Why ChatGPT will be the next big growth channel, 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. Startups is a game of trying to get distribution before the incumbent can copy. So it's this kind of concept of escape velocity. And so on that note, which I think is like 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 has gotten way harder

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. So all of these things, right? And so I think the natural reaction is why would I play this game if I'm a startup?

My hypothesis, and I think there's a lot more consensus around this now than there might have even been three months ago, is that the moat is really about context and memory. These models, by themselves, if you compare them side by side, they kind of generate the same result. And so the actual difference maker is which one has more of your context. And because it's the context plus the model that produces the best output. And then that kind of starts to accrue to this loop around memory

The key point here you're making is that there's almost a number of distribution channels emerging, many of them will be niche. So I think of LinkedIn, if I want to, like LinkedIn for me is a very targeted audience for folks that listen to this podcast. So yeah, 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. So 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

We're going to see the next major steps of this play out over the next six months. And so I think we just saw one of the pieces drop around this, which was ChatGPT's recently launched agent mode. And so 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 kind of figuring out and placing it in the different tiers and business models. But it's likely that no general purpose agent is going to fulfill all of the infinite use cases successfully

At this part of the cycle, you're placing bets. We don't, 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. 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. Everybody right now, you're probably at the cycle and trying to figure out, we all need to figure out where are we going to place our chips?

The advice here essentially is integrate with ChatGPT, maybe Gemini, maybe if Apple has something, is actually integrate with what they launch. So it could be a login thing, it could be a search thing, it 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

The most impactful thing that you can do is form really 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 their stages and they set a benchmark that we will be one-fifth. Each of our functions will be one-fifth the size. And 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

In every transformation, what we see is essentially three groups of folks. You see your catalysts, the people leading the charge. You then have your converts. These are folks that will make the transformation, they will adapt, but they need structure. And then inevitably you have a certain percentage that are anchors. They're dragging their feet, they're kind of silently creating friction in the background. And there's a big difference in how companies are treating those folks. Some are passive, others have set a hard deadline: transform by X date or we will exit folks

Cultures thrive on density, right? And that's why they're sometimes the best ones feel like cults. And so as a result from that perspective, it's like, hey, like we, 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 or principles and stuff. And if that's not you anymore, then we're defining a plan to exit it. But I would say that less than 10 percent of companies we see are taking this hard stance. But they are probably the ones that are farthest along, getting the most adoption and are seeing the most results