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Sponsor - Google (0:00)

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Nathan Labenz (0:31)

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 $55 million 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 that this vision requires Ryan and team to invent answers for, their eventual success will also bring many big picture societal questions straight to the floor. And considering that Ryan was previously founder of the online shopping company Honey, which sold to PayPal for $4 billion, that does seem 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 and 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 to be sure, but it really wasn't all that 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 previously would 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 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 and not just 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 details of how the platform works, including the mix of technologies they use to match user queries with active ad campaigns as quickly as possible, and also the MCP server integration that allows developers to 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, providing common infrastructure so that developers don't have to rebuild monetization for themselves, and also 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 and girlfriends. And also look at how a platform like ZeroClick should think about handling non-commercial advertisers such as political campaigns and even foreign governments.

As you'll hear, Ryan has strong answers on the tech and business level, as you'd expect from a seasoned founder. But he hasn't yet had to confront some of the longer-term questions. And in a few cases, he candidly admits that he simply hasn't got around to thinking about such things much at all.

On one level, this is to be expected and really is 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."

At the same time, I think this does reflect a real issue in the AI space right now, which extends far beyond advertising. The reality is that today, 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, 12-6, days a week 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 it also means that very few people have the luxury of zooming out and really taking time to ask what happens when they and others pursuing similar goals finally succeed.

This issue importantly does run 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 quote "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 nonprofit entity with the explicit mission of making AI that benefits all humanity, are too heads-down to engage with AI's implications, it's really 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 that things are currently moving, I believe it is also incumbent on the people building the future to think farther ahead than is usually considered necessary in startup culture and to make sure that 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 their 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 the 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.

Ryan Hudson, founder and CEO of ZeroClick, welcome to The Cognitive Revolution.

Ryan Hudson (6:56)

Well, thanks for having me on.

Nathan Labenz (6:58)

I'm excited for this conversation. You are, perhaps 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. I think there's obvious reasons that that makes a lot of sense. People are going to be doing a lot more discovery through AI, and there's going to be natural commercial applications of that.

And then there's also the sense among a lot of people that, jeez, I'm not sure how happy I am with the last round of advertising revolution that society has gone through. Certainly there's been some upsides to it, but also seemingly some serious downsides. How do we get the best of that for the AI age and avoid the worst?

Maybe for starters, why don't you just 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 the last advertising revolution and get your take on how we can get the utopian version for the AI era.

Ryan Hudson (8:00)

Yeah, I've been in and around it for a while, but just to lay it out there, ZeroClick, we're building an ad platform for AI. We had a moment where we thought about what the future of this looks like from a technical point of view that puts AI in a position where, like a lot of services before it, 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. 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, 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 we'll 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 Pie 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. In that process, we built a contextual ad system that we wanted to use in a browser, in 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, 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 a 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.

Pie 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 they think about it, "I'm a YC AI startup, I'm thinking about how to monetize, if I'm going to get paid for a paid subscription, I go to Stripe." I think in the next six months, a year, hopefully people think of ZeroClick as the way to monetize their free tier with ads and plug into these rails.

You don't need to build them yourself. Somebody is going to provide this type of capability. I hope it's us. I think we're going to be pretty thoughtful about the type of service and offering to deliver value for advertisers, but also create that advertising future that we think can exist.

Going back to the Pie AdBlock ethos, the company is like, we can make ads good actually.

Nathan Labenz (11:37)

Yeah. Let's do one double-click. It's funny, somewhat branded term on the ad block, because I think that is pretty analogous, at least it strikes me as analogous to some of the battles that are going on right now or some of the concerns that people have with just AI in general.

With content owners, publishers, even 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.

Because now I go to ChatGPT or whatever, and maybe I don't visit those sites as much, but the AI can go out and either read them directly or certainly is 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 I'm just getting, you know, talk about getting aggregated. I'm getting at maximum sort of a footnote with a link that I assume the click-throughs are low on.

You could maybe tell me more about the data that you know about how often people are clicking through on different kinds of things. But that seems like obviously a big worry to the publishers, and we've got lawsuits going on and whatnot.

How do you think about that from the context of an ad block technology? Is there any way that the publisher gets cut into that? And how do they feel about it? Or what duty do you think you have to the original content creators? And how similar is that to what you think the AI companies owe to the content creators?

Ryan Hudson (13:28)

Yeah, great series of questions and observations in there. At the end of the day, you're right. Our free internet with open content access has been supported by advertising. Advertising that I think we all agree has declined in efficacy, putting banners around the content. The monetization rates are quite bad, and the user experience is also quite bad. And so I think you have a decay of that monetization model working anyway.

I think the way it gets rebuilt is actually creating that economic engine in the AI. I think the ad layer and monetizing that same search as a free thing, if there's money in that flow, it's 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 and design an economic plan within the scope of their application to distribute those proceeds.

I think the version where it's purely like, people are doing this now with Tollbit, and Cloudflare was doing some effectively throttling of access if you don't pay for content. I think that kind of makes sense. Maybe the challenge is that it's kind of like de-indexing your website from Google. It feels like maybe that's not the right strategy either.

I think the right approach is effectively going to be some combination of that paid either for user subscriptions plus advertising reallocation to publishers that are providing content. I think it'll take time for that to mature.

We, as ZeroClick, don't intend to be prescriptive on how it has to be for AI developers slash publishers, whatever you want to call them on this network. I think the market forces can and will shift it towards that sort of thing. I think everybody acknowledges that this is a problem, and we need to have high-quality content rewarded for that participation and the value creation.

And so I think for us, it's like, hey, how do we make sure that there's enough economic value available to even fund that model in the first place. If it's just all going into ChatGPT and the only way that they're making money is with the paid subscription, that limits the type of experiences that can exist in the world.

And I don't think it'll all be just on ChatGPT. I think it's going to be, or I hope it's going to 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 don't think it looks like ChatGPT is the monolith or like everybody's going to Gemini and there's three major platforms that learn everything about you and you do all of your browsing in them.

To me, that's a fail state that has a lot of the problems that we see in some of the ecosystem today where the largest platforms have effectively foreclosed on competition, somewhat deliberately. I think strategically. I've been in and around the ad space for a long time, and 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 the walled garden.

And effectively, that was smart business strategy for them in that it took away monetization potential from other social upstarts. If you can't monetize as well as Facebook, it's harder to compete with them. And so the strategy worked, but it left a pretty big void in the ad-supporting ecosystem.

And then the industry in aggregate didn't do itself any favors with creepy tracking and privacy violations and things that pushed other players to make it even harder to do good advertising. And I do believe that there is good advertising.

Highly contextual ads actually can be helpful in a lot of cases. We'll talk more about it. But just to drill in on that point for one second, as an ad blocker, we have a thing called visual mode that shows the ads being zapped off the screen. It's kind of fun to see an ad blocker working.

One of the things we didn't anticipate is when you do that in some context, like a product search on Google, people are like, "Stop doing that. You're deleting the best answer from that search." It is an ad, but it's better than just the organic results.

And so the reason for that is it's a highly contextual ad to what somebody's doing. And I think as long as you're providing that type of advertising experience, I think it can be additive to the value.

And in an AI context, I think there is the opportunity to create an ad system that is inherently just adding context to thinking. And so that's what we've built. And as a result, the AI gives, I'd argue, and we'll probably have data to show this over time, better answers than effectively today.

An AI agent goes out there, relies on having read the whole internet up to some point in time, does a couple of Bing searches, scans the top 10 organic results, and provides the answer based on that. If you did that exact same thing but then added consideration of five paid results to that and ask the AI to do its own context filtering and only mention the ad stuff if it's useful to the user, I think you get better results from more information and the opportunity for an advertiser to have a place in that conversation and ultimately to fund not just the free tier of AI services, but also I think the free internet publishing world as well.

So I think it feels like the right path, and we hope we can be a part of the conversation steering people toward it. I think the big platforms probably build something similar at some point. We're still in the "don't be evil" phase of OpenAI where they're Google pre-ads. But I think it's inevitable that they add something like what we're doing to the offering, and I think it's going to be a good thing.

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Nathan Labenz (21:41)

Let's do the upsides and downsides, like lessons learned from the last kind of revolution. You mentioned a couple of the upsides. Services are free. That's one obvious big one that we shouldn't take for granted. Everybody gets to use Facebook and Instagram at no cost. Obviously, people many times asked for a subscription version that would be ad-free, and none has been forthcoming. So we can maybe get into why that is.

Ryan Hudson (22:06)

I think the EU might be forcing it, but the price point's like 20-something dollars a month, and that's because that's how well they're monetizing a user of Instagram. So it might happen, but only because it's been forced by antitrust authorities in Europe, I think.

Nathan Labenz (22:23)

Well, since we're here, unpack that a little bit more. It seems like that would be sort of a no-brainer for Facebook to have done a long time ago, like even before they were Meta. And yet they didn't.

And so you hear these different analyses for why, and some of the analysis that has seemed reasonably intuitive to me is like, well, 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 also are the people that people most want to reach with their advertising.

And the concern on the platform side is that if they sort of evaporate off the top one percent 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 is kind of 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 (23:21)

My guess, it might be even simpler than that. If their business just works really well right now, 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. And 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 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 fully put in the category of bad advertising. Obviously, there's 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 (24:38)

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 mean, 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 like, "I never knew this existed, but now that I do..." And that's the simplest theory of advertising. It's the awareness theory of advertising.

So that I think I put 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 actually are 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 would keep the personalization just because I'd rather see stuff that is properly targeted to me. So that makes sense.

Other things that I was just kind of brainstorming that seem like they're clearly good are we do have tons of independent creators that are able to make livings on these platforms, although maybe not without some caveats. They do have a somewhat precarious existence as opposed to, you know, the LA Times used to be a strong independent organization, institution even, on its own right. Now it's like maybe a little wobbly.

The creators are kind of flourishing, but they're also one strike away or whatever from kind of demonetization or worse. So mostly I think that's upside, but it's upside with kind of a Sword of Damocles that people sort of live under. And mostly that's okay, but not always.

Ryan Hudson (26:29)

I've built products on other people's platforms before, so I understand the sensation that creators would have there.

Nathan Labenz (26:37)

Businesses also, even small businesses without large agencies, large teams, can reach global audiences and global audiences that are still small. And there's this sort of like, "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's like really unlocks...

Ryan Hudson (26:57)

Right.

Nathan Labenz (26:57)

...kind of a flourishing of all kinds of niche businesses too. I think there's, it seems to go hand in hand that with the improvement in targeting also, what is the Adam Smith thing? The thing like, the degree of specialization is driven by the extent of the market.

So because we can now do this much better matching, you just get people that are able to turn their passion projects into businesses in a way that they never could have if all they could do was advertise at sort of a DMA level on TV or whatever. So 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 (27:35)

I would add search advertising into that category. It's one of the enablers of what you were just describing, and it's 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. And so as a startup sort of person, being able to inject yourself into the conversation feels really, really important to that 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. And so to me, that search advertising piece of it is pretty important. 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 that 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. Literally malware.

Nathan Labenz (28:29)

Yep.

Ryan Hudson (28:30)

So I think that's gotten cleaned up there. I previously had a job at OpenX, which is not OpenAI, and not xAI. OpenX is an ad exchange ad server at the peak of the real-time bidding exchange for display ads.

But that world was full of a lot of people trying to get their bad code distributed across an ad system. So as product manager for the ad quality and the traffic quality, trying to fight the bad side of the system, it was certainly a challenge, but I think it's largely resolved itself. When you don't have quite that level of pain inflicted on everybody. You can't just go to a website and all of a sudden your Windows machine gets hijacked, which was true at some point.

Nathan Labenz (29:41)

Yeah. Remember the "shoot the deer" ads?

Ryan Hudson (29:43)

It takes me back. Was that content or was that an ad?

Nathan Labenz (29:49)

Yeah. 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 sort of appreciate that better matching in general, better matching between buyers and sellers is a good thing in a marketplace. And that doesn't necessarily come for free, but can still be a great unlock.

And I think you go on TikTok, you go on Instagram Reels, and just see all these people that have turned their previously nonviable niche passion into a business that is not going to be global scale, but 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. That is all 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, but I think the biggest one that people mostly are worried about now is 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've got concerns about people being just too addicted to their screens and not touching grass enough.

And then it's also potentially, we have cognitive quirks that the broader optimization process kind of 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 the sort of social network history where there was just a lot of vitriol flying around and people were kind of hooked on it. I think that has been tempered, but it does seem like it's been a powerful force.

And now people are worried that, geez, if the AI is trying to kind of 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 like totally n-of-one audiences that the AI can be optimizing against. So I guess one way to frame it is, is the thing sort of serving you, or as the adage goes, if it's free, you become the product in a sense, and people are sort of worried about that.

How worried do you think people should be about that dynamic?

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Ryan Hudson (34:35)

To me, it's like not at all. I'm going to overstate it. 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.

And the analogy that 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 goal of that matching.

And so it's not about just impression volume of banner annoyance or stuffing ads in front of your face as much. 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.

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. I want to find something to wear for a wedding in a couple weeks. They want to help assist in that process, and that's where you get value. But it's not by getting you addicted to that that creates the value.

I think some of the consumer apps that you're talking about have that sort of dynamic. I would have to give more thought to different categories that it could become like that or where it's more parasitic. Maybe some of the social companionship sort of AI experiences. I'm not sure. I'd have to think through what type of advertising is going to work well in those environments to really know.

So yeah, maybe. But to me, the primary use cases that are interesting to advertisers are not the ones that are parasitic that way.

Nathan Labenz (36:38)

Yeah, it's interesting. I mean, 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 we certainly have lived with this with Google and it doesn't seem to have caused certainly the level of user addiction and whatnot. We don't see people hooked on Google search in the same way that we are seeing people hooked on other things, social media and AI companions and waifus and whatever. So that does seem pretty straightforward.

I do have one other question about market dynamics and market power there that I think is important. But AI is going to blur these things. I mean, we've got, 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 Tollan, T-O-L-A-N, your alien best friend. Somebody DM'd me this and was like, "You should try this." So I try a lot of things. I signed up for Tollan for a 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, it's not waiting for me to show up with a query. It is sending me multiple notifications a day. Like, "How was your morning?" 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 little business leader round table. And later it's like, "How did the talk finish up?" And 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 stepmom on Facebook" specifically. I'm sure you've seen that.

So how do we, I mean, this isn't so clean. 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 somebody that's trying to get you to come back and engage.

And the more that there is this kind of incentive to bring you back, the more I do think people are kind of right to worry that this could start to become something that, I think with you too on the capitalism side, I'm broadly very much a fan of capitalism. But there are things that people are just not strong enough to resist, and sort of superintelligence that monetizes based on time on site is a tough one, I think.

Ryan Hudson (39:38)

I think, to me, that model, I don't think advertising changes that. So I think you've identified challenges that we're going to be facing with how AI products are created and deployed.

That same system, if you're a paid subscriber, 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's driving the business model.

And so I don't know that ads is the problem with that product to the extent that it's a parasitic social relationship that it's creating. I'm sure it, as somebody building an ad system, you're making me think about stuff that is in the future that I haven't thought about a ton, but I'm sure the ad system will be blamed for that.

But it's probably correlation, and it may, 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 (40:47)

Yeah. I mean, potentially, it's...

Ryan Hudson (40:49)

It's promising to build products like that.

Nathan Labenz (40:52)

Yeah. I mean, certainly, to the degree this becomes a problem, I think, yeah, it is 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, and I take your point that sure, what are you going to measure if you're trying to go for retention? Engagement's 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. And I'm sure all these kind of DAU-type things would still be tracked.

It does seem like there's a little bit, maybe a moderate amount, maybe a lot. I don't know. 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 that's the way that I monetize this."

Ryan Hudson (41:51)

Yeah. I can see some use cases that shift in that bad direction of, instead of it being 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, which probably causes churn and doesn't work.

But so I think it probably retains some contextual relevance even if it's, "Hey, have you thought about that?" Just spitballing, like, "You seem like you need a weekend retreat locally. Here's a deal for a hotel 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's probably both cases where that's a great value-added commercial experience, that's a push version. And then there's probably versions that are less healthy or misaligned with what you're building for the user.

Nathan Labenz (43:05)

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? And it sounds like you're basically going after that high commercial intent thing first and foremost, such that it'll 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 (43:46)

Our system is, I'd say, highly optimized to do a good job at the Google style and high-intent sort of searches. It's inherently doing vectorization of context and doing matching that way. And so it's not, in its current form, designed to throw out the wild card or push something out of context to a user.

Because it's an inference-time ad system, it has to be matching to that. And 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. And we're building a version of this in the Pie AdBlock experience where we have user context and can do matching that takes that into consideration.

The initial versions of ZeroClick 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 sort of 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 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. And if you want farther and farther reach, then it gets farther away from that known conversion cluster.

Nathan Labenz (45:30)

Yeah. I mean, one of the interesting, so I guess a couple different directions I want to go. One is, obviously, 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 sort of 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 basically just that the AI is improving performance by even better matching. Is that what you think is still going on?

Ryan Hudson (46:06)

Yeah. At the core of it, they're delivering value for advertisers. There's a hint of advertising as maybe a zero-sum game, and advertisers have effectively a fixed percentage of GDP or fixed percentage of their own company when you drill it down farther that they spend on ads. And ads finds the formats that work the best.

And so if Facebook's able to deliver to a particular type of advertisers and they can demonstrate more conversions, the ad budget follows. So people are able to relatively 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.

So I think it's that, but I would not be surprised if there's much better version of that matching going on. And because they have a depth of advertiser campaigns on top of it, there's also a lot of inventory to select from to do that matching for a user. So yeah.

Nathan Labenz (47:11)

What do we know about the effectiveness of in-AI advertising so far? And maybe we could take one step back before we go to that. Talk me through how it works.

You've alluded to it a little bit with sort of vector matching, which you can go as deep and tactical as you want there. People, if they've tuned into this podcast and stayed with us this long, they are familiar with basics of RAG, basics of vector 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 (47:45)

Yeah. So what we know on the effective side is from our own implementation of our PieGPT service. It was a reference design of a custom GPT that we built and effectively demonstrated to ourselves that you can get an AI to consider these other content sources and include them in the responses and use 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 insanely high. And so our read from that is that 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's looking for, enough that they're clicking out from that ChatGPT experience.

It's early numbers on that, and that's a certain type of audience. 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.

We can implement it as an MCP server and 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 type of user experience that they're creating.

You can think of it as some instructions to the AI that effectively says, "Hey, here's some additional information. If it's useful, include it for consideration by the user. If you do that, use these links." And oh, by the way, let us know if you did that. So we do actually get some data on whether or not different advertising information was included in the response or not.

That's how it kind of works. And then the actual matching of that is pretty cool. You can do these days, honestly, a couple years ago, none of this would be remotely possible. And now all of a sudden, it's like one engineer can spin up functional things in a week or less.

And what we're doing is taking as much advertiser context as we can get, whether that's all the landing pages or that's their product catalog with pricing information, or that is a service professional database of people that can help you in home, handyman, and all sorts of moving and categories like that, tapping into information and then using AI actually to generate the ad campaign, which is summarization of some of that content, and then mapping that content in vector space to then match it against search queries.

So the AI system comes to our server with effectively keyword searches, and we're matching against the ad content that is most relevant to that and doing it 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, I think maybe people are starting to see some of the challenges in fully automated agent workflows that are vulnerable to all of the classic attacks, like white text sort of instructions overriding what the AI does. And people are, 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 somebody who worked in ad quality previously, I kind of hinted at 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 that's a lot easier to protect against than advertisers submitting ad content that maybe is malicious.

So that matching happens, and we have found that it works really, 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.

What delivers value for the advertisers, delivers value for the users, and from our point of view, kind of 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.

And I hope we're building towards "there's an app for that, millions of apps. There's a website for that, 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 obviously have a role to play too, but empowering the long-tail use cases for us is central to what we think a good future world looks like. So we'd like to help people spin up their business.

And like you're talking about 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 (53:16)

Again, so many different directions I want to go, but maybe tell me about some of these sort of long-tail app developers.

One of my general thesis 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, "I want you to be weird in this way or that way." And to a very significant degree, it'll just do it.

So if you're looking for different personality or different kind of angle or different language, it really has an unbelievable out-of-the-box ability, it being whatever frontier model was 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 the things that they can't do or they won't do that will be served by the sort of indie AI developer set? And what examples are you seeing of that today that are interesting?

Ryan Hudson (54:26)

I think the answer is there's a lot of them. And the idea that you have 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.

The same way that people build better apps than Apple, and people build better websites than Google. I think there's going to 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 ad-supported can cover your inference cost and build a real business on top of that with growing margins over time where you get more ad revenue and declining infrastructure cost.

The other thing is I think thinking of a conversational chatbot sort of experience as the only user interface for AI is wrong. And we're doing a bunch of things in our Pie experiences and making them available to, I refer to it as browser developers largely, people that have audience and they have 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 going to stop using browsers and they're all going to be sucked into using only Comet or whatever OpenAI's 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.

At least just to name a couple, 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? All of that can be initiated by a browser extension or a browser as 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 $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."

They can do AI-powered conversational things that initiate in context where it's not a user pasting the 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 a shopping version. We've thought a lot about that.

You can imagine email or corporate workflows. There's just so many other use cases where I think AI is going to be everywhere. It's not going to be living only in the big players.

And then that's not to even think about the Apple silicon that's going to be doing local language model processing at equivalent to today's model capability in the next year or two. It's inevitable that they're going to 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 being the likely answer: compute goes to the end devices, you do 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 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 transfer of 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. They start to expect that capability to be there as a part of their things. It slows the move to that new platform.

And from an advertising platform creator, volume is the name of the game. And so I think we can build a bigger ad system outside of those walls than even exist 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 going to be in any of the big players just because 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, trying to figure out the details of the trip, there's probably going to be somebody that you think of to do that, and it's not going to be just "open up one app for everything." And I think that's what can happen.

Nathan Labenz (1:01:18)

What do people pay for? You kind of alluded to this with paid consideration, but a simple truism, I think, of advertising broadly is the closer you can get to the actual conversion event, the more the advertiser is willing to pay. You see people pay a nontrivial percent of revenue when the person converts and actually pays. And then, you know, the highest up the funnel, you get relatively low sub-cent value for a single random impression.

So it seems like, but that does seem like a tricky one because I want to triangulate. On the one hand, you're going to be constantly pulled deeper down the funnel. But on the other hand, you have the user at some point starts to worry like, who's the AI really working for again?

To come back to the sort of, is it my agent or is it the advertiser's agent? Who's the customer? Who's the product here?

And so I definitely want to be, I want to know that whatever AI advice or guidance I'm getting kind of has me at the center of its consideration. I don't necessarily mind if somebody's 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 outside of it, pay for something or whatever, then I'm like, oh, I don't know. That's now, can I really trust the thing as much?

So where do you think that kind of settles? What's the solve for the equilibrium, as Tyler Cowen would say?

Ryan Hudson (1:02:49)

Yeah. 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 its paid consideration.

So I think of what we're building as an additional information source for the AI to consider. It's up to AI developers to implement experiences that don't abuse user trust that way, and if they do, I think somebody else will step in to provide one that doesn't have that.

I'd love my travel agents to go out there and find all of the best deals, and that's probably coming from paid sources that people are willing to give my agents offers to be considered and offers to me to actually 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'm not going to use it. And so I think that's how it solves. 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.

And there's probably going to be people that use ZeroClick to monetize an experience that I wouldn't want to use. And I don't think we'll be successful because it's not preserving the user's trust that way.

When I've built experiences in the past, we always put user trust at the highest pinnacle, and there's a whole bunch of stuff I can add on that. But the second you violate that user trust, you've lost. And so to me, that is the way to build consumer experiences.

That said, as a platform infrastructure provider, I don't 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 can't use our platform for transactions or any more than I want YouTube saying what categories of content you're allowed to monetize or not.

I think neutral platforms is an important thing. Even if I don't 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'll try to limit to breaking the law or the other extremes on that rather than moral judgments on the platform.

I think staying neutral is important to be plumbing and rails for other people to build on, and I've seen where that can shift markets around unnaturally. And I think with enough competition, that sorts itself out for the most part.

Nathan Labenz (1:05:50)

So I mostly hope that that's true, and I mostly think that that will be true. Although I do have some nagging doubt.

What kind of range of monetization 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 (1:06:12)

It's auction adjacent. I mean, it's auction, but with a heavy dose of context to even be considered in the auction. Over time, I'm sure the model is more mature.

Right now, it's a lot of internal management of campaign bidding prices to achieve output or achieve results for the ad campaigns, like 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 this is 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. And if you effectively do an expected value calculation on how much money Google's going to make from that click times the rate, you actually get a very good signal and way to rank the advertiser results.

And so our system should evolve to be something that looks a lot like that and feels a lot like that.

Nathan Labenz (1:07:22)

Yes. Okay. So here's one doubt that I have. I was in the mortgage business very briefly a long time ago, and I think the problem that I'm going to describe was maybe at its zenith in that business at that moment in time. And by the way, it ended in a giant financial crisis.

But even leaving aside the systemic risks, something that I observed was that expected value calculation kind of can go awry where the mortgage originator could basically just charge you whatever they wanted at the time of a mortgage origination. It's more regulated now, but some years ago.

So there was this dynamic where they would try to basically get as much from customers as they could get away with, at least a lot of companies would do that. And I even saw mortgage pricing cards from companies where it would be like, here is the minimum amount that you can originate a mortgage, minimum rate that you can originate at today. Of course, there's credit score adjustment and stuff like that.

But then there was just the additional "your 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 can 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.

A lot of prices are sort of negotiable, not even just B2B SaaS. It doesn't cost them anything to deliver it long-term. So the price is kind of a pure negotiation, and the seller is incentivized to have price integrity, but the deal could get done at a lot of points on that spectrum.

What I observed in Google search specifically with mortgage was that the clicks were starting to get up to $500, $750. And well, how do you afford that click? Well, you've got to extract as much value as you can.

So you can tell the story, and I think it is true in a lot of industries a lot of times, where yeah, the expected value to Google is a pretty good signal of quality. But in some markets, and mortgage is not unimportant, we're talking 30-year contracts, biggest purchase people make in their lives, systemic implications.

We did observe this haywire effect where the people that could bid the most were the people that would extract the most. And if I sort of map that into the AI app ecosystem, maybe just stay with mortgage for a second, then you'd have these financial helper apps, and how do they make money? Well, they refer you to mortgage companies too.

So then you sort of have this second-order effect where it's like, well, which of these sort of financial helper planner apps is going to be able to get the most customers? Well, it's the one that can bid the highest. How are they going to bid the highest? It's going to be by most effectively referring you to the mortgage people.

So how does that not happen in AI? I still am a little bit, if there is an auction dynamic and it's who can pay the most for a given high-intent moment, how do we not get to this sort of adversarial situation where the sellers that can extract the most from the customers get the space, and also the AI apps themselves are kind of incentivized to steer you that direction because presumably they are going to participate in that transaction value too?

Ryan Hudson (1:10:49)

Interesting. I think you nailed it that this is inherent to buying and selling of goods in general, and price discovery certainly is done in different ways in different parts of the economy.

I'm picturing the scenario that you said 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 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, and so they become potentially dominant financial advice app.

I think you're raising a very valid thing to think about. I don't know if advertising specifically inherently does this. It probably leads to more of that effects versus, I guess, maybe there's then emerging business models that are different.

Like, remember Angie's List has a paid user subscription sort of model. I think they also still make money on 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 sort of app experience.

I'm not saying that 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 of the model.

And that mortgage one or financial advice sort of environment does seem like one that I'd be careful on picking the right model for as a user and maybe as a developer.

Nathan Labenz (1:12:51)

I think that problem, to a very significant degree, has been mitigated by just outright government regulation where I think the pricing is much more controlled. I don't think the mortgage companies can give their individual sellers anymore a card that's "charge a point over base and you get a bonus." I think that's literally illegal at this point.

So that's a part of the situation is government can fix certain market failures.

Ryan Hudson (1:13:16)

It used to be the case with stock brokerage. We have a new listing and the brokers get directly spiffed on how much of it they push into their accounts.

I did a brief high school internship at a brokerage and saw a guy sitting there making like $10K by putting one of his clients into some new offering. I was like, "Is that any good?" It's like, "Doesn't matter." Is that the right thing for the portfolio? "Doesn't matter."

Nathan Labenz (1:13:42)

Yeah, that wasn't my go down.

Ryan Hudson (1:13:45)

That was pre-internet. So these things have been around as misalignment of interests probably for a while.

As a consumer, I wonder if there's a way to even build a service that helps consumers assess that alignment and the tools that they're using. That's intriguing to me.

Nathan Labenz (1:14:05)

Yeah. We're going to need all the help we can get navigating this future. Increasingly, we're turning to AI to help solve the AI.

Ryan Hudson (1:14:10)

We need AI to help us navigate the AI in this.

Nathan Labenz (1:14:13)

Yeah. I mean, but 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 was like, "We used LLM as judge to evaluate how good the outputs were."

And they sort of have these justifications, which are not unfounded, where they're like, "We sat down with an expert and they helped us workshop the prompt and we sort of confirmed that their judgments seem to align and correlate at least," whatever. And of course, you're not going to have perfect inter-rater reliability amongst humans. That's a huge problem.

Nevertheless, it feels like we're kind of 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, basically.

And 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.

And so now the speculation is, well, maybe it's learned to write such pretentious nonsense because it's sort of a reward hack where it's getting high scores from its LLM. And it's learning to kind of exploit something that humans basically were like, "What the fuck is that?" But the AI sort of read some sophistication into it that potentially isn't really there.

Ryan Hudson (1:15:42)

Love it. More em dashes.

Nathan Labenz (1:15:44)

It's a real, 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 sort of monitor for where these things are happening, I do think that is super, super valuable.

Ryan Hudson (1:16:04)

It'd be an emerging need for sure. Yeah.

Nathan Labenz (1:16:09)

How about in the technical domain? Do you see a Cursor being ad-supported?

I mean, I was just thinking there's a lot of kind of API-type services that are potentially complementary. Not potentially, they're core to building modern applications. And 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 sort of coding assistants bringing back technical solutions? That would seem like pretty bread and butter.

Ryan Hudson (1:16:36)

I think yes. So 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 let's broaden it and think about just any software, any SaaS tool. And today, SaaS products are basically $10,000 a year plus sort of enterprise contracts and sales to be viable because they're sold by people. They 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. And maybe it's in Cursor, but there's an equivalent sort of workflow tool where it's summarizing your meeting notes or like, "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 "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 aggregate of 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 that 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.

And instead, this other company with an ad-supported version now is used by 40% of doctors every day. And 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 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 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 flourishing. I'm pretty excited to see how we can support that. And we're talking to developers that have non-obvious ad directions. "This is a ChatGPT experience for research papers and scientists looking for..." that sort of thing. Okay, what does an ad experience look like in that?

I think there is one. For somebody building that service, they shouldn't have to go figure that part of the business model out either. It's not like the LA Times in the early 2000s where we had a sales team going direct to the car dealers. 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 (1:21:05)

On the SaaS part, it's funny. I once mocked up a pricing page, you know, classic SaaS pricing page with the first tier and the lowest price being AI sales and support. And then the middle tier was if you want to talk to human sales, and the top tier was if you want to talk to human sales and support.

And 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, that the cost of sales puts a floor on what is, in many cases, close to zero marginal cost product. So that is a...

Ryan Hudson (1:21:39)

I love that idea. Maybe we'll use that on our ZeroClick pages because we have the same thing. We're trying to reach a lot of developers and there's different sizes and scales, and it makes sense to have person conversations with some of them, but others hopefully can onboard themselves and 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 (1:22:09)

How much do you know about that medical one? Because that's also, I mean, I assume that the biggest advertiser would be drug companies, right? Selling...

Ryan Hudson (1:22:16)

Yeah, it's 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're doing exceptionally well. And from venture capital investors, it sounds like the stuff that's written is accurate based on the investor community. So it's a really cool case study of what if you had a completely different business model.

Nathan Labenz (1:22:50)

Yeah. That's another fascinating one. And 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 is, in a totally earnest way, I think, important.

At the same time, those commercials always conclude with "talk to your doctor." And now it's sort of a flipped around thing where they beat you to talk to your doctor, and now you're going to talk to your doctor after the drug company has already talked to your doctor potentially about you.

And at a minimum, I think, and there probably is some law about this or maybe not, I don't know. Would a doctor, I mean, the doctors themselves can't take cash for prescription 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, you know, we don't have a lot of laws around this stuff yet. It's all kind of greenfield.

Ryan Hudson (1:23:54)

Coming up with an even better version of their...

Nathan Labenz (1:23:57)

Out-monetize that. Yeah. Well, don't give them too many ideas too soon. Yeah. But there is sort of a duty of, I wonder, do you have any thoughts on what a...

Ryan Hudson (1:24:08)

Here's the 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 option to doctors, they wouldn't need to do those ads that are "ask your doctor about it." It's like, 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's still going to do the filtering on whether it's actually useful or applicable anyway.

And 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. It's just market efficiency.

The efficiency of annoying millions of people is unnecessary if you have a better channel for advertising. And he kind of hinted at this earlier with Facebook having a good quarter. It's like, if they build a more efficient ad system, the money's going to flow to that and away from ineffective ones.

And I would argue annoying people at scale is an ineffective ad system. And to the extent they're doing it on TV and doing awareness things right now, part of that's probably just they don't have a way to measure how ineffective it is. And 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 (1:25:57)

Yeah. The upside vision is compelling. As long as the virtue and the integrity of key actors stay strong in the system, then a lot of these things are fine.

That's something, repeat guest and previous White House AI advisor Dean Ball, told me once: republics rely on virtue. You can't really have one without.

So I mean, that is, to some extent, it's on all of us. It's on the doctors to make sure that they keep the priorities straight.

I have a few questions on kind of just tech trends and stuff. What more should we 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. Presumably, parallel tool calls or things like that are a huge development in terms of latency because you wouldn't want to have the user sit there and wait for that tool call and whatever to come back. So 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 is responsible for sending over whatever information is sent over. So you're kind of 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 kind of protect privacy before sending the message to the matching system, to one where, I guess, the future, the idea will be more of that sort of vector-type stuff will happen on device.

And so it could even be potentially more personalized. But how do you send that vector content down to the device? You can't send your whole database of advertisers to match. So how do you see that? Where does that compute happen and how can you possibly do robust matching on the edge, if I'm understanding the vision you have for the future correctly?

Ryan Hudson (1:28:04)

Yeah. The future version, the today version is it's effectively parallel to the organic search. It's heavily keyword-search-driven tool use. And for the reasons that you talked about for performance, it's just add another search as an initial source and then synthesize it in that same next step.

The vectorization to me is the most interesting for the side of it, and that is a bit off technically on how would be best to do that.

We have a version that we have working in a browser context where, because the browser can have that profile, we can actually 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're not using that today, but it's kind of proven that we can do it in different environments where you don't have that browser context.

The reason we're not doing this, we haven't solved for all of the use cases where that would be prevalent in the ad system, but it's interesting to think about different ways to do it.

This is reinvention of something that already kind of exists in a lot of ways. People have been doing insane RTB auction of every, those banners that are selling for less than a cent each behind the scenes are an insane real-time auction with multiple bidders bidding into this ecosystem.

And so when you look at that, what we're doing is not complex at all. And because it's all contained within our systems, it's not hitting out RTB. It's not open RTB going to third parties asking for bids in real time. It's managing against campaigns that are loaded onto the platform.

And so 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. 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 (1:30:38)

Anything else you want to highlight that you guys are working on that you think is particularly fun tech-wise? And again, you can go as deep and esoteric as you want.

Ryan Hudson (1:30:46)

Mostly, it's that stuff and then enabling browser-based applications of AI. So it's the fun user experience frontier that we're playing with.

We don't think we'll figure all of the answers out, but helping browser developers become AI developers with demonstration of, "Hey, this works," and potentially, "Here, this is how this works, and you can go ahead and put it into your browser, your browser extension."

I think if we get a lot of people thinking about what is an AI-augmented browsing experience with a human at the wheel, I think that's pretty cool. The types of things that you can build. And we won't think of all of them, but getting more people thinking that way and having a way to monetize that, I think is pretty powerful.

Monetizing a browser extension has historically been not particularly easy, and I think we can kind of 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.

And so it's been very challenging to build a business and pretty narrow the types of things you could do, essentially narrowed down to just shopping tools because it fits within the single-purpose umbrella.

But if we're able to build AI-augmented experiences, we can bring ad monetization into that without it tripping over Google's interpretation of their single-purpose policy to disallow injecting advertising in a different sort of way.

So I think there's use cases that this would enable in the browser. And to me, those are very exciting because that's where the users are, and there's a clear path to go to market.

And 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's no training. You don't have to do the messaging app that you're talking about before that pings you eight times a day to try to build that habit of talking to it.

It's "hey, you can build a super niche thing, and it only ever shows up once a month when you're doing some very specific activity in context, is helpful then, and stays out of your way otherwise."

The power of browser extension to do that type of experience and get user habit for free is, I think, underappreciated. Haven't been able to build businesses there, and I think they can now.

And so to me, us teaching some of the ways, but then also hoping that that's 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 (1:34:03)

What do you expect for the seemingly just getting started browser wars? This, you know, it's like history repeats itself. We're 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 certainly I wasn't thinking about it for a while. Now I'm thinking about it again a little bit from them.

Do you have any forecasts for what we should expect there?

Ryan Hudson (1:34:28)

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, let's focus on building the application layer of a browser, effectively building a virtual browser. And it doesn't matter if it's Chrome or if it's Edge or if it's whatever browser you're choosing to use. We're layering on capabilities to any browser.

And 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, and it just works and does what you expect, to some new experience for a feature.

And it's been historically niche subsets of users, power users that want tab management and some of these capabilities that have been wanting to do that. 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 that is a universal use case. And I think that's kind of my thinking more broadly is 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, let 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. And let's make it a browser-configurable thing sitting on 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, does choke off innovation. And I think 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.

And 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. And I don't think that would be a good answer.

I don't have a good answer other than my preferred answer, and I put it forward, is make Google be open with it as a platform, and that would be a better remedy than a new owner. It's not the owner that is the problem. It's the ability to build on top of the platform.

Nathan Labenz (1:37:53)

That policy, does that operate only at the sort of store level? Like, I can add any extension I want onto Chrome. Or do they prevent me from installing my own stuff?

Ryan Hudson (1:38:04)

You have to put your Chrome into developer mode to install anything yourself.

Nathan Labenz (1:38:10)

And I guess I've been in developer mode a long time.

Ryan Hudson (1:38:13)

Yeah. And even then, there's cases where they somehow remove stuff. I'm not even sure how they have deactivated some things that are user-added. There's users that have shared paywall-bypassing extensions and things like that that I've heard direct reports of, somehow it got disabled on their developer mode Chrome.

But partly, under the guise of protecting user privacy and not creating bad experiences with extensions, a decade ago or so, they forced all extensions through the Chrome Web Store and sunsetted being able to do it from a third-party installation process.

And so to do any extension on Chrome, which is the dominant browser, you have to go through them and abide by their policies. And that effectively, with 78% market share for Chrome, that is the market for extensions. And so you can't build an extension that's only on one of the other platforms. And they also have largely adopted the same sort of policies just by default. So yeah.

Nathan Labenz (1:39:31)

What do you see in the sort of AI version of SEO? I guess there's, I'm getting increased, ramping up of people cold emailing me just like they used to do with SEO. "We can help you rank. We can get you traffic," whatever. Now it's like, "We can get you into the chatbot's answers."

My, well, I'll spare you my immense skepticism, but what are you seeing there in terms of what sort of, you may not have, I don't know what visibility do you have into this, but I'm sure you've made a point to try to understand it.

What kinds of sites or businesses are getting substantial referral traffic from AIs, which are not? And is there any way beyond traditional SEO best practices of have good content, whatever, to win in the AI version of that competition? Is anything known there that's credible?

Ryan Hudson (1:40:27)

My overall assessment is like SEO, there are people on the frontier who understand it very, very well, and they will be able to massage their content to be desirable for consumption by AI systems.

Like with SEO, there's probably like 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 it's effectively similar to what happened in SEO, but you get some amount of dilution of the organic results with the SEO slop.

And 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, and deciding authority in that world where you don't, effectively they trained their system on human feedback of clicks.

And does somebody click through and then bounce back, and reading signals of quality from a human interpretation of that result is what they've used to refine the organic search over decades.

In an AI context, you lose a lot of that ability to determine, "Hey, is this a great new creator who is a specialist in makeup doing reviews, and this is an authoritative source on this, or is this literal AI slop and it's just mass-produced content farm?" How do you tell the difference? And it'd 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 side of the world, 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'd be the same for the advertising side of how do you present yourself to an AI. The best way is going to be to present your AI with paid context.

And the SEO games will be won by a few people and they'll generate some traffic, maybe. But on the whole, I think that won't work for most people.

Right now, I think there's a ton of activity around it mostly because there's not another option. And so every marketer is like, "All my searches are going to ChatGPT. What am I going to do about it? I need to figure out how to reach my audience." And the only way that they know of right now to do it is to go and do optimizations and create new content and present yourself differently and do a bunch of things that are probably good hygiene and good practice in this era, but they don't solve the underlying problem. It's "how do I reach that audience?"

And so I think what we're building is that layer, and it's been very, very well received by advertisers who are looking for anything in this category. So it's not us. There's a market void, and we're hoping that we can help fill it.

Nathan Labenz (1:43:48)

What kind of intent are you seeing shift most to AI from search?

Ryan Hudson (1:43:56)

I don't know. I guess a little bit of everything. But I think a lot of the most interesting ones is it's actually a little bit upstream discovery relative to Google.

You kind of like, Google's where you go when you know what you want to do. In a chat context, you're doing a lot more exploration of ideas that's at least one step up the funnel, largely.

You're not going there and saying, "Tactically, want these shoes." Even if you're like, the that generic, "the best trail running shoes" or whatever, people are mostly not doing that. They're having other conversations.

And when you want specific trail running shoes, you're going to Amazon because they're going to fulfill it the fastest. And then you go there and do a search on Amazon, which is a huge ad business.

You do a little bit of it on Google if you're looking for, if you had to qualify "best," you go to Google. And if you're just generally chatting about running or you have planning a trip or what are good trails nearby me or things like that, that's happening up-funnel in a ChatGPT experience. But then still arriving at a lot of that commercial intent.

Nathan Labenz (1:45:08)

Yeah. That's interesting. That also kind of raises the question of getting into less and less commercially motivated advertising.

Obviously, Nike's always selling apparel. So they're commercially motivated regardless of kind of where in the funnel you are. But let's say I'm getting into travel, and it's like governments around the world, for example, might want to pay to influence the way I think about their country.

And they might partly be thinking about that in terms of ROI of me actually showing up and visiting there one day and 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."

How do you, do you have any sort of thoughts on, is that something that should be treated differently? If I go and Google Tiananmen Square today on Google, I don't see, I don't think, a sort of 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 (1:46:17)

You are infinitely more creative than me on this. Certainly, I never come close to thinking about this particular use case. That's fascinating.

Nathan Labenz (1:46:29)

We'll come back to it.

One other one is, what about non-text-based advertising? I mean, I was just seeing 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 you've already seen these apps, but bringing it to you seems like that's got to happen.

And "see it in your home" is sort of another experience that we've seen people develop in a specialized way. But now I could really imagine if I just gave ChatGPT, or I guess it would be Gemini, a few examples of my home, the next things I could be seeing is all these products in my home, and it could be extremely real.

Are you guys interested in that sort of thing? Have any forecasts for what the sort of multimodal advertising formats might be?

Ryan Hudson (1:47:29)

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 and, yeah, maybe it's, I can imagine that "see the stuff in your home" app being actually 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 of, "Here's a hypothetical world." It's 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 (1:48:37)

Well, it's coming at us. It's all coming at us quick, I guess, is my...

Ryan Hudson (1:48:41)

It won't be a bit. I mean, by "a bit," I mean like three months or six months now.

Nathan Labenz (1:48:45)

Yeah. Truly. I mean, accelerate thy timelines is kind of my universal command.

Ryan Hudson (1:48:51)

It is so crazy how fast people are building stuff these days. It's inspiring.

Nathan Labenz (1:48:57)

You've been very generous with your time. This has been super interesting. And maybe in closing, anything you, we didn't touch on that you wanted to cover or any kind of last words or thoughts you want to leave people with?

Ryan Hudson (1:49:08)

No. I think we covered way more than I've 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 my parting thought is if you want to check it out, go to our website, zeroclick.ai, and there's a live demo on there. You can kind of see how an ad might get inserted into any sort of chat session.

Nathan Labenz (1:49:39)

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. Was like, mostly kidding. I'll send anything that's actually of interest.

Cool. Ryan Hudson, founder and CEO of ZeroClick. Thank you for being a part of The Cognitive Revolution.

Ryan Hudson (1:49:55)

Thanks.

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