00:34:41 · Sandy Diao
Like, one example is one of the companies that I that I advise or work with is called TeachShare. They help teachers or educators of any traditional or untraditional kind create educational curriculum. And generating this content, sure, you could do it with GPTE. You could do it with Claude. You could do it with any foundational model. But what they add on top of it is this design experience where all the variables that you need to think about, like, what is the grade level? What is the level comprehension? What is the format? Is it, you know, more image based? Is it tracing based? Is it text based? Is it more comprehension based? All these factors are basically, like, packaged neatly for you so that as the educator, you don't need to think about all these different factors. And then it makes it also easy for you to be able to keep up to date and edit and come back to your repository of the content that you're creating. So I think, you know, regardless of what it is that you're building, I think that some of the factors of building for a market still remain true, meaning that you wanna go for product market fit, meaning that you wanna make sure your product is serving a market, look for the classic signals thereof, are these users retaining? And then, you know, ultimately, try to figure out a distribution channel that fits that as well. So can I sustainably, for example, with TeachShare grow in the teacher and educator segments? And do I have channels that show me that that is working? So, you know, kind of in my in my view there, I think that TAM is very large. People are willing to use multiple tools, and just because the foundation model is building for this use case doesn't mean that you cannot tackle that yourself as a foe.
00:36:19 · Guillaume Moubeche, Sandy Diao
[00:36:19] S1: And to build upon your point, I think, like, for the company you mentioned, I think what's interesting also is, like, not only they have, like, the package design, and they simplified a lot the experience for the end users, so there is a lot of additional value, but I assume that in the way they build their products, because they leverage all these new models, when the models get better, the product experience also get better, which means that they are not like, you know, fighting existing models. They just can leverage the improvements to bring even more value to their end users. [00:36:57] S0: Exactly. The... As the models get better, you're right. The product experience gets better, so you're not having this existential threat of, I get outcompeted because the foundational models themselves, like, outperform my product itself. In fact, my product has the actual data points as well of teachers and educators creating content. And therefore, the way that, my evals are set up, I'm actually actually gonna have a net better product and output than even a foundational model with far less context on a specific use case.
00:36:57 · Sandy Diao, Guillaume Moubeche
[00:36:57] S0: Exactly. The... As the models get better, you're right. The product experience gets better, so you're not having this existential threat of, I get outcompeted because the foundational models themselves, like, outperform my product itself. In fact, my product has the actual data points as well of teachers and educators creating content. And therefore, the way that, my evals are set up, I'm actually actually gonna have a net better product and output than even a foundational model with far less context on a specific use case. [00:37:25] S1: Definitely. No. That's really interesting. And you mentioned, like, to look at the retention, and also that people, you know, like, have lower and lower switching costs. Now that with AI, you know, you... A lot of people feel that they can build everything they want, and in a very quick way, even though it's it's more complex than this, actually, but eventually it might get there over time. Like, do you think about AI and retention, and what are the things that you would focus on to make sure that your retention doesn't go to zero with the the switching costs, you know?
00:38:00 · Sandy Diao
[00:38:00] S0: Yeah. It's honestly so hard. I think a lot of the retention strategies will remain the same, and that you still need to figure out how a user continuously gets value with your product over time. And some of these are going to be figuring out, like, what is ultimately the strategy around how the longevity of your product, referencing the... Maybe the, you know, the educator tool for a second here. One common behavior that people are gonna have is figuring out how they can, like, repeatedly come back to make refinements and edits to their products, how they can store their products and view their products and share their products. So you... We have to, you know, utilize some of the same retention strategies that we would pre AI as well, which is how do you build a product experience with longevity that people can constantly come back and and reference and and continue to use. On the other hand, one of the biggest drivers of retention, the input to retention, which is new user activation or activation itself, has wildly changed. Right? AI has kind of trained us to expect value really, really quickly, and really, really fast, and very, very generously. And part of that is the fact that we've been spoiled by these big companies who have this seemingly infinite budget to burn around giving away AI credits and tokens so that people can get a taste of what good looks like. And so in order for smaller companies or newer players to keep up with that, they need to figure out, how do I let somebody peek at the value? How do I let somebody see what's behind the curtains without burning myself out? Without costing me a fortune in order to run this business? And I think that's that's really hard. But I think there are some design patterns that we can utilize to make that happen. You know, for example, one of the experiences that you get when you use any Vibe coding tool is they allow you to start typing your prompt right away. They say, what is it that you want to build? And then they let you hit, like, generate. But for the most part, most of these tools don't actually run the generation. They bring you to a sign up wall, and they try to add all of these little steps that help the user be more serious about their intention to use the product.
00:40:04 · Sandy Diao, Guillaume Moubeche
And I think that's one of the things that we need to look out for in growth, which is we wanna give away value, we wanna show people what's possible, but we also need to make sure that these are real serious users, not someone who's just gonna be casually browsing, burning us of, you know, our our our cash and our capital here in order to run these models. And, of course, that will become more and more negligible over time because the cost of running these models gets cheaper and cheaper over time. But I think, you know, for now, one of growth's responsibilities is to figure out what are those distinct points. Do we continue to identify intent early on, but still match the expectations of this ever changing consumer whose expectations are now being seriously molded by their experiences with free and fast AI. [00:40:50] S1: Yeah. I I agree. And how would, like, one full stack growth person leverage AI in the day to day? Like, what's what's the typical... What are the typical workflows that you personally leverage whenever you advise companies or... Yeah.
00:41:09 · Sandy Diao
[00:41:09] S0: Yeah. I would say the following. The first one is that, maybe unexpectedly, a lot of growth in marketing teams aren't necessarily adopting AI as much as you think they would. And and I think one of the reasons here is that, especially in the role of growth, a lot of the... Even the channels and levers we just covered are very cross functional. They're product led. They're marketing led. They're sales led. They're community led, etcetera. And one of the challenges with AI right now is getting all of that context and data to a single place in a way that makes sense, in a way that breaks past the walled gardens. You know, not every one of these tools has an API that you can use to nicely and neatly connect all of these data points together. So there's still this role of the orchestrator, if you will, who has to look across these channels, use their intuition about the business, look at the data from various sources, or at least figure out how to, generally speaking, automate reading some of these reports, and then figure out how it all coalesces. I think the manually building the stack, building the data sources still lives on, and for good... Very, very good reason. Some of the areas where agents and AI automations are starting to play more of a factor are things like automated ads reporting, automated ads optimizing, stuff that is very, I think, process oriented. You can really write out the guidelines and say, here are things that I have to do. Even things like influencer marketing outreach, where maybe your guidelines for identifying who an influencer is, you can now write into a natural language prompt. And you can, you know, build, say, a Claude skill around it so that it runs every single week, every single morning, however frequently, you know, your your outbound influencer marketing campaigns work. So I do think that every team, for the most part that I've seen, have a relatively unique stack based on who's responsible, whether they're more of, like, that general growth manager or maybe more of a performance specialist who's focused on one channel or the other. I think the opportunity here is having more of a unified growth system that has the context across all of these different systems here.