The Exchanges

Q I totally agree with you. It's like totally company dependent, and it sets you up for a, like, very challenging answer. My question to you is, you said about post PMF there for Loom. You've said before in some of your work that product market fit isn't enough. What does that mean? Because founders chase this product market fit. Like, what should it be, and how could it be changed?

A Well, to add nuance to that, I would say that my quote is product market fit is not enough to build a venture scale business, meaning like a hundred million plus, I kind of pick a number out, a hundred million plus in revenue within some, um, you know, reasonable time period. Um, and so I think there's like a few, few pieces of this. One is that I think everybody thinks about, I think one of the mistakes is people think about product market fit as a binary thing, and it actually lives on a spectrum. There's two vectors. Factors of, of product market fit. There's how strong of a fit I have and with how big of a market, right? And there's some kind of efficient frontier on that graph of like you cross over and it's like, ah, it's strong enough with a big enough market that, um, that this is like a venture where we're on the venture viable path. But there are companies that certainly live with very strong, um, Product market fit, but with a strong, with a small market that just aren't venture viable. And there are actually, I've seen companies that have a very large market, but weak product market fit. And those are also not venture viable, but in either of those cases, they are viable businesses in the grand scheme of things. They're just not this little tiny world that we live in around venture. It's, it's not, it's, it's not part of it. So that's the first part of it. The secon…

AI assessment note: “my quote is product market fit is not enough to build a venture scale business”

Q do we do then? Do we go, right, we're predicting saturation, and we're gonna actually divert a little bit of resources, and really try and carve out a content strategy, a SEO strategy, a you name the channel, or do you say, hey, we've got one channel that's saturating, let's split the resources between four, and see which one springs with hope. No. Definitely. Which one is the right approach?

A Okay. Biggest mistakes of this are, one, not starting early enough. Um, we already talked a little bit about that. Two, underestimating the amount of time that it takes to get a new channel or new product, uh, going. And three, over, three, over-resourcing it. And then four, um, essentially, uh, this is an area where I would actually take a couple bets. But not like 10 bets. The way that we did this at HubSpot, um, you know, when I came in there, my mission with a couple others was help, uh, establish a second product category for the company and a second channel. Go from the sales and marketing motion we had to more of a product-led motion. We had those two missions, uh, to, to figure out. And the way that the, I did not come up with this, this really came from Dharmash and Halligan, the two founders, which was, um, the way that we established The philosophy behind it was, okay, we are going to start with multiple bets. We are going to treat each one of these bets like a new venture investment. We are going to seed fund this. And so they like made us come and they would pitch for our seed funding for the idea, which was basically one year of funding for a small team, like a four or five person team. And then at the end of that year, uh, and then, and then Halligan JD, who was the COO and they're like, They would act like our board essentially, and we'd have to go do like board…

AI assessment note: “this is an area where I would actually take a couple bets. But not like 10”

Q I totally agree with you. It's like totally company dependent, and it sets you up for a, like, very challenging answer. My question to you is, you said about post PMF there for Loom. You've said before in some of your work that product market fit isn't enough. What does that mean? Because founders chase this product market fit. Like, what should it be, and how could it be changed?

A Well, to add nuance to that, I would say that my quote is product market fit is not enough to build a venture scale business, meaning like a hundred million plus, I kind of pick a number out, a hundred million plus in revenue within some, um, you know, reasonable time period. Um, and so I think there's like a few, few pieces of this. One is that I think everybody thinks about, I think one of the mistakes is people think about product market fit as a binary thing, and it actually lives on a spectrum. There's two vectors. Factors of, of product market fit. There's how strong of a fit I have and with how big of a market, right? And there's some kind of efficient frontier on that graph of like you cross over and it's like, ah, it's strong enough with a big enough market that, um, that this is like a venture where we're on the venture viable path. But there are companies that certainly live with very strong, um, Product market fit, but with a strong, with a small market that just aren't venture viable. And there are actually, I've seen companies that have a very large market, but weak product market fit. And those are also not venture viable, but in either of those cases, they are viable businesses in the grand scheme of things. They're just not this little tiny world that we live in around venture. It's, it's not, it's, it's not part of it. So that's the first part of it. The secon…

AI assessment note: “product market fit is not enough to build a venture scale business”

Q do we do then? Do we go, right, we're predicting saturation, and we're gonna actually divert a little bit of resources, and really try and carve out a content strategy, a SEO strategy, a you name the channel, or do you say, hey, we've got one channel that's saturating, let's split the resources between four, and see which one springs with hope. No. Definitely. Which one is the right approach?

A Okay. Biggest mistakes of this are, one, not starting early enough. Um, we already talked a little bit about that. Two, underestimating the amount of time that it takes to get a new channel or new product, uh, going. And three, over, three, over-resourcing it. And then four, um, essentially, uh, this is an area where I would actually take a couple bets. But not like 10 bets. The way that we did this at HubSpot, um, you know, when I came in there, my mission with a couple others was help, uh, establish a second product category for the company and a second channel. Go from the sales and marketing motion we had to more of a product-led motion. We had those two missions, uh, to, to figure out. And the way that the, I did not come up with this, this really came from Dharmash and Halligan, the two founders, which was, um, the way that we established The philosophy behind it was, okay, we are going to start with multiple bets. We are going to treat each one of these bets like a new venture investment. We are going to seed fund this. And so they like made us come and they would pitch for our seed funding for the idea, which was basically one year of funding for a small team, like a four or five person team. And then at the end of that year, uh, and then, and then Halligan JD, who was the COO and they're like, They would act like our board essentially, and we'd have to go do like board…

AI assessment note: “this is an area where I would actually take a couple bets. But not like 10 bets.”

Q Listen, I heard nothing but wonderful things. I want to start, though, with a little bit of an entry point, which is how did you first make your first foray into the world of growth as a starting point?

A I made my way into growth where I would say, like, 70% of, like, the growth OGs made into growth, which was the early Facebook platform social gaming. Days. If you look at like some of the top people in growth, there's like a lineage. It's like almost like a coaching lineage, like all the way back to the social gaming days. And so I had started this company, uh, shortly after school called Viximo. And, uh, we were in the social gaming space, uh, for a while. And then it kind of transitioned to mobile gaming. And that was like the perfect petri dish To essentially like create growth people because what the Facebook platform did was that it opened up a ton of like viral channels as well as like paid acquisition channels. The games were very driven by like product driven levers, uh, like virality. It was a highly quantitative game. You were playing this high, like arbitrage game. You were constantly seeking out like the things in the API's that people out still like didn't understand. And games are so focused on, um, like the psych, the psychological aspect of users to a degree that other software products aren't. And when you combine all of those things together, that's really kind of what growth turned into, which was understanding how your product grows, which is different than how your business and company grows. And we can talk a little bit about that later. Um, combining it …

AI assessment note: “I made my way into growth where I would say, like, 70% of”

Q from those early Facebook gaming days, so totally get you there. I do want to ask, I think so much is gained in, in hindsight and with years of experience. If you could go back to your first day prior to your first role in growth, and tell yourself one piece of advice, cool yourself up that night before, what would you tell yourself, knowing all that you know now?

A Oh boy. Uh, probably two things. One is like, there is, um, there's not an infinite world of growth options. It's actually, uh, it's fairly well defined and constrained, and that's something that I've, like, I've learned, um, pretty dramatically, especially through Reforge, where I get to see inside, you know, the The growth of thousands of different companies, including the top public ones all the way down to your early stage ones, and it's not like there's an endless list of ways to grow. There's actually a fairly set menu of things, and you can innovate within that menu, but it's actually pretty rare that a new dish gets added onto the menu. Knowing those sets of things, I think, helps really start to hone in on What your realistic opportunities are and not. And then the second piece of that is conviction and patience. When you look at a lot of the highest flying growth strategies, right, what is underneath them is what we call every forge growth loops, where other people call flywheels, but they're basically systems that work like compound interest, right? They're things that feed, uh, itself and grow over time. And the really hard part about their systems It's just like compound interest. It looks like tiddlywinks at the beginning. Right. And it doesn't, it doesn't look like much. And so what that does is it puts a lot of, uh, a lot of pressure. And I see a lot of founders…

AI assessment note: “probably two things. One is like, there is, um, there's not an infinite world”

Q works and seeing the success in a channel, obviously, the decision around whether it's successful or not is largely made by the metrics that you see and how you interpret them. You said before that people often get metrics wrong. I agree with you, but I'd love to hear, what do you think are the biggest ways that you see founders and growth teams really misunderstand or get metrics wrong?

A Yeah, well, the first and foremost is metrics before strategy. I won't go too deep on that, because I feel like that's like a common thing. That's the biggest thing wrong, which is your metrics are there to help answer the question, is your strategy working? Right. Um, and so they are not there to determine your strategy. You have to have a reasonable hypothesis of what your strategy is first. Like what is, what is your core growth loop? Like all of these things. And then you can say, well, what are the metrics that we would use to measure that strategy and what would success look like? So that's number one. Number two is they basically go quant before qual. So, um, To understand what your metrics should be, you have to understand the qualitative underpinnings, and a great example of this is especially around retention. Um, like setting retention metrics, for example, actually it's, I would say, is 80 to 90% understanding the qualitative definitions of what is the problem that you are solving, what is the natural frequency that that problem occurs in In your target market's life, right? Um, so, and then, and then you can start to match the metric to it. Like, should we be tracking this on a weekly active user basis or a monthly active user basis? Active, what does active mean? Well, what is the behavior in the product that indicates we are solving the problem for this user? But…

AI assessment note: “the first and foremost is metrics before strategy.”

Q from those early Facebook gaming days, so totally get you there. I do want to ask, I think so much is gained in, in hindsight and with years of experience. If you could go back to your first day prior to your first role in growth, and tell yourself one piece of advice, cool yourself up that night before, what would you tell yourself, knowing all that you know now?

A Oh boy. Uh, probably two things. One is like, there is, um, there's not an infinite world of growth options. It's actually, uh, it's fairly well defined and constrained, and that's something that I've, like, I've learned, um, pretty dramatically, especially through Reforge, where I get to see inside, you know, the The growth of thousands of different companies, including the top public ones all the way down to your early stage ones, and it's not like there's an endless list of ways to grow. There's actually a fairly set menu of things, and you can innovate within that menu, but it's actually pretty rare that a new dish gets added onto the menu. Knowing those sets of things, I think, helps really start to hone in on What your realistic opportunities are and not. And then the second piece of that is conviction and patience. When you look at a lot of the highest flying growth strategies, right, what is underneath them is what we call every forge growth loops, where other people call flywheels, but they're basically systems that work like compound interest, right? They're things that feed, uh, itself and grow over time. And the really hard part about their systems It's just like compound interest. It looks like tiddlywinks at the beginning. Right. And it doesn't, it doesn't look like much. And so what that does is it puts a lot of, uh, a lot of pressure. And I see a lot of founders…

AI assessment note: “probably two things. One is like, there is, um, there's not an infinite world”

Q they've done amazingly well on TikTok and YouTube Shorts, but there's no obvious, um, correlation to customer signups or to revenue, and so it's like, yeah, a ton of people are watching your videos, uh, But what? And it's, I didn't really know how to advise them. What do you advise when there's like a channel that's working in terms of attention, but not obvious value derived back to business?

A It's adapt or kill. So we went through this at HubSpot. We were, you know, the first product we built, uh, it's now called, referred to as the sales hub. At the, at the time it was essentially a bottoms up tool for sales folks that let them, you know, track their emails and, and contract opens and stuff. And at the time that thing was magical. Now it's like commonplace in every, every single, every single product. There was like, kind of like two things that were working for us that were driving Quite a bit of growth at the time. Um, and we had this, like, incentivized referral loop, so you got to, like, track a certain number of emails per month, and if you hit the limit, then you can invite a couple of your colleagues and essentially get a few, uh, free months. That was working for a while, as well as, uh, you know, we actually, because of the bottoms up, low friction nature, we were getting, you know, paid acquisition to work. The problem that we ran into, uh, at that Point was that even though it was growing, those two things actually had pretty good connection with, um, driving growth for that product. Uh, we got to, I don't remember, it was like maybe like a 100,000 weekly active users or something, and then we looked at the user base, and there was a huge mismatch between the types of used, a lot of the types of users that were being, that were showing up in that product…

AI assessment note: “It's adapt or kill. So we went through this at HubSpot.”

Q works and seeing the success in a channel, obviously, the decision around whether it's successful or not is largely made by the metrics that you see and how you interpret them. You said before that people often get metrics wrong. I agree with you, but I'd love to hear, what do you think are the biggest ways that you see founders and growth teams really misunderstand or get metrics wrong?

A Yeah, well, the first and foremost is metrics before strategy. I won't go too deep on that, because I feel like that's like a common thing. That's the biggest thing wrong, which is your metrics are there to help answer the question, is your strategy working? Right. Um, and so they are not there to determine your strategy. You have to have a reasonable hypothesis of what your strategy is first. Like what is, what is your core growth loop? Like all of these things. And then you can say, well, what are the metrics that we would use to measure that strategy and what would success look like? So that's number one. Number two is they basically go quant before qual. So, um, To understand what your metrics should be, you have to understand the qualitative underpinnings, and a great example of this is especially around retention. Um, like setting retention metrics, for example, actually it's, I would say, is 80 to 90% understanding the qualitative definitions of what is the problem that you are solving, what is the natural frequency that that problem occurs in In your target market's life, right? Um, so, and then, and then you can start to match the metric to it. Like, should we be tracking this on a weekly active user basis or a monthly active user basis? Active, what does active mean? Well, what is the behavior in the product that indicates we are solving the problem for this user? But…

AI assessment note: “the first and foremost is metrics before strategy.”

Q I, I mean, I'm an early stage investor. I work with many, many kind of, especially kind of PLG SaaS companies, very similar to Loom in terms of kind of go to marketing business model, and I think all the founders would say they understand the machine and the system, but they don't know what the constraint is. What do you advise founders who don't know what the constraint is?

A Well, I, I would, I would first go back, which is, I actually think most people, A lot of people don't understand what the machine is. And the reason is, is if you ask them to, uh, draw you a picture that answers how does the product grow, right? They'll either struggle to draw that picture or it'll like, it'll look like such a jumbled mess that you as the recipient can't really understand it. That that's actually a signal that not only have they kind of boiled it down and they understand it, but they're not able to communicate it in a way that the rest of the team understands. And then the rest of the team doesn't understand it. That's where you run into these problems like, oh, we're gonna, you know, come up with these 10 different tactics, try them all, hope one of them works, right? Uh, because they don't really understand how, how things like map, uh, actually like map to, uh, the equation. But understanding the constraint, um, ends up coming down to the quantitative part of this, right? The, the exercise I just, that I just mentioned is more of the qualitative part is like, hey, can I, Draw a bunch of boxes and arrows that say, um, as a new user enters the system, they walk through these four steps, and if they complete these four steps, another new user, like come, like comes out of it, right? That's kind of what that picture looks like. Um, the quantitative is now takin…

AI assessment note: “understanding the constraint, um, ends up coming down to the quantitative part of this”

Q I say you should hire it before, because you need to throw enough people at the machine to know if you have product market fit, you need enough data to understand if some segment of people liked it, and a growth hire can help you get that data to understand. Do you agree with me, or do you agree with everyone else, that growth is a post-product market fit higher?

A It's an and, not an or question. So I think this gets back to what is, what is your type, what is the type of product and what is the system? So it also gets to the question is, what do you mean by hiring a growth person, which is its own mess of a question in itself. Uh, and so I think I agree with you that you have to have enough volume coming into the system to really understand, uh, What these, like, what these levers are in the product and what you can, and what you can play with, right? And sometimes pre-product market fit, you, you need to hire a dedicated person that's just focused on bringing volume into this, bringing volume into the system. Sometimes you actually don't, you know, to give you an example, like if I was to start, I, let's, uh, if I was to start an amplitude competitor, right? For example, a new analytics product, right? Uh, you know, that thing is, um, I think it's not, that thing's not viral at all, right? Like you're buying that thing for tens of thousands of dollars as a whole, like set up costs, like all that kind of stuff. There's probably a team that's really focused on building that, building that product and building that engine, you know, engineering. And, but you need somebody in that case that, that, that growth model is going to be very marketing driven versus product driven. And as a result, you need a totally different skill set of person …

AI assessment note: “It's an and, not an or question.”

Q I, I mean, I'm an early stage investor. I work with many, many kind of, especially kind of PLG SaaS companies, very similar to Loom in terms of kind of go to marketing business model, and I think all the founders would say they understand the machine and the system, but they don't know what the constraint is. What do you advise founders who don't know what the constraint is?

A Well, I, I would, I would first go back, which is, I actually think most people, A lot of people don't understand what the machine is. And the reason is, is if you ask them to, uh, draw you a picture that answers how does the product grow, right? They'll either struggle to draw that picture or it'll like, it'll look like such a jumbled mess that you as the recipient can't really understand it. That that's actually a signal that not only have they kind of boiled it down and they understand it, but they're not able to communicate it in a way that the rest of the team understands. And then the rest of the team doesn't understand it. That's where you run into these problems like, oh, we're gonna, you know, come up with these 10 different tactics, try them all, hope one of them works, right? Uh, because they don't really understand how, how things like map, uh, actually like map to, uh, the equation. But understanding the constraint, um, ends up coming down to the quantitative part of this, right? The, the exercise I just, that I just mentioned is more of the qualitative part is like, hey, can I, Draw a bunch of boxes and arrows that say, um, as a new user enters the system, they walk through these four steps, and if they complete these four steps, another new user, like come, like comes out of it, right? That's kind of what that picture looks like. Um, the quantitative is now takin…

AI assessment note: “understanding the constraint, um, ends up coming down to the quantitative part of this”

Q British Airways or Delta. I, I, I know it sounds stupid, but that was the first thing that came to mind. High price point, high luxury, like good. Is that what we're talking about? Cause like, does that mean enterprise products should not do YouTube and Twitter? And do you know what I mean? I'm just trying to understand what that kind of means then for the high price products.

A You know, I live in a software driven world versus, uh, You know, uh, um, the luxury purse world. So, uh, and so most of my thinking and is very specifically targeted at software companies. And I think, I think for other companies, there's probably a set of different, different rules that, that rule the roost, you know, in your example, an enterprise product, should they not do YouTube and stuff? It's more about what the purpose of, of like YouTube and stuff is driving for that company. You know, yes, it might be driving, uh, Like awareness and, and these, and these other pieces, but that alone is not going to drive customers for an enterprise product. And even if you were doing it, if you looked at the overall mix of like, you know, at scale of what's driving customers, it's going to be a pretty likely a pretty small fraction. And so it kind of gets to an enterprise product. You have to make your sales machine work. And if you can't make your Pure noise. And so you got to get the core machine working, and then you can add these other things that help accelerate it as an example. I think where the, you know, the focus piece of that is, is I can try, I can do a bunch of these, I can do a bunch of these things, but it's actually pulling attention away in the early days from actually understanding and getting that, that core machine working. Riverside is actually probably a good e…

AI assessment note: “an enterprise product, should they not do YouTube and stuff? It's more about what”

Q I say you should hire it before, because you need to throw enough people at the machine to know if you have product market fit, you need enough data to understand if some segment of people liked it, and a growth hire can help you get that data to understand. Do you agree with me, or do you agree with everyone else, that growth is a post-product market fit higher?

A It's an and, not an or question. So I think this gets back to what is, what is your type, what is the type of product and what is the system? So it also gets to the question is, what do you mean by hiring a growth person, which is its own mess of a question in itself. Uh, and so I think I agree with you that you have to have enough volume coming into the system to really understand, uh, What these, like, what these levers are in the product and what you can, and what you can play with, right? And sometimes pre-product market fit, you, you need to hire a dedicated person that's just focused on bringing volume into this, bringing volume into the system. Sometimes you actually don't, you know, to give you an example, like if I was to start, I, let's, uh, if I was to start an amplitude competitor, right? For example, a new analytics product, right? Uh, you know, that thing is, um, I think it's not, that thing's not viral at all, right? Like you're buying that thing for tens of thousands of dollars as a whole, like set up costs, like all that kind of stuff. There's probably a team that's really focused on building that, building that product and building that engine, you know, engineering. And, but you need somebody in that case that, that, that growth model is going to be very marketing driven versus product driven. And as a result, you need a totally different skill set of person …

AI assessment note: “sometimes pre-product market fit, you, you need to hire a dedicated person”

Q If we just dive on that, do you think it's obvious when channels are starting to deteriorate in effectiveness and growth?

A This is as, this is, I would actually say even more, Harder than understanding your constraint in the system. Essentially, what you're trying to do is predict a point of saturation and, uh, of, of what you're, of what you're doing. And it's, it's hard on multiple dimensions. One is that I think most people, I think the tendency is to actually think something is going to saturate much more quickly than it actually does. And that's because If you look at the really high growth companies, you know, taking HubSpot as example, they, along their way on content, their, their path to really establishing that content loop, they hit ceilings along the way, right? And it wasn't like, you know, they stopped at that first seal and been like, oh, throw the hands up, like we're done. Every, like every step of the way, there was some new idea, some innovation that unlocked like Another pool. Right. And so going beyond, you know, kind of digging beyond that, um, is really, really hard work because you're basically trying things that others aren't trying. And that's like actually just very hard for both people and, uh, and a company, um, to do it. So I actually think predicting saturation is, um, Well, one of like the hardest things, um, for companies to do HubSpot, I would say is actually. Like one of the biggest things I learned there is, uh, they are probably one of the best, if not the best …

AI assessment note: “predicting saturation is, um, Well, one of like the hardest things”

Q they've done amazingly well on TikTok and YouTube Shorts, but there's no obvious, um, correlation to customer signups or to revenue, and so it's like, yeah, a ton of people are watching your videos, uh, But what? And it's, I didn't really know how to advise them. What do you advise when there's like a channel that's working in terms of attention, but not obvious value derived back to business?

A It's adapt or kill. So we went through this at HubSpot. We were, you know, the first product we built, uh, it's now called, referred to as the sales hub. At the, at the time it was essentially a bottoms up tool for sales folks that let them, you know, track their emails and, and contract opens and stuff. And at the time that thing was magical. Now it's like commonplace in every, every single, every single product. There was like, kind of like two things that were working for us that were driving Quite a bit of growth at the time. Um, and we had this, like, incentivized referral loop, so you got to, like, track a certain number of emails per month, and if you hit the limit, then you can invite a couple of your colleagues and essentially get a few, uh, free months. That was working for a while, as well as, uh, you know, we actually, because of the bottoms up, low friction nature, we were getting, you know, paid acquisition to work. The problem that we ran into, uh, at that Point was that even though it was growing, those two things actually had pretty good connection with, um, driving growth for that product. Uh, we got to, I don't remember, it was like maybe like a 100,000 weekly active users or something, and then we looked at the user base, and there was a huge mismatch between the types of used, a lot of the types of users that were being, that were showing up in that product…

AI assessment note: “It's adapt or kill. So we went through this at HubSpot.”

Q If we just dive on that, do you think it's obvious when channels are starting to deteriorate in effectiveness and growth?

A This is as, this is, I would actually say even more, Harder than understanding your constraint in the system. Essentially, what you're trying to do is predict a point of saturation and, uh, of, of what you're, of what you're doing. And it's, it's hard on multiple dimensions. One is that I think most people, I think the tendency is to actually think something is going to saturate much more quickly than it actually does. And that's because If you look at the really high growth companies, you know, taking HubSpot as example, they, along their way on content, their, their path to really establishing that content loop, they hit ceilings along the way, right? And it wasn't like, you know, they stopped at that first seal and been like, oh, throw the hands up, like we're done. Every, like every step of the way, there was some new idea, some innovation that unlocked like Another pool. Right. And so going beyond, you know, kind of digging beyond that, um, is really, really hard work because you're basically trying things that others aren't trying. And that's like actually just very hard for both people and, uh, and a company, um, to do it. So I actually think predicting saturation is, um, Well, one of like the hardest things, um, for companies to do HubSpot, I would say is actually. Like one of the biggest things I learned there is, uh, they are probably one of the best, if not the best …

AI assessment note: “predicting saturation is, um, Well, one of like the hardest things”

Q Sorry, can I just interrupt you? How is finding your aha moment automated? Like, is it not highly nuanced and contextual dependent on the millions of infinite options of what a product could be?

A I guess what I'm saying here is like before, um, You know, your top tier growth person, uh, seven or eight years ago would know, um, how to do that analysis essentially like by hand, right? Because the tooling and technology wasn't necessarily there to, to, to do it any other way, right? Um, I think like today it's already kind of automatically done inside tools like Amplitude and others. But I think there's another layer of that where all of these things get essentially the friction to do them doesn't require the same skill set that it required you eight years ago. But this kind of gets to the dichotomy that I was talking about. The things that don't change, right, that I don't think AI is going to really help you with that much is understanding, like, all of these qualitative underpinnings Uh, of your product, of your use case, of your problem, of your natural frequency, and then the psychological levers that you can tap into, um, to move a user's behavior. I think AI is going to have a really hard time at, like, understanding that and doing not thinking. But in addition to that, I think I kind of put out this, uh, Like a little bit of a lighthearted, but serious tweet at some point, which was like growth boils down to four things, which is you find an arbitrage opportunity. You use that to spark some type of compounding system, some type of growth loop. You optimize the crap…

AI assessment note: “today it's already kind of automatically done inside tools like Amplitude”

Q Have they hit a scene? If you were, if you were to be head of growth at Twitter, what would you do?

A Oh, uh, I, I think this gets back to what we were talking about earlier, which is like, you can have, you know, product market fit lives on two vectors, the, uh, both the strength and the size, the strength of the fit and the size of the market. I think what you're really seeing, and I, I think it was like Eugene, um, way who captured this well in his essay, which is, Hey, they actually had really strong PM fit with a smaller market, which like they have these like subcultures of Twitter of like tech and, and, and, uh, politics and, and other things. And what they've done with a bunch of their changes, like the algorithmic feed and all that kind of stuff is they're essentially searching for, uh, PM either they're searching for that PM fit with a larger market. Um, a larger market of folks. The challenge with that is that you, you do that and there's a risk of breaking like your original PM, like your original PM fit. And that's going to be the big question, right? Like that's going to be the big question is like if they keep pushing aggressively in that direction. So, you know, I, I think, uh, I don't know what I would, I would do ahead of, ahead of growth of Twitter. I think the challenge that they're caught in is the reason they're making all of these changes is because, um, Of Elon's forty four billion dollar buy and needing to make that money back. So he has to find PM fit …

AI assessment note: “I don't know what I would, I would do ahead of, ahead of growth”

Q is such an arbitrage. Yeah, a hundred percent. I, I agree with you, um, and I hope it does, and I agree with you in terms of chaos injecting opportunity and innovation, but you've had an amazing career. When you think about, and I think lessons are learned from often mistakes, what is the biggest growth decision you've made that went wrong, Brian, and how did it change your mindset?

A Uh, so, so my fundamental view is, like, the only way to figure things out is to, like, create. Um, you can do some thinking up front, right? But then you gotta get into, like, the making, um, the creation of these things. And you learn through that creation of, like, what's right and wrong, and then you go back to, like, the start of the process and think a little bit. I get really frustrated with folks who think they can think their way to all the solutions, and I'm like, that's impossible. Like, we, we just need to get into the, we just need to get into the making. Of things, right? I think, like, as a result is, like, I think a lot of people look back at your question that you asked, and they're like, well, what is something that I thought long and hard about, but I was wrong, you know? Um, and as a result, it, like, had a huge, had a huge cost. I just try to view things as, like, it's just always a natural, like, being wrong is, like, a natural evolution to, um, you know, its next step. But I will answer your question directly to satisfy To satisfy you. My biggest mistake at HubSpot was a little bit of what we touched on before, which is that I was in love with, like, the virality and other things that we had going with the early product, but there was a disconnect with the core target market of the core business. I was pushing for, like, that virality, and I kept going do…

AI assessment note: “My biggest mistake at HubSpot was a little bit of what we touched on before”

Q British Airways or Delta. I, I, I know it sounds stupid, but that was the first thing that came to mind. High price point, high luxury, like good. Is that what we're talking about? Cause like, does that mean enterprise products should not do YouTube and Twitter? And do you know what I mean? I'm just trying to understand what that kind of means then for the high price products.

A You know, I live in a software driven world versus, uh, You know, uh, um, the luxury purse world. So, uh, and so most of my thinking and is very specifically targeted at software companies. And I think, I think for other companies, there's probably a set of different, different rules that, that rule the roost, you know, in your example, an enterprise product, should they not do YouTube and stuff? It's more about what the purpose of, of like YouTube and stuff is driving for that company. You know, yes, it might be driving, uh, Like awareness and, and these, and these other pieces, but that alone is not going to drive customers for an enterprise product. And even if you were doing it, if you looked at the overall mix of like, you know, at scale of what's driving customers, it's going to be a pretty likely a pretty small fraction. And so it kind of gets to an enterprise product. You have to make your sales machine work. And if you can't make your Pure noise. And so you got to get the core machine working, and then you can add these other things that help accelerate it as an example. I think where the, you know, the focus piece of that is, is I can try, I can do a bunch of these, I can do a bunch of these things, but it's actually pulling attention away in the early days from actually understanding and getting that, that core machine working. Riverside is actually probably a good e…

AI assessment note: “It's more about what the purpose of of like YouTube and stuff is driving”

Q is such an arbitrage. Yeah, a hundred percent. I, I agree with you, um, and I hope it does, and I agree with you in terms of chaos injecting opportunity and innovation, but you've had an amazing career. When you think about, and I think lessons are learned from often mistakes, what is the biggest growth decision you've made that went wrong, Brian, and how did it change your mindset?

A Uh, so, so my fundamental view is, like, the only way to figure things out is to, like, create. Um, you can do some thinking up front, right? But then you gotta get into, like, the making, um, the creation of these things. And you learn through that creation of, like, what's right and wrong, and then you go back to, like, the start of the process and think a little bit. I get really frustrated with folks who think they can think their way to all the solutions, and I'm like, that's impossible. Like, we, we just need to get into the, we just need to get into the making. Of things, right? I think, like, as a result is, like, I think a lot of people look back at your question that you asked, and they're like, well, what is something that I thought long and hard about, but I was wrong, you know? Um, and as a result, it, like, had a huge, had a huge cost. I just try to view things as, like, it's just always a natural, like, being wrong is, like, a natural evolution to, um, you know, its next step. But I will answer your question directly to satisfy To satisfy you. My biggest mistake at HubSpot was a little bit of what we touched on before, which is that I was in love with, like, the virality and other things that we had going with the early product, but there was a disconnect with the core target market of the core business. I was pushing for, like, that virality, and I kept going do…

AI assessment note: “My biggest mistake at HubSpot was a little bit of what we touched on before”

Q Have they hit a scene? If you were, if you were to be head of growth at Twitter, what would you do?

A Oh, uh, I, I think this gets back to what we were talking about earlier, which is like, you can have, you know, product market fit lives on two vectors, the, uh, both the strength and the size, the strength of the fit and the size of the market. I think what you're really seeing, and I, I think it was like Eugene, um, way who captured this well in his essay, which is, Hey, they actually had really strong PM fit with a smaller market, which like they have these like subcultures of Twitter of like tech and, and, and, uh, politics and, and other things. And what they've done with a bunch of their changes, like the algorithmic feed and all that kind of stuff is they're essentially searching for, uh, PM either they're searching for that PM fit with a larger market. Um, a larger market of folks. The challenge with that is that you, you do that and there's a risk of breaking like your original PM, like your original PM fit. And that's going to be the big question, right? Like that's going to be the big question is like if they keep pushing aggressively in that direction. So, you know, I, I think, uh, I don't know what I would, I would do ahead of, ahead of growth of Twitter. I think the challenge that they're caught in is the reason they're making all of these changes is because, um, Of Elon's forty four billion dollar buy and needing to make that money back. So he has to find PM fit …

AI assessment note: “I don't know what I would do ahead of growth of Twitter.”

Q ask you two things that can actually come from Kit Bodnar at HubSpot, um, related to channel. He said on the show before that one channel working really well will get you to fifty million in ARR. Two channels working really well will get you to a hundred million in ARR. First question, what do you advise founders in terms of channel selection? How to know which channel to choose?

A So I agree with Kip, right? Which is that, um, once you figure out kind of that core, that thing that's working, it's almost always in a venture scale company, the better thing to do Is to focus your firepower, your limited attention, your limited capital, your limited talent on fueling that thing as fast as possible, which is very counter to the, like a lot of the common advice, which is like, ooh, you don't want to be too reliant on one thing. You want to diversify, like all of that kind of stuff. That is a, that is a bad investor's point of view, right? Like, yes, that might work for money management, right? It does not work For growing a business because it ignores all of the complexity that comes with diversification of products or channels or any of these types, any of these types of things is like, if you find something that's working, you better focus that firepower. Um, but the second piece of that is that you then need to anticipate when that one thing might start running out of fuel. So you're not like, Caught with your pants down in trying to find that second thing that Kip is talking about too late, because that, that creates this stall out effect and is really hard to, to, to reignite. Um, it's really hard to like get yourself going from a stall out effect.

AI assessment note: “once you figure out kind of that core, that thing that's working, it's almost always”

Q Sorry, can I just interrupt you? How is finding your aha moment automated? Like, is it not highly nuanced and contextual dependent on the millions of infinite options of what a product could be?

A I guess what I'm saying here is like before, um, You know, your top tier growth person, uh, seven or eight years ago would know, um, how to do that analysis essentially like by hand, right? Because the tooling and technology wasn't necessarily there to, to, to do it any other way, right? Um, I think like today it's already kind of automatically done inside tools like Amplitude and others. But I think there's another layer of that where all of these things get essentially the friction to do them doesn't require the same skill set that it required you eight years ago. But this kind of gets to the dichotomy that I was talking about. The things that don't change, right, that I don't think AI is going to really help you with that much is understanding, like, all of these qualitative underpinnings Uh, of your product, of your use case, of your problem, of your natural frequency, and then the psychological levers that you can tap into, um, to move a user's behavior. I think AI is going to have a really hard time at, like, understanding that and doing not thinking. But in addition to that, I think I kind of put out this, uh, Like a little bit of a lighthearted, but serious tweet at some point, which was like growth boils down to four things, which is you find an arbitrage opportunity. You use that to spark some type of compounding system, some type of growth loop. You optimize the crap…

AI assessment note: “this kind of gets to the dichotomy that I was talking about.”

Q ask you two things that can actually come from Kit Bodnar at HubSpot, um, related to channel. He said on the show before that one channel working really well will get you to fifty million in ARR. Two channels working really well will get you to a hundred million in ARR. First question, what do you advise founders in terms of channel selection? How to know which channel to choose?

A So I agree with Kip, right? Which is that, um, once you figure out kind of that core, that thing that's working, it's almost always in a venture scale company, the better thing to do Is to focus your firepower, your limited attention, your limited capital, your limited talent on fueling that thing as fast as possible, which is very counter to the, like a lot of the common advice, which is like, ooh, you don't want to be too reliant on one thing. You want to diversify, like all of that kind of stuff. That is a, that is a bad investor's point of view, right? Like, yes, that might work for money management, right? It does not work For growing a business because it ignores all of the complexity that comes with diversification of products or channels or any of these types, any of these types of things is like, if you find something that's working, you better focus that firepower. Um, but the second piece of that is that you then need to anticipate when that one thing might start running out of fuel. So you're not like, Caught with your pants down in trying to find that second thing that Kip is talking about too late, because that, that creates this stall out effect and is really hard to, to, to reignite. Um, it's really hard to like get yourself going from a stall out effect.

AI assessment note: “So I agree with Kip, right? Which is that, um, once you figure out”