Stack Overflow users don't trust AI. They're using it anyway

21:12Nilay Patel:We have to take a short break. We'll be right back.

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22:46Nilay Patel:Upwork is a one-stop platform to find, hire, and pay expert freelancers across web and software development, data and analytics, marketing, business operations, and more. They help grow your business by giving you fast access to specialized talent across 125 plus categories. So you can fill skill gaps, launch projects faster, and scale support up or down without committing to full-time headcount. And with Business Plus, you can access the top 1 % of talent on Upwork. And with AI-powered shortlisting, you'll get matched to the right freelancer in under six hours. No endless searching required. You can visit Upwork.com right now to post your job for free and connect with top talent ready to help your business grow.

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25:38Nilay Patel:That's S-E-R-V-A-L dot com slash decoder.

25:53Welcome back.

25:54Nilay Patel:I'm talking with Stack Overflow CEO Prashanth Chandrasekhar about what an existential crisis for the company the launch of ChatGPT was. And of course, that led me to ask, why would anyone new come to Stack Overflow now? Do you think new users are going to come to Stack Overflow? Stack Overflow is a product of the mobile era, right? There's an explosion of software development. There's an explosion of community. There's a culture in the valley of building apps and services. And there's new tools. Stack Overflow is one of the central points of gathering for that community in that era. New developers today might just open Cloud Code or Cursor or GitHub or whatever and just talk to that.

26:30Nilay Patel:and never actually venture out into a community in that way. Do you think you can get people to come to Stack Overflow directly and seek out answers from other people, or are they just going to talk to the AIs? I think that for simple questions, by the way, when we saw the decline in questions early on in 2023, what we realized is that pretty much all the question declines had to do with very simple questions. Complex questions absolutely still get asked on Stack because there's no other place. The LLM is only as good as the data, which is typically human-curated, and we're one of the best places for that, if not the best for technology.

27:07It's still a very active site, a lot of engagement, a lot of monthly active usage. And the questions being asked are quite, I would say they're advanced questions, but what we're also increasingly seeing through our new mechanisms that we've opened up, because we want to answer your question, we want to give people other reasons to come to the site other than just getting their answers. So we have had to broaden our site's purpose and hence the mission of cultivate community, power learning, and unlock growth. So what we've done with those things is let's open up new entry points, new ways for people to engage.

27:39So we have, in fact, for example, nearly unlocked the ability for humans to chat with each other to get directional guidance. So that's been a very, very popular feature on the site where people are engaging with other experts. So we have, for example, people asking OpenAI API questions, as an example. and they can go into the open AI chat room and be able to engage with other people having, you know, that have similar questions, as an example, or Python experts, as an example. We also opened up ability for people to get, demonstrate their knowledge by opening up challenges. So we, you know, effectively like hackathons, that's another idea that we've got where we've opened up a whole bunch of series of challenges, very popular feature.

28:16Now people spend time to go and solve these challenges that we post. And then that way it can accrues to their ability to showcase their understanding of the fundamentals, which I think is very important in terms of where the world is going. Because if people are just using vibe coding tools and cogent tools, I think companies that are bringing in young talent ultimately have to know that they're relying on the people who not only took the shortcut, but also understand the fundamentals. And so we're one of the few places where you can actually prove that you've actually learned the fundamentals.

28:47So that's the other reason why we've opened up these two mechanisms. And then the third reason, third part of the mission, which is unlocking growth, is we also want to enable people. There's going to be a lot of job disruption as a function of all this. And the people's jobs are going to change quite dramatically. Junior developer jobs, even though I think it's a short-sighted move by many companies to stop hiring them, considering you need a pipeline, those people are going to need a home. Then you're going to need to connect with other people to be able to sort of progress and learn and get jobs.

29:14And so jobs is a very important part. We struck a partnership with Indeed this past year so that they partner on tech jobs. So it's just to broaden the scope of our site so that there are many other reasons other than asking the questions they will still do. But we want to give them more reasons to come to the site also.

29:31Nilay Patel:This comes, I think, to the big tension in all of this. And I see it playing out on all kinds of different communities. I see it playing out in our own comments in a lot of ways. You want to build a community of people who are helping other people get better, and that is being disrupted on every side by AI. And communities that are built around people are pretty resistant to the incursion of AI. This has definitely happened on Stack Overflow. Your moderators have essentially revolted over the ability to remove AI-generated answers as fast as they want to. When you partnered with OpenAI, a bunch of users started deleting content so it wouldn't be fed into OpenAI for training, and you had to ban a bunch of them.

30:09Nilay Patel:How are you managing that balance? Because if you build communities around people, I would say right now, anyway, the culture is those communities will push back against AI very hard. I would say one of the most important things that we're focused on and I've spent time on over the past few years is this whole push and pull, as you describe it, of like, how do we think about AI in the context of our site? Because it's pretty clear to us and to me that if we don't modernize the site in the context of us leveraging AI as an entry point, et cetera, that it's going to be less relevant over time. And so that's not good.

30:46So we've taken a very aggressive stance by incorporating AI into the public platform now with AI Assist, as I mentioned, which has been fantastic to see. And I'll walk you through the decision on why we did that. And then same thing on the enterprise side. But there's definitely, you know, if I think about the user base at Stack Overflow, it's kind of like a big nation, right? Like we've got 100 million people. And there's definitely, you know, people on both sides of the spectrum. What's interesting is that there is a, you know, we have something called a 1-9-90 rule. 1 % are the hardcore users who have been spending a lot of time, you know, with their blood, sweat, and tears, curating knowledge, spending their time on the site, et cetera, contributing.

31:219 % doing it in a medium way, and then 90 % sort of consuming and mostly being a lurker on the site. And the distribution on the site, when we ask people on whether or not they're using AI, our own surveys basically say, if we look at the Stack Goal Flow 2025 survey, over 80 % of our community members are using AI or intend to use AI. 80%, right? But the trust level on that answer, when they're using AI, is only about 29%. Only 29 % of our user base actually trusts what's coming out of AI, which is actually quite appropriate considering where we are because there should be skepticism of this new technology.

31:58So there's enthusiasm to try it, but not to fully trust it. And with this 1.990 rule, I think what we have is we have a core group of users that are always going to be the protectors of the original mission of the company, which was to create this kind of knowledge base that was completely accurate and do nothing more than just that. And then you're going to have, we have a very large number of people who are, let's say, younger developers, people, the next generation of developers who are looking to leverage the latest and greatest of tools. And it's very clear to us, based on surveys that we've done and additional research, that they want to use natural language as the interface to be able to do this.

32:33It is the most meaningful seed shift sort of change in terms of, you know, computer science development. If you look back to all the way to even object-oriented programming many, many decades ago, that wasn't such a huge boom, actually. It didn't actually create this new change. But now we're in this moment where everything's been unlocked. So I think it's a huge change effort. And we've had to sort of decide that, hey, we've got to be able to respect the original mission, keep accuracy at the heart of it. And so we're not comfortable with using, for example, AI for answers because it will generate slop because it hallucinates.

33:06And that's hence the trust score is low. But why don't we incorporate natural language interfaces so that's the preferred way to sort of engage? And so we ended up doing that both on the public side as well as on the enterprise side of the house. And that's been really well received by the vast majority of users. But there will be always a vocal minority that will push back against incorporation. Because there's a lot of, you know, beyond just the site, there's just a level of, I think, broader concern about what all this does to jobs. And if we let the cat out of the bag, then what's going to happen?

33:37So I think there's that obviously concern also, which is understandable.

33:40Nilay Patel:Let me put a pretty fine point on that. I think I understand that in maybe a sharper way. If I am somebody who, in your 1%, who spent a lot of time on Stack Overflow helping other people, and the reason I answer questions for free on your platform, which you monetize in lots of ways, is because I can directly see that my effort helps other people grow. And I'm helping other people solve problems. That is one very self-contained dynamic. The last time you were on the show, our entire conversation was about that dynamic. Yes. Right? And how you got people to participate in that dynamic and the value of that dynamic.

34:15Nilay Patel:And then suddenly, there's a very clear economic benefit to the company that owns the database because they're selling my effort to open AI. Right? Which is a thing that is happening across the board. Right? We're going to do all these data licensing deals with all these AI providers. They're going to train on the answers that I have painstakingly entered into this database to help other people. And now the next generation of software engineers is going to get autocomplete that's based on my work. And I've gotten nothing. I mean, I've heard that from lots and lots of people. I've heard that in our own community.

34:48Nilay Patel:I think I have felt that as various media companies have made these deals. How do you respond to that? Because that feels like what you were providing was a database that you had to monetize in some ways. But the people, the interaction that the people had was the value. And now there's another kind of economic value that is maybe overshadowing it or recasting or recharacterizing the interaction that the people have. There are a couple of points there. One is, I think, if you think about the original DNA of this company and why people came together to do this thing. You know, when I joined the company, I asked a question, like, what's people's incentive to spend time doing this?

35:23And I asked the founders, specifically Joel Spolsky, about this. And his point is that the software development community is effectively, it's very altruistic. People just want to help each other out, you know, because people understand how frustrating. I used to write code many years ago. I recently picked it back up with, you know, some of the code gen tools, which is interesting. Compare and contrast. But I just remember how frustrating it was if you get stuck on something. And so Stack was obviously a huge boon when it was created to unlock this. And so it was truly out of that. That was the reason.

35:52We also asked a question, even before ChatGPT, should we incentivize users by paying for that? Should we give them monetary benefit? And that wasn't like a high ask by a user base. We went and researched people. So people were not in for the money. Plus, it complicates things because how do you judge the payment for a particular JavaScript question relative to a particular Python question? You know, it's just a very, very, it goes down a rabbit hole, which is, I think, a kind of a sort of a untenable sort of rabbit hole. And so this commercial aspect, so that's one. So what is the original reason why people got together?

36:30It was about the mission. Secondly, in terms of like, I think why we have to do this and, you know, is it unfair and so on. The primary reason why we have to go down this data licensing route or why we've had to do it is because the model of the internet has literally been turned upside down. People relied on, and I know you talk about this, Nile, with the DoorDash problem. I think the model of the internet has literally where people go to search engines and go to websites and you monetize off of ads. I really empathize with content sites that are heavily dependent on advertising because I think most content sites, their traffic is down 30%, 40%, something like that.

37:13With this huge seed shift and where companies that support these platforms have to ultimately bear business, ultimately. What do we have to do? We have to do what is necessary to adopt a new business model to survive and thrive and do all the things. Thankfully for us, we've had an enterprise business. which is independent of all of this. Thankfully for us, we were now able to, we still had the advertising business for large advertisers still cared about this, you know, our community. And so data licensing only felt right in terms of making sure that we can, you know, effectively capitalize in the moment, plus also be able to invest back into our community so that people who are there for the right reasons saw the benefits of that.

37:58So we've invested with all these new features I just mentioned, you know, whether that is all these new content types or challenges or chat or AI assist or any of these things all takes resources to go and build. And so we've had to go and leverage these funds that we received to be able to go do that. Now, in the future, we may consider other ways to make it, you know, for example, should we pay our users, give them a piece of the data licensing revenues? Perhaps, you know, we'll ask that question always. There's always ways for us to continue. But right now, this is the current setup that we have.

38:31Nilay Patel:You mentioned to get to the data licensing deals, you had to put up a bunch of anti-scraper tools. You had to go into secondary and tertiary layers of the stack to get deals from Databricks and other kinds of providers. The AI companies, they were just scraping your site before. They're probably still hard. Whether or not they're paying you, they're probably still just going through the front door because all of them appear to be doing that. Did you have to say, we're stopping you and then go get the deal? Or did you say, hey, we know you're doing this, but you have to pay us or we're going to start litigating?

39:02Somewhere in between, I would say. I think we put up the anti-scrapers very quickly. We even sort of changed the way in which people received our data dumps. And, you know, because we want, against it as a balance, because we never wanted to prevent our community users from grabbing our data for their legitimate needs, you know, for them to do their school projects or PhD theses or anything like that. So we've continued to be open about our data for our community members, but they have to be community members, and there can be companies looking to commercialize off the data. So we were very specific about the policy terms, putting up technology that prevented people from grabbing it so we know exactly who is scraping, who is not scraping.

39:45Some parts of those were outreach to those folks to say, look, stand down because you're obviously putting a lot of pressure on the servers by doing what you're doing. So, you know, take it easy.

39:57Nilay Patel:But I think my characterization of those companies is they don't care. Or some of them care and they want to be good citizens. And some of them absolutely do not care. And they would prefer the smoke. You can just categorize. And there's a reason Amazon is suing perplexity. They told perplexity to stop it and perplexity won't. As we're speaking today, this morning, the New York Times is suing perplexity. Then there are other players who are acting in different ways and they're striking different kinds of deals. Walk me through one of those deals. When you went and struck your deal with OpenAI, was it, we're going to stop you, and if you want the door to be open again, you have to pay us?

40:29Nilay Patel:Or was it, you know this is wrong, we can take all the technical and legal measures, but we should actually just get to the deal correctly? Walk me through that conversation. With some folks like them, we were already, you know, we were incorporating something like OpenAI into our product. Remember the Code Red situation where we were about to announce our AI response? So we were actually using that technology to do what we had to do to incorporate AI into the public platform as well as our enterprise products. So we had a relationship with them. And we also said, look, this is not going to work.

40:59It's not tenable. And this is the new way of working. And so we need a new arrangement, a business arrangement for you to use the data. And so let's actually have a conversation. And to credit to them, they were very partner-centric around that. I was very impressed by both OpenAI and companies like Google who are all very open to engaging on this topic and wanted to be responsible AI partners. They got it immediately, even before we asked them. It wasn't like this kind of big, you know, let's go have this conversation from the ground up and justify why it had to be done. We just said, look, this is what needs to happen because it's a new business model.

41:34And we got into the conversation pretty quickly in that, OK, let's actually have a constructive way to, you know, what exactly are you looking for? which format of data do you want to scrape the content? Do you want bulk uploads? Do you want API calls? What do you want? So we got into that whole mix. And then, of course, there is the conversation around, and these are recurring, just mind you, Nile, that these are recurring revenue type of deals, that these are not one-time payments. So yes, they were very collaborative partners. But you're right, there are players who are contradictory. They say something and their actions, I think, prove other things, you know, in terms of how they've engaged.

42:16And so there are people that are holdouts for sure, people that are not exactly consistent with their word. And that's unfortunate. You know, it's a, and I think every company like us has to decide what we do about that. And, you know, we're in various stages of these conversations with various people on, you know, how to make sure that we sensibly get them to sort of do the right thing.

42:37Nilay Patel:Now you have to name one of those companies. Who do you think is holding out differently than their public posture. I'd rather not be, but I think you've actually characterized all the usual suspects that you are covering are the usual suspects that we are encountering, is how I would put that. Yes. Let me ask you about the recurring revenue piece, and then I want to get into the decoder questions, because I think they'll be illuminating after this conversation. There's a sense that we've done all the pre-training that we're going to do, that scraping the internet is not the future of these models, that there needs to be some other leap.

43:09Nilay Patel:Stack Overflow's existing corpus of information is the valuable thing, right? There's a lot of information. There's 20 years of stuff in that database. What's the value of, you have to pay us again to train the next version of Gemini or GPT, and the value of there's incremental information being added to the existing database? Because that seems like a clear split to me. The way that we have thought about this is that every model that's being trained, you're training it on some corpus of information. You're going from GPTX to Y. And so in the new model that you're training, if you're leveraging our original data or some derivative of that from a prior model, then you have to pay us for it.

43:52That's effectively the legal requirement to be able to do that. And so it's a cumulative aspect, right? So let's not forget that. And so people have to pay for the cumulative data. It's not just the fact that it was used back in the day. And yes, obviously, relative to 20 years, one year's worth of information is going to be less. But that's why you're getting 20 plus one. You know, that's the idea. And so that's just the way the legal agreement has been set up.

44:16Nilay Patel:Is it per year? Is it every year's worth of data is a chunk of money? Or how does that work? No, it's just it's a cumulative. It's like the whole corpus. Past, you know, historical data as well as, you know, anything that's going forward for the following year. All that is sort of one accumulated sort of data set. And that's, you know, charge is one effectively. But that doesn't – so, like, this year's data doesn't get pulled into the training set for Gemini 3, which just came out, right? Every new question and answer in Stack Overflow since Gemini 3 has come out is not incorporated in Gemini 3's training.

44:52Nilay Patel:So, you're kind of betting that they're just going to train ever bigger models. That's right. Is that how it's structured in your mind? Yeah. And some companies have asked, you know, their wide use cases. You know, there's pre-training use cases. There are, you know, even beyond that, you know, you can leverage the data in many, many different ways for AI and non-AI use cases, search use cases and so on. But correct, I think that we, in some, there may be scenarios where more larger models are built and our data is going to be useful for those scenarios. But there's going to be RAG indexing. There's going to be post-training needs.

45:27There's going to be all sorts of scenarios. And it's quite interesting to see some of the frontier labs ask for very specific slices of data that they find very useful. And we're able to remember we've got a lot of, there's not only just a question and answer, but we've got the comment history. We've got the metadata history. We've got the voting history. We've got the user A has gone down this path history. You know, so it's a lot of excellent context for things like reasoning and to really sort of be able to be useful to really mimic the human brain. It's effectively one human brain that's been documented almost.

46:05Nilay Patel:We have to pause here for another quick break. We'll be back in just a minute.

46:16Nilay Patel:Support for today's show comes from Upwork. The fastest growing businesses aren't doing more. They're delegating smarter. Upwork helps you bring in expert freelance help fast so you can delegate and keep moving. Upwork is a one-stop platform to find, hire, and pay expert freelancers across web and software development, data and analytics, marketing, business operations, and more. They help grow your business by giving you fast access to specialized talent across 125 plus categories. So you can fill skill gaps, launch projects faster, and scale support up or down without committing to full-time headcount.

46:53Nilay Patel:And with Business Plus, you can access the top 1 % of talent on Upwork. And with AI-powered shortlisting, you'll get matched to the right freelancer in under six hours. No endless searching required. You can visit Upwork.com right now to post your job for free and connect with top talent ready to help your business grow. That's U-P-W-O-R-K dot com. Upwork.com. Support for the show comes from Shopify. Starting a new business, it could be a lonely endeavor, especially in the beginning. And when you're starting out, it's more important than ever to make sure you have the right tools at hand. Shopify is the commerce platform that millions of businesses around the world rely on to sell their products online.

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48:22Nilay Patel:That's shopify.com slash decoder.

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49:03Nilay Patel:And I'm Adam Grant. And we're here to invite you to the Curiosity Shop. A podcast that's a place for listening, wondering, thinking, feeling, and questioning. It's going to be fun. We rarely agree. But we almost never disagree. And we're always learning. That's true. You can subscribe to the Curiosity Shop on YouTube or follow in your favorite podcast app to automatically receive new episodes every Thursday.

49:31Nilay Patel:Welcome back. I'm talking with Prashanth Chandrasekhar, CEO of Stack Overflow. We've talked a lot about AI and models and disruption. These are all big issues. So how do they fit into the fundamental work of being a CEO, which is to make decisions and keep the company running? This, I think, brings me to the decoder questions. You've restructured the company. There's been some rounds of layoffs. You've obviously refocused on the SaaS business in a real way. I think we should talk about that. But the idea that we're going to train ever bigger models and that will be the growing part of the business versus we might just want some slices versus it's actually retrieval augmented generation or RAG that's going to be the future for a lot of these other businesses.

50:11Nilay Patel:You would make different decisions based on which one of those is going to grow faster. And I don't think anybody knows. Maybe you know. You can tell me if you know or you know someone who knows, but we're in a very nascent period for all of this kind of development. How have you structured the company to even out all of that risk and be prepared for how do people actually need the data in the future? Because I don't – maybe you know, but I don't think anybody – I don't think I know. Yeah, yeah. It's hard to predict clearly. And also, you know, who knows? We've got some brilliant minds like the Demis Haspis at Google and others who are coming up with the next generation leap of whatever time.

50:46the equivalent of transformer technology is to keep going towards this ultimate goal that they're all pursuing, which is AGI. So, yes, you're right. It's hard to know exactly what shows up and when. However, the way we structure as a company is effectively two parts, Anil. So the one part is the enterprise business. So we have a product team, engineering team. We have obviously a go-to-market team focused on that. And that's very, very clear, the enterprise products business. And then the other side of the house is what we call the community products side of the house. And the community products team focuses on the public platform, all the features that we talked about so far, AI Assist and all the subjective new questions and chat and all these things.

51:28And there's the community side. And the data licensing business sits in that group. And so that they are obviously tied to the engagement on the site. And so there's a kind of a virtual cycle there. So that's how we're split. And that includes, again, product resources, a small go-to-market team, engineering folks, et cetera. And then I've also a community management team, which spends a lot of time with the moderators, et cetera, to engage there. So it's split down the middle. And of course, our other functions support both.

51:59Nilay Patel:How big is Stack Overflow today? I know you laid off almost a quarter of the company in 2023 because of traffic declines. You've built other businesses. How big are you today? We are about 300 people or so. And do you think the revenue you're going to see from data licensing or your SaaS business is going to allow you to grow again? We believe so, yeah. We're a growing company. We're profitable. So, you know, financially, we're thankfully in a very good spot. And now it's all about placing bets on the highest growth opportunities. And we believe creating this knowledge intelligence layer inside the enterprise through our stack internal product is a phenomenal growth option because customers are pulling us in that direction, which is fantastic to see.

52:39Nilay Patel:That's right. Those are some of your announcements. I do want to wrap up by talking about those. I'm just focused on the future is the SaaS business for enterprise. The future is data licensing. Are you still seeing declines on the public website? I would say it's been stabilized for a few months. I think the engagement and the activity on the site are actually pretty stable. The drop in questions that I mentioned previously were all the simple questions. And it seems to me now has sort of come to a place where the complex questions are being asked. We have a consistent number of people on the site every day.

53:10We have something called a heartbeat. In fact, anybody can go check it out. There's always, you know, if you go to stackoflore.com, you'll see it at the bottom, how many users are online at the moment sort of thing. And so you'll always see a very consistent number there. So I think it's hard to know how to predict the future. But certainly I think the worst of it was back in, you know, 23, 24, for sure.

53:31Nilay Patel:The question I ask everybody in Decoder, as you well know, is how do you make decisions? The last time you were on the show, you said that you wanted to be on the front lines as much as possible, and you wanted to be informed by people who are on the ground. Has that changed in the past three years? What's your decision-making process? Yeah, not really. I think it's very important for leaders and people like CEOs, et cetera, to have the full context because these decisions cannot be delivered. You can't have filtered information. So I spent a lot of time with users, a lot of time with customers, really understanding what they care about.

54:05And that's how we've even decided, even something like as controversial as the AI assist feature to our conversation. It wasn't obvious if you were not around to say, let's go and, you know, build that. Because if you just listen to the headline statements, it seems like, hey, people don't want that, want to create AI into Stack Overflow. But the reality is that many, many users, the 90 % I was mentioning, wanted a natural language interface. That's what they are comfortable using these days, and that's what they wanted. So that's why we decided to do that.

54:35Nilay Patel:One of the things that I see everywhere is that split. You mentioned it before, 1990, right? There's a very vocal minority. We see it in our own traffic on The Verge. We cover AI deeply. We are told that everyone hates it. I understand why. I understand the comments. That's what I'll say. I get it. And then I see the usage numbers. I see the traffic on our coverage of AI tools. I see companies like yours saying everyone's using it. And there's some gigantic split there that is unlike any other split I think I've ever encountered in covering technology for the past 15 years. Where everyone says they don't like it and then they're using the hell out of it.

55:13Nilay Patel:The only other one I can think of that is lightly comparable is how people feel about Adobe. Everyone uses the tools and everyone's mad at the Creative Cloud subscription fee. is basically the only comparison I have. It's not a good one. It doesn't map one-to-one, but it's as close as I've come to that split. What, in your mind, accounts for that split with AI, where people don't like it, they're very vocal about not liking it, and then we see the numbers and everyone's kind of using it anyway? I think it comes down to that data point I shared earlier, which is that 80-plus percent of our user base wants to use AI or are already using AI for code-related topics, but only 29 % of that population trusts AI.

55:49And trust is a very deep word. You know, it's like, why don't you trust something? You don't trust something because you don't think it's producing high integrity answers, accurate answers. You may not trust it because it may replace you one day. And, you know, you don't like that either. So, but at the same time, you're obviously going to be curious on what is this force that's going to be such an economic force. And so you want to keep trying and using it and perhaps getting better and ultimately, hopefully, leveraging it to your benefit in a way that you can be relevant as an individual developer in the future, being able to go a lot faster and so on.

56:25So I think that's probably the reason, because I think especially the developer audience, I think they're a very discerning, a kind of a, let's say, analytical audience. And they can be prickly if things are not, let's say, deterministic, right, the way it has been for a very long time. And so this is a very probabilistic sort of technology. So the fact that it's almost like going to a casino and using a roulette wheel, you're going to get a different answer every time. it's not necessarily comforting for somebody who is writing very, very specific code, looking for very specific outcomes. And I think that people will get used to that over time.

57:05It is a mind shift change for people writing software. And that may be the reason that why people are intrigued, because it is so powerful as a technology. Don't get me wrong. We use Vibe Coding all over the place at Stack, right? All the features I mentioned to you, our designers and our product managers Vibe Coded it first to show it and get user feedback before we went and built it. So we've embraced these tools internally for their benefit. So there will be ways in which you can feel comfortable using it, but still, I think that's the core reason.

57:33Nilay Patel:I actually want to talk about that dependency, right? You know that it's not trustworthy, but you are building products with it. You are building products to enable it. You've mentioned it several times. The big announcement this week is Stack Overflow AI Assist. You've talked about it several times throughout this conversation. You're betting that this is what people want, right? You're betting that an AI-powered tool on Stack Overflow will help more people, and then maybe that thing is going to hallucinate like crazy and give people the wrong answer. How do you make that bet when you know that the users don't trust it, but you still have to roll out the tools because that's where the industry is going?

58:05We believe we've actually unlocked a very important aspect of that trust issue, which is responding to it. Our AI Assist feature provides a RAG plus LLM solution. So effectively, it provides an answer that first goes out into our corpus of information of tens of millions of questions and answers. We have 80 to 90 million. And those are first used to produce a response. And then if they don't, then there's a fallback option where it goes and leverages OpenAI, for example, who's our partner, to be able to go and produce trustworthy knowledge from other parts of the web. And so it's first searching through our trusted, attributed knowledge base.

58:48It produces the links so people can go down that path and learn more about it, which is very important to us, attribution and so on. And so that's how we are navigating this element of hallucinations, and we're constantly testing it. And it's not perfect, right? There will always be improvements, but we're also looking at where the world's headed. And if these models continue to get better, then we should benefit from those improvements. And ultimately, we should have the best solution because you've got grounded human context plus the LLM strengths as well.

59:21Nilay Patel:I think the thing that I'm most interested in is the faith that the models will continue to get better. I'm not 100 % sure that's true. I'm not sure that LLM technology as a core technology can actually be intelligent. As you're saying, people are very attracted to the natural language component of these models and the interface shift that's happening. The platform shift that we're all going to develop software with natural language or let the LLM's reason basically self-prompt themselves into an answer, right? There's something there that seems risky. Are you perceiving that risk today? Are you factoring that in or are you saying this is where we're at now and we have to continue until something changes?

1:00:00I think the first thing around the improvement on the LLM, let's call it plain. I'm with you, right? It's hard to know how things are going to improve. When you just think that, okay, it's plateaued, when you thought about the past six months, boom, here comes Gemini 3. And again, we're proud partners of Google. And here we go. It is a seed shift. It blows every other model out of the water. And now we've got a code red situation in other companies, other LLM competitors.

1:00:27Nilay Patel:Sam Allman did type the words code red, by the way. I want to be doing that. So that's very good. Perhaps that was the way for me to go back in the day. But I would say, but the point being that that is, so it is surprising that you're able to produce that sort of a leap when you seemingly things have plateaued. So I don't know. I'm not, I can't predict that, right? Because these folks are deep, deep in the subject, Demis and others. And so that's true. But there's also going to be other, I'm sure, innovations that we are not even privy to. Like I was explaining previously. like if, you know, transformers were obviously a huge development in this space, there may be something that these AI research labs come up with that we're not even aware of that's going to be ultimately pushing things.

1:01:11Ultimately, we know that the compounding effects are very real. You know, we've got unlimited compute. You've got, you know, extremely powerful chips and GPUs that are now even lowering their costs. And I was at, you know, AWS Reinvent this week where they talked about the Tranium chips and Tranium 3 and Tranium 4 being built out. So, you know, there's going to be just the proliferation of these compounds. And then you've got access to data that we've already talked about. And so these things, when they combine and compound together, it's going to produce very magical outcomes. So I think that's the belief and why it's rooted, why my own assumptions are rooted in the fact that it's going to improve.

1:01:48Nilay Patel:The split, and the reason I'm asking about this in the context of the tools you're building and everyone using it and only 29 % of people trusting it, is you've got to bring that number way up to reach the returns that every company investing in AI is trying to reach, including yours. I don't know if the core technology can do that. I don't know if you can stack a bunch of technologies to do that. But I do know that one version of the future of software development looks like everything is vibe coding all the time. Right. Another version of the future of software development looks like writing intensely long prompts for models that are pages and pages themselves, which seems ridiculous to me, but maybe that is the future.

1:02:26Nilay Patel:And another version looks like, oh, we return to humans at the cutting edge of software development, and they are co-developing with an AI model and then maybe asking Stack. And that feels like a richer, more interesting future. But it's unclear to me where we are on that spectrum or how that even plays out. We obviously have not only a bird's eye view in the context of our public community with this 29 % data point as an example, but our enterprise customers give us a clear view on where they are. because the ROI question is being asked very heavily inside companies. And clearly, I think the 2026 is going to be the year of rationalization.

1:03:04If 2025 was the year of agents, at least, where every tool is being tried out inside these companies, there's a very open landscape for CTOs to go and buy various tools, test various tools. And so I think it's been a tremendous time for some of the companies building these tools. But 26, the CFO pressure with, hey, look, okay, there's productivity improvements have to come from these. We're going to hire less people as annual planning happens. There's going to be tremendous pressure in the system to prove out what the real value is. Everybody at a senior level that I've talked to acknowledges that this is a big shift, and they all are leading it pretty hard.

1:03:44They're all waiting for the improvements. I think most people, most companies will say that they have seen improvements in the small groups where they've tested these tools. But that's a sort of a self-selecting group because they're the enthusiasts and so on. And they will see great productivity gains, which is probably true. But there's an absolute drop off in productivity as you think about the adoption across the enterprise. And probably for the same reasons, by the way. You know, you're telling employees to use tools which may put them out of a job. So why would they want to do it? Or more fundamentally, if these tools are not perfect and they're hallucinating and they're going to be held accountable, then that's not good either.

1:04:18And then, of course, is the process changed, the mindset changed, the ability to completely change the workflows of how you work, all the enterprise change management work, which is why our solution, which is Stack Internal, is building this human curation layer, a knowledge intelligence layer, so that you can, with the MCP server on top of it, the knowledge base in the middle, and then our ability to ingest knowledge from other parts of the company to create these atomic Q &A that are extremely helpful to root your data. enterprise knowledge in through these AI agents. That is that solution. That's why we've gone and really gone hard at producing that.

1:04:56And we've seen a really, really strong response from our customers. We have some of the world's largest companies leveraging and testing and building this with us, HP and Eli Lilly and all these companies, Europe. And so it's been amazing to see them gravitate to us because they want to fulfill that ROI point that you're making. ages. And what are the gaps? It's trust, again. And so they want this trust layer through a company like Stack that they can actually insert in between their data as well as their AI tools.

1:05:25Nilay Patel:When you say the age of rationalization, what I hear is you think the bubble's going to pop in 2026? Yeah, I think certainly the exuberance in just trying out various tools and unlimited budgets on this AI budget, I think will ultimately, it'll come to roost. I'm not sure about the bubble bursting. There's definitely going to be corrections along the way. There's no question about it, if you look at history. But the number of vendors that are selling into these companies, what I'm very surprised by is that there's similar functionality. There's four of them being tested within these companies.

1:05:58Certainly, all these companies may have received, whatever, they've got to$100 million in ARR. But at some point, there's going to be churn when the CTO decides, you know what, I'm only going to use maybe one and maybe a second one as a backup. No different from the cloud. If you think of the multi-cloud world back in the day, people didn't have three clouds out of the gates. I mean, now you have maybe one primary cloud and one secondary cloud. This is different, but at the same time, I don't think you're going to have four different wipe coding tools, in my opinion.

1:06:26Nilay Patel:When you think about Stack Overflow as being a trust layer, right? I mean, that's the value add. That's what maybe you can charge a premium for over time. You're still dependent on a tool that only 29 % of your users trust. And I know you're talking about RAG and your other systems for doing that. How do you think you bring that number up with Stack Overflow? Is that possible for you to do, or is that the ecosystem has to do it for you? I think it's an ecosystem point, more generally speaking, because that is more a reflection of people's, what they have access to beyond Stack. They've got access to all these other options.

1:06:58And so what we can focus on is being the most vital source for technologists. And so for us, it's about making sure this content is excellent, it's high quality, but also it's a great place for people to, again, cultivate community, connect with each other, learn, and grow in their careers. But I think the way in which we can do it is through the fact that we are working with all these big AI labs and the fact that our trustworthy knowledge that's been human curated painstakingly is going to flow into these LLMs, which ultimately produce trustworthy answers. So we're sort of one layer behind, but that's, I think, where we operate.

1:07:37We operate in that trust layer or the data layer, if you will, in the context of LLMs. So that's our indirect contribution to that 29%.

1:07:45Nilay Patel:Prashant, this has been a great conversation. You're going to have to come back sooner than three years next time. What's next for Stack Overflow? What should people be looking for? Our biggest focus is going to be making sure that we build this enterprise knowledge intelligence layer for companies to truly use AI agents in a trustworthy way. So, you know, our stack internal product that we launched a couple of weeks ago at Microsoft Ignite, in fact, is very, very excited about that on the enterprise side, as well as, of course, on the public platform, as I've mentioned throughout, to help our community users connect with each other, really learn as well as grow their careers.

1:08:24And there are going to be so many avenues and new entry points like our AI Assist and our subjective content and chat and other things that people hopefully find very useful as things change around them very rapidly and they can be part of this amazing community and help each other out. So those are the two focuses, enterprise as well as our public community.

1:08:42Nilay Patel:All right. Well, when the bubble pops next year, We're going to have you come back. Let's talk. And you're going to say you predicted it. Thank you, Dillian. I appreciate it. I'd like to thank Prasant for joining me on Decoder, and thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode or really anything else at all, drop us a line. You can email us at decoderattheverge.com. We really do read all the emails. You can also hit me up on Threads or Blue Sky. You can also leave a comment on our fancy new YouTube. You can watch full episodes at DecoderPod, and we have a TikTok and an Instagram.

1:09:07Nilay Patel:They're also at DecoderPod. They're a lot of fun. If you like Decoder, please share with your friends and subscribe wherever you get your podcasts. Media Podcast Network. The show is produced by Kate Cox and Nickstat. It's edited by Ursa Wright. Our editorial director is Kevin McShane. The Dakota Music is by Breakmaster Cylinder. We'll see you next time.