Microsoft CTO Kevin Scott on how AI can save the web, not destroy it

27:32So even if, let's say, you and your 11 friends in 2025 are going to go start a TikTok channel to talk about tech. If one of the things that you're doing there is doing a bunch of reviews about tech products or tech sites, and you want to reach an audience and the audience is sitting inside of Copilot or ChatGPT or something like that, you're going to want those agentic pieces of software to have some way where they can reach into your media channel so that you're sort of exposing that audience to what you're putting out there. And NLWeb might be a good way to do that, where maybe you're not offering up everything, but you're offering kind of what's happened with search, like teasers, like snippets, like things where it's like, okay, you know, you ask your agent to like, hey, I'm trying to buy like a new phone.

28:29You know, here's kind of what I think I want. Can you go find me some sources of information about this? And, you know, if you have from your TikTok channel, a way to let your agent know what your content is, like maybe that's the referral traffic back into TikTok from the agents like, hey, go watch this video. Like this is super interesting. Yeah. I'm really curious to see if the big platforms enable themselves to be searched or acted on by agents in the way that they somewhat had to allow themselves to be searched by the big search engines, right? There wasn't a choice. I think maybe the biggest platform that recused itself from search for a minute was Facebook, but Instagram is still searchable, There was a trade where you wanted to be exposed and to be found on these tools.

29:21So everyone sort of opened up. The dynamics of how you will open up to agents, I think, for a variety of reasons, many of which make sense, are just not clear. Why would we do this when we could build our own agent? We're still in those early stages. To put it in search terms, are you going to do vertical search or big horizontal search? And horizontal search sort of totally won. Yeah, and it's just sort of hard to say what exactly might happen here. I think it will largely get decided by users. So like, you know, one of the things that's going to happen is users will just decide what it is they're going to tolerate.

29:57So if using an agent to help you sort out your life and what you're doing becomes such a huge preference that people have, things that aren't connected to the agent will kind of turn invisible for folks. Like you'll just sort of think, oh, well, X isn't reachable by my agent. Like maybe X is kind of broken and I'll find another way to do that. And I think the thing that you want is the market kind of figures out what it wants is you want as many open protocols as humanly possible so that people can make those late binding decisions about like, okay, like this is what the users have chosen. Like they're expressing their preferences, like at least like have this thing be open so that I can opt into it.

30:45Yeah. When the preference is clear. Yeah. I'm dying to know how this plays out. I can see a bunch of sites like TripAdvisor and others that would really want this kind of distribution. Obviously, we need to build the front-end tools, the aggregators that say, okay, here's your agent that's going to go out. Do you think, having looked at the agents that have been demoed so far or announced and then not shipped or announced and shipped to five people, do you think that initiating MCP across the web like this is a necessary condition for agentic systems to operate? Because none of them work so far.

31:19Yeah, I think something like this is really kind of necessary. I mean, I remember back when I was working in the early days of mobile on advertising, and the reason that I worked on mobile advertising is I wanted to figure out a way to help people who were building mobile apps and services to figure out distribution and to figure out how they monetize themselves. And before things like AdMob came into existence, the only way that you could get distribution is you went and cut a BD deal with a mobile phone company. And they would decide whether or not they were going to give you placement on – at the time, it was WML, and it was placement on their deck.

32:05And it's just kind of a barbaric arrangement. You know, it made perfect sense back then. But like, if you sort of look at what happened with the technology and fast forward through time, it's like, yeah, why would anyone choose that? And so I think there's a little bit of that dynamic right now where people are absolutely finding utility in these agents, like even as constrained as they are right now. And so in places like software development where you need to have a much narrower range of things that the agents can actuate, like where you build some completeness there, like, oh, my God, the adoption is great.

32:46And people love what these things are doing. And there's a ton of competition. And it's just really transforming how software development is working. So what I think we're going to see, and this is me, Kevin Scott, the optimist, is if you had a real complete agentic web where MCP was sort of the lingua franca of this agentic web, kind of like HTTP was. Everybody can just sort of stand up an HTTP server and start serving HTML, and they get to decide what the HTML payload is. You're going to see this really interesting organic unfolding of what's possible. I don't know what the moral equivalent of the Amazon.com or the early winners in the web are, where when you get enough of that plumbing hooked up that things are going to be super useful.

33:46But yeah, I mean, I think that some of this protocol stuff has to happen before you get to full utility. It's why MCP is interesting. We kind of think about NL web as kind of like the HTML layer. So it is a thing that lets you not have to go do a really tremendous amount of low-level work in order to be able to sort of plumb your stuff up to the agentic web. Yeah. You know, there's the parallel that comes to mind here is Apple's attempt to build an agentic Siri, which is built on a thing in their operating system called App Intents, where iOS apps will expose themselves to Siri in some way and let Siri take action inside those apps.

34:28There are rough parallels here. Obviously, MCP is more open standard. It's more nascent. App Intents has run into the same business model problems. Like if you're an app developer on iOS, why would you let Siri use the app and not the user so you can upsell them or sell them an app subscription? Whatever you're going to do. That's one parallel. The other parallel is Alexa Plus, which I joked earlier has launched to some people. No one knows who they are. Google has some agentic ideas. There's computer use from Anthropic, OpenAI. None of it's worked yet. Have you seen anything that says this is definitely going to work?

35:06To answer your question very specifically, like outside of software engineering and outside of demos, like I haven't concretely seen the thing that works. And I'll just sort of ground it even more. So if I look at my everyday life and I'm sort of looking at how I use these things outside of software development, there's just not an awful lot I can go – that I'm choosing, Kevin Scott is choosing, to go delegate a bunch of stuff to this agent to do on my behalf. But I can kind of smell it with MCP. And I do think it actually has to be open. I think it's kind of hard to do this in a vertically integrated way.

35:50The other question I want to ask, I'm really putting the media training to use here. I'm going to ask you about Google. Google had an opportunity to achieve some of the success it did because Microsoft was in the throes of antitrust pressure. Microsoft bundling Internet Explorer, putting pressure on Netscape that led to whatever amount of legal trouble Google was able to swoop in. It created the application layer. was able to put Chrome on Windows. Everybody has a story. That really created the antitrust pressure on Microsoft really created the opportunities for Google to succeed. Here we are.

36:23It's many years later. It's decades later. There's a lot of antitrust pressure on Google, particularly in regards to how it controls the web, both in the advertising layer and the search layer. There's the suggestion that the government make Google divest Chrome. This is a lot of antitrust distraction. And here I am talking to a Microsoft executive about new ideas for the web and new standards for the web. Do you see the opportunity as the same, that there's space because Google is being distracted? Look, I think the opportunity is the moment that we're in. Like the technology itself is just ready for some of this stuff to happen.

37:00I mean, this demo that you saw that Guha gave you, you couldn't have done it two or three years ago just because the technology wasn't ready. It would have been just impossibly difficult to do. And there was no way that TripAdvisor could see a thing on a Tuesday and be demoing it on their own data on a Wednesday. That is entirely a function about technology maturity. So I don't know what's going to happen with anything that the government is doing with any of the other tech companies. But I think part of what's happening right now is like you just have a new set of technologies that are capable of a new set of things.

37:42And you've got a bunch of big tech companies and small entrepreneurs who see the possibility in the thing. And like you like the thing that I want is I want to see as much energy in this ecosystem becoming more mature as humanly possible. and again you know like i my my pattern matching is you know goes back to the last time that i was a happy young developer which was uh like when the internet was emerging and just that sensation that you can have when a bunch of hard things just became easy and a bunch of the protocols are open yeah and you don't have to ask anyone's permission to go try something wild like that's when interesting things happen.

38:31But let me just put a little bit of pressure on this and I want to actually broaden out and just talk about AI generally. You know, if you had showed up and said, okay, here's a new standard for accessing the web and structuring websites from Microsoft. Yep. Two years ago, everyone would have said, great, we're going to wait for Google's riff on this or for Google to adopt the standard. Google's under a lot of pressure. A lot of trust has been erased from Google. You now do have the opportunity for OpenAI and Anthropic and Microsoft to show up with a new standard and to feel that there might be real adoption here and that Google can't show up with its own standard tomorrow and take the wind out of your sails.

39:10That has to be true for you, right? You feel that because the comparison I would make is, I don't know, in the late 90s or early 2000s, someone would announce a new standard and Microsoft would show up with like a proprietary Windows riff on that standard and the other thing would go away. And that was part of the problem. Do you see that reflection right now? Yeah, I don't know. And I'm not trying to be – sometimes I am trying to be evasive. I'm not trying to be evasive here. Sometimes it feels to me as an engineer like certain things are technically inevitable. Like I've had a bunch of conversations about, you know, with folks about MCP, like inside of Microsoft, where it's like, oh, you know, like, you know, this isn't exactly what we would have chosen.

39:58And I'm like, yeah, but it kind of doesn't matter. Like sometimes there is a real problem in an ecosystem where the simplest solution that everybody can choose to adopt is the winner because we all win because ubiquity is the thing that really matters. And it feels like we've got a bunch of those opportunities right now. And so the thing that I think that's really beneficial is some of them have become such simple things where you actually don't need a multi-trillion dollar company to go do an enormous amount of work in order to create the conditions for adoption to happen very quickly. in some sense, you know, with MCP and NLWeb, you actually don't need a big tech company pushing for it.

40:52Like, we're just sort of a voice out here saying, like, hey, here's this interesting thing. It's open. Like, you know, go do with it what you will. And, like, that's all I can do. Like, you know, I don't have the ability, you know, in terms of open protocols to tell anybody to do anything. Like, we will shine a light on it and hope good things will happen. We need to take another quick break. We'll be right back.

41:18We're back with Microsoft CTO Kevin Scott. Before the break, we were discussing some of the practical implications of the agentic web and where it might run into competing pressures from platforms like Google Search. But I wanted to make sure I asked some bigger questions about AI, in particular, Microsoft's OpenAI partnership, which Kevin was instrumental in building. I also wanted to know how Kevin's thoughts on copyright and compensation in the creative community have evolved since we last talked about these issues a year and a half ago. Let's talk about the industry kind of broadly. You described it as middle innings.

41:48I'm describing it as sort of act two. This technology exists. Everyone has used it. We've all played with a chatbot. Some reporters have had the chatbot ask them to leave their wives. I will never stop making this joke.

42:02Broadly, what did you get right in your initial bets and what did you get wrong? What surprised you? Well, I think we accurately spotted the scaling law trends with the reasoning power of foundation models. I think we've been more right than wrong with our conviction that those trends will continue to play out. I think we still have a tremendous amount of progress to be made, increasing the reasoning power of models. but like they kind of like I don't want to make light of how difficult it will actually be to continue the scaling but they feel like a set of reasonably solvable problems if you've got the you know right resources and focus I think the thing that's sort of you know hard right now is I feel like we've got this capability overhang with models where the models are actually capable of a lot more than what they're being used for.

43:04And so I, even inside of Microsoft, I maybe overestimated how quickly people were going to, you know, just lean all the way into the platform capabilities of the, you know, basic AI models. So I think we're a little bit behind right now on product. And like by we, I don't mean Microsoft, I mean everybody other than, you know, this rapid progress we're seeing in software development tools. So I think there are things in healthcare that could be a lot better than they are right now. Like, you know, a bunch of things are inhibited by some basic plumbing stuff, which is, you know, the topic of this particular conversation.

43:45But like a lot of it is just, you know, we, we, we just sort of need a lot more companies to get created and a lot more products to get made just to use what's already possible with these models. And like a little bit of it too is like, you know, I have this conversation over and over and over again. I was at a gathering with developers late last week. There's this interesting conservatism that is especially unhelpful with exponentially improving platforms. And it doesn't even look like conservatism if you didn't have the exponentially improving platform. It's like somebody will sort of look at a thing and say, ah, you know, Like this is a little bit too expensive for me to use for this particular thing that I'm – or particular problem I'm trying to solve.

44:33Or it's like marginally useful right now. So, you know, it gets things right about 30 % of the time, you know, but like that's only marginally useful. And, you know, and then it's like, okay, like I'm just going to pause and wait. wait and that may be the right thing to do but like the wait part of the pause and wait is too long in many cases right now where you know the next time people go in and sample to see if it's gotten cheaper or more capable like it's already sort of raced past where it needed to be and then you're just too late uh trying to get your thing to market and so i think that's the thing that i'm seeing over and over again like where we collectively are making mistakes uh where our pattern matching isn't as good as it could be.

45:21Right. You're saying that you should envision the products even if they're not 100 % great yet. Yeah. It's interesting you mentioned that because you were one of the architects of the open AI relationship at Microsoft. A couple of years ago, you were on the show. I asked you about that relationship and where it came from. And you described that relationship in terms of platforms. Yep. Right, that Microsoft was a platform company. Obviously, Azure is a massive platform. and you said OpenAI is aligned with you on the platform vision and we wanted to structure a partnership together so we could go build the platform together.

45:54Things have changed in two years, I would say. The companies have pulled apart a little bit, maybe a lot. I watched the Senate hearing on AI the other day. I noticed Brad Smith from Microsoft and Sam Altman from OpenAI. Opposite ends of the table. OpenAI has become much more of a consumer company. They're obviously trying to go make big consumer products, not platform products. Anthropic is much more of a platform company, I would say, than OpenAI is today. How do you see that relationship now? Is it fully decoupled? Are you working together still? Are you still trying to build a platform? Yeah, look, I still spend a huge amount of my time on OpenAI things.

46:31And there's a huge amount of technical stuff, just sort of me as an engineer. We're building big computing systems together. They're a gigantic Azure customer. like their workload is a like really, you know, non-trivial part of our platform, like particularly in terms of AI compute. Yeah, I mean, so like we're working with them all the time trying to make sure that we are building things for them that they need. And like still just a ton of work that we're doing together like across the board. Just from everything from how do we optimize the infrastructure that we're building to how do we take these models that we're training and get them optimized so that they can actually be platform components.

47:28all the way to, you know, like we still operate a joint deployment safety board where like we help each other assure that the things that we're releasing to the public have gone through a rigorous, responsible AI review before they launch. So, yeah, just a ton of work that we're doing together. If you listen carefully, there's a qualitative difference in what you said from before, right? There are huge Azure customers with a big workload. Of course, everybody works with their biggest customers closely. Yep. Previously, it was we're interdependent and their models are powering every copilot across the company.

48:07It very much sounds like Microsoft has moved OpenAI to the category of big customer we work with closely from interdependent technology partner. Yeah, they are very different from any other big customer that we have. The models that they are training on Azure supercomputers are still very, very important to the things that Microsoft is building. Their components that they are building are important parts of the Azure platform. So, like, they are both customer and they are, like, platform building partner. And look, they have a bunch of things that they're off trying to do on their own that are independent from us, like ChatGPT, for instance.

48:51And that's awesome because their success with ChatGPT is helping put a bunch of super good pressure on the Azure platform. And that is another consistent thing. I don't know whether I talked about this the last time we talked about the OpenAI partnership, but like one of my core theses when we were doing the first deal, like when is it like five, six years ago now? Like it was, we need the best AI workloads in the world to be running on Azure so we can ensure that Azure is building itself in a world-class way for those AI workloads of the future. And so like the more successful chat GPT is like the better Azure gets.

49:37Yeah. Seeing those AI workloads, my colleague Tom Warren has reported that Elon Musk and XAI are preparing to host Grok on Azure. He tells me to ask you, there's some angst within Microsoft about working with Elon and whether you can trust that company, especially with these other dependencies. Do you feel that angst? Yeah, I'm actually not super plugged into that conversation. I know we're doing it. The thing that we're trying to do with the model marketplace on Azure is we want to make sure that all of the good open source models that developers want to use are available and easy for them to use.

50:18Everything that we can offer there, we do offer. Do you still control the GPU budget at Microsoft? This is a thing you said to me several years ago, and I've never stopped thinking about it. You don't anymore? I do not, thank God. What happened there? Was it just like, this is too much? Because you described it as a horrible job. Oh, it was a horrible job, yeah. Is the pressure on needing GPUs, is that lessened or is it increased? Yeah, we still need lots and lots of GPUs. Because there is also reporting from Reuters and others that Microsoft has slowed down some of its data center investment or reallocated it as the models have gotten cheaper to run, as things like DeepSeq have shown up.

50:55we still are urgently deploying capacity and the thing that I will say is if you are sitting inside of Microsoft and you are talking to all of your teams that are building AI products or doing AI research I have seen no lessening because of any technology trend in the desire for more GPUs Do you think we can do AGI in the current hardware? This is a thing that's floating around that I keep hearing from people across the industry. I don't even know what AGI is, which is a thing I've been a little bit confused by since I wrote my book many years ago. I think first you'd have to define what exactly it is you think that means.

51:43I think if you look at the current generation of hardware that's rolling out, we're getting a big performance win from this next generation that's deploying right now. And so if you are thinking about what's going to happen over the next 12 months, it's going to be a pretty substantial leap forward in performance just across the board of everyone's systems. Because this current hardware generation and optimizations you can do on top of it are kind of extraordinary. Do you think that we're going to get more capacity because of the optimizations or because the hardware is more powerful? It is absolutely true that most of the performance wins are coming from the optimization.

52:24So you get, you know, order 2x improvement in price performance for every hardware generation, which is extraordinary, by the way. Like that's – you never got that every 18 months with Moore's Law. Like it was a little bit slower than that. And so the hardware advancements here are just breathtaking in terms of how good they are. But the software performance optimizations on top of the hardware is much bigger than that even. So we're just very reliably getting order of magnitude every year or so when you combine those two things. How would you categorize those optimizations? Because a lot of the early wins in model capability came from just ingesting more data.

53:09We just made the models bigger and that is how they got smarter. How would you characterize these optimizations? Yeah, look, it's a bunch of things. So there are things that you do with how you're training the models. There's a whole bunch of things that a lot of the wins have come from being able to effectively use smaller data types to store model activations both on the inference and the training side, which means that you can do a lot more arithmetic in parallel because you have smaller numbers that you are using in your arithmetic operations. I mean, it really is kind of crazy to see the breadth of the optimizations from what you're doing in the training side to just people fundamentally rewriting the numeric kernels for the inference stack.

54:07And then there's a bunch of things that you can do using your standard bag of computer science techniques around prompt optimization and caching and using more than one model to service prompts. Like you don't always need to send every prompt to the most expensive model. Like we now have a big enough portfolio of models where you can sort of choose to handle certain things with models that are like hyper performance optimized, but less general. And, you know, send the more complicated things to the, you know, to the bigger, more expensive models. It's funny, when I talk to the other agentic AI CEOs, they describe that kind of orchestration as the key.

54:57And you're talking about MCP is the key. And I'm curious which one has to come first, right? Like, we got pretty far in orchestration. Yeah, look, I think, again, going back to this capability overhang, like, I think we have more reasoning power in these models right now than we're effectively using. And so my hypothesis is that one of the things that is preventing us from having more useful things is just action taking. And the entirety of the action space is too constrained right now. And so I'm not saying it's an either or. I just think it's going to be super hard work to get that action space opened up.

55:37And so we just need to get on it as an ecosystem right now. I want to end by talking to you about my favorite thing to talk to you about, which is the relationship of technology and art. You did write a book. It's called Reprogramming the American Dream. The first time we ever talked, it was about that book. Somewhat notably, the foreword to that book was written by J.D. Vance, who's now the vice president of the United States. I don't think you saw that one coming at that time. You did see a lot of stuff coming about how AI would reshape the economy or at least threaten to reshape the economy.

56:06Yeah. As we talk about the models getting more capable and the ways they're getting more capable, the very notion that we could make the models more capable by just ingesting more data has hit a limit, right? We've ingested all the data. And now there's, you know, a lot of lawsuits about whether that ingestion of data was legal, whether it should be compensated. You're an author. I'm going to put these to you as simply as I can. If I stood outside the bookstore and everybody who came looking for your book, I stopped them and said, I can just make you a podcast about that book? You just send me a note, like just text me and I'll send you a full podcast summarizing that book.

56:44Do you think that would increase or decrease sales of your book? Well, look, I will say something that's about Kevin that I don't know whether you can or should generalize to authors in general. Like I'd be fine with like people doing whatever they want to with the content of my book. Like I - If you had to make your living based on sales. Yeah, yeah. So like Which is why it's super different. Look, I think authors, if you spend all of your time and dump your heart and soul into making a thing, you deserve to be compensated for it. Now, I think there's a lot of different ways to be compensated for a thing.

57:27I'm not even following what's going on with a bunch of this litigation closely enough that even if I could comment on it, it would be useful commentary. I think going back to the conversation that we started with, open protocols for this agentic web is that people who are making things get more, particularly here at the beginning where the whole landscape hasn't really sorted out what the business model is. I think people can sort of jump in and have more agency around business model. But when you – I mean, did you make a Studio Ghibli meme when I released that tool? No, I didn't. You didn't? Okay.

58:10I'm curious. A lot of people did. I'm not saying I didn't. No comment from me on that. My point is, as somebody who makes creative work, you're a maker yourself. There are lots of people who their livelihood depends on economic exchange for their creative work. Yeah. And their criticism of the AI industry broadly is you've created all of this capability, maybe more capability than we're even using, as you're saying. Yep. But we have received nothing in return. And we're just we're in it now, right? Act two, middle innings. And it doesn't feel like that has changed, but for some litigation. I'm just wondering if your thinking has evolved or matured.

58:46Well, I think the way that I was thinking about this in the beginning is the, you know, the, the way that I'm thinking about it right now. So like, I certainly wouldn't want to see anything cause Hayao Miyazaki to make less beautiful things. Like I'm like maybe one of the biggest fans in the world of like what he and Studio Ghibli have done over the years. Like I think it's just I mean, it's like some of the most beautiful art that was created in the 20th and early 21st century. And so, yeah, like I like people like that. I want them to have every incentive in the world to like do more of what they're doing.

59:24I mean, the thing, though, too, with the platform is like I will just sort of, you know, come out and say it like I'm. not really super interested in these image generator things. Like what I'm interested is a model that can do medical diagnostics for my mom who lives in rural central Virginia and doesn't have access to really super high quality healthcare. And there are just, you know, tens of millions of people like her in the United States who are in the same predicament. It doesn't get better over time because of the demographics and the United States. Studio Ghibli content has nothing to do with whether or not AI is good at medical diagnosis or not.

1:00:11And so that's the thing I want to make sure of in this debate that we can have the part of the debate, which is there's you know this creative economy that like i don't want disrupted at all because i'm i'm a fan of and i appreciate you know those folks and there was i was having this conversation with reed hoffman and jj abrams a few uh you know months ago yeah where you know like if anything like i would love for it to be the case that ai made it easier for people like jj abrams uh to do more of what they do. And I think is, you know, like you just sort of have more stuff floating out there that like that's what people will want.

1:00:58They'll want more of the, you know, hey, here's, I'm a fan of J.J. Abrams and his voice and his work. Like, give me more of that. Like not, you know, this random crap, you know, like what some teenager has gotten out of an image generation model. That's a very important debate to have. I don't want that to overshadow like this other thing, which is like these tools can be enormously useful for solving some very important problems. And we don't want to have this conversation that we're having over here, which is important to have to impede our ability to push forward with this other stuff, which is also super important, if that makes sense.

1:01:42It does. It does. It's interesting because you are describing what more or less is the framework that the copyright office just released before Trump fired the Librarian of Congress and the Register of Copyrights, which appears to have backfired. And now there's an even more copyright maximalist person in that position because that report said, look, some of these uses for training data are obviously fair use. Scholarship and research and all this stuff. And then I'll just read the quote from the Copyright Office, preliminary report that came out last week. Making commercial use of vast troves of copyrighted works to produce expressive content that competes with them in existing markets, especially where this is accomplished through illegal access, taking the stuff without permission, goes beyond established fair use boundaries.

1:02:29So, right, here's the distinction. There's some stuff where we say medical imaging, the utility of this is so high and the work is so transformative, maybe fine. And then some of the stuff where you've just copied every YouTube video in the world and you're just letting people make more YouTube videos, probably not. Can you envision a framework where that would apply to the tools Microsoft is building, where you would say this is stuff we're going to do and this is stuff we're not going to do? Yeah, look, I think we're open to having any kind of sensible conversation. I think you just have to sort of show up and there's some technical limitations and constraints on like, you know, what's possible and what's not possible with the fundamental technology.

1:03:13But I think there's a super rich dialogue to be had here. There's also this interesting thing where I think increasingly you accurately identified that we've kind of exhausted all of the data that is available to train models. And so we're now in a regime where a bunch of these systems are being trained with a set of techniques where they don't depend as much on data as they once did. And so there may be all sorts of technical ways to enhance the reasoning capabilities of models that aren't quite as dependent as they might have been at some point on ingesting a bunch of sort of organic tokens of data.

1:04:02I mean, it's also true, like, you know, I think we talked about this last time that like there's an increasingly good understanding of the quality of data, like, you know, how much a token of data contributes to the reasoning power of the model. And then, like, you know, the biggest thing, like, my bugaboo in general with all of this stuff is thinking of models as databases, like, as information retrieval systems is kind of like you want to talk about a suboptimized system. Like, they're kind of terrible as databases just from an efficiency perspective. And so, again, you go back to things like NL Web, here's a model and it has learned how to reason the same way that you might have taught a biological brain how to reason.

1:04:54And then, like, once you have a certain level of reasoning capability, like, the interesting thing is, like, you know, prompt by prompt, task by task, like, what information do you have access to to reason over? And, like, you know, how you monetize those two things and, like, what the share of the, you know, business is between those two things can be very, very different. Yeah, with one, for instance, if you need a model that's reasoning over breaking news, if you have something like NLWeb and a subscription to a bunch of news outlets, you can provide the agent access to those subscriptions if the publisher wants to allow that using the user's authorization tokens and then let the model reason over that information.

1:05:44and like you're sort of paying a subscription fee to like have this ephemeral content to reason over. So like I think there are all sorts of ways like we may be able to sort the business model stuff out over time. Yeah, I mean, I want to bring this all together. It sounds like with the new search project, NLWeb, and with the investment in MCP and wanting it to be more widespread, it just feels like you're trying to create a wholesale architecture shift for the web, right? Like this is the new kind of web and you're trying to just sort of incept and incentivize its creation because the deal of the old web appears to be up.

1:06:22Is that a fair characterization? I don't know whether the deal of the old web is up, but like it's kind of time for us to be thinking about some new, I think. Yeah. And, you know, like I think as we're all collectively thinking about the new, like we should do what every good architect does when they're thinking about the new. It's like, you know, what has worked and what hasn't worked for, like, all of the constituents and stakeholders over recent years. And, like, let's try to go make something better that works for everyone. And, like, we'll get to the best outcome when everybody's incentives are aligned, where the creators and the consumers are, like, have their interests balanced.

1:07:01And, you know, there aren't a bunch of, like, weird, you know, intermediaries, like, constraining, like, how utility and value gets exchanged. Well, I wish you luck because so far the creators have a very clear point of view on where their incentives are. Kevin, I could talk to you forever, obviously. You've got to come back soon. I want to keep an eye on this web project and see how it's going over time. Yeah. Thank you so much for having me.

1:07:28I'd like to thank Kevin Scott for taking time to join Decoder today. 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 decoder at theverge.com. We really do read all the emails. You can also hit me up directly on Threads or Blue Sky, and we have a TikTok and an Instagram. They're both at DakotaPod. If you like Dakota, please share it with your friends and subscribe wherever you hear podcasts. If you really like the show, hit us with that five-star review. Dakota is a production of The Verge and part of the Vox Media Podcast Network.

1:07:53Our producers are Kate Cox and Nick Stat. This episode was edited by Xander Adams. The Dakota music is by Breakmaster Cylinder. We'll see you next time.