, we decided to essentially do the opposite, where we chose to be subscription first and destination first. And we decided to build proprietary technology and software that could prioritize those direct relationships so that we, not intermediaries, could really deliver trusted and differentiated value to our audience.

And that was that was one of the very the most important decisions that we've made. That still is a guiding principle to how we lead and build products and make decisions. And it laid the foundation for us to then make another consequential decision, which was to decide to become the essential subscription for, again, every curious person who is seeking to understand and engage with the world, and not just for news, but now also daily life.

So this is your second stint at the New York Times, and after your first stint, you spent some time at Meta, and it was a really crucial time for the United States and the world. So talk a little bit about that.

I'd be happy to. So I first ended up at the New York Times almost twenty years ago, feeling like really one of the luckiest people in the world. And it was a really interesting period. I was there from 2006 to 2016, and it was a period of really deep transformation for the company. I was leading our mobile team, and back in 2006, we had no presence whatsoever on mobile. And so it was really, really great experience that I then took when I went to Meta.

It was 2016, and I decided to go to Meta to lead a team where we were helping micro sellers in low bandwidth parts of India come online for the first time and transact using social commerce and really looking at the interoperability of the Meta family of apps. And it was awesome work, but the 2016 election then happened. And as you referenced, um, it was a really intense time of reckoning internally, externally, a lot of, um, really important conversations about misinformation, elec election integrity and more were really taking place.

And what I found myself doing there was attempting to do things to the Facebook platform that Facebook was not really built to do. Like, how do we classify and algorithmically rank accurate, high-quality information? That's just not what News Feed was meant to do. How do we reduce spam and abuse? How do we introduce fact-checking? Um, how do we grow local journalism at the community level and really help bring people together at a time of really deep division?

And so it was it was really hard. It was really interesting work, incredibly purposeful. But I remember thinking I'm so passionate about the intersection again of journalism and technology. Journalism is not core to the mission of Meta, and Meta's been very upfront about that. Mark has spoken very, very, very clear. And so I, in my bones, was like, how can I help bring these lessons in scale and ambition and product driven growth back to the mission of journalism?

And so that's when I ultimately found myself back, uh, at the New York Times in late 2019, right before the pandemic.

I want to ask you a a meta question. So at the beginning of 2025, Meta canceled its fact checking program. What are your thoughts on that decision?

I haven't been at Meta for quite some time. I can't comment on the specifics of what's happening there right now, um, just cause I'm not privy to it. But I do think in general, the need for veracity of information is only going to grow. And so when you look at kind of the bifurcation of the internet, you have all of this synthetic slop or dubious content, the provenance of which is increasingly difficult to identify, especially in a lot of these AI native platforms.

But the human need to know what you are reading and that it is accurate and that you can use that information to make really consequential decisions in your life. That is, that is not a need that I'm seeing abate. I'm only seeing it grow in terms of, um, its essentiality. But when I also think about what it means to develop a really high quality information ecosystem, I do think that some of it lies with us as well.

Like trusted institutions has been eroding for decades, and we're seeing more and more powerful actors trying to discredit serious journalism. And so how can we come together to build an information ecosystem that is healthier, more accurate, and more trustworthy? And I just don't think that as an industry, we have done enough to make our reporting as accessible, as approachable, and as transparent as it can be and as it needs to be. And so when you look at our products now, we're doing so much to deepen how our journalists engage directly with the public.

How can we make our work more approachable, more transparent? If you open the New York Times on a given day, you'll see in our app we have more reporters on camera who are speaking directly as experts to our audience about the process behind their stories. It feels like a friend who's just a very smart friend who's approachable and is saying, like, let me just tell you why this is such an interesting and important story, and why it's one that hasn't been told, but people need to understand.

So so as AI becomes much more ubiquitous and it becomes much more embedded across our daily lives, this work of building authentic relationships between our journalists and our audience and earning their trust just becomes so much more essential.

In a minute, how the New York Times is using AI across their product portfolio, including in their recipes. Stay with us. The companies winning right now aren't just building great AI. They're building the infrastructure to actually run a business on top of it, which is where Stripe comes in. Stripe is built for whatever comes next, with planetary scale financial infrastructure to help you power any business model. As a matter of fact, 86% of the Forbes AI fifty run on Stripe, along with industry leaders like Salesforce and Pepsi.

Last year, Stripe users processed $1.9 1 trillion combined. See how Stripe can power what's next for your business at stripe dot com. So let's switch to AI. You're not just leading product, uh, for the Times, you are also leading the organization to become an AI-driven organization. So I want to dig into that. There's this tension between using AI tools and being at the forefront of this incredibly powerful innovation, but at the same time doing it in an intentional and thoughtful way while protecting kind of the company's IP.

So how do you navigate that while kind of embracing, uh, the adoption? What what does that mean for for the Times?

Yeah, so so two things can be true at the same time, which is what you're really getting at. There's a way for us to have an approach for how we use AI responsibly, and I'll talk about that. And then there's also, um, an approach where we can say we also believe that our intellectual property must be valued and respected. So so let's take the first one. So I would say that broadly, um, we have used technology for decades to help during each big moment of transformation.

We, um, did this with mobile, um, as I mentioned when I was leading mobile. We have done this with audio when you think about, um, our early investments in audio, which have really helped us, you know, enter people's lives and homes and earbuds. Machine learning is something that we have been using in our journalism and in our products now for a very long time to power ad targeting, to power our subscription model and our dynamic paywalls, personalized recommendations.

Um, and now AI, just like any other technology, is something that we feel can be quite powerful. But the goal isn't to be first to market necessarily, always with every new technology. It's to be the best at using technology to amplify the impact of our journalism, to really meet our audience's most important news and life needs, and to solve real problems at scale versus chasing fads. And this is something that we really try to focus on because we could get pulled in a million directions.

So there are a few principles that I'm happy to share that really guide our work and how we use AI responsibly. The first is that we really see AI as a tool in service of our mission, and that mission is to seek the truth and to help people understand the world. And Rana, there's actually something that I've heard you talk about on some of your previous podcast episodes about this idea that AI should be about augmenting and amplifying human ability, and ultimately it can unlock greater human potential.

And that's how we see AI, right? We see it as a tool to assist our journalists and to accelerate our business. The second, though, is that we always have a human in the loop, and this is really important. The expertise and the judgment of our journalists, those are our competitive advantages, and that's what machines simply can't match. Because if you think about it, journalists are out in the world and they are finding information that does not want to be found to tell important stories.

So this is information that no LLM has access to. And I really believe that that is information that will become even more important in the age of AI. At the end of the day, our brand promise is that we are always responsible for what we report, however the report is created.

Yeah, I love the kind of the human-centric approach to harnessing AI, cause as you as as you mentioned, this has been the common thread in my entire career. And I think it is a combination of keeping the human in the loop and keeping the human accountable, but also thinking about how to use these tools to augment and amplify and tell stories that we've not heard of yet.

100%. And, you know, just to sort of give you an example of, like, what this looks like in in the real world, there was so, um, it was, I wanna say it was maybe ten days before the election. The twenty this was the 2024 election. Two of my colleagues in the newsroom, Ali Brezon and Nick Carasentini, they were reporting on Trump's campaign election lies. And what they got their hands on were over 500 hours of video recordings of the group that was behind this movement.

But the problem was there were fewer than 500 hours before election day, right? So, like, even if we put the entire newsroom on the task, we wouldn't have gone through every single video in time. So this is where we were able to take two members of our AI team and embed them with a journalist to transcribe the videos. And then they fed the videos more than 5 million words of transcript into an LLM, which Ali and Nick, using their expert judgment, used to search and prompt and surface surface examples for their reporting.

And this reporting was enabled to be published, you know, in a matter of days so that people had access to this information before going to the ballot box to decide who they were going to vote for. So that's just, I think, such a great example where, you know, the LLM on its own doesn't do much. But when you have a high-quality data set and a set of expert journalists who know how to prompt it and ask the right questions, you can really unlock a lot of assistive potential that can really help make journalists all the more powerful.

Okay, so so my son is doing this archival research, looking through Egyptology archives, and there's thousands of documents in Arabic, and he he's not very fluent in Arabic, but he's been using a variety of AI tools to essentially streamline some of that process and help kind of this professor he's working with to sift through all these archives. So I think what I'm hearing you say is AI's going to change a lot of the ways we uncover data and uncover stories, and, yeah, accelerate some of this kind of research.

It's it's pretty cool.

I I think I think just research in general is definitely a place as long as you have a human in the loop to then fact check. Like that that again, it just all comes back to to making sure that you close that loop. But I do think that that's a very relevant and very prevalent use case. How do we support the craft and the making of journalism? But in no way is AI going to replace it.

I love it. What are some other examples of how AI is being used in the newsroom?

So I would say that at the story level, there are so many other examples of machine learning engineers being paired with journalists, um, to sift through giant public data sets and to find and uncover really important patterns. But beyond beyond that, you know, there are so many other examples across our products. And so this this crosses the newsroom and our product teams, where we're really using AI to make all products in our portfolio so much more accessible to more people.

Um, and you might not know this, but we actually have generative AI now embedded in many of our larger consumer products. So in news, for instance, if you just open up the product on any given day, over 90% of our news and opinion stories now have an automated voice mode attached to it. And so this is, you know, a very simple way for us to take a story that traditionally would have only been in text format and create a different modality.

So that if people are commuting, if they just prefer to listen, maybe honestly for for other reasons, audio is the only way that they can access and retain information. It just unlocks from one original story all of these other high quality derivative versions that can just travel more widely to reach and engage more people. Cooking is another one of my favorites, and I know you mentioned that, you know, your one of your children, um, is into cooking.

If you're ever outside of the US, you know, historically, it was hard to cook with us because our recipes were in imperial units. And so we've been using generative AI to do very simple things like help automatically transfer ingredients from imperial to metric units. And these are small grace notes, but you can find that these types of things when you really understand a pain point in your product and you can thoughtfully apply generative AI. We now have over 50% of our international users using this feature and just finding that the product helps them cook in a very easy and effective way.

So there there's so many ways like that where it might not be the most splashy use case, but what it really is doing is unlocking the power of our journalism for so many more people. And that's incredibly valuable, and that's the type of impact that we want to see.

Yeah, and it's enhancing audience engagement, right? Like, back to kind of building this authentic relationship with with your audiences, that's kind of one way to do it, right?

100%. And what we're finding is that for people who already have a relationship with us, it is helping to drive more engagement because they're just getting so much more value out of the product. But then for other audiences, where there might have been a little bit of friction based on the way we were distributing our content, the way that we were presenting it, we weren't necessarily multimodal. The more that we reduce those barriers to entry, the more that they're finding their way into the New York Times universe as well.

And as we take the long view to build the next generation of future readers and subscribers and listeners and viewers, um, it's a it's a really important part of that strategy.

Yeah, were were there some tools or use cases that were completely off the table?

So so the AI use cases that I think are off the table are the ones that in any way compromise the independence, the originality, and the trustworthiness of our journalism. So I don't have a a specific example because in in many ways, the principle is kind of put just enough guardrails so that there's a lot of room for experimentation, but we are also not off exploring things that we would just never ship. So there there are some pretty hard lines there about what we will and will not explore when it comes to the journalism itself.

But beyond that, we have a really high tolerance for, um, experimentation internally. So just as an example, a couple of weeks ago, we had one of our internal hackathons called Maker Week. And this is where we have so many colleagues from across the company. Anyone can basically come and play and use AI tools to figure out, you know, how to solve a problem that's been on their mind that they wanted to solve, how to unlock new value.

And we ended up with over a hundred different prototypes leveraging AI. Doesn't mean we're going to ship all of them, but there's some really amazing ideas that have come out of them. And

Can you share some?

uh, sh- sure, I mean, I'll I'll give you an example, you know, like one, um, is we had one team that was looking at how to apply AI to create a simpler and a safer and an easier New York Times cooking experience for kids. Again, I don't know if this is a product that will ultimately find product market fit within scale, but as an idea and a concept, it's so driven in a in a real customer need.

And the teams can see how AI can help solve for that, which I just think is wonderful.

One of the areas with my investor hat on that I'm really intrigued by is the kind of virtual human economy where people can create digital video or voice clones of themselves to scale their impact. So I'm I'm wondering if this is something that your journalists have considered playing with, like, is that an area that you're looking into?

It it's an area that I'm seeing a lot of others look into. Um, for us, what we actually see is the power of being able to engage with the actual reporter, the person is the thing that is so much fa- so much more valuable, um, to our audiences. And so, you know, if people really want to understand, let's say from Maggie Haberman, you know, what is she seeing? She has studied this administration and this president more deeply than anybody else.

It's so much more beneficial to come hear from her directly on a podcast, in a video, in a story, than it is to engage with a theoretical AI version of her. So so I really, really feel like the authentic, um, expertise and humanity of our journalists is what actually draws people into the New York Times and will only have increased value as that becomes, you know, a little bit more of a rare experience. Because so many people will be, you know, engaging instead with synthetic information and AIs over humans.

Yeah, you you mentioned how the New York Times is very multimodal in their approach to sharing and reporting on news. So it's not just text-based, there's voice, there's video. I believe that the current kind of interface to AI today, which is mostly text-based chatbots, is not gonna be the ultimate. It's not an AI native interface, and I'm really curious about, like, what an AI native interface looks like, where you can access these AI tools anywhere you are.

I I don't know if that's something you've thought about at all. Like, is it is it gonna be like, you know, my son wears Meta glasses?

Mine too. Really? H- isn't that Oh, yeah. The the barrier to entry is so low. I mean, what what does your son use them for? What are what are some of the most important use cases? We can trade notes.

Okay, so we're on vacation or whatever, and we're all walking around Mexico City. And he's sixteen, he's got his glasses on, and and I'm talking, and I don't realize that he's got his music on. I'm like, you're not listening to me. So I don't know if I like the use case, but he he does that.

A hundred, 100%. We. It's funny. The the sneaky use case I see with my son is taking, um, photos and videos in addition to listening to Spotify in the background. So I I'm, I'm with you. That is that is a common pattern. Um, but but what I y- you're asking a really a really important question. This is something that I think is still to be determined, but that we're we're really thinking deeply about. Cause if you if you sort of go back to a lot of the the main modes of interaction with information in the internet, you know, traditionally it was it's like query based, and that was search.

And now we're seeing that shift more to, you know, chatbots and and a lot of the the kind of, you know, different LLM products that we're that we're seeing out in the world. There's also been this, you know, very dominant form of serendipitous browsing, right? And scanning, um, which really came out of social feeds, but is also something that is very much that that plays to, you know, one of the modes that we have as an interface with our product.

And that I don't think is going away anytime soon. And then there's another one, which is, you know, having a real connection to a show and a personality. And so when you look at YouTube and you look at Spotify and this idea of, you know, more appointment viewing, that is also a little bit of a a different mode and a different engagement pattern. And so what you're talking about in terms of, you know, voice, super interesting.

We have, as I've mentioned, most of our coverage now available in automated voice. And so you can kind of see how there are a lot of interesting ways for us to think about using our incredible corpus of of IP to potentially engage people in really new and novel ways going forward.

Yeah, that that could be really exciting, right? So as people are exploring new spaces or new experiences, what if you could access that information in real time? Like, and and uncover some of these stories that connect you to a place, right, that you wouldn't have ordinarily, you know, I, maybe I don't necessarily track the news in Mexico, but while I'm there, right, and walking through these spaces, that's an opportunity to uncover some of these new stories.

That's that's super cool. I love it. We're going to take a short break. Stay with us. So let's switch focus to responsible uses of AI. And I want to start with, you know, in June, Sam Altman of OpenAI joined Hartfork's live show, and we've had Kevin Roose on the show. We're big fans of him and Hartfork. But but basically had Sam Altman on the show, and he brought up the New York Times lawsuit against OpenAI pretty early in the interview.

So so that that was pretty cool, interesting. Um, so for listeners who aren't aware, the New York Times is suing OpenAI and Microsoft for using its copyrighted reporting into, uh, training the OpenAI models. Can you talk a little bit about New York Times position and and where where is that lawsuit landing?

Yeah, so the the lawsuit is active. Um, can't comment on where it's landing, um, for for obvious reasons. But what I'll say at a at a high level is that we, like many creative IP holders, we just believe that it's imperative for us to protect our expertise and ensure that our our IP is is respected. And when you think about a company like the New York Times, we have this enormous amount of high value intellectual property.

We've developed it now for over 175 years, and we produce new high value IP every day. And it's at great expense and often at great risk to our journalists who are on the ground doing really hard things to get access to important information. And so we believe that AI developers need to pay to use that high quality proprietary IP. And the truth is, they clearly see a lot of value from it. If you look at most of the major AI developers, news is a big use case on their platform.

And when we look at the law, we believe that the law is on our side. We are very clear that we're prepared to do what it takes to enforce our rights in court. And I just have to say that as a as a human, I think a world with less quality information is dangerous for democracy and society. And so I really see, um, fighting for IP protection as fighting for funding the truth. And there are, I just have to say, other organizations who we have very productive partnerships with.

Uh, we just announced, um, about two months ago, a partnership with Amazon, and they really recognize sustainable and fair value exchange that gives us appropriate compensation for our work and appropriate controls over our content. So we have a long track record of working very productively with a lot of tech platforms and companies. And I'm actually, you know, optimistic that we'll be able to do more of that in the future.

I want to double click on that because you talk about this equal value exchange, this partnership with Amazon as an example. What does this equal value exchange look like?

It's a it's a great question. I mean, it there's no single answer for every single um partnership. But I would say, you know, in general, not specific to Amazon, because I can't comment on the specifics of that deal. You know, you look at historically, value exchange came through an agreement between a publisher and a platform where you gave a certain amount of your content to the platform. And in exchange, you received direct referrals and audiences back to your own destinations.

And that was that was the that was the agreement. That is still one form of value exchange, but now we are seeing, you know, many other types of value exchange just out in the market. So again, not specific to the New York Times, but we're seeing licensing of high quality news and information. We're seeing co-development of products, right? It really comes down to the incentives of two, two organizations, and where can they find middle ground to create net new value together that really helps support both of their businesses.

So I want to talk about how AI is shaping the future of journalism, and I want to share a statistic. So in 2024, Pew Research released a study that showed that roughly half of US adults believe that AI will have a negative impact on the news people receive over the next twenty years. What's your perspective on this?

I mean, I I I'm really taking the long view, and I do think that, you know, in a world saturated with commoditized content and dubious sources, high quality fact check journalism, it's not a niche, it's an essential service. And so that is really what we are focused on because, uh, this keeps me up at night. It keeps me up at night when I think about my kids and the relationship that they have to news and information.

But it's also such an animating force for us. We all see, you know, the same potential risks and dangers, but it really actually strengthens our conviction in the value of our mission and the work that we're doing every single day.

Yeah, amazing. I guess what is your advice to other news organizations, but other organizations in general, trying to adopt AI in in a meaningful and responsible way?

I it's a great question. I think it just comes back to not treating AI as a strategy or a roadmap unto in itself. If you are really clear about who you are, radically clear about your identity, your mission, your purpose, the unique value that only you can provide to your customers and your audience every single day. If you're if you know that, then it becomes very easy to think about how AI can be a force multiplier to help you do that.

But if you don't have that vision and that clarity, and if the rest of the organization is not fully fully behind you on that, that is where I feel like you might, even with the best of intention, use AI and get the wrong outcomes. So I think it really just starts by being so clear about who you are, your purpose, your mission. And then you can start to unlock some really creative and novel use cases for responsible AI, but it's all in service of your mission, not the other way around.

Yeah, absolutely. Okay, last question, and it's a question I ask of all my guests. What does it mean to be human in the age of AI?

Oh, that is such a wonderful question. Um, I think it means being curious, humble, and also a little more skeptical, just to make sure that you're a little bit more aware about where the information that you see is coming from and making sure that you're making the best decisions with what you've got.

I love it. Curious, humble, and skeptical. That's awesome. Alex, it was great having you on the show. Thank you for joining us.

Thank you so much, Rana. This was a pleasure.

We often think of chief product officers in the context of tech companies. So it was fascinating to see how this role operates at a media organization. The New York Times is a product led company with a suite of products beyond the news. And as an organization, they are embracing AI, everything from using AI in the newsroom to helping drive audience engagement. I particularly love their human-centric approach to AI. They use AI as a tool to amplify and augment human potential.

It's not taking away the job of a journalist. But there is tension here. At the same time that the New York Times is implementing AI, they're also in an ongoing lawsuit against some of the big players making them. This is a more extreme example, but a lot of companies are grappling with how to drive value from AI while also mitigating the risks. Maybe there's something to Alex's advice about using a different starter word every time you play Wordle.

Follow your curiosity, experiment, and be open to new strategies. AI isn't so different. Don't forget to leave us a review and rating wherever you're listening to us now. Your feedback helps other people find the show, plus we read all of your comments. Thank you. Pioneers of AI is a Wait What original. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa, and our associate producer is Jordan Smart. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pew.

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