Why ChatGPT will be the next big growth channel (and how to capitalize on it) | Brian Balfour

Introducing the Guest

Lenny Rachitsky: Everyone’s always complaining SEO’s dead, it can’t grow. Word of mouth is so hard.

Product Isn’t Enough, Distribution Is Key

Brian Balfour: All of the ingredients for new distribution platform are essentially happening. My prediction, the new distribution platform will be ChatGPT. There’s a bunch of signals that they’re about to launch that.

Lenny Rachitsky: This is a huge opportunity for companies to get on it.

Why Distribution Is Getting Harder

Brian Balfour: It ends up being a prisoner’s dilemma. Don’t trick yourself into thinking that you can’t play the game. The cycles seem to be getting shorter and shorter, so you actually have a smaller amount of time. If you don’t do it, your competitors are going to go to the new platform and your customer expectations change. There is no opting out of the game.

The Four-Step Cycle of New Platforms

Lenny Rachitsky: This is the opportunity to disrupt an incumbent.

Prediction: ChatGPT as a Distribution Platform

Brian Balfour: If you’re a late-stage company, you place multiple bets. For startups, it’s a totally different ballgame. You have to choose one and go all in.

Lenny Rachitsky: Think about companies like Zynga that grew on Facebook and then became massive companies.

Breaking Down the Four-Step Framework

Brian Balfour: Building a great product is one of those things that’s necessary, but not sufficient. And actually the separation is between those that build really great distribution.

Lenny Rachitsky: What would be the backup if not ChatGPT?

The Rise and Fall of Facebook

Brian Balfour: My hypothesis of who’s best-positioned would actually be…

Lenny Rachitsky: Today, my guest is Brian Balfour. Brian is the founder and CEO of Reforge, a company that I’ve been a long-time fan and advocate of. Historically, Reforge has focused primarily on teaching courses on product and growth, but more recently they’ve transitioned to building their own products, including a product called Reforge Insights and a bunch more really cool stuff coming very soon.

Prior to Reforge, Brian led growth at HubSpot, and over the course of his career, he has seen the rise and fall of every major distribution channel, including Facebook’s ad platform, Google Ads and SEO, and the Apple App Store. Based on what he’s seeing, he is predicting the emergence of a brand new and powerful distribution channel that will likely arise in the next six months, centered most likely around ChatGPT. It is really rare for a new growth channel to open up. It’s been a long time since the last one appeared, and the people who recognize this and hop on it early are the ones that reap the most rewards. So this is a huge deal.

In this conversation, Brian shares what he’s predicting, what he’s seeing, why this is a big deal, and what you should be doing about it right now. I highly recommend you listen to this full conversation and discuss the ramifications with your team. If you enjoy this podcast, don’t forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a bunch of incredible products for free for one year, including Lovable, Replit, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, and Mobbin. Check it out at lennysnewsletter.com and click Product Pass. With that, I bring you Brian Balfour.

The Two Sides of Competition

Brian Balfour: Yeah, thanks for having me. Excited for this one.

Lenny Rachitsky: I’m really excited to have you back. We’re just going to dive right in. Essentially, you’ve uncovered a really important trend or insight about how products are going to grow differently in the future, how growth is changing, and this is something that I think a lot of people need to hear, so I asked you to come on to share what you’re seeing. I also think this is just very timely. I think you said you’re going to say in the next six months things might significantly change, so I’m really excited to do this. We’re going to spend this whole conversation on this insight. To set us up, what is just the big idea? What’s the high-level idea here?

Google: A Long Harvesting Cycle

Brian Balfour: Just like you, I’ve spent my whole career just really passionate about startups, figuring out how to build products that win, that emerge in new markets, and one of the things that I have learned over time or one of the things you hear a lot from a lot of folks is, to win, you have to really build a great product. A lot of advice boils down to that. And one of the things that I feel like I’ve banged my head against the wall in a lot of ways in my career is actually telling people that building a great product is one of those things that’s necessary, but not sufficient, and actually the separation is between those that build really great distribution.

So this general partner, his name’s Alex Rampell, he’s at Andreessen Horowitz, actually wrote this blog post 10 years ago back in I think 2015. In the essence of the blog post, he basically says one thing, which is that startups is a game of trying to get distribution before the incumbent can copy. It’s this kind of concept of escape philosophy.

On that note, which I think os a very good summary of what you’re trying to do in a startup and distribution, is that we’re right now living in this environment where that game of startups getting distribution faster than the incumbent has gotten way harder in a lot of ways, and in some small cases has gotten a little bit easier. But if we think about this, the way that it’s gotten harder and some of the things that probably a lot of founders or folks working on the growth side that probably feel is that, one is that incumbents can copy faster these days. That window that you have to get that escape velocity has actually shrunk. It’s decreased.

The second thing is that a lot of the organic distribution that we’ve had, especially over the past few years, has really shrunk as well. Everybody’s talking about the decline of SEO and clicks declining, but you also see it in some other cases. A lot of these social platforms don’t really let you send as much traffic to sites. LinkedIn just changed their algorithm, which has really dropped organic distribution. Obviously the Twitter-to-X, transition that happened, right? TikTok’s almost always been like that.

And then the third way that it’s gotten harder is that AI’s really good at writing software and code generation, and so everybody’s feeling this infinite increase of competition, especially at the startup level. YC is pumping out six of the same thing every single cohort. That’s what it literally feels like.

So it’s gotten way harder. This game, this escape velocity game has gotten a lot harder. It’s gotten easier in some very exceptional cases like the Cursor or something where AI has been like the spark. I know you wrote the blog post about the race car engine, and I think you said there’s the spark plug in the engine. AI really created that, a new type of spark, a new type of interest of early adopters to fuel some new players in a short period of time. So it’s amazing to see something like Cursor overtake market share of something like GitHub Copilot in nine months or less. That’s how fast it happens. It’s kind of crazy.

But the main thing that people need to understand is, okay, well, if that’s the game I’m playing, how to get to escape velocity before the incumbent? What are all the ways to do that and to really figure that out? There’s multiple ways that this can happen, but one of the major ways, one of the major, major ways that we always see is that this can happen when new distribution platforms emerge, because when new distribution platforms emerge, startups are usually the fastest to take advantage of them. It’s slower for the incumbents to move. It gives startups this opportunity essentially to play this game.

Casey Winters wrote this blog post about two years ago, maybe 18 months ago, about the AI technology shift. His key point was the AI technology shift has been a technology shift that has not come with a distribution shift yet. If you look historically, we’ve had a bunch of technology shifts from the internet to the cloud to mobile to social, all of these different types of things. Some of them come with new distribution platforms, new ways to distribute products, and some of them don’t, but the most powerful ones, the most impactful ones are the ones that do come with these new distribution platforms. His second key point was that these two things don’t actually happen at once. Usually you get the technology shift, then you get the distribution shift a little bit later.

Now we’re a couple years from that post. We are a couple years into AI technology shift, and one of the things that I am seeing is all of the conditions, all of the ingredients for a new distribution platform to emerge are essentially happening. So I think we’re at an inflection point where we’re going to see this emerge really fast.

The key thing for everybody to know is that as new distribution platforms emerge, they follow the same four-step cycle and it’s a game that you’re playing, that everybody’s playing. Just like any game, you need to know the rules of the game. You need to know the steps of the game in order to have any sort of opportunity to win. That’s the thing that I’ve lived through once again, both painfully and also in good ways, and is something that I’m keeping my eye on and something that I’ve been talking about. Before we go into that four-step cycle, I figured I’ll pause there to see if you have any follow-up questions on that.

Lenny Rachitsky: Okay. This is amazing. Essentially what you’re saying is we follow these ways to grow, there’s SEO, there’s paid growth, there’s sales. All these channels have been around for a long time. They’re extremely saturated. Everyone’s always complaining SEO’s dead, it can’t grow, the SEO, anymore. It can’t grow. Word of mouth is so hard, there’s so many amazing things now that’s hard. Paid is so hard. It’s just all this money just-

The Repeating Cycle: Mobile and LinkedIn

Brian Balfour: Tax are rising.

AI Platforms: The Next Key Window

Lenny Rachitsky: Exactly.

Where to Build Your Moat

Brian Balfour: All these things. Yeah.

Lenny Rachitsky: So all these saturated channels, and what you’re saying is there’s an emerging new channel that has not yet been saturated, and this is a huge opportunity for companies to get on it. And you’ll talk about timing because it’s a little tricky to even know exactly when to go big on this.

Signals from Third-Party Platforms

Brian Balfour: That’s right. Yeah.

Google’s Window of Opportunity

Lenny Rachitsky: But that’s a huge deal. This has been a long time since there’s a new way to grow that you can actually use as a lever for growth and not just hope for the best. Okay.

Brian Balfour: Yeah. That’s right.

Anthropic and Claude’s Differentiation Strategy

Lenny Rachitsky: Before you get into the cycles, do you want to tease what the answer is, just to give people a little hint, or do you want to keep it secret?

Commercial Opportunities in AI Agents

Brian Balfour: Well, to be clear, okay, so my prediction, we don’t have a clear winner yet. My prediction of the new distribution platform will be ChatGPT, in some ways that people probably already think it’s happening in some ways that it won’t, but the thing that is less important or that is more important than whether I have predicted the exact winner correctly, the thing that’s more important is to understand the cycle and evaluate how to determine where you want to place your bets and how to place those bets, which I know we’ll talk about.

I could be wrong about the ChatGPT prediction and what’s going to happen there. I think there’s going to be two parts of it. There’s going to be what they do with a ChatGPT search experience, but I think the bigger thing will be whatever they do with launching a third-party platform on top of ChatGPT, there’s a bunch of signals that they’re about to launch that, I’m pretty sure it’s going to be ChatGPT.

The thing I’m way more sure about is that some new distribution platform will emerge and it will follow the same four-step cycle. That’s the key. Could be wrong on the first piece, I am very confident on the second piece.

Lenny Rachitsky: Okay. Excellent foreshadowing. I completely agree, if it’s anything, it would be ChatGPT at this point. Let’s get into it. What are the cycles that platforms generally follow?

Timeline for ChatGPT’s Platformization

Brian Balfour: Yeah, and I’ll give some examples of this, but let me explain the four steps of the cycle first and then we’ll go through a bunch of examples of all those individual steps. The four steps are essentially, one is I call a Step Zero. It’s the conditions of the market have been met. Step One is about a moat, Step Two is about a platform opening, and Step Three is about the platform closing for control and monetization. Let me briefly explain each one.

Step Zero is about the competitive market being met, the conditions being met, and there’s a few part piece of this. One is that typically what happens is that there is consensus that there is going to be this new huge category. Think social, think mobile, like all those types of things. In this case, these AI like chat platforms, like a ChatGPT or a clock. There’s consensus about that, but there’s no clear winner yet. We typically have somewhere between five to seven major players really battling it out and they’re all looking for what is the edge? What is the thing that is going to help me win? Because all of these dynamics, in all the history, they either end up in monopolies or duopolies, and so the stakes are really large and so the competition is fierce.

That’s Step Zero. I think we could all agree that we are in that mode right now. We’ve got OpenAI battling with Claude, battling with Gemini, and Google with whatever Meta comes out with their new team, so on and so forth. There’s huge amounts of capital, there’s consensus, all the types. They are in a fierce composition. That’s Step Zero.

Step One is then these players, somebody essentially identifies whatever the moat is, the thing that is going to help build them defensibility and help them hit escape velocity and become that monopoly or duopoly in that single category. Once they figure out what that moat is, then they need to press the advantage. They need to figure out how to gather that moat as fast as humanly possible. It tends to be that you can’t do that by yourself, so you need the help of an ecosystem in order to gather more of that moat.

That typically comes down to third-party content creators or app developers and other businesses. So they all establish a third-party platform that has some incentives built in, and usually the value exchange is, hey, you develop on top of my platform, you add more use cases, more engagement, all of these things to my platform, and in exchange, I’m going to give you something in return. Usually that thing that’s in exchange is, I’m going to give you some new form of distribution for your application and for your business.

But what essentially happens over time is that we go into Step Three, which is the closing period, which is at some point, all of these companies end up starting to lock down the platform. This tends to happen for reasons of monetization and growth. They either competitively don’t want somebody to use their own platform to disrupt themselves. We saw that in the early Twitter days with things like Vine and Periscope, shutting those things down unceremoniously, or they need to find ways to monetize at a deeper and deeper level because all these companies, they have to grow.

Google’s the classic example here of just more and more real estate has either been taken up by either ads or their own first-party applications. That’s the key is they close it down by doing by one of a few things. They either shut it down entirely, two, they develop their own first-party applications to absorb the highest use cases, or three, they artificially depress the organic distribution that they gave you in the step prior to push you towards paid mechanisms in order to monetize. I think we should go through multiple examples here, but that’s the core essence of the four steps. I’ll pause there.

Lenny Rachitsky: Awesome. So it’s essentially figure out what’s going to make, create defensibility long-term with your moat, bring everyone in, “Hey, everyone, welcome to Facebook,” everyone joins Facebook and then, okay, and all the developers build on Facebook to bring in more people on Facebook and then they’re like, “Okay, now you got to pay. There’s a toll,” but you love this so much and you’re so hooked to all your friends that you’re here, you may as well stick around.

Betting Strategies for Startups

Brian Balfour: That’s right. That’s right.

HubSpot’s Strategic Dilemma

Lenny Rachitsky: Amazing. Okay, so yeah, a few examples would be great.

Brian Balfour: Yeah. You just hit on the first one. This is the first one that I always think about because this is where I learned about this cycle very early in my career. One of my first companies was during the Facebook platform boom, social gaming, all of those applications, and I lived the full cycle in a very short period. I lived the glory days and just the absolute horror days, and it was very painful, but this is exactly what happened.

Let’s go through the four steps. Step Zero. Facebook was in a brutal battle with MySpace, Friendster and a few others. People forget this. People forget that there was actually a bunch of competitors at that time, and in fact those competitors were bigger than Facebook. They had more users back in 2007 when Facebook launched their third-party platform. But one of the key things is that Facebook was very early to the insight about the direct network effects in that there’s going to create real lock-in that the more friends, the more of the global network that was on there, the more that it was just going to feed and hit this escape velocity.

At the time they launched their platform, I think they were maybe one-fourth, one-fifth the size of something like MySpace or even Friendster, Orkut, these are some of the names at that time, but they opened up their third-party platform. What was the value exchange? They went to third-party developers and they said, “Okay, we’ve created this canvas,” they used to call it the canvas, and they were like, “You can put anything in the canvas that you want: an app, a game, whatever. You can monetize in any way you want. We just want this sidebar real estate on the ads. That’s what we’re really interested in.” There was this mad gold rush on that Facebook.

Oh, sorry, the other part of that was, “Not only will you put it there, we’re going to give you access to all of these notification channels and feed to get distribution for your application.” That was the other piece of it. You had this mad rush of developers coming in and you had this huge social application, social gaming boom. People just grew incredibly virally very fast, but eventually, essentially what happened over time is they kept peeling back that value exchange.

They first were like, “Ah, actually those dollars that you’re making inside that canvas area, well, we want a percentage of that.” So they changed that. And then they figured out their ad systems and then they started peeling back. They started suppressing access to all of the organic channels that they had. Eventually, they went all the way towards absorbing the highest use case into their own first-party platform, things like first-party applications, things like events, photos, all those types of things, and basically shut down the platform for dead.

These companies that have basically built on top of this platform, the other thing is by the time they started closing all those things down, all those competitors that we talked about, they were so far ahead at that point because they had built off the back of all these developers coming, adding use cases, bringing more users onto the platform, identifying that moat. They were so far ahead, it didn’t matter. It didn’t matter what the other folks did at that point, and that’s what really gives you confidence to start closing down. But there’s so many other examples of this if we go through it.

Criteria for Choosing a Platform

Lenny Rachitsky: Just before you give other examples, just something I’ll highlight here. One is the moat they identified in theory was the friend graph, I imagine?

Brian Balfour: Yeah.

New Traffic from ChatGPT

Lenny Rachitsky: Just once we have all your friends, you’re not going to want to go anywhere. I imagine it’s also important to note, this is a natural thing that would happen if you build the thing and it grows and you’re like, “Oh, maybe we should change strategy.” I imagine not everyone even knows this is what will happen and they organically evolve their strategy, or do you think everyone’s just like, “This is now going to be our plan, Step 1, 2, 3, 4?”

Reforge’s Pivot and Enterprise AI Adoption

Brian Balfour: I think a different version of that question is I think some people could sit here and interpret this as all these folks are evil. That’s not what I’m saying. That’s actually not what I’m saying. I want to be very clear on that, because I think this cycle happens because of competitive and capitalistic dynamics and pressures. It’s the same environment that enables creating amazing new companies here in the US.

And there’s two sides of the coin. You go through this cycle because it’s a competitive environment. You’re trying to figure out how to beat competitors, and this is one of the strategies to beat competitors. But at some point you just have to continue growing. You have to grow those dollars. The market does not reward flat companies, if anybody’s noticed. You have to keep growing, and so they have to keep finding ways to grow as well as prevent their own disruption. TThey can get so big and they can give access, so much access of distribution to new developers, they don’t want to enable their own disruption as well as they need to keep growing.

My guess is anybody who is sitting in their shoes owning their platform is going to follow the exact same playbook and the exact same reasoning. Look, sometimes it happens also because it actually is the best thing for the user. Facebook’s channels did get super spammy and all of those things, and that was part of the reason they’d play this, but let’s be honest, it wasn’t the only reason. A lot of it was for these other reasons. I don’t think it’s evil. You just need to know how to play the game. That’s competition, that’s business. They’re playing you, so you need to play them. That might be a little sadistic or something, but that is business. You’re in a game of competition.

Lenny Rachitsky: Essentially, the incentives are pointing you in this direction. Capitalism, they say capitalism works, and so it’ll pull everyone in this direction even if maybe they want to avoid it. Let’s do a couple more examples.

Setting Hard Constraints

Brian Balfour: Yeah, we’ll go through them quick. I think everybody’s probably… Google’s an interesting one because it played out over a much longer period of time. Facebook happened over the course of about in five-ish years, something like that. Google did it very slowly over years, but same thing, early massive competition against Yahoo, I don’t know, AltaVista, Lycos, you name them all. That was even before my time. They were first to really identify these data moats and incentivizing essentially web developers, content folks to optimize for their search algorithms, create this great distribution mechanism. Everybody’s building content and everything for them, but over time, slowly but surely they did two things.

One is more and more that real estate became ads that they were monetizing, so they’re suppressing organic distribution in order to push people towards the ads, as well as absorbing a bunch of the highest value first-party use cases, things like travel as an example, or even restaurant search and all those types of things. The former Yelp CEO and founder has been out there saying a lot of things about these practices. So, same exact cycle.

Mobile went through the exact same cycle. iOS created a new distribution mechanism. They had a ton of competition among different phones when they first started on. They found the defensibility was more about the apps, the data and all the developers, created the App Store, all of these types of things, but over time, we’ve seen more and more restrictions there on that front.

And then most recently, we’ve seen this happen in smaller places, too. LinkedIn, as an example, first went through this wave with company pages. They were like, “Ah, companies, come on, promote your company page. Bring in more users, all that type of stuff, and then get all these followers.” And then of course you get almost no distribution now through your company page because they’re pushing you towards ads. And then they recently just did this with personal profiles, too, which is they really boosted distribution for individuals to create content for that platform. They then introduced the thought leader ad format, a way to monetize those individual posts, and now you’ve seen them really pull back on that organic distribution.

So this happens in big forms and it happens even in smaller use cases as well, but once again, the steps of the cycle are exactly the same. The key part about this, too, is that the broad trend is that the cycles seem to be getting shorter and shorter and shorter and shorter, so you actually have a smaller amount of time to play the game.

Lenny Rachitsky: Okay, and the big a-ha here is, yes, this will end maybe not great for you, but there’s this magical period when they’re open to customers and users where you can grow like crazy because they want everyone to come and they give you a distribution. What you’re saying essentially is ChatGPT, potentially some other platform, maybe is about to enter this moat.

Three Types of People During Transition

Brian Balfour: Yeah. Well, before we get to ChatGPT, I think the natural reaction when you first realize this is, “Screw them, I’m not playing that game.” That’s what I feel like most people, how they react. Because the unfortunate truth is that a lot of companies don’t predict that last stage and end up in a really hard position. So many companies got completely killed during the crash of the Facebook social platform. Apple’s 30% tax basically destroyed a bunch of types of applications and business models because you feel like it just wasn’t margin-effective. So many companies built on SEO loops that are in serious, serious trouble right now if that’s their only channel. So all these things.

I think the natural reaction is, why would I play this game if I’m a startup or a company? You can even see this with ChatGPT, as an example. They just launched these deep research connectors. One of them was my former company, HubSpot. If you sat inside HubSpot and you were just thinking in isolation, you would be like, well, why would I want to make all of my data accessible through ChatGPT and have all of the usage you start to accrue there? That doesn’t really make sense in isolation. But we don’t operate in isolation. Once again, we operate in a competitive environment.

What’s going to happen is that if you don’t do it, your competitors are going to certainly go to the new platform and your customer expectations change, and you have to rise to those customers’ expectations. They’re going to start expecting you to be in these new experiences and all these things. It ends up being a prisoner’s dilemma, which is, there is no opting out of the game. You have to play the game. So it’s better to be early than to be super late to this game, especially, especially if you are a startup. That’s the key opportunity.

We will talk a little bit more about how to play the game more, but it’s better to be early as well as, then the key, the harder part about it is anticipating that last stage of the cycle and figuring out how to sequence away from something before that last cycle comes. I think that’s the key part, but let me pause there and then I’ll talk a little bit about ChatGPT and some of my reasoning behind that.

Lenny Rachitsky: Cool. So what you’re saying is not only is there going to be this big opportunity to grow, if you don’t take advantage of it, somebody in your space will. It’s not only there’s an opportunity, but this is something you need to do because you might miss the boat.

I think about companies like Zynga that grew on Facebook and then became massive companies. If they didn’t do that, they would’ve missed the boat, someone else would’ve eaten that lunch. I don’t know, I’m thinking about the Technology Bros podcast on Twitter right now, TBPN, where they basically figured out on Twitter you can create this livestream and you see it all day in your Twitter feed just like, hey, they’re broadcasting, and it’s a really cool distribution channel.

I think there’s a big call to arms here almost of just the opportunities emerging and you basically need to pay attention. You can’t opt out.

Why You Must Take a Hard Stance

Brian Balfour: That’s right. That’s right.

Lenny Rachitsky: Awesome.

The Disconnect Between Executives and Frontlines

Brian Balfour: Exactly.

Lenny Rachitsky: Okay, so let’s talk ChatGPT.

The Slowest Part Determines Overall Speed

Brian Balfour: Look, let’s go through this cycle. Right now we’re in that competitive environment. Like we said, all those players we talked about, ChatGPT, Claude, Gemini, all these folks, they are battling it out. We’ve seen this with the Talent Awards especially over the past month or so. There’s no clear winner yet, but there’s consensus around the category.

The second thing is then, okay, what’s the moat? Has the moat been identified? And who seems to have identified it the first or as furthest along? My hypothesis, and I think there’s a lot more consensus around this now than there might’ve even been three months ago, is that the moat is about context and memory. These models by themselves, if you compare them side by side, they generate the same result, and so the actual difference-maker is which one has more of your context, because it’s the context plus the model that produces the best output, and then that starts to accrue to this loop around memory. The more you use it, the more it’s able to store a memory around you, which feeds more personalized context, which produces better outputs. It ends up being another one of those flywheels, another one of those loops.

If you look at who’s farthest on this, it definitely is ChatGPT. They were the first ones to memory. They’ve been investing a lot in these different types of data connectors, essentially context connectors, gathering all of this context, so you can really start to see it in the usage.

The second thing is, and one of the pushbacks I’ve gotten on my prediction has been, well, what about Google and Gemini? They have so much distribution through Chrome and all of this other stuff. Deedy Das, who’s a VC at Menlo Ventures, actually published some good data on retention of all of these different ones.

I think the second reason I predict ChatGPT is if you look at history once again, it was never the person who had the biggest distribution at the moment of time. It was the one that had the best retention and engagement. Google had the best retention and engagement over the others. Facebook was smaller, but had way better retention and engagement over the others, so on and so forth.

The data that Deedy published clearly show that both the retention curves, which I know you and I have both written about at exhaustion, level off at significant portions higher than all the other platforms, as well as those retention curves have been shifting up dramatically over time, you can start to see the effects of memory. They have the very elusive smile curve, the ones that you just like. I’ve seen all of those dynamics very few times in my career, and they tend to be the folks like Slack and all of the big winners. It’s just so elusive

Lightning Q&A Round

Lenny Rachitsky: The smile curve, just to people who don’t know what that is, is essentially retention goes up over time, it goes down a little bit, and then you come back to it and you use it more.

Brian Balfour: Yeah, that’s right, and it’s usually the result of some type of network of factor or something else, and it’s an early indicator that that platform is on a trajectory to hit escape velocity.

The third piece is that, and they haven’t really hidden these, but there’s all sorts of signals that they’re about to launch a third-party platform. They’ve been hiring for a bunch of roles. I’ve seen multiple postings on product manager engineering roles, all that kind of stuff for, quote, unquote, “agent platform” and all those pieces. It feels pretty inevitable that one of these players will need to launch a third-party platform in order to serve all the possible use cases on these tools. There’s going to be some value exchange, which is like, hey, for your agent to be effective, you probably need access to the context and memory and distribution, so there’ll be some value which is, “Integrate to us and we’ll give you those three things which is going to drive more users and more usage,” and we’re going to go through the steps of the cycle.

You can already see this. They’re starting to form preferred partnerships with some of the bigger players, which paves the way for smaller third-party players. It lends credibility to the platform. It’s like, well, if HubSpot and XYZ are doing it, then I should probably do it, too. It’s like that type of mentality. But that’s why I think out of all of these platforms, ChatGPT has the best shot right now.

And then, a bunch of folks are always like, “Well, what about Claude? I really like Claude. I use Claude.” Well, the problem with that is I think ChatGPT at this point has at least a 10x difference on MAU. If you’re a developer and you’re comparing those two platforms and you’re looking at it and you’re like, “Well, ChatPT has 10x the number of users and better retention engagement,” it’s like, what’s the logical choice of which one you’re going to prioritize your scarce resources on?

Those are just some of the reasons that my prediction is on ChatGPT. In the blog post that I wrote about this, I actually then played my own devil’s advocate and said, “Okay, here are some reasons why it might not be ChatGPT,” but I think we’re in that part of the cycle. That’s my prediction. I might be wrong in the prediction of ChatGPT, but I really think, I feel very confident we’re going to see this cycle play out again.

Lenny Rachitsky: Two follow-up questions here. One is, what would be the backup if it’s not ChatGPT? It sounds like it might be Gemini or Google?

Brian Balfour: My hypothesis of who’s best-positioned but is not executing on it right now would actually be Apple-

Lenny Rachitsky: Whoa.

Brian Balfour: … because through the devices, they basically can see everything. They have the ultimate view into your context. They’re sitting at that level But I don’t know what they’re doing. From an execution standpoint, maybe they’re going to surprise us with something crazy magical, but we haven’t seen any external signals around this. That’s probably just based on what real estate and where people live in the stack would own.

And then, I think right behind that, I would probably put Google because of owning the context of things like email and the distribution points of search and Chrome and Android and those types of pieces. A lot of people point to them, but my experience with all of their products, going back to the retention engagement thing, is that if we could take a look inside their metrics, I think what we would see is a bunch of fly-by users in their mouse. They’re sprinkling the Gemini bucket everywhere. I’ve literally clicked on it accidentally multiple times. My guess is a huge portion of their mouse is exactly that of what’s happening right now. Look, they just acquired a very talented team from Windsurf and from-

Lenny Rachitsky: And just the team. Just the team, part of the team.

Brian Balfour: Yeah. We’ll see. Things are changing dramatically on a week-to-week basis, so we’ll see if they’re able to press those advantages in a very clear way. But I think the window is very small for them if ChatGPT plays their cards right, because they clearly have the escape velocity right now. If they just keep pressing that advantage in the right way, I think it’s going to be very hard for Google to counter in the amount of time that’s left

Lenny Rachitsky: On the Claude piece, I’ll just throw this nugget out, I had Mike Krieger on the podcast, Head of Product, CPO, at Anthropic, and asked him just, “You’re losing to ChatGPT. How do you approach the future of Claude?” He very specifically said, “Yes, they’ve caught lightning in a bottle. This is just going to win based on what I’ve seen at Instagram. So we are specifically focusing on what is Anthropic and Claude incredibly good at, which is developer tools, coding, backend stuff.” So they’re actually leaning more and more into that. If you’ve seen the revenue recently, they’re making, I don’t know, approaching 10 billion a year or some crazy amount of money. They’re actually doing super well, just in a different use case.

Brian Balfour: I’m glad you mentioned this because this brings up something that we skipped, which is, there are smaller platforms that have existed and will also emerge in this environment as well, and that’s what you’re alluding to. This tends to happen is things end up growing into more niches. Even if you look at social, like LinkedIn emerged as a subset of the social world, but even on these smaller platforms, these new distribution channels, they go through the same cycle.

I’ll give something, really a very opposite example of the ones that I gave. Look at the platform Udemy. They are a platform for course creators. I don’t know if most people know this, but when they started, their rev share to creators was something like 80% to creators. They started very high. That brought on all the course creators, got their whole marketplace going, so on and so forth. I believe it was about a year ago they announced that they’re essentially pushing that rev share down to somewhere between 15 and 20%.

Lenny Rachitsky: Wow.

Brian Balfour: They’re somewhere at 25 and 30%. Another example of they close down organic distribution in order to monetize, all that kind of stuff. The same thing will happen in this AI world. Cursor, it’s very clear Cursor’s on the path to also probably create some type of agent platform for developers. That’ll be a smaller ecosystem to play in for some products. It feels like everybody has the same strategy at this point is everybody wants to launch an agent platform. I imagine some of these other horizontal productivity tools will do the same thing, maybe like a Notion or an Airtable or a Monday.com or something like that.

There will be smaller platforms that will emerge, and they will follow the exact same cycle that I’m also discussing, but in terms of the biggest consumer one, that’s where I think ChatGPT has probably the most escape velocity and others will focus on different areas. Just to be clear, I love Claude. I actually use both Claude and ChatGPT-

Lenny Rachitsky: Same.

Brian Balfour: … for different things. I have lots of love to go around for all these tools. My prediction has no bearing on which product I like the most right now.

Lenny Rachitsky: Also love Claude. So the key point here you’re making is that there’s almost a number of distribution channels emerging. Many of them will be niche. I think of LinkedIn. LinkedIn for me has a very targeted audience, for folks that listen to this podcast. Even though it’s not, I don’t know, Google or Facebook or whatever, it’s still incredibly valuable for this specific thing that I do.

Brian Balfour: For sure.

Lenny Rachitsky: I think this is even more interesting that there’s going to be a number of distribution channels that emerge out of this whole AI wave.

The other thing I’ll note real quick, you mentioned this idea of everyone’s building agents. I just had Brett Taylor on the podcast who’s building Sierra, and he made me realize why everyone’s building agents partly. One is because the outcome-based pricing that you can charge with agents is incredible because, one, you can actually attribute their impact on your business’s ROI. You can actually see this is saving an agent $15 because it solved the case. And it’s attributable and it’s autonomous. It’s just doing it on its own. With that, you can charge per outcome. You can say, “We’ll charge you a dollar,” every time it solves an issue. So the monetization opportunity is huge and the margins go up like crazy.

Brian Balfour: Can I just ask a question about that?

Lenny Rachitsky: Yeah.

Brian Balfour: Do you think that has longevity in the sense that… That makes sense in the current environment that we’re sitting in right now because people are comparing these outcomes relative to what it costs them today with pure humans. But once again, competition comes in at some point, and so that feels like that creates a pretty ripe opportunity to undercut and come into… and then you have the disruption theory playing out as well. It obviously depends on the infrastructure costs and compute costs to run these things, but I just wonder how much of that is temporary versus something that’ll be long-term.

Lenny Rachitsky: So you’re saying that dollar will come down to 50 cents, 25 cents, or you’re saying someone’s going to come with a whole new business model and disrupt that whole approach?

Brian Balfour: More the first, yeah. It’s just competition erodes that away, essentially, right?

Lenny Rachitsky: Yeah, that’s a good point. So margins will be higher for a while, and then they’ll come down.

Brian Balfour: Unless there’s something else that creates a durable pricing power, right?

Lenny Rachitsky: Yeah.

Brian Balfour: That’s probably the second piece of this. Yeah, that’s probably the second piece of that hypothesis, I feel like.

Lenny Rachitsky: Yeah. I guess the opportunity there, the moat would be the data, similar to how Cursor is collecting more feedback on what people want in their code suggestions, maybe in theory CRF and has more and more data over time, and there’s this network effect-

Brian Balfour: Yeah. That’s right. Let me revise that. I believe in that as long as it’s paired with-

Lenny Rachitsky: Some moat?

Brian Balfour: … this second piece. Otherwise, it gets competed away.

Lenny Rachitsky: Good tangent. Okay, one more question. What is your prediction on timeline for when the opportunity appears, and what do you predict, as of the day we’re recording, what do you predict will be the next couple things that ChatGPT, and let’s just focus on that, releases to start to open up this platform to get everyone in there?

Brian Balfour: Well, look, I’ll first give the disclaimer that I feel like any thoughts on timing in the AI market have been very hard to predict. It’s always shorter. That’s where we should buy us. It’s always shorter than you think of when something’s going to happen. That’s what it’s felt like from the seat that I’ve been sitting in. But my guess is this: we’re going to see the next major steps of this play out over the next six months.

I think we just saw one of the pieces drop around this, which was, ChatGPT’s recently launched Agent mode. It’s kind of a general-purpose agent, and I think that starts to introduce all of the users to using agents and they’re figuring out and placing it in the different tiers and business models, all of those pieces. But it’s likely that no general-purpose agent is going to fulfill all of the infinite use cases successfully. There’s two reasons for this.

Users struggle with horizontal tools. They can do everything, and that’s exactly why they struggle to adopt, and so they typically need more specific entry points. But also, the more specific use case you get, sometimes you need specific UI, specific data, other specific ingredients to properly fulfill that use case for a given audience. I think their Agent mode was a step in this direction.

What I would expect to see play out next is that they will either launch, they will announce the platform with preferred partners, or what they’re going to announce first is basically a set of preferred partners, the guinea pigs, an initial 10 to 20 folks that are bringing agents to their platform. What that does is essentially, once again, it’s a credibility card. You do special deals with some brand names to give the platform credibility, and it creates this desire from everybody else to come on to the platform.

And then the step after that is starting to opening up the platform. This is where we’ll really start to figure out what this game is going to look like because they basically have to define what the value exchange is. What are they giving you access to, and what are they incentivizing you with to come onto the platform? That’s one version of it.

The other version of it is just the replacement to search. You can also see them starting to make more moves here, which is deeper attribution in some of the results, those types of pieces. They’re bringing in shopping. That’s one of their recent announcements as well, native into the UI. Essentially they will form new monetization mechanisms around that stuff as well. That’s actually going to be very important because, going back to the moat around memory and context is that they will want to incentivize as many people to their free tier as possible, but given the cost of AI, they have to cover it somehow, so they’re going to need some monetization mechanisms. The more that they can cover that free usage with things that aren’t subscriptions, I think that probably also feeds the moat.

I think those are some of the next steps on two different vectors, more of a third-party developer platform and more of the content, whatever you want to call it, AEO, GEO. I don’t know what acronym is we’ve all decided on yet. Let me know if we have. I think those will be the next steps that we’ll see.

Now, that’s what I think for ChatGPT. I think the thing that we should talk about is, essentially what I would advise folks, especially startups, is you’re placing bets. At this part of the cycle, you’re placing bets. The winner is 100% guaranteed, as I mentioned, and so you essentially at some point will need to make some decisions about where to place your bets.

In the Facebook days, all those other social networks, they also came out with their own platforms. iOS had Android and some failed initiatives from Windows. I don’t even remember what that platform was called. You can look back and whoever placed through… the iPhone was actually a very, and iOS is a good one, which is, if you had only aligned your bets to Android, you probably lost. If you somehow found a way to play on both ecosystems, you could be a winner. But if you only aligned to iOS, you could also be a winner. You had to have iOS as part of your betting strategy in order to win.

Everybody right now, you’re probably at this cycle and you’re trying to figure out, well, everybody will need to figure out where are they going to place their chips. How are they going to bet? Depending on how you bet really depends on what your current position is in the marketplace. If you’re a late-stage startup, let’s start with that, or a late-stage company, you can afford the luxury to place multiple bets and spread your chips and wait it out a little bit to see who the winner is, and then really throw your muscle behind that winner. You have that luxury a little bit. But the risk of that, the risk of that is that sometimes the incumbents wait too long to make that decision, and that’s the key question they will need to answer.

The key question for startups is totally different. You don’t have the luxury to spread your chips. You have to go all in. You have to choose one and go all in. You have scarce resources, scarce attention from the market. It’s a totally different ballgame. Higher risk, higher reward, for sure. That’s part of the betting strategy for startups. That’s what you have to do is you have to figure out your betting strategy, and then we can talk a little bit about how you might evaluate and pick the right course for you. That’s where we’re all at right now is we just entered the casino, we just put some cash in for some chips, and now we’ve got to figure out what tables and where to place those chips.

Lenny Rachitsky: I love this analogy. Just to be crystal clear about what listeners should do, what founders should do, what product teams should do, the advice here essentially is integrate with ChatGPT, maybe Gemini, maybe if Apple has something, actually integrate with what they launch. It could be a login thing, could be a search thing, could be a connect and suck up your memory in context. The advice here is you need to do this because this is potentially the way that most companies will start to grow and your competitors may overtake you.

Brian Balfour: Yeah. If we had to really simplify it, it’s essentially, play the game. Don’t opt out of the game. Don’t trick yourself into thinking that you can’t play the game. That’s number one. Number two, no matter who you bet on, just make it a focused bet. Because if you look back, all the failures are the ones that tried to play multiple games at once with scarce resources, and that just tends to never work if you’re an early-stage startup. So those two things: play the game, put a focused bet.

Lenny Rachitsky:

Miro has been empowering teams to transform bold ideas into the next big thing for over a decade. Today, they’re at the forefront of bringing products to market even faster by unleashing the combined power of AI and human potential. Guests of this podcast often share Miro templates. I use it all the time to brainstorm ideas with my team. Teams especially can work with Miro AI to turn the unstructured data, like sticky notes or screenshots, into usable diagrams, product briefs, data tables, and prototypes in minutes.

You don’t have to be an AI master or to toggle yet another tool. The work you’re already doing in Miro’s canvas is the prompt. Help your teams get great work done with Miro. Check it out at miro.com/lenny. That’s M-I-R-O dot com slash Lenny.

So you were Head of Growth at HubSpot for a long time, and you gave that as an example, HubSpot, why would they integrate, why would they give away all their data so that ChatGPT can suck it up, and you never have to go to HubSpot, you’re just working through their agent. Would you at HubSpot be like, “Yes, we got to do this. This is the game we got to play”?

Brian Balfour: Yeah, 100%, and that’s exactly what I think you see them doing. Look, to be very clear, I have not talked to anybody at HubSpot about this. I have not talked to Dharmesh about this, but Dharmesh I think has also published about this. The right thing to do is, essentially, even though you understand how the cycle plays out and you don’t necessarily understand what your exit strategy is once you get out, it’s better to be early, know that you need to figure out an exit strategy and figure out that exit strategy along the way versus waiting and then being super late and then know what the exit strategy is.

I think that’s exactly what you see them doing. They’re trying to be as early to this stuff as possible. I think it’s a pretty smart play, even though we might not necessarily see what the exit strategy is out of this cycle for them.

Lenny Rachitsky: Going back to that amazing quote that you shared at the beginning of the conversation by, I think it was Alex Rampell?

Brian Balfour: Yeah.

Lenny Rachitsky: Of that startups win by finding a distribution channel before the incumbent copies them. What you’re saying here is this is the opportunity for startups to disrupt an incumbent. This is the opportunity for someone to disrupt Salesforce, I don’t know, ServiceNow, all these guys that have been around for a long time.

Brian Balfour: Yeah, it’s going to be one of the major ones. Now look, you’ve already seen players that have been able to hit this escape velocity, the Cursors and stuff of the world. Once again, there’s multiple ways to hit that escape velocity, but this is one of the major ways to do it is to basically hitch yourself to a new platform. Look, you did it yourself, actually. You hitched yourself to Substack super early. You took a focused bet.

Lenny Rachitsky: I was going to say that.

Brian Balfour: Yeah, yeah. I don’t know why that just hit me, but you took a focused bet and you’ve benefited from it in a disproportional way than those that came later. I think that’s actually a great meta example here as I sit here and think about this.

Lenny Rachitsky: Yeah, that’s actually the way I thought about when I was moving to Substack. I feel like there’s this wave rising and I want to ride this wave, even if maybe it’s not the best place or they take a cut, all that stuff. But it worked out really well.

Brian Balfour: That’s right. I think it worked out very well.

Lenny Rachitsky: It worked out really well. To be honest, it felt like it was too late when I started six years ago.

Brian Balfour: It felt too late?

Lenny Rachitsky: Yes.

Brian Balfour: Oh, say more about that.

Lenny Rachitsky: It always feels too late, I think, to people that join… Silicon Valley, or sorry, Marc Andreessen has this famous quote. He’s like, “I came to Silicon Valley in the ’80s. I thought it was over, I was too late. I missed all the opportunities.” That’s fair. There was just a lot of newsletters. They were doing really well. Millions of subscribers. I’m like…

Brian Balfour: And what do you say to people now who want to join Substack?

Lenny Rachitsky: “Learn from this example.” A lot of times when people think that it’s too late, it’s definitely not too late, and it’s always only just getting started. Especially if you’re on Twitter all day listening to podcasts like this where we’re surrounded with this bubble of everyone talking about something, when in reality, 1% of people know anything about what you’re hearing about every day.

Brian Balfour: Yeah. Yeah, that’s so interesting.

Lenny Rachitsky: Okay, so coming back to the advice, say someone is sitting there and talking to their manager like, “Brian just shared all this mind-blowing advice. We got to pick our battles, we got to pick our platform,” what would your advice be for them to decide where to place their bets?

Brian Balfour: I think this is a great question because, once again, put my personal prediction aside for a second, and I would encourage everybody to think about it from first principles from who their audience is, what their product is, what stage of company they’re at, their current strengths and weaknesses, you got to take all this into account, but if I had to boil it down to a few criteria, the main things I would think about is, when you’re looking at new distribution channels and new platforms to choose on, one, going back to what we said before is the better signal is retention and depth of engagement of the users on this platform than it is pure user level like MAU or some other number of signups, one of those vanity metrics. Look at that number one.

Number two is, there’s some element of user quality and ability to monetize the users on this platform. I think the starkest example here would be iOS and Android. Even today it’s something like Android has 70-some percent of devices, but only 30% of the market share by dollars, and it’s the exact flip for iOS. It goes back to what we were talking about earlier, which was, if you bet on Android only, you probably lost, but if you bet on iOS only, even though smaller user base, you were still able to parlay that into a win later.

The third thing to look at is, as these platforms emerge, just analyze what the value exchange is. What are they giving you to incentivize you to develop on their platform? All these platforms, it’s a bit of a game of whoever understands the rules and how to arbitrage the rules the best tend to be the ones with the edge and figure that out.

And then finally, fourth on my criteria would be pure scale. Obviously, even if you have those other three, but there’s a 200x difference in scale and momentum, obviously you probably have to choose the bigger platform.

But last but not least is as you go through these criteria, these are how you think through entering the game. Once you enter the game, then you immediately need to move to starting to think about how do you exit the game. Knowing once again that that last step is going to come at some point in the future, that there’s going to be some closure for monetization, then that’s where you have to start thinking through your strategy to exit.

That comes down to things like, okay, well how are you going to own an important part of the user experience or workflow? Or how are you going to accumulate specialized data in context that the major platforms don’t have? Or how do you create different types of micro network effects? All of these types of things. So just once again, there is the entrance criteria, but once you figure that out and you feel like you’re in the game, you immediately need to move towards, okay, what’s my exit plan here, knowing this is all coming.

Lenny Rachitsky: It’s interesting that another way to think about this model you’ve described as building, the strategy of building on top of LLMs and becoming a GPT wrapper. Because essentially this tech allows you to, say, create a cursor that is incredible, and then you could argue, oh, you’re just going to be this wrapper, and they’re getting all the money here, everyone can copy you. What’s your defensibility long-term? The answer is, what is the moat you will build over time sitting on top of this thing that will make you more and more valuable on term and not have to rely on this thing? It feels like you could use the same framework for building a GPT wrapper business, to use that euphemism.

Brian Balfour: Yeah.

Lenny Rachitsky: Say someone is sitting there today. Is there anything they can do to start making a bet? Is it simply creating an MCP that allows LLMs to suck in your data? Is that the one thing you could do today? Is there anything else that’s available today to start using these platforms, or is it just a little too early and they haven’t released the good stuff yet?

Brian Balfour: It might be just a tad too early. We’re right on that edge. Some of the questions I’m asking myself is, I’m going through all of these players and where our customers and target audience live, and I’m asking myself the question, okay, if this player launched some type of platform, how would we evaluate it? So on and so forth.

You can also try to cozy up to these folks. I would place a large portion of my net worth right now that if we could sit in the OpenAI offices at that front desk, that they are having meetings with potential preferred developers talking about this, we could probably sit there and log it. I do think some people are going to be in a place to develop preferred relationships and make a note. If you’re in that spot, then you should definitely play that card. A lot of early-stage startups will be in that place.

Other than that, I would say, once they launch these platforms, you can’t do much else, so you really know what the value exchange is and what they’re going to expose for you, but also just be prepared to turn your strategy on a dime and go all in. I think that’s probably one of the hardest parts of this is that these things emerge and you have to capitalize extremely quickly. A lot of times, it’s hard for leaders to do that because they don’t want to create a feeling of whiplash into the unknown, and we’ve got all these projects in play. You know all the things. I think that’s probably the last part of what we can be doing right now versus just staying on top of everything as it emerges.

Lenny Rachitsky: As you were talking, this reminded me, I recently noticed that ChatGPT is driving me to my newsletter more traffic than Twitter, and I feel like that recently shifted. I didn’t even know this was a thing until I just started looking through my referrals. I’m like, “ChatGPT? What the hell is going on there?” It’s like a different version of what you’re talking about, but essentially, in theory I could block ChatGPT from… I don’t know. I don’t even know if I can from… including all my stuff.

Brian Balfour: You can in Substack now, yeah. I just saw that setting in there.

Lenny Rachitsky: Okay. Oh, interesting. That’s the similar kind of decision is, is it better for me for it to be recommending my stuff and telling people, “Hey, go check this thing out,” or is it better to block it off? I think, per your point, and this is the way I felt, take it all. It’s good. In that, it’s better that it’s from Lenny’s newsletter than something else. So someone else will come in and eat that market share.

Brian Balfour: Yeah, that’s right. If you don’t do it, somebody else is. I think that’s also what all the major media publishers are really contending with right now.

Lenny Rachitsky: I guess I need a licensing deal with New York check. Anyway. Okay. I want to go on a totally different tangent. We weren’t planning to talk about this. I know that I said this, we’re going to be fully focused on this one topic, but there’s something you mentioned to me before we start recording that I think will be really interesting to a lot of people.

You guys at Reforge are now building actual SaaS products that people can buy. It’s not just courses. I don’t know if people know that, but let’s make sure people understand this. There’s actually products for product teams, so maybe just explain that briefly, but the thing that I think is really interesting here is you work with a lot of companies now selling them AI tools, and you have noticed a very big difference between the companies that are really good at adopting AI tools and seeing gains from them from those that don’t. Talk about just what you see there, and because this is in theory going to be really helpful to companies that are struggling with adopting AI tools and seeing gains.

Brian Balfour: Just to quickly explain that transition so it makes sense for people, which is, I started Reforge just with the interest that there was all these incredible leaders out there growing on the front lines of some of the fastest growing companies and they have all this amazing knowledge and I wanted to encode it in useful and practical ways for others. That took the form of courses and content and product, all that kind of stuff at the beginning. Along the way, everybody kept asking us to essentially build the tools to implement what we taught. Because with anything, you can learn as much as you want. You can listen to my podcast, your podcast, Lenny, whatever, as much as you want, but if you don’t actually put it into action and implement it, then it’s not really going to create value.

People kept asking us to really close that gap and we said no for the longest time. And then about a couple of years ago when AI really started to inflect, it really created this moment that, oh, wow, now there’s this opportunity not just to encode this knowledge into content, but also into the products, the software, the tools that we use ourselves. So we started to take a really big bet on that and started to develop this new platform for AI-native product teams.

The first product we launched is called Reforge Insights, which acts like your AI product researcher, aggregates all the feedback from all the sources, uses AI to analyze it, helps you explore it, but also will start to identify what are the gaps, the things that you don’t have in your feedback today and auto-generate the research to go gather all those new insights, so complete the full cycle. We’re going to launch two other major products as part of this platform before the end of the year, but we’ll save that for some future episode.

So that’s been our journey. We’ve seen inside companies that are going through this transformation from two perspectives. One is obviously selling in that tools, but the other perspective is, for 10 years, companies have been coming to us to help them try some sort of transformation with our learning product.

Most companies are not coming to us to just throw a bunch of courses in front of, they’re trying to solve some big business problem, some transformation. Now that used to be things like, we’ve got to figure out this growth thing, or I’m going from sales-led to product-led, or I have more project managers and I need to transition to product managers, something like that. There’s some business problem, they’re going through some transformation and they saw us as part of that transformation and we got to partake in quite a few of those types of transformation.

Now of course, the transformation that everybody’s going through is, okay, how do I become more AI-native? How do I adopt this stuff? We’ve seen a pretty wide spectrum and from both perspectives of how companies are approaching this. I’m sure everybody’s seen that AI, we’ve been calling them the AI manifesto memos from CEOs out there that proclaim, “We are now AI-native,” in some grandiose way, but behind the scenes, there’s actually some incredibly stark differences in the actual teeth of what backs up those memos and backs up those executive decrees that we should all be AI.

Just to point out a few of them, which is, one is that I think the most impactful thing that you can do is form really hard constraints. There’s other parts that’s like, okay, you want to communicate this, you want to establish an owner of who’s going to drive this, you want to build an incentives and rewards, and you see this all playing out in things like building it into your career ladders, or some people are starting to introduce this as questions into their performance reviews, all those types of pieces. But the thing that is actually moving the needle are the companies that are defining incredibly hard constraints.

One company that we worked with developed this constraint that they benchmarked against other companies of their revenue size and the team sizes for those stages, and they set a benchmark that we will be one-fifth, each of our functions will be one-fifth the size. What that did is it created a constraint that you couldn’t hire above that level, and it forced people to essentially find ways to adopt AI and do things to replace that. So that was one.

You’ve seen these other ones, I can’t remember from what company, that might’ve been Shopify or another, who was like, you are not allowed new headcount until you prove to us that you are not able to accomplish this with AI. That’s another hard constraint. But you also see these other constraints on a smaller level, which is executives saying, “I will not do a product review or review a PRD unless it comes with three prototypes.” Something like that. That’s the hardest one. Those are the biggest constraints.

I think the biggest change that I’m seeing is, and the things that separates out the top few percent making this change and everybody else is essentially making the hardest decisions, and that hardest decision is going to come down to exiting people. In every transformation, what we see is essentially three groups of folks. We call them the catalysts, the people leading the charge, the people who are experimenting, doing this on their own time, all that kind of stuff.

You then have what we call your converts. These are folks that will make the transformation, they will adapt, but they need structure, they need permission, they need a clear outline, they need a clear plan. I don’t say this in a negative way, it’s just that that’s how some people operate. That’s where things like all the things that we were talking about before, which was the decree, the permission, the clear budgets, the rewards, all of those types of things.

But then, inevitably you have a certain percentage that are anchors. They’re dragging their feet, they’re silently creating friction in the background and all those pieces. There’s a big difference in how I think companies are treating and thinking about their strategy for those folks. One group is like, ah, we’re going to work with them very passively. Others have set a hard deadline. They’re either going to make the transformation by X date or we’re going to exit folks.

A lot of people look at this as being really harsh. I think a lot of people would think that, especially individuals, but let me explain it from more of a CEO perspective. A lot of these companies are seeing this AI transformation, the ones that are taking it more seriously, as this isn’t adopting new tools, this isn’t a light change. This is a fundamental culture change of how we operate as a company. You can’t have 20, 30%, whatever meaningful number of it is of your company trying to operate in a completely different way, in a completely different culture.

Cultures thrive on density, and that’s why there’s sometimes the best ones feel like cults. As a result, from that perspective, it’s like, hey, for us to be successful, for this to be the best thing for all employees, we all need to be operating around the same culture of principles and stuff. If that’s not you anymore, then we’re defining a plan to exit it.

I would say that less than 10% of companies we see are taking this hard stance, but I would say they are probably the ones that are farthest along getting the most adoption and are seeing the most results of the ones that are taking those hard stances. There’s a bunch of other stuff I could talk about, but that’s the high level of what we’ve seen across a bunch of different companies.

Lenny Rachitsky: That is incredibly interesting. I’m glad we went there. I have a newsletter post coming out soon, probably before this episode, that touches on a lot of advice along these lines. I am excited for you guys to keep seeing these insights into companies and sharing more of this because this is I think what a lot of people are looking for, just like things aren’t quite clicking at our company. We keep hearing everyone just getting so much more productive. All these companies are running more efficiently and it’s not working here. I think that’s the kind of advice a lot of people are looking for, so thank you for sharing all that.

Brian, is there anything else that you wanted to touch on? Anything else you wanted to leave listeners with before we get to our very exciting lightning round?

Brian Balfour: Well, actually, just a couple more points on this topic we should go. There’s probably two more things I would say about this. One is that, if you’re a CEO listening to this, I would say that most CEOs or most executives are incredibly disconnected from the actual AI adoption taking place inside their companies. I think a lot of executives who have done these decrees and all that kind of stuff think it’s happening naturally, but we talked to both groups. We talked to tons of end users and we talked to tons of executives.

The story we hear from the end users, the PMs, the eng, all that kind of stuff that we talk to using all this stuff, one of the main questions we ask them is, if we’re talking to somebody who’s picked up a prototyping tool, say, “Well, how many other people on the product and design team are using this?” Almost 90% of the time it’s like, “Ah, it’s me and this one other person,” and everybody else hasn’t taken it up. So there’s a huge disconnect.

We heard one story, and I can’t say the name, but it’s a company we all know. It’s a major tech company, a tech-forward company. CEO’s been out there talking about being AI-native. We talked to one of their principal PMs. Person was early to the prototyping tools. This person shared a prototype with the designer, the eng manager. The designer and eng manager escalated it to the VPs. It caused this whole conversation. Month later, it was still stalling out. This PM happened to then attend a happy hour where the CEO was at and approached the CEO and told the CEO about the experiment that they were running with prototyping and stuff, and the CEO was like, “This is fantastic. Where is it at right now?” He was like, “Oh, well, X, Y, Z happened.” The CEO had no idea. And then the CEO is like, “Okay, let me take care of it,” and then the next day, it happened.

So one is that you have to go to the ground floor on this stuff. Some of the best companies like Shopify and others are measuring actual adoption and usage. They’ve gone to the extreme on that front to get a bunch of signals and close to the ground. It just goes to show that this is… I don’t think we want to talk about going founder mode, but the reality is it’s not just about getting into the weeds of your product, but with something this sizable, you got to get into the weeds of the transformation to really understand what’s going on and adopt it. That’s point number one.

The second point I would say is Fareed, we do this podcast called Unsolicited Feedback, Fareed Mosavat had this great quote on it. He was like, “Look, your output is constrained by the slowest part of your system.” That stuck in my head because it’s absolutely true. If you think about AI adoption as a system, there’s all parts of the system that could be slowing adoption. It might be that people don’t feel permission or they don’t have the budget or they don’t have the knowledge, all these types of things. In a lot of these cases, it’s things like IT, legal, procurement are the slowest part of the friction and are setting the pace of all of this output.

You can also see this in just product teams. There’s been all this talk about product managers are becoming the new bottleneck because engineers are speeding up. Well, that’s because people are speeding up one part of the product system and not the other parts, which makes sense. They adopted all of this tooling for engineers because they’re the biggest head count and the most expensive and all that type of stuff, but product is an output of design, PMs, and engineering. The system is there not to produce code, it’s to ship product, and shipping product is the function of those three things.

If you just accelerate one part of the system, you’re just going to move to the bottleneck to another part and your actual product output, the output of the system doesn’t accelerate, either. I think people have to really understand those two things: what is actually happening on the ground floor, and what is the slowest part? What is the thing that is causing the slowest part of the adoption? Just attack them ruthlessly if you’re really serious about making this transition.

Lenny Rachitsky: What a wild time we’re living through. So much changes.

Brian Balfour: It is a wild time, yeah.

Lenny Rachitsky: All these ways that we’re all so used to, okay, this is how we do it.

Brian Balfour: Yeah. It’s exciting and exhausting at the same time, man. That’s how I think about it.

Lenny Rachitsky: Such a simple way of describing the road.

Brian Balfour: Yeah.

Lenny Rachitsky: Oh, my God. Okay, Brian, is there anything else before we get through our very exciting lightning round?

Brian Balfour: That’s it. Let’s do lightning round.

Lenny Rachitsky: Here we go.

Brian Balfour: Zap, zap, zap.

Lenny Rachitsky: Ding, ding, ding. All right, Brian, I’ve got five questions for you. Are you ready?

Brian Balfour: Let’s do it.

Lenny Rachitsky: Okay. What are two or three books that you find yourself recommending most to other people?

Brian Balfour: My God honest answer is that I have not had the time to finish an entire book since I had my second child. From a complete book standpoint, I have not been able to… Things that I actively read on a regular basis, just other content out there that I’ll throw out there is, gosh, Jamin Ball from Altimeter Capital writes this great newsletter called Clouded Judgment, which is mixture of market thoughts as well as market stats. That’s really useful to help me keep a pulse on the market. I was just reading through some stuff from NFX that’s been pretty good lately on all of this. I know James Currier and I, we lived a lot of the same cycles through social and stuff, so I tend to identify with that. I don’t know, those are two things that I love reading.

Sorry, I’ll give one more shout-out to a different podcast, which is from two guys at Spark Capital, Nabeel Hyatt, which I know from my early Boston days, and Fraser, sorry, I’m blanking on the last name right now, who was Head of Product at OpenAI, and they’ve got a great format where it’s just those two riffing on some ideas and stuff. I highly suggest that one. I like that one a lot.

Lenny Rachitsky: Here’s my reading tip that has changed my reading habits. Bryan Johnson, the longevity guy, he has this advice for better sleep, which includes, before you go to sleep, read for 10 minutes in bed.

Brian Balfour: It does put you to sleep. I don’t feel like I retain anything that I read that close to bed, though. Do you feel like you retain it?

Lenny Rachitsky: I do, I do. I’m reading fiction. It’s nonfiction. Sorry. You want to read something calm, not like I’m learning. I’m reading fiction and it’s really nice. Knowing that this is going to help me sleep better makes me motivated.

Brian Balfour: There’s an incentive. There’s a reward there.

Lenny Rachitsky: The reward, yeah.

Brian Balfour: They’re talking about rewards and creating behavior change, yeah.

Lenny Rachitsky: Exactly. The reason to do it is this whole thing is you want to get to low resting heart rate, and that helps lower your resting heart.

Brian Balfour: I’ve got some other sleep tips on that front if you want to go down that path, but we’ll save that for you.

Lenny Rachitsky: Please, for the third podcast. Okay, next question. Do you have a favorite recent movie or TV show that you really recently enjoyed?

Brian Balfour: It’s not new, but I just rewatched Silicon Valley that I hadn’t watched in a number of times. It’s painful because the first few seasons, I went through almost every one of those moments in my first startup, like hiring the gray-haired CEO, the funding falling through at the last second, all the crazy stuff, but going back and watching that, there’s just some extra nuances and stuff that I feel like they wrote really well that I thought was really good. I’ve really been watching that.

The other thing that I’ve watched is just more of a just pure entertainment, calming thing, turn the brain off is Owen Wilson’s new show on Apple TV, Stick, which is about him as a former professional golfer and all that. I won’t ruin the show and stuff, but it’s a very nice calming, little bit fun type of show.

Lenny Rachitsky: I’ve been seeing that on my Apple TV. Maybe I should check it out. Good tip. Do you have a favorite product you’ve recently discovered that you really love? It could be a gadget, it could be a app on your phone, it could be something in your computer. It could be nothing at all.

Brian Balfour: You can’t see it, but I just changed my whole setup. Now I have a UltraGear super-wide curved screen with a very nice standing desk from, I believe it’s called Ergonofis. Er.

Lenny Rachitsky: Ergonofis?

Brian Balfour: Yes. I think it’s Ergonofis, E-R-G-O-N-O-F-I-S.

Lenny Rachitsky: All right.

Brian Balfour: It’s a very nice, sleek standing desk. Very stable, very quiet. Very much enjoy.

Lenny Rachitsky: Excellent tip. And the curved monitor, very cool. Okay, two more questions. Do you have a life motto that you often come back to and find useful in work or in life, something you share with folks, something that you think about when times are hard or just generally?

Brian Balfour: Look, it’s a little cliche at this point, but it’s somewhere around here, I used to have the quote printed out about the man in the arena. Especially in times like this where so many things are changing and there’s so much competition, but so much opportunity for great, I really both respect and enjoy the game and spending time with folks that are in the arena figuring this stuff out, tinkering with things. That’s what I keep coming back to, especially been at Reforge for 10 years, that’s a good portion of my life, and we’ve gone through some great periods and some tough periods, so I tend to come back to that.

Lenny Rachitsky: And that’s what always separated Reforge from so much other content and advice is it’s people in the arena sharing their wisdom, not just a bunch of influencers. It’s sad that Chamath made that quote so cringey.

Brian Balfour: I know, I know. That’s why I said it’s a little cliche cringe right now.

Lenny Rachitsky: Screwed it up for everyone.

Brian Balfour: Yeah.

Lenny Rachitsky: Final question. Brian, you don’t know this, but your parenting advice on Adam Fishman’s podcast-

Brian Balfour: Oh.

Lenny Rachitsky: … really impacted my parenting philosophy, specifically this line you had about independence.

Brian Balfour: Oh, yes.

Lenny Rachitsky: I’d love for you to just share that insight about how you think about raising kids,

Brian Balfour: I wish I could remember where I grabbed this from so I could attribute it properly, but basically, the philosophy is, if you think about going from when they’re born until they’re essentially 18 and leave the home, your job as a parent is to essentially make them more and more independent. What that involves is continuously looking for opportunities for them to make even bigger and riskier decisions for themselves as they grow up and you’re there as a support to those decisions, but letting them make those decisions on their own so that by the time they’re 18, they are a fully independent person able to think through those decisions themselves.

Now, look, my sons are young. They’re five and three, so it’s not like I’m having them make life and death decisions or where we might buy our next house or stuff like that, but it’s even small things at this age of… My oldest, five and a half, is really starting to learn and get curious about money and how you spend money and where new things come from and how you earn money. Rather than just buying things for him, he’s got money from his grandparents and stuff saved up, and we can be like, “Okay, you can buy that thing, but you’re going to spend this,” and try to teach him the consequences and all that kind of stuff, and then when he breaks something…

It’s just small things like that, but thinking about the time from zero to 18 as this spectrum of independence and being a supporting role in what you’re essentially doing is you’re trying to move as many decisions, the percentage of decisions you make for them down to zero by the time that they’re 18. That’s something that I’ve kept in the back of my head since really seeing that.

Lenny Rachitsky: Thank you for sharing that. I know I didn’t tell you that I was going to ask you about this, so that was a beautiful way of summarizing it.

Brian Balfour: Yeah, I couldn’t remember that whole podcast. I had no idea what I said, but-

Lenny Rachitsky: I would say you nailed it.

Brian Balfour: … that’s a good one, yeah.

Lenny Rachitsky: Brian, two final questions. Where can folks find you if they want to reach out and where can they find the products you guys offer? Whatever you want to plug. Also, how can listeners be useful to you?

Brian Balfour: Check out reforge.com. Check out our new products like Reforge Insights. They’re on the website. You can find me personally, my writing, including a bunch of the stuff that we talked about today now on Substack. Just recently moved. You can either go to my website, brianbalfour.com, where I have some info or just the blog.brianbalfour.com where all of my new writing is taking place. Those are the two major pieces.

Last but not least is that, as I mentioned, Fareed Mosavat, who I used to work with at Slack, we have this fun podcast. The two of us get on there and riff like we were having dinner every couple of weeks about different product and strategy types of things. It’s a fun format for us, so if that’s something you enjoy, it’s called Unsolicited Feedback, where we give feedback and advice to nobody that ever asked for it.

Lenny Rachitsky: That’s amazing. Perfect title. Brian, thank you so much for being here.

Brian Balfour: Yeah, thanks for having me again. This is great.

Lenny Rachitsky: Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

Glossary

English	中文
Adam Fishman	Adam Fishman（保留原文，播客主持人）
AEO	AEO（保留原文，AI Engine Optimization 的缩写）
Agent mode	Agent 模式
AI manifesto memo	AI 宣言式备忘录
AI-native	AI 原生
Alex Rampell	Alex Rampell（保留原文）
Altimeter Capital	Altimeter Capital（保留原文）
Andreessen Horowitz	Andreessen Horowitz（保留原文）
Android	Android（保留原文）
Anthropic	Anthropic（保留原文）
Apple	Apple（保留原文）
Apple Podcasts	Apple Podcasts（保留原文）
Brett Taylor	Brett Taylor（保留原文）
Brian Balfour	Brian Balfour（保留原文）
Bryan Johnson	Bryan Johnson（保留原文）
Casey Winters	Casey Winters（保留原文）
Chamath	Chamath（保留原文，指 Chamath Palihapitiya）
ChatGPT	ChatGPT（保留原文）
Claude	Claude（保留原文）
Clouded Judgment	Clouded Judgment（保留原文，newsletter 名称）
Cursor	Cursor（保留原文）
data moats	数据护城河
Deedy Das	Deedy Das（保留原文）
deep research connectors	深度研究连接器
Dharmesh	Dharmesh（保留原文，指 HubSpot 联合创始人 Dharmesh Shah）
duopoly	双头垄断
eng	eng（保留原文，engineering 的简称）
Ergonofis	Ergonofis（保留原文，品牌名称）
escape velocity	逃逸速度
Fareed Mosavat	Fareed Mosavat（保留原文）
first principles	第一性原理
first-party use cases	第一方用例
flywheel	飞轮
founder mode	创始人模式
Gemini	Gemini（保留原文）
GEO	GEO（保留原文，Generative Engine Optimization 的缩写）
GitHub Copilot	GitHub Copilot（保留原文）
GPT wrapper	GPT wrapper（保留原文）
HubSpot	HubSpot（保留原文）
incumbent	现有巨头
iOS	iOS（保留原文）
James Currier	James Currier（保留原文）
Jamin Ball	Jamin Ball（保留原文）
Lenny Rachitsky	Lenny Rachitsky（保留原文）
man in the arena	竞技场上的人（西奥多·罗斯福著名演讲）
Marc Andreessen	Marc Andreessen（保留原文）
MAU	MAU（月活用户）
MCP	MCP（保留原文，Model Context Protocol 的缩写）
Menlo Ventures	Menlo Ventures（保留原文）
Meta	Meta（保留原文）
Mike Krieger	Mike Krieger（保留原文）
moat	护城河
monopoly	垄断
Nabeel Hyatt	Nabeel Hyatt（保留原文）
net worth	净资产
network effect	网络效应
NFX	NFX（保留原文）
OpenAI	OpenAI（保留原文）
organic distribution	自然分发
outcome-based pricing	基于结果的定价
PRD	PRD（保留原文，Product Requirements Document 的缩写）
preferred developer	优先开发者
pricing power	定价权
prisoner’s dilemma	囚徒困境
product-led	产品驱动
Reforge	Reforge（保留原文）
Reforge Insights	Reforge Insights（保留原文）
retention curve	留存曲线
rev share	收入分成
SaaS	SaaS（保留原文）
sales-led	销售驱动
Salesforce	Salesforce（保留原文）
SEO loops	SEO 循环
ServiceNow	ServiceNow（保留原文）
Sierra	Sierra（保留原文）
Silicon Valley	《硅谷》（HBO 剧集）
Slack	Slack（保留原文）
smile curve	微笑曲线
Spark Capital	Spark Capital（保留原文）
Spotify	Spotify（保留原文）
Stick	《Stick》（保留原文，Apple TV 剧集名称）
Substack	Substack（保留原文）
TBPN	TBPN（保留原文）
Technology Bros	Technology Bros（保留原文）
thought leader ad format	思想领袖广告格式
Udemy	Udemy（保留原文）
Unsolicited Feedback	Unsolicited Feedback（保留原文，播客名称）
Windsurf	Windsurf（保留原文）
YC	YC（保留原文）
Zynga	Zynga（保留原文）

---

Reformatted by reformat_english.py