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.
00:43:20 · Sandy Diao, Guillaume Moubeche
But I I think right now, what I'm seeing and probably the way I recommend growth teams to think about it now, is really thinking of ourselves as individuals who are responsible for growth to be general orchestrators. Figure out what is worth automating, but figure out the manual stuff first. Because if you don't have the manual stuff down, there's no point in scaling it. And that agent's only gonna be as good as the underlying assumptions and logic behind, like, why we did this thing in the first place. [00:43:51] S1: And when you join, like, a a company to to advise them, like, what's your typical process? Like, what are the steps you go through to kind of analyze what's working, what's not working, and what can be done better?
00:44:03 · Sandy Diao
[00:44:03] S0: Great question. The first step that I take when I meet a company is I just kind of wanna hear from them what their growth challenges are. And the second thing that I do is I pair that with an audit of what actually is happening in the business. And the realization that we typically have at the end of that process is that our assumptions on what's working or what our problems are are typically not actually what's happening in the business itself. So that tends to be the early on process. And then we have to align on what are the growth problems that we meaningfully want to solve. From there, it's figuring out, you know, what are the the the quick wins or the fast things that we can do to show some initial lift and impact so that we can have confidence to continue to double down or continue to invest in other things. That tends to be my road map. And then from there, we build something a little bit more long term. That's like, okay. Here are good foundational things that we should probably invest in and do, but we don't have to do right away. So an example is some quick win type of things that we tend to do early on. I mentioned this concept earlier, but what I've seen in most, probably a 100% of early stage startups I worked with is there are power power law outcomes to user acquisition. Firstly, every business wants to solve user acquisition. Even if they have tons of users, they're like, oh, I'm afraid it'll dry up at some point, and it's not sustainable, and it's not scalable, etcetera. So everyone has that problem. But what I've seen for every business I've I've worked with or ever touched is that parallel outcomes drive the results, meaning that one or two channels drive the majority of users. Again here, meaning that we need to figure out what those best fit channels are. And one of the inclinations that a lot of teams I work with tend to have is they wanna test more and more channels. So they're all saying, what do think about influencer? What do think about paid ads? What do you think about Reddit? What do you think about, you know, all these like different referral programs and everything else? And my response to that usually is, I don't think you should be testing all these all at once. We should find out which channels have the best fit for your product.
00:45:53 · Sandy Diao, Guillaume Moubeche
So for example, if you have a product that's very socially shareable, very visual, let's start with social channels first, cause you're gonna have a better fit there. Or if you have a product that has a lot of existing demand, a lot of people understand, maybe consumer product, let's focus on paid first or SEO. So it it really kinda depends on the the the setup of the product itself. And then, you know, we we launch an experiment or two, and that gets us really strong results. Either it works like crazy well, and we're like, wow. We should hire a paid specialist, or we should hire an agency, or it goes, oh, wow. I thought for the longest time that this was going to work, and it and it didn't work. Let's move on to the next thing. But that's typically how the road map looks, and it's also the same approach that I take when I'm full, you know, full time in house in a team, and my mandate is also to, you know, to grow the team. [00:46:40] S1: And what do you think when when you look at a at a growth moat for a company, what do you think have changed before AI versus now? What kind of moats do you feel will remain stronger and the one that will actually not be as strong as as they used to be?
00:47:02 · Sandy Diao
[00:47:02] S0: It's a really interesting question. I think about this one a lot. What is going to be a moat with growth? Well, here's how I view it. I think that firstly, when people ask, like, what your moat is, it's this unique competitive advantage that's supposed to sustain for the long term. But I think by definition, in the world of AI, there is going to be very few growth modes that remain the same. In fact, I think that growth modes are always constantly going to be changing depending on the size, the scale of the company, and just distribution channels themselves are changing. So by definition, you cannot have a persistent growth mode that is gonna take you from, you know, c to series a and series a to c and c plus. Right? It's just... It's not gonna work that way anymore. And one of the reasons that's true is because, again, the distribution channels are changing, things are just working a lot faster. Also, the nature of these distribution channels, like GEO or generative engine optimization, products are now being served on a platter to users. Right? Users are asking, what is this exact product that I need for my specific profile with all this context about, like, what this model understands about me? That model will just say, like, here's the thing you should use. Right? There's no more comparison. There's no more reading these lists of, like, check boxes that says, like, this solution is better than the other. There's a little bit of that and, you know, sort of referencing it. But, you know, for the most part, products are being brought closer to the point of per... Conversion with AI as one of these distribution channels. And same with video content. Right? Algorithms are getting better and better at surfacing content that is unique and custom to you, and so you don't need to do that much more research the same way that you would have been in sort of this pre AI world. And so in order to succeed here, the growth mode itself is, like, one, being adaptable and making sure that you're constantly checking on and making sure that you're aware of and changing how your distribution works. But but two, I would say that this other concept that, you know, I referenced earlier as well, finding what your natural distribution advantage is is even more important.