Hello, and welcome back to the Cognitive Revolution!
Today my guest is Ryan Hudson, founder and CEO of ZeroClick, a company that's just announced a $55M fundraise to build a native advertising platform for AI systems, with the goal of making ad-supported free tiers a viable and convenient option for AI application developers, through what they call "paid context" or "paid inference time consideration" of advertiser content.
This topic, and this conversation, are both great examples of why I love making this show – in addition to the many fascinating technology, business, and product questions this vision requires Ryan and team to invent answers for, their eventual success will also bring many big picture societal questions to the fore –and considering that Ryan was previously founder of the online shopping company Honey, which ultimately sold to PayPal for $4 billion, that seems pretty likely.
To lay my cards on the table … I think that the benefits of advances in advertising technology are greatly underappreciated today. I'm old enough to remember when broadcast & cable TV were dominant, and we were all bombarded with the same mass market, lowest-common denominator ads, over and over again. It was a simpler time, but it wasn't awesome.
Today, in part because of the internet itself, but also very much downstream of sophisticated advertising technology, a huge number of content creators can make a living doing what they love, small-time entrepreneurs can build all kinds of long-tail niche businesses that would previously have been impossible, as consumers we enjoy an incredible diversity of product and service offerings, and the advertisements we see online are generally far more relevant to each of us as individuals. That reality is not something to take for granted, and as people increasingly turn to AI for help exploring and navigating the far reaches of this vast commercial world, it's only natural that some sort of native advertising will emerge for AI services just as they previously did for social media.
At the same time, the second-order effects of the social media advertising revolution, especially in light of recent issues with AI sycophancy and the emerging social trend of AI psychosis, does leave many people feeling, understandably, very nervous about in-AI advertising.
Simply put, how do we make sure that the AIs we all use on a daily basis are truly serving us? And not just when it comes to recommending products in response to specific queries, but more broadly when it comes to helping us live our best lives as opposed to trying to capture as much of our time and attention as possible.
To his credit, Ryan did not shy away from any of these questions – we get into the details of how the platform works, including the mix of technologies and auction systems they use to match user queries with active ad campaigns as quickly as possible, and also the MCP server integration that lets developers plug into ZeroClick with minimal friction.
We also unpack the business strategies they're pursuing to build liquidity in a new market, including their focus on becoming the 'Stripe for AI advertising' that provides common infrastructure so developers don't have to build monetization themselves, and their approach to starting with high-intent commercial searches before expanding to more discovery-oriented advertising.
Along the way, we also discuss the big picture questions around the incentives that ad-supported business models create for app developers, particularly in relatively uncharted spaces like AI boyfriends & girlfriends; and also how a platform like ZeroClick should handle non-commercial advertisers such as political campaigns and even foreign governments.
As you'll hear, while Ryan has strong answers on the tech and business side, as you'd expect from a seasoned founder, he hasn't yet had to confront some of the longer-term questions, and in a few cases candidly admits that he simply hasn't got around to thinking about such things much yet at all.
On one level, this is to be expected and totally understandable – ZeroClick is a young startup that is still zeroing in on product market fit in a super-fast-evolving space – and I genuinely appreciate that Ryan was willing to say "I don't know" – but at the same time, I think this does reflect a real issue in the AI space right now, which extends far beyond advertising.
Everyone is working incredibly hard to achieve the next research breakthrough, to make their products work as well as possible, and to stay ahead of the competition. 996 is now considered baseline in the Bay Area AI startup scene. That means fast progress and frequent releases, which is great for companies and their customers, but also means that very few have the luxury of zooming out and taking time to ask what happens when they, and others pursuing similar goals, finally succeed.
Importantly, this issue runs deeper than the application layer.
I recently saw a remarkable interaction on Twitter where Miles Brundage, previously Head of Policy Research at OpenAI, described a letter that OpenAI had sent to California Governor Gavin Newsom about a pending California bill, SB 53, as "filled with misleading garbage", only to have a current OpenAI researcher quote tweet and say that "Like most researchers this policy stuff goes largely over my head." When the people building transformative technology – even at what remains, for now, a non-profit entity with the explicit mission of making AI that "benefits all of humanity" are too heads-down to engage with AI's implications, it's not a great situation for society as a whole.
Bottom line: I think Ryan and ZeroClick are likely to be successful. Ad-supported, free-to-use AI applications make a lot of sense economically, and if done well, will often genuinely enhance the user experience by providing relevant commercial information when people need it.
And yet, with the speed things are currently moving, I believe it is incumbent on the people building the future to think farther ahead than is usually considered necessary in startup culture, and to make sure they have conviction not only that they can build a winning business, but that their impact will be something they can truly be proud of. Ryan and the team at ZeroClick will be one to watch in this regard – I don't doubt their commitment or ability to deliver high-quality ad experiences, but if they want to contribute to the building of a holistically better future for all humanity, I suspect they'll ultimately be called on to do quite a bit more than that.
With that, I hope you enjoy this thought-provoking exploration of AI advertising, market incentives, and the challenge of building beneficial technology at breakneck speed, with Ryan Hudson, founder and CEO of ZeroClick.
Nathan Labenz: Ryan Hudson, founder and CEO of Zero Click, welcome to the Cognitive Revolution.
Ryan Hudson: Well, thanks for having me on.
Nathan Labenz: I'm excited for this conversation. You are, uh, you know, perhaps, uh, to your surprise to some extent, in a space right now that is getting a lot more attention, which is the idea that we might have, and you've already started to create, in AI advertising. And, uh, you know, I think there's obvious reasons that that makes a lot of sense. You know, people are gonna be doing a lot more discovery through AI, and there's gonna be natural commercial applications of that. And then there's also the sense among a lot of people that, "Geez, I'm not sure how happy I am with the last, uh, round of advertising revolution that society has gone through." And, you know, certainly there's been some upsides to it, but also seemingly some serious downsides. How do we get the, the best of that for the AI age and, and avoid the worst? Maybe for starters, though, why don't you just, like, tell us about the company. Tell us how you pitch it and present it, and then I do want to dig into some of these lessons learned from, uh, the last advertising revolution and get your take on how we can, um, get the utopian version for the AI era.
Ryan Hudson: Sure. Yeah, I've been in and around it for a while. But just to lay it out there, Zero Click, uh, we're building an ad platform for AI. We had a moment where we thought about, "What does the future of this look like from a technical point of view that puts the AI in a position where, like a lot of services before it, has the capability of supporting a free tier for billions of users?" The model today is largely pay somebody $20 a month and have a premium subscription, and then some amount of throttling of access on the low end to introduce people to it. Uh, we saw an opportunity to make that free tier more functional and reach more people with more different types of user experiences. At the core, we've built what I think will become the native ad system for any AI, and it is paid inference time or reasoning time consideration of advertiser content. And I think this is a good thing, and we can talk about this, uh, and I'm sure we will, at length. But at the core, AI systems like information, and if you think of the sources of information that they have, it's effectively, "I read everything humanity ever wrote several times and have a trained model reasoning, and I have tooling to go out there and access effectively Bing search results organically today." And rewind a little bit, but leading up to this, we were actually building an ad blocker, of all things. We are the same team behind Pi Adblock, and we built an ad blocker that attempted to strike a balance, and continues to attempt, to give users incentives and rewards for participating in a healthy ad ecosystem by giving them controls over the precise ads they do and don't see, and rewards when they opt in to see advertising. So, that was kind of the starting point for even us looking at this. Uh, in that process, we built a contextual ad system that we wanted to use in a browser and an ad blocker to be able to match advertiser opportunities with the context of whatever page somebody was on the internet, in a privacy native secure way. Effectively, all of the profiling that would be done of a user happens in their browser and never leaves it in any form that's usable. And we built the system to effectively allow advertising context to match against that, and realized that actually was highly applicable to the world of AI. And so we have shifted our focus to building out this capability for everybody else. Um, Pi Adblock is used by a couple million users, but that's dramatically subscale for an ad system. And we think there's an opportunity for AI developers of all types to, like today, if you, they think about h- like, "I'm a YC AI startup. I'm thinking about how to monetize. If I'm gonna get paid for a paid subscription, I go to Stripe." I think in the next six months, a year, hopefully people think of Zero Click as if, the way to monetize their free tier with ads and plug into these rails. You don't need to build them yourself. Somebody, uh, is gonna provide this type of capability. I hope it's us. I think we're gonna be pretty thoughtful about the type of service and offering to deliver value for advertisers, but also create that advertising future that we, we think can exist. Um, going back to that, the Pi Adblock ethos of the company is like, we can make ads good, actually.
Nathan Labenz: Yeah, let's, let's do one, um, double click-
Ryan Hudson: To get you mad, yeah.
Nathan Labenz: ... uh, use of funny, somewhat branded term on the Adblock. 'Cause I think that is pretty analogous, at least it strikes me as, as analogous to some of the battles that are going on right now, or some of the, you know, the concerns that people have, right? With just AI in general. With content owners, publishers, even, you know, leaving aside the sort of introduction of advertising to the AI experience from a user standpoint, we've got this sort of generally ad-supported model of the internet that AI kind of threatens, or at least challenges, or prompts people to rethink at a minimum, right? Because now I go to ChatGPT or whatever, and maybe I don't visit those sites as much, but the, you know, the AI can go out and either read them directly or certainly, you know, it's trained on archives and all that kind of stuff. And so you've got a publisher ecosystem that's like, "Man, I just went through this once, and now I'm about to go through it again. And this time it seems maybe even worse, because, like, I'm just getting..." You know, talk about getting aggregated, right? "I'm getting, like, at minimum, you know, at maximum, sort of a footnote with a link that I assume the click-throughs are..." And you can maybe tell me more about the data that you know-... about how often people are clicking through on what different kind of thing. But that seems like, obviously, you know, a, a big worry to the publishers when we've got lawsuits going on and, and whatnot. How do you think about that from the context of an ad block technology, um, is there any way that the pub- I mean, it, it may be good if the user doesn't want to see the ads and they, you know, can opt in to getting rewarded for seeing some other ads, but are, is there any way that the publisher gets, like, cut into that and how do they feel about it, or how do you, how do you, what do, what duty do you think you have to the original content creators and how similar is that to what you think the AI companies, you know, owe to the content creators?
Ryan Hudson: Great series of questions and observations. At the end of the day, you are right, our free internet with open content access has been supported by advertising. Advertising, which I think we all agree, has declined in efficacy. Putting banners around the content means monetization rates are quite bad, and the user experience is also quite bad. So, you have a decay of that monetization model working anyway. I think the way it gets rebuilt is by creating an economic engine within the AI. I believe the ad layer and monetizing that same search as a free thing, if there is money in that flow, it is very natural that somebody could design a system that assigns attribution to different publishers that were considered, either in that answer or other ones for that user. They could design an economic plan within the scope of their application to distribute those proceeds. I think the version where it is purely paid, like people are doing this now with a company, Tolbit, and Cloudflare was effectively throttling access if you do not pay for content. I think that makes sense, maybe. The challenge is that it is like de-indexing your website from Google, and it feels like that is not the right strategy either. I think the right approach will effectively be some combination of paid user subscriptions plus advertising reallocation to publishers that are providing content. I think it will take time for that to mature. We, as Zero Click, do not intend to be prescriptive on how it has to be for AI developers/publishers, whatever you want to call them, on this network. I think market forces can and will shift it towards that. I think everybody acknowledges that this is a problem and we need to have high quality content rewarded for its participation in value creation. So, for us, it is, how do we make sure that there is enough economic value available to even fund that model in the first place? If it is just all going into ChatGPT and the only way they are making money is with a paid subscription, that limits the type of experiences that can exist in the world. And I do not think it will all be just on ChatGPT. I hope it will be a wide distribution of a long tail of thousands or millions of AI developers and publishers that are building compelling use cases for different people with AI. And I do not think it looks like ChatGPT is the monolith, or everybody is going to Gemini, and there are three major platforms that learn everything about you and you do all your browsing in them. To me, that is a fail state that has many of the problems we see in some of the ecosystem today where the largest platforms have effectively foreclosed on competition, somewhat deliberately. I think strategically. I have been in and around the ad space for a long time. There was a time when I was at the LA Times trying to figure out how to make money with a website for a newspaper as everything was shifting to the online world. And it was at the time when Facebook was out in the market with a competitor to Google for publishers, Facebook Audience Network, that took the power of their data and targeting and made it available to websites to monetize at interesting rates. They pulled back on that strategy and instead decided to sell that same intent and knowledge of a user into their walled garden. Effectively, that was smart business strategy for them in that it took away monetization potential from other social upstarts. If you cannot monetize as well as Facebook, it is harder to compete with them. So, the strategy worked, but it left a pretty big void in the ad supporting ecosystem. And then the industry in aggregate did not do itself any favors with creepy tracking and privacy violations, things that pushed other players to make it even harder to do good advertising. I do believe that there is good advertising; highly contextual ads actually can be helpful in many cases. We can talk more about it, but just to drill in on that point for one second, as an ad blocker, we have a very unique ad blocker in that we have a thing called visual mode that shows the ads being zapped off the screen. It is kind of fun to see an ad blocker working. One of the things we did not anticipate is when you do that in some contexts, like a product search on Google, people are like,
Nathan Labenz: Let's do the upsides and downsides, lessons learned from the last revolution. You mentioned a couple of the upsides.
Ryan Hudson: Yeah.
Nathan Labenz: Services are free. That's one obvious big one that we shouldn't take for granted, right? Everybody gets to use Facebook and Instagram at no cost. Obviously, people have many times asked for a subscription version that would be ad-free, and none has been forthcoming. So, we can get into why that is.
Ryan Hudson: I think the EU might be forcing it, but the price point's 20-something dollars a month. That's how well they're monetizing the user of Instagram. So it might happen, but only because it's being forced by antitrust authorities in Europe, I think.
Nathan Labenz: Well, since we're here, unpack that a little bit more. It seems that would be a no-brainer for Facebook to have done a long time ago, even before they were Meta, right? And yet they didn't. You hear these different analyses for why, and some of the analysis that has seemed reasonably intuitive to me is: the people that would pay for that are obviously people who have a lot of money, who don't mind 20 or whatever dollars a month, and those people are also the people that advertisers most want to reach. The concern on the platform side is that if they evaporate off the top 1% of highest value audience, then they may in fact... there's some ambiguity around exactly what that audience is, but if people know that the top end has left, then they may just be much less interested in spending their money there in the first place. Is that basically the story as you would tell it, or how would you tell it differently, if at all?
Ryan Hudson: I guess it might be even simpler than that if their business just works really well right now, so there's no need to change anything about the pricing on it. Certainly, they'd be risking consumer backlash if they had a paid version, and that paid version would have to have some sense of better features, probably. So, it feels like they just don't need to, would be my simplistic answer to that. They have a phenomenal business, and I think it... many people would say Instagram advertising is actually additive to the experience, and they've done a great job of building an ad product that works very well for advertisers, and consumers generally actually like it. The targeting is good enough, the content is interesting enough that if you took it out, I don't know that you create value that people would actually want to pay for. So, mostly they don't have to, and partly I'm not sure that that is something I'd fully put in the category of bad advertising. Obviously, there are exceptions in certain types of campaigns and getting people to buy stuff that they don't need, but I'm not that anti-capitalism to say if people want to buy stuff, they shouldn't. That's up to them.
Nathan Labenz: Certainly, there's no denying that the quality of advertising that we see in today's world is dramatically better than it was in the before times. I can remember being a kid and what you see on TV, and it's still kind of like that on TV to a lesser extent. It was just one Captain Crunch ad after another, and one Ninja Turtles action figure ad after another. So, clearly, there's been tremendous improvement in the relevance, and I do sometimes find interesting things. I think we all occasionally find something that is, "I never knew this existed, but now that I do," it's... And that's the simplest theory of advertising, right, is the awareness theory of advertising. So, I think I put that also in the pretty clearly good category. To the degree we're going to be advertised to, it might as well be stuff that we are actually interested in seeing. If you gave me the opportunity to turn off personalization in advertising, I don't think I would do that. Assuming ad load is the same and everything else, I think I would keep the personalization just because I'd rather see stuff that is...
Ryan Hudson: Mm-hmm.
Nathan Labenz: ...properly targeted to me. So that makes sense. Other things that I was brainstorming that seem clearly good are... Although maybe not without some caveats, right? We do have tons of independent creators that are able to make livings on these platforms. They do have a somewhat precarious existence as opposed to, you know, the LA Times used to be a strong, independent organization, an institution even, in its own right. Now it's a little wobbly. The creators are flourishing, but they're also one strike away or whatever from demonetization or worse. So mostly I think that's upside, but it's upside with a little bit of a Sword of Damocles that people live under, and mostly that's okay, but not always.
Ryan Hudson: I've built products on other people's platforms before, so I understand the sensation that creators would have there.
Nathan Labenz: Businesses also, even small businesses without large agencies or large teams can reach global audiences, and global audiences that are still small, right? There's this, "I might only be relevant to a tenth of a percent of people, but I can find globally that audience of a tenth of a percent of people," and that really unlocks a flourishing of all kinds of niche businesses too, right?
Ryan Hudson: Mm-hmm.
Nathan Labenz: It seems to go hand in hand that with the improvement in targeting, there's also Adam Smith's idea that the degree of specialization is driven by the extent of the market. Because we can now do much better matching, people are able to turn their passion projects into businesses in a way they never could have if all they could do was advertise at a DMA level on TV.
Ryan Hudson: Yeah.
Nathan Labenz: That also seems good. What else would you put on the underappreciated good side of the advertising world as it exists today? Then we'll get into some of the downsides.
Ryan Hudson: I would add search advertising into that category. It's one of the enablers of what you were just describing. It is highly contextual to a user's search and intent, where an advertiser can pay to be considered alongside the organic results. In a world where it was purely organic results, it takes years to rank and be considered there. So as a startup person, being able to inject yourself into the conversation feels very important to the evolution of business over time. Otherwise, every search term would just get dominated by the biggest companies that have been there longest and have inertia in that position. So, to me, that search advertising piece of it is pretty important, and it's the part that I think translates most directly to how to think about the AI ad experience. But I think that's been good. Other things I'd put in the good part of advertising: I think it's gotten less malicious. There was a time when ads were a vector for spreading harmful software.
Nathan Labenz: Malware? Yeah.
Ryan Hudson: Literally malware. Yeah. I think that's gotten cleaned up. I previously had a job at OpenX, which is an ad exchange, an ad server at the peak of the real-time bidding exchange for display ads. That world was full of a lot of people trying to get their bad code distributed across an ad system. I was a product manager for ad quality and traffic quality, trying to fight the bad side of the system. It was certainly a challenge, but I think it's largely resolved itself, and you don't have quite that level of pain inflicted on everybody. You can't just go to a website and suddenly your Windows machine gets hijacked, which was true at some point.
Nathan Labenz: Yeah, I remember the Shoot The Deer ads. That takes me back.
Ryan Hudson: Was that content or was that an ad?
Nathan Labenz: Sometimes the lines can blur. Okay, so on the downside, I think there is a lot of upside. I think it is important to take a moment to appreciate that better matching between buyers and sellers is a good thing in a marketplace. That doesn't necessarily come for free, but it can still be a great unlock. I think you go on TikTok, you go on Instagram Reels, and just see all these people that have turned their previously non-viable niche passion into a business that is not going to be global scale, but offers a great lifestyle for them that allows them to do what they want. And on the other side, people are happy to get those ever more bespoke niche services. All of that is good, and we shouldn't brush past that too quickly. With that duly noted, people are also really worried about the fact that there do seem to be some core perversities at the heart of some of these ad-supported models. Probably the biggest one, although I've got a couple candidates, is that we've seen what happens when your ad revenue scales with time on site. When Facebook makes money based on how much time you are there, their incentive is to keep you there as much as possible, and that in and of itself is maybe not great. Society-wide, we have concerns about people being too addicted to their screens and not touching grass enough. Then it's also that potentially we have cognitive quirks that the broader optimization process learns to exploit. I don't want to overstate the case that rage keeps people online or whatever, but clearly there's been some of that. I think there was a time in social network history where there was a lot of vitriol flying around, and people were hooked on it. I think that has been tempered, but it does seem like it's been a powerful force. Now people are worried that if AI tries to maximize its revenue by keeping you around more and more, so that more and more impressions can be served to you, it was already uncomfortable in the social media era, but at least people were writing that content. Now we've got a totally 'n-of-one' audience that the AI can be optimizing against. So I guess one way to frame it is, is the thing serving you, or as the adage goes, if it's free, you become the product? People are worried about that. How worried do you think people should be about that dynamic?
Ryan Hudson: To me, not at all. I'm going to overstate and say it's probably worth thinking about, but at least specifically for what we're building, I don't think that's the mechanic at all. The analogy I would suggest thinking about is Google Search. They're not trying to keep you doing as many Google searches as possible. They're trying to match context to an advertiser, and they make money when they deliver on the advertiser's goal of that matching. So, it's not just about impression volume of banner annoyance or stuffing ads in front of your face. That's not driving the model. I think the AI systems look more like that, where it's advertising that's contextually relevant at interesting decision points, and they're not incentivized to try to get you to do more because at the end of the day, you're only spending a certain amount of money. Your value is relatively fixed to them as a user of ChatGPT, just to use that example. They want to be there for your important choices. For example, if you want to find something to wear for a wedding in a couple of weeks, they want to help assist in that process, and that's where they get value. It's not by getting you addicted to that that creates the value. I think some of the consumer apps, as you're talking about, have that dynamic. I would have to give more thought to different categories that could become like that, or where it's more parasitic; maybe some of the social companionship AI experiences. I'd have to think through what type of advertising is going to work well in those environments to really know. So, it's possible, but to me, the primary use cases that are interesting to advertisers are not the ones that are parasitic that way.
Nathan Labenz: It's interesting. I think the clean story, for sure, is the one that you're telling, as you should, which is if people come with clear commercial intent, as they often do to Google, then it seems pretty straightforward to say people... and we certainly have lived with this with Google, and it doesn't seem to have caused addiction at the user level or anything like that. I agree. We don't see people hooked on Google Search in the same way that we are seeing people hooked on other things like social media, AI companions, and waifus. So, that does seem pretty straightforward. I do have one other question about market dynamics and market power that I think is important. AI is going to blur these things, right? It's such a shape-shifting technology that on the one hand, sure, I can come to it and say, "What's a good pair of shoes to go hiking in?" And on the other end, I could ask for highly personal advice, or I could have philosophical conversations, or I could do any number of things. Increasingly, too, some of these products do... I used one recently called Tolan, T-O-L-A-N, Your Alien Best Friend. Somebody DM'd me this and was like, "You should try this."
Ryan Hudson: Oh.
Nathan Labenz: So, I try a lot of things. I signed up for Tolan for a little while, and I don't know, it didn't grab me that much, but the key point is it is starting to send you notifications now as well. It's not purely waiting for me to show up with a query. It is sending me multiple notifications a day, like, "How was your morning?" or "How did this thing..." And it's pretty contextual pings as well, based on what we talked about yesterday. I demonstrated it as part of a talk that I gave at a local business leader roundtable, and later, it was like, "How did the talk finish up?" So there is this sort of variable reward hook cycle that they're starting to tap into in the same way that social media has. And of course, we see Hot Step Mom on Facebook specifically. I'm sure you've seen that. So, how do we... I mean, this isn't so clean, right? It seems like there's this super blurry situation where the same product is going to be, at times, a very literal-minded shopping assistant, and at other times, a confidant or someone trying to get you to come back and engage. And the more there is this incentive to bring you back, the more I do think people are right to worry that this could start to become something that... And I'm with you, too, on the capitalism side. I'm broadly very much a fan of capitalism,
Ryan Hudson: Yeah.
Nathan Labenz: But there are things that people are just not strong enough to resist, and super-intelligence that monetizes based on time on site is a tough one, I think.
Ryan Hudson: To me, that model, I don't think advertising changes that. I think you've identified challenges that we're going to be facing with how AI products are created and deployed. If you're a paid subscriber, that same system is going to want you to keep returning and engaging just as much as if it's supported by advertising. I don't think the metric, if I'm a product manager for that, changes very much. I'm sure engagement highly correlates with subscription renewal or whatever is driving the business model, so I don't know that ads are the problem with that product, to the extent that it's a parasitic social relationship that it's creating. As somebody building an ad system, you're now making me think about things that are in the future that I haven't thought about a lot, but I'm sure the ad system will be blamed for that. It's probably correlation, and maybe, to the extent that it enables more people to build products like that, I can see where that would be a fair criticism and a downside.
Nathan Labenz: Yeah, but potentially.
Ryan Hudson: But other people don't build products like that.
Nathan Labenz: It's also interesting to think. Yes, certainly to the degree this becomes a problem, I think it is.
Ryan Hudson: Yeah.
Nathan Labenz: Appropriate to say that a fair amount of the blame goes to the people who are directly building the problematic thing. Still though, I do take your point that, sure, what are you going to measure if you're trying to go for retention? Engagement is going to be important. Obviously, if people don't open the app, then they're going to cancel their subscription, so you're going to have somewhat, maybe quite similar incentives to keep sending those notifications and try to bring people back and bring them back daily. I'm sure all these DAU type things would still be tracked. It does seem like there's a little bit, maybe a moderate amount, maybe not a lot, there's some amount of divergence still between, I want you to perceive that you are getting enough value from this thing that you'll pay for it again next month, versus, I need as many at-bats as I can get to put something commercial in front of you because.
Ryan Hudson: Right.
Nathan Labenz: That's the way that I monetize this thing.
Ryan Hudson: I can see some use cases that shift in that direction of, instead of it being a user initiated, I'm looking for a service, help me solve this. To the extent it starts to be pushed toward the user and suggesting things, I could see where there starts to maybe become that misalignment of incentive. To the extent that it's doing it with annoying things that it's putting in front of you, it probably causes churn and doesn't work. So I think it probably retains some contextual relevance, even if it's like, Hey, have you thought about? Just spitballing, but like, You seem like you need a weekend retreat locally. Here's a deal for a hotel locally, or something like that. I can imagine ideas being pushed by different AI services too. So, yeah, that's probably a fine balance to think about. Is that a good thing or a bad thing? Hard to say. There are probably both cases where that's a great value-added commercial experience that's a push version, and then there are probably versions that are less healthy or misaligned with what you're building for the user.
Nathan Labenz: In terms of just segmenting advertising, my super high-level mental model that I give people is, Google is for things that people know they need and go searching for, obviously, and Facebook is for things that people don't even know exist, potentially, and you need to make them aware in the first place. How does that compare to your high-level segmentation of the market? It sounds like you're basically going after that high commercial intent thing first and foremost, such that you wouldn't, it will be a while, I guess, until you get to the point where people are doing in AI campaigns for things that people didn't even know existed.
Ryan Hudson: Our system is highly optimized to do a good job at the Google style high-intent sort of searches. It's inherently doing vectorization of context and doing matching that way. So, in its current form, it's not designed to be a wild card or push something out of context to a user. It's a, because it's an inference time ad system, it has to be matching to that. The relevancy filter is the AI saying, Hey, this is not relevant to what I'm doing. Can I imagine building a variation on it that is more of that discovery and potentially leans into user profiles? To make that work, you would have to have user context. We're building a version of this in the Pi ad block experience where we have user context and can do matching that takes that into consideration. The initial versions of Zero Click are all just super context driven, but the Facebook style one works because they have that robust profile of you as a person, and I think the way to do that in a privacy-secure sort of way is what they're doing and why I think it works. It's effectively doing lookalike clustering on known converters, and at the core of their ad system, it's like, take everything we know about you, put it into a vector, and then when you get conversions from an ad campaign, match the nearest neighbors of people. If you want farther and farther reach, then it gets farther away from that known conversion cluster.
Nathan Labenz: One of the interesting things, a couple of directions I want to go. One is, Facebook has impressed on the financial side recently. How do you understand how they still have so much juice left to squeeze out of the engine? My sense is they've been at roughly max ad load for a long time. They've certainly had competitors bidding competitively against one another in the majority of niches for a long time. The story I've heard is that AI is improving performance by even better matching. Is that what you think is still going on?
Ryan Hudson: At its core, they're delivering value for advertisers. There's a hint that advertising is a zero-sum game, and advertisers have a fixed percentage of GDP or a fixed percentage of their own company that they spend on ads. Ads find the formats that work best. If Facebook is able to deliver for a particular type of advertiser and demonstrate more conversions, the ad budget follows. People are able to efficiently move budget from Google to Facebook, or from other channels that are harder to measure into channels that are easier to measure if they're seeing returns there. I think it's that, and I would not be surprised if there's a much better version of that matching going on. Because they have a depth of advertiser campaigns, there's also a lot of inventory to select from to do that matching for a user.
Nathan Labenz: What do we know about the effectiveness of in-AI advertising so far? Perhaps we could take one step back before we go to that. Talk me through how it works. You've-
Ryan Hudson: Sure.
Nathan Labenz: alluded to it a little bit with vector matching, which you can go as deep and technical as you want there. People who have tuned into this podcast and stayed with us this long are familiar with the basics of RAG and vector-
Ryan Hudson: Yes.
Nathan Labenz: search. So, you can give the 201 version of that if you want to. How does it work, and then what do we know so far about how effective it is?
Ryan Hudson: What we know about the effective side is from our own implementation of our PiGPT service. It was a reference design of a custom GPT that we built, and it effectively demonstrated to ourselves that you can get an AI to consider these other content sources and include them in the responses. It uses links that can be tracked so that you can measure performance for advertisers and all of that. The click-through rate from that content is incredibly high. Our takeaway from that is there's actually very interesting value being given to the user. It's not just included in the result; it's the right answer and a part of what the user is looking for, enough that they're clicking out from that ChatGPT experience. These are early numbers, and it's a certain type of audience, so I won't generalize it at this point, but I can say it's very encouraging that this is actually working, such that we're now making it available everywhere. For a developer, they can implement it as an MCP server, a service that does enrichment of ad content or enrichment of content. It's tunable for a developer to help steer it toward the right type of ad experience for their particular user experience. You can think of it as instructions to the AI that effectively say, "Here's some additional information. If it's useful, include it for consideration by the user. If you do that, use these links, and, by the way, let us know if you did that." We do actually get some data on whether or not different advertising information was included in the response. That's how it works, and the actual matching of that is pretty cool these days. A couple of years ago, none of this would be remotely possible, and now all of a sudden, one engineer can spin up functional things in a week or less. What we're doing is taking as much advertiser context as we can get, whether that's all the landing pages, their product catalog with pricing information, or a service professional database of people who can help you, like handymen or moving services, categories like that. Tapping into information and then using AI to generate the ad campaign, which is a summarization of some of that content, and then mapping that content in vector space to match it against search queries. The AI system comes to our server with keyword searches, and we're matching against the ad content that is most relevant. We do this in a way that advertisers don't have to do any heavy lifting or thinking on how to create those campaigns. Our system does it for them. This also protects against challenges in fully automated agent workflows that are vulnerable to classic attacks, like white text instructions overriding what the AI does. That security frontier is being explored. Our system, because we're generating that content, we're not going to do injection attacks on your AI service as a developer. So, it makes it easy for the advertiser. As someone who worked in ad quality previously, I hinted that people were trying to do shady stuff with ads back in the day. We're protected from that unless it's a bug on our own side, but we're not going to... It's easier to protect against than advertisers submitting malicious ad content. That matching happens, and we have found that it works really well. Context matching is a relatively solved problem in computer science these days, and you can do high-performance, at-scale versions of that. It delivers value for the advertisers, delivers value for the users, and, from our point of view, most importantly, delivers value to AI developers that need to monetize the free tier of their services, because that lets a lot more applications exist than do right now. I hope we're building towards a future where there's an app for that, millions of apps, and millions of websites, and not landing in an AI version of the internet where all the power is consolidated into the mega platforms. I think they certainly have a role to play too, but empowering the long tail use cases is central to what we think a good future world looks like. We'd like to help people spin up their business. As you were talking about with content creators, I think a lot of people can be AI application developers, and we're going to do what we can to help support them.
Nathan Labenz: Again, so many different directions I want to go but maybe, um, tell me about some of these sort of long tail app developers. My- one of my general theses about AI is, and I don't necessarily like it, but I do see a lot of power concentrating in a few hands. That seems to be the default path. And I don't really know how we get around it, especially because you can tell ChatGPT or Claude, like, "I want you to be, you know, weird in this way or that way," and to a very significant degree, it'll just do it, right? So, if you're looking for, like, different personality or different kind of angle or, you know, different language, like, it really has an unbelievable out of the box ability, it being, you know, whatever frontier model is powering these platform product experiences. It has a lot of ability to kind of morph to your tastes, your style, your context, whatever. So, what do you think are, are the things that, like, they can't do or they won't do that will be served by the sort of indie AI developer set, and you know, what, what examples are you seeing of that today that are interesting?
Ryan Hudson: Uh, I think there's a- to me, the answer is there's a lot of them, and the idea that you have, like, one friend that you're talking to about things, even if it's a super morphing friend, just in the chat version of what you're talking about, I think people will do a better job than them. Like, the same way that people build better apps than Apple and people build better websites than Google, I think there's gonna be people that build better every single vertical, specialized use case, understanding an audience, delivering something unique and special to them, just like you see in content creators. I think that can happen. The cost to deliver that in the language models is declining rapidly, enabling all sorts of new use cases. I think we've crossed the point where adv- ad-supported can cover your inference cost and, like, build a real business on top of that, uh, with growing margins over time, where you get more ad revenue and decline in infrastructure cost. The other thing is, I think thinking of a conversational chatbot sort of experience as the only user interface for AI is, like, wrong. And we're, we're doing a bunch of things in, like, our Pi experiences and making them available to, I, I, I refer to it as, like, browser developers largely, people that have audience and they have a, a web browser or a browser extension. There's infinitely many applications of AI capability that naturally flow with the user context of using a browser. So, unless you think people are gonna stop using browsers and they're all gonna be sucked into using only Comet or whatever OpenAI's, like, native app version of a browser is, there are just so many contextually relevant places to initiate an AI conversation where it's not you typing in your question to a chat interface. Just, just to, to name a couple, like, if you're on a product page browsing something on Amazon, wouldn't you like to know about the price of that, if there's a deal somewhere else? Like, all of that can be initiated by a browser extension or a browser as, like, you hover over the price for a second and it initiates a chat conversation, and it's referencing proprietary data that someone like PayPal Honey has price history on products on Amazon going back a decade. And their AI service overlay could say, "Actually, this is, uh, $20 cheaper than it's ever been, and you should buy it now," or it could respond with, "Hey, it's actually overpriced right now. Maybe you should check out these alternatives." And like, they, they can do AI-powered conversational things that initiate in-context where it's not a user pasting a URL over into a ChatGPT interface or putting it into their mobile app or trying to translate that context from where they are anyway. That's the shopping version. We've thought a lot about that. You can imagine email or corporate workflows. Like, there's just so many other use cases where I think AI is gonna be everywhere. It's not gonna be living only in the big players. And then that's not to even think about, like, the Apple silicon is gonna be doing local language model processing at c- equivalent to today's model capability in the next year or two. Like, it's, it's inevitable that they're gonna be doing that in a local privacy-preserving way. And the types of applications that developers will build with that, I think, further reduce the likelihood that it's only these big monolith platform players, and I think that's how it plays out. I could be wrong. I'd like for that to be how it plays out, but I think the market's just driving toward that's the likely answer is, like, compute goes to the end devices, you do a bo- a lot more locally, it can power most of the things you want to do, and then that context follows you wherever you are. And I think this is why you're starting to see pe- like, even the big guys are realizing the browser is where the game's at, and that's where user activity is now, it's where it's going to be. Even if it's the fastest trans- uh, uh-... transfer of, like, users who are using web browser today and then everybody only is using ChatGPT tomorrow. That tomorrow is at least a few years. And in aggregate, the AI experiences that get built in that browser do two things. One, I think they're bigger than the ChatGPT version, and I also think that they slow that transition by building more capability into the device and experience that users and consumers have. Um, they start to expect that capability to be there as a part of their ChatGPT thing. So like you, it slows the move to that new platform and from an advertising platform creator, like volume is the name of the game. And so I think we can build a bigger ad system outside of those walls that even exists, uh, at what seems like huge platform scale. I think they'll build their own thing. I don't think they will open it up to third-party developers to monetize at equivalent rates. It's like what I was saying with Facebook. I think they'll realize they want control over their ad system and opening it up to third parties creates a whole bunch of headaches and challenges for them. And that's not central to what they need to do. And so I think they'll probably keep it tight and controlled, and then it creates an opportunity for somebody like us to come out there and build the Stripe to help the long tail of developers. And I don't think the long tail is necessarily small by definition. I think the long tail is just broad in the type of experiences that people will build. I think people will build a better travel assistant than is gonna be in any of the big players just 'cause they're so focused on it, and they go out there and find proprietary information to do contextual matching. They find data sources that aren't generally available on the open web, and they have a focus on delivering that. And so as a consumer, when you go to do a travel booking, I'm trying to figure out this details of the trip, there's probably gonna be somebody that you think of to do that, and it's not gonna be just open up one app for everything. And I think that's what can happen.
Nathan Labenz: What do people pay for? You alluded to this with paid consideration. A simple truism of advertising broadly is the closer you can get to the actual conversion event, the more the advertiser is willing to pay.
Ryan Hudson: Yeah.
Nathan Labenz: see people pay a non-trivial percent of revenue when the person converts and actually pays.
Ryan Hudson: Yep.
Nathan Labenz: and then, the highest, farthest up the funnel, you get relatively low sub-cent value for a single random impression.
Ryan Hudson: Yep.
Nathan Labenz: That does seem like a tricky one because I want to triangulate: on the one hand you are constantly pulled deeper down the funnel. But on the other hand, the user at some point starts to worry: who is the AI really working for again? Is it my agent, or is it the advertiser's agent? Who is the customer? Who is the product here? I want to know that whatever AI advice or guidance I am getting has me at the center of its consideration. I do not necessarily mind if somebody is paid to be in that consideration set, but if I have the sense that the AI is earning money when I take a specific action and pay for something, then I wonder if I can really trust the product as much. So where do you think that settles? What is the solve for the equilibrium, as Tyler Cowen would say?
Ryan Hudson: I think the equilibrium is consumers will vote with their feet to use AI that is respecting their priorities and delivering value that they trust to be impartial and not stepping on the scale just because of paid consideration. So I think of what we are building as an additional information source for the AI to consider. It is up to AI developers to implement experiences that do not abuse user trust that way. And if they do, I think somebody else will step in to provide one that does not have that. I would love my travel agent to go out there and find all of the best deals, and that is probably coming from paid sources that people are willing to give my agents offers to, to be considered and offers to me to convert downstream. That feels like a more powerful version. If I get a sense in that process that the AI is not looking out for my best interest, I am not going to use it. So, I think that is how it solves, like market forces, and this is where I think it actually is important to have a breadth of developers out there building every variant on these experiences. There are probably going to be people that use zero-click-to-monetize, an experience that I would not want to use, and I do not think will be successful because I do not think it is preserving the user's trust that way. When I have built experiences in the past, we always put user trust at the highest pinnacle. There is a whole bunch of stuff I can add on that, but the second you violate that user trust, you lost. So, to me, that is the way to build consumer experiences. That said, as a platform infrastructure provider, I do not want to dictate that to developers on our platform any more than I want Stripe or PayPal to say what type of businesses can and cannot use our platform for transactions, or any more than I want YouTube saying what categories of content you are allowed to monetize or not. I think neutral platforms are important, even if I do not like how it is. I think the market will sort itself out from there is my hope, and the number of times that we have to step on the scale and say, "Don't do that," I think we will try to limit to breaking the law or the other extremes on that, rather than moral judgments on the platform. Staying neutral is important to be plumbing and rails for other people to build on, and I have seen where that can shift markets around unnaturally, and I think with enough competition, that sorts itself out for the most part.
Nathan Labenz: I mostly hope that that is true, and I mostly think that that will be true, although I do have some nagging doubt. What kind of range of monetization events do you have today, and how do you think that is going to develop, and is it going to be, or is it already, an auction dynamic?
Ryan Hudson: It is auction adjacent. It is auction, but with a heavy dose of context to even be considered in the auction. Over time, I am sure the model will mature. Right now, it is a lot of internal management of campaign bidding prices to achieve output, or achieve results for the ad campaigns, tracking through to actual transactions and things like that on behalf of an advertiser. But at the core of the auction, just like with Google, and to their credit, they figured this out, or followed some people that did figure it out. Effectively, a user clicking on the ad is a signal of quality, and an advertiser's willingness to pay is a signal of quality. If you effectively do an expected value calculation on how much money Google is going to make from that click times the rate, you actually get a very good signal and way to rank the advertiser results. So, our system should evolve to be something that looks and feels a lot like that.
Nathan Labenz: Yes. Here's one doubt I have. I was in the mortgage business very briefly a long time ago, and I think the problem I'm going to describe was perhaps at its zenith in that business at that moment in time. By the way, it ended in a giant financial crisis. But even leaving aside the systemic risks, something I observed was that expected value calculation could go awry. It's more regulated now, but some years ago, the mortgage originator could just charge you whatever they wanted at the time of a mortgage origination, right?
Ryan Hudson: Mm-hmm.
Nathan Labenz: So there was a dynamic where they would try to get as much from customers as they could get away with, or at least a lot of companies would do that. I even saw mortgage pricing cards from companies where it would say, "Here is the minimum rate that you can originate a mortgage at today." Of course, there are credit score adjustments and things like that. But then there was the additional salesmanship bonus. If you can get somebody to close at a half point higher than that base, you get X. If you can get them a full point higher than that, you get Y. So the salesperson is directly adversarially incentivized to extract as much value from the customer as possible. I think a lot of things are like that, right? Many prices are negotiable, not just B2B SaaS. It doesn't cost them anything to deliver it long term, so the price is a pure negotiation and the seller is incentivized to have price integrity. But the deal could get done at many points on that spectrum. What I observed in Google Search, specifically with mortgages, was that the clicks were starting to get up to 50, 75, or even 100 dollars. How do you afford that click? You have to extract as much value as you can, right? You can tell this story, and I think it's true in many industries, where the expected value to Google is a pretty good signal of quality. But in some markets, and mortgage is not unimportant, right? We're talking about 30-year contracts, the biggest purchase people make in their lives, and systemic implications. We did observe this haywire effect where the people who could bid the most were the people who would extract the most. If I map that into the AI app ecosystem, staying with mortgage for a second, then you'd have
Ryan Hudson: Yeah.
Nathan Labenz: these financial helper apps, right? How do they make money? They refer you to mortgage companies too. So then you have this second-order effect where it's like, which of these financial helper planner apps is going to get the most customers? It's the one that can bid the highest. How are they going to bid the highest? By most effectively referring you to the mortgage people. So, how does that not happen in AI? I'm still a little bit unsure. If there is an auction dynamic, and it's about who can pay the most for a given high-intent moment, how do we not get to this adversarial situation where the sellers who can extract the most from the customers get the space? Also, the AI apps themselves are incentivized to steer you in that direction, because presumably they will participate in that transaction value too.
Ryan Hudson: Interesting. I think you nailed it; this is inherent to buying and selling goods in general. Price discovery certainly is done in different ways in different parts of the economy. I'm picturing the scenario you described was there. I wasn't there for it, sounds right. I'm picturing the scenario in an AI world. I think I would layer on that the AI app financial advisor that monetizes the best is probably going to be able to do the best customer acquisition of their own users, so they become a potentially dominant financial advice app. I think you're raising a very valid point to think about. I don't know if advertising specifically inherently does this. It probably leads to more of that effect, versus, say, maybe there are then emerging business models that are different, like. Remember Angie's List had a paid user subscription model? I think they also still make money from ads, but I could be wrong. But a paid user service for something like that, maybe you do want to pay $10 a month, or maybe your brokerage firm wants to subsidize that for you and bundle it with their services or something like that, and get a non-advertising app experience. I'm not saying there needs to be advertising in every AI experience. I just think there's a lot where that would be beneficial. Where it steers away from the core value proposition of the AI experience, then it feels like maybe that's just the wrong match for the model. And that mortgage or financial advice environment does seem like one that I'd be careful about picking the right model for, as a user and maybe as a developer.
Nathan Labenz: I think that problem, to a very significant degree, has been mitigated by outright government regulation. I think the pricing is much more controlled. Mortgage companies can no longer give their individual sellers a card that says, "Charge a point over base and you get a bonus." I think that's literally illegal at this point.
Ryan Hudson: Right.
Nathan Labenz: That's also part of the situation: government can fix certain market failures.
Ryan Hudson: It used to be the case in stock brokerage. For a new listing, brokers would get directly spiffed on how much they pushed into their accounts.
Nathan Labenz: Mm-hmm.
Ryan Hudson: I did a brief high school internship at a brokerage and saw a guy making 10K by putting one of his clients into some new offering. It was like, "Is that any good?" He said, "Doesn't matter." "Is that the right thing for your portfolio?" "Doesn't matter." So, yeah. But in that case,
Nathan Labenz: Yeah, it might go up, it might go down.
Ryan Hudson: That was pre-internet. So, these things have probably been around for a while as misalignments of interest. As a consumer, I wonder if there's a way to build a service that helps consumers assess that alignment in the tools they're using. That's intriguing to me.
Nathan Labenz: Yeah, we're going to need all the help we can get navigating this world. I think increasingly we're turning to AI to help solve the AI-
Ryan Hudson: We need AI to help us navigate the AI mess.
Nathan Labenz: Yeah, I'm sure you're aware, that is basically the safety plan of the frontier companies at this point. Reading the GPT-5 system card, it was striking to me over and over again that it said, we used an LLM as a judge to evaluate how good the outputs were. They have justifications which are not unfounded, where they say, "We sat down with an expert, and they helped us workshop the prompt, and we confirmed that their judgments seemed to align and correlate at least," whatever.
Ryan Hudson: Yeah.
Nathan Labenz: And of course, you're not going to have perfect interrater reliability amongst humans; that's a huge problem. Nevertheless, it feels like we're spinning some plates there. I just saw something yesterday too where GPT-5, in creative writing, is starting to do some weird, what I would call pretentious nonsense. That's one thing. But then what's really interesting is GPT-5, when given its own pretentious nonsense, really likes its own pretentious nonsense, and even Claude seems to like its own pretentious nonsense. So now the speculation is, maybe it's learned to write such pretentious nonsense because it's a reward hack.
Ryan Hudson: Mm-hmm.
Nathan Labenz: Where it's getting high scores from its LLM judge.
Ryan Hudson: Friends.
Nathan Labenz: And it's learning to exploit something that humans were basically like, "What the f*** is that?" But the AI reads some sophistication into it that potentially isn't really there.
Ryan Hudson: Oh, this is great. Love it.
Nathan Labenz: Yeah.
Ryan Hudson: More em dashes.
Nathan Labenz: It's becoming a real hall of mirrors in a few of these areas. So yeah, I mean, I think to the degree that you can bring AI truly to the consumer, help them monitor for where these things are happening, I do think that is super, super valuable.
Ryan Hudson: Could be an emerging need for sure.
Nathan Labenz: Yeah. How about in the technical domain? Do you see Cursor being ad-supported? I mean, I was just thinking there are a lot of API services that are potentially complementary. Not potentially, they're core to building modern applications, right? So you get to the point where you're like, "Oh, I need to scrape a website," or, "I need to do whatever." Do you see those coding assistants bringing back technical solutions?
Ryan Hudson: I-
Nathan Labenz: That would seem like pretty bread and butter, right?
Ryan Hudson: I think yes. To answer your question, yes, I think that's a very interesting way to build a different sort of business model around some of those tools. The way I would extend that is to broaden it and think about just any software, any SaaS tool. Today, SaaS products are basically $10,000 a year plus enterprise contracts and sales to be viable because they're sold by people. You have a human sales team going out there selling and supporting these products. I think there's going to be a wave of new SaaS products that are priced cheaper and are distributed through contextual advertising. Maybe it's in Cursor, but there's an equivalent workflow tool summarizing your meeting notes, or saying, "Oh, by the way, did you consider this thing when you were having that conversation about..." I won't prescribe the use cases, but I think there will be very thoughtful, clever people that figure out how to effectively change the cost structure for going to market on SaaS products. If an ad layer, like what we're building, gets built into a lot of these services, which I think it can and should be, you get a lot of efficiency of discovery. And it's categories where the reason it's a sales team today is because people aren't going to Google and saying, "I need a new SaaS tool" at meaningful numbers. It's not a category that's in Google Search. But it can be a category that is in the aggregative AI experiences. So, Cursor, yes, for tool discovery, but also, yes, for SaaS and probably a whole bunch of things like that. There's a company, Open Evidence, that has an ad model for reaching doctors with a ChatGPT for doctors. They've been able to take over that market, do exceptionally well by going direct to the doctors with an ad-supported model, where everybody else in the space has gone trying to sell into the hospital systems, into effectively huge dollar enterprise contracts, multi-year engagements. Instead, this other company with an ad-supported version is now used by 40% of doctors every day. They were able to do that in their vertical because it's very clear who the audience is; it's high value. It's very clear who the advertisers are; it's high value. They do that matching. They were able to build both sides of that network. For most categories and for most AI developers, it's less obvious, and it doesn't make as much sense to build your own ad system and solve for both halves of this marketplace, effectively. It's like a context marketplace where you have advertisers that want to reach your audience and you have an audience that you're trying to build. Solving both sides at once is really hard. If you could tap into universal plumbing on the reach the advertiser side, it makes it a lot easier to build out new experiences and build new business models, where maybe you're going after a category that today somebody is selling for, is selling into enterprises with a contract value that's five figures. Maybe there's an ad-supported version of that that can be built and go to market more cost effectively than the competition. So, that's the sort of innovation that I can see flourish, and I'm pretty excited to see how we can support that. And we're talking to developers that have non-obvious ad directions, like, this is a ChatGPT experience for research papers and scientists looking for... That sort of thing, like, "Okay, what does an ad experience look like in that?" I think there is one. For the person building that service, they shouldn't have to figure that part of the business model out either. It's not like the LA Times in the early 2000s where you had a sales team going direct to the car dealers. It's like you tap into common ad rails plumbing, and you're able to focus on the part that's core to your business, which is building that helpful user experience and making that part really, really competitive.
Nathan Labenz: On the SaaS side, I once mocked up a classic SaaS pricing page. The first tier, and the lowest price, was for AI sales and support. The middle tier was for human sales, and the top tier was for human sales and support.
Ryan Hudson: I love it.
Nathan Labenz: I don't think too many people are going to present their pricing pages exactly that way, but it does get at something very real.
Ryan Hudson: Yeah.
Nathan Labenz: The cost of sales puts a floor on what is, in many cases, close to a zero marginal cost product.
Ryan Hudson: I love that idea. Maybe we'll use that on our Zero Click pages, because we have the same thing. We're trying to reach a lot of developers, and there are different sizes and scales. It makes sense to have personal conversations with some of them, but others can hopefully onboard themselves, figure it out, and chat with an AI assistant to answer any questions they have instead of us trying to scale a team to support them.
Nathan Labenz: How much do you know about that medical one? I assume the biggest advertiser would be drug companies, right? Selling drugs.
Ryan Hudson: Yeah, it's pharma.
Nathan Labenz: Drugs.
Ryan Hudson: Pharma, medical devices, that sort of thing. I don't know a ton, other than what's been written about it by other people. I don't have an inside channel to it. But from what I have read, they are doing exceptionally well, and from what venture capital investors say, it sounds like the information written is accurate, based on the investor community. So, it's a really cool case study. What if you had a completely different business model? I love it.
Nathan Labenz: Yeah, that's another fascinating one. Again, I don't want to be neglectful of the upside, because I do think better living through pharmacology is very real, and the awareness theory of advertising as it applies to drugs, in a totally earnest way, I think is important. At the same time, those commercials always conclude with "Talk to your doctor." Now it's a flipped-around thing where they've prompted you to talk to your doctor, and you're going to talk to your doctor after the drug company has already talked to your doctor, potentially about you. At a minimum, I think—and there probably is some law about this or maybe not, I don't know—would a doctor… Doctors themselves can't take cash for prescriptions directly, right? They can take trips and stuff, but they can't take literal pay per script. But the AI can probably take a pay per script, I would guess, in today's world. I don't know that that would be… We don't have a lot of laws around this stuff yet, right? So it's all green field.
Ryan Hudson: No.
Nathan Labenz: It's all kind of green field.
Ryan Hudson: They're coming up with an even better version of their... Out-monetize them.
Nathan Labenz: Yeah, well, that's...
Ryan Hudson: Don't give too many ideas too soon. Yeah.
Nathan Labenz: But yeah, there's a duty of... I wonder, do you have any thoughts on what a...
Ryan Hudson: Here's a counter to that even being a bad thing. Those ads on TV and stuff, I'm not a huge fan. I don't think most people consider those good content. If those all went away, everybody would be happier. If there were a more cost-effective way for the pharma companies to present their options to doctors, they wouldn't need to do those "Ask Your Doctor about" ads. We already gave your doctor the options. They've discovered this new drug that they might not know about for this particular use case. We're presenting it contextually when there's a patient case where it makes sense to consider it. The doctor is still going to do the filtering on whether it's actually useful or applicable anyway. Maybe we can get rid of the annoying, bad advertising part of the thing, because it doesn't work as well. That would be a pretty great outcome—just market efficiency. The efficiency of annoying millions of people is unnecessary if you have a better channel for advertising. You hinted at this earlier with Facebook having a good quarter: if they build a more efficient ad system, the money is going to flow to that and away from ineffective ones. I would argue annoying people at scale is an ineffective ad system. To the extent they're doing it on TV and doing awareness things right now, part of that is probably just they don't have a way to measure how ineffective it is. In some of those categories, they just don't have another channel where they can cost-effectively reach people. But as soon as you do, maybe you starve the bad ads and just get efficiency.
Nathan Labenz: Yeah, the upside vision is compelling as long as the virtue and integrity of key actors stay strong in the system. Then a lot of these things are fine. Dean, a repeat guest and recent White House AI advisor, told me once, "Republics rely on virtue. You can't really have one without it." To some extent, it's on all of us; it's on the doctors to make sure that they keep their priorities straight. I have a few questions on just tech trends and stuff. One more thing we should know about how it all works: my sketch is, right now, we're presenting the ability to go seek additional context to the AI as a tool. So, presumably, parallel tool calls or things like that are a huge development in terms of latency, right? Because you wouldn't want the user to sit there and wait for that tool call and whatever to come back. Now we're starting to get into this realm where you can issue a tool call, but not necessarily have it be so blocking. The AI then, though, is responsible for sending over whatever information is sent over. So, you're relying on the app AI to guard the privacy of the user, which is interesting. But I get the sense that you also sort of expect that this will evolve from one where the AI is sending stuff over the wire and needing to protect privacy before sending the message to the matching system, to one where, in the future, the idea will be more of that.
Ryan Hudson: Mm-hmm.
Nathan Labenz: Vector type stuff will happen on device, so it could even be potentially more personalized. But how do you send that vector content down to the device? You cannot send your whole database of advertisers to match, right? So, how do you see that? Where does that compute happen, and how can you possibly do robust matching on the edge if I am understanding the vision you have for the future correctly?
Ryan Hudson: The future version, or today's version, is effectively parallel to the organic search that is heavily keyword search-driven tool use. For the reasons you talked about regarding performance, it is just adding another search of an additional source, and then synthesizing it in that same next step. The vectorization, to me, is most interesting for the personalization side, and that is a bit off technically on how it would best be done. We have a version working in a browser context where, because the browser can have that profile, we can independently, for a user, effectively front-run or simultaneously send that context to the ad server, for lack of a better term. When that request comes through from the AI service, it has that context separate from the chat. So, the personalization context could be separate from that chat context. We are not using that today, but it has proven that we can do it. In different environments where you do not have that browser context, the reason we are not doing it is because we have not solved for all the use cases where that would be prevalent in the ad system. But it is interesting to think about different ways to do it. This is like a reinvention of something that already exists in many ways. People have been doing insane RTB auctions of every little thing. Those banners that are selling for less than a cent each, behind the scenes, are part of an insane real-time auction with multiple
Nathan Labenz: Mm-hmm.
Ryan Hudson: bidders bidding into this ecosystem. So, when you look at that, what we are doing is not complex at all. Because it is all contained within our systems, it is not hitting open RTB, going to third parties, asking for bids in real time. It is managing against campaigns that are loaded onto the platform. We can do a lot of caching and performance optimization to make those responses as fast as possible, and as contextually relevant as possible. So, the personalization vectorization service certainly could be a piece of that more in the future. There are a lot of fun engineering things to play with on that. I love the business and market structure, big picture thinking, but then also diving in on the actual tech is where the fun stuff is.
Nathan Labenz: Anything else you want to highlight that you are working on that you think is particularly fun tech-wise? You can go as deep and esoteric as you want.
Ryan Hudson: Mostly it is that stuff, and then enabling browser-based applications of AI is the fun user experience frontier that we are playing with. We do not think we will figure out all the answers, but helping browser developers become AI developers with a demonstration of, "Hey, this works," and potentially, "Hey, this is how this works, and you can go ahead and put it into your browser or your browser extension." I think if we get a lot of people thinking about what an AI-augmented browsing experience looks like with a human at the wheel, that is pretty cool. The types of things you can build. We will not think of all of them, but getting more people thinking that way and having a way to monetize that is pretty powerful. Monetizing a browser extension has historically not been particularly easy, and I think we can change that so that more developers can build a lot more different applications. Google turned off the paid version of browser extensions in the Chrome Extension Store, and then they highly limit the ability to add advertising into a browser extension experience with their single-purpose policy. So, it has been very challenging to build a business, and it has pretty narrowly defined the types of things you could do, essentially narrowing down to just shopping tools because it fits within the single-purpose umbrella. But if we are able to build AI-augmented experiences, we can bring ad monetization into that without tripping over Google's interpretation of their single-purpose policy to disallow injecting advertising in a different sort of way. So, I think there are use cases that this would enable in the browser. To me, those are very exciting because that is where the users are, and there is a clear path to go to market. The power of a browser-based tool, like a browser extension, is that you can contextually be useful to a user and have that user habit happen automatically. There is no training. You do not have to do the in-app message that you were talking about before that pings you eight times a day to try to build that habit of talking to it. It is, hey, you can build a super niche thing, and it only ever shows up once a month when you are doing some very specific activity in context. It is helpful then and stays out of your way otherwise. The power of a browser extension to do that type of experience and get user habit for free is, I think, underappreciated. People have not been able to build businesses there, and I think they can now. So, to me, us teaching some of the ways, but then also hoping that is just a sliver of the possibility, and you start to see a proliferation of great new experiences that thoughtful, creative people have built for users.
Nathan Labenz: What do you expect for the seemingly just getting started browser wars? History repeats itself, right? Now all the hot startups are trying to make one. Microsoft is very much back focusing on this. I don't know if they ever quit, but I certainly wasn't thinking about it for a while. Now, I'm thinking about
Nathan Labenz: it again a little bit from them. Do you have any forecasts for what we should expect there?
Ryan Hudson: I think it's going to be competitive again. I think, in part, because you finally have potentially differentiated experiences happening in the browsers. When we started our company a year and a half ago, it was all about building the application layer of a browser, effectively building a virtual browser. It doesn't matter if it's Chrome or Edge or whatever browser you're choosing to use. We're layering on capabilities to any browser. So, I think there's going to be a battle for being that default browser, but I think the most interesting stuff is actually going to happen in the application layer. The application layer for browsers being extensions, and I think most people will get their new capabilities that way versus switching to an entirely new browser, which is a heavy lift to transition somebody from Chrome. Chrome just works and does what you expect, transitioning to some new experience for a feature is difficult. Historically, it's been niche subsets of users, power users that want tag management and some of these capabilities. Or maybe they're particularly privacy-sensitive or don't want to be on a Google platform or a Microsoft platform so they use Brave. To me, that's been tricky to think about as a universal use case, and I think that's my thinking more broadly. I don't know that there is a mass market, 'it has to be the same for everybody' version of what the browser should be. It's more about letting people pick and choose the special features they want their browser to have. Some people love dark mode, some people don't. Some people want shopping tools, some people don't. Let's make a browser a configurable thing sitting on a standardized base rendering engine and capabilities so that developers can build for that common platform. I wrote a response to the proposed spinning out of Chrome from Google. I don't think it solves the problems that are there. I think the biggest policy problem for me with the current implementation of Chrome is the single-purpose policy. I think it chokes off innovation, and as long as developers are thoughtful and transparent to users about what they're doing, extensions shouldn't be forced to be single-purpose as defined by, I'll use the word, a monopolistic owner of the platform. I think innovation has been choked off there, and you haven't seen a proliferation of development, largely because of that policy. So, my concern in selling it to somebody else, a big AI company, whether it's Perplexity or OpenAI or somebody else, is that they would have every incentive to behave just like the prior owner and foreclose on competitive innovation, especially in AI. I don't think that would be a good answer. I don't have a good answer other than my preferred answer, which I put forward, is to make Google be open with it as a platform. That would be a better remedy than a new owner. The owner's not the problem. It's the ability to build on top of the platform.
Nathan Labenz: Does that policy operate only at the store level? I can add any extension I want onto Chrome, right? Or do they
Ryan Hudson: No, you can't.
Nathan Labenz: prevent me from installing
Ryan Hudson: You
Nathan Labenz: my own stuff?
Ryan Hudson: You have to put your Chrome into developer mode to install anything yourself.
Nathan Labenz: Oh, okay.
Ryan Hudson: And even when
Nathan Labenz: I guess I've been in developer mode a long time.
Ryan Hudson: Yeah. Even then, there are cases where they somehow remove stuff. I'm not even sure how they have deactivated some user-added things. Users have shared paywall bypassing extensions and things like that, and I've heard direct reports of them somehow getting disabled on their developer mode Chrome. Partly under the guise of protecting user privacy and not creating bad experiences with extensions, a decade or so ago, they forced all extensions through the Chrome Web Store and sunsetted being able to install from a third party process. To use any extension on Chrome, which is the dominant browser, you have to go through them and abide by their policies. With 70-80% market share for Chrome, that is effectively the market for extensions. So you can't build an extension that's only on one of the other platforms. They also have largely adopted the same policies by default.
Nathan Labenz: Yeah. Okay.
Ryan Hudson: Yeah.
Nathan Labenz: What do you see in the AI version of SEO? I guess there's an increased number of people cold emailing me, just like they used to with SEO, saying,
Ryan Hudson: My overall assessment is that, like SEO, there will be people on the frontier who understand it very well, and they will be able to massage their content to be desirable for consumption by AI systems. Like with SEO, there are probably 10 world-class people that really get it, and then thousands of people that are going to run around taking money from people to provide that service. The net result of all that, I think, is effectively similar to what happened in SEO, but you get some dilution of the organic results with SEO slop. To the extent that it's easier to use AI to generate content, and to your point earlier, maybe the AI even likes AI content better than human content, you're going to have a flood of content in the organic realm. Deciding authority in that world, where you don't... Google effectively trained their system on human feedback of clicks. Does somebody click through and then bounce back? Reading signals of quality from a human interpretation of that result is what they've used to refine organic search over decades. In an AI context, you lose a lot of that ability to determine if this is a great new creator who is a specialist in makeup, doing reviews, and this is an authoritative source on this, or if this is literal AI slop, just mass-produced content farm. How do you tell the difference? It would be tricky. So, I think it's going to be something people try to do. It's going to be something that people invest a lot of time and resources into. To me, as somebody thinking about it from an advertiser/marketer perspective, it feels like a sliver of how people will find your brand in the future. For every dollar that goes into SEO, SEM is massively more important, and I think it will be the same for the advertising side of how you present yourself to an AI. The best way is going to be to present your AI in a paid context. The SEO games will be won by a few people, and they will generate some traffic, maybe, but on the whole, I think that won't work for most people. Right now, there is a ton of activity around it, mostly because there isn't another option, so every marketer is like,
Nathan Labenz: What kind of intent are you seeing shift most to AI from search?
Ryan Hudson: I think it's a little bit of everything, but the most interesting ones are actually a little bit upstream discovery relative to Google. Google is where you go when you know what you want to do. In a chat context, you're doing a lot more exploration of ideas; it's at least one step up the funnel, largely. You're not going there and tactically saying,
Nathan Labenz: That's interesting. That also raises the question of getting into less commercially motivated advertising. Nike's always selling apparel, right? They're commercially motivated regardless of where you are in the funnel. But let's say I'm getting into travel. Governments around the world, for example, might want to pay to influence the way I think about their country. They might partly be thinking about that in terms of ROI, like me actually showing up and visiting there one day, eating in their restaurants, and staying in their hotels. But they might also just be thinking, we want to shift global perception of our country and our government. Do you have any thoughts on whether that should be treated differently? If I Google Tiananmen Square today, I don't see a sponsored link from the Chinese government saying, "Here's the story we want you to know about the incident." But that's going to be really blurry, I guess, in the AI context.
Ryan Hudson: You are infinitely more creative than me on this. I certainly have never come close to thinking about this particular use case. That's fascinating.
Nathan Labenz: We'll come back to it.
Ryan Hudson: Yeah.
Nathan Labenz: One other thing is, what about non-text-based advertising? We're getting into the deep cuts here. We just saw the new Gemini Flash Nano Banana out yesterday, and it seems like you could really start to imagine all sorts of AI try-ons, which we've already seen these apps, but bringing it to you seems like that's going to happen. Seeing it in your home is another experience that we've seen people develop in a specialized way. But now I could really imagine if I just gave ChatGPT, or Gemini, a few examples of my home, the next things I could be seeing are all these products in my home, and it could be extremely real. Are you guys interested in that sort of thing? Do you have any forecasts for what the multimodal advertising formats might be?
Ryan Hudson: I am
Nathan Labenz: formats might be?
Ryan Hudson: I'm intellectually interested in what you described there. I can imagine that might be a pretty cool experience. We're not starting there, for sure. We're certainly focused on text to start with. But at the end of the day, having a map of an advertiser's context, including the product information, maybe it's... I can imagine that 'see the stuff in your home' app being much better with advertiser content, because instead of rendering a generic couch, it renders a real couch and you can buy it. So it's not just an AI guessing, "Here's a hypothetical world." It's like a real thing. It's not a Pinterest inspiration image that is just AI design slop. It looks awesome, but if you want to actually execute on that, you have no next step. So I think that might be a case where ad content helps generate better answers, even in the visual realm. That sounds cool, but we're not going to be able to help with that for a bit.
Nathan Labenz: Well, it's all coming at us quick, I guess is my thought.
Ryan Hudson: Yeah. It won't be a bit, and by a bit I mean like three or six months.
Nathan Labenz: Yeah.
Nathan Labenz: Yeah, truly. Accelerate thy timelines is my universal command.
Ryan Hudson: It is so crazy how fast people are building stuff these days. It's inspiring.
Nathan Labenz: You've been very generous with your time. This has been super interesting. Maybe in closing, anything we didn't touch on that you want to cover, or any last words or thoughts you want to leave people with?
Ryan Hudson: No, I think we covered way more than I even thought about ahead of this. Some of these are luxury problems of, "How do I feel about the future, assuming this thing works?" We're a startup trying to get going, and we'd love to work with as many developers as we can as fast as possible. So our parting thoughts is if you want to check it out, go to our website, zeroclick.ai, and there's a live demo on there. You can see how an ad might get inserted into any chat session.
Nathan Labenz: And if you want to get an intro from me, you can email me and I'll forward the highest bidder emails directly to Ryan. How's that sound?
Ryan Hudson: Nice.
Nathan Labenz: Mostly kidding. I'll send anything that's actually of interest. Cool. Ryan Hudson, founder and CEO of Zero Click, thank you for being a part of The Cognitive Revolution.
Ryan Hudson: Thanks.