Who Will Win the Go-To-Market AI Race? with Stage 2 Capital Co-founder Mark Roberge

I'm very blessed to be able to talk to a lot of entrepreneurs and thought leaders and serial entrepreneurs and VCs that I respect every year. We're talking a lot about AI, and I haven't seen the energy in great entrepreneurs, thought leaders, visionaries that I see in their eyes today in like 20 years. And I share the same sentiment. And I'm trying to understand why and respect the fact that like when the internet technology kind of came out, we knew what it was in like whatever, 1997. This is kind of the same time as what's going on in AI right now. That like some people at this conference weren't even born yet. That's how long ago it was. And I'm trying to explain like why do I have this feeling and why do our peers have this feeling. And I think it's like, for the first time in 20 years, it's a blank canvas. It's complete artistic freedom, especially in our area of B2B software. We can reimagine everything. And that didn't happen with the other, like blockchain, like I don't know what kind of impact that's going to have on society. If crypto comes back or whatever, but like I never saw the blockchain thing in our sector, B2B software. I was just like, I look at Plays, I'm like, Can't you just use a QR code or like encryption? And like, yeah, you kind of can. And then like social was big. Social was huge for our society. For B2B, I don't know. I kind of rewrote some of the marketing playbooks and maybe the way we collaborate. Mobile was huge for society, but like, I don't know. Like I use the mobile version of Slack and Zoom and the CRM, but like, kind of is based on the web version. But the internet, when the internet came out and we knew what the technology could do, we could reimagine everything. And that's how these visionaries feel with AI. And I think that's really actionable for us. Because as you start thinking about it, you become paranoid that everything you're working on is irrelevant. I don't know if you feel like that. I feel like that sometimes. And I've been pushing our entrepreneurs to think that way too, because I think there's a decent chance that whatever you're working on as a startup, as a use case, as a problem, it might be irrelevant. It might be like it's 1997 and you're working on a client-server on-premise piece of software and you're about to get the rug pulled out from under you. Pulled out from under your feet. And so that's just the one thing I'd really push you on is that's our job as founders, is to be visionaries. This is an exciting time to be a visionary. But try to think what a post-AI technology world looks like in 8 years and reimagine what your startup is doing. Reimagine how the use case or the problem that you're solving is solved in that world. Because otherwise, I think we face irrelevance. Now, it gets even more complicated. Because the other narrative that I'm hearing that I agree with is that we're in a hype cycle. I'm showing the Gartner hype curve here. This was first mentioned to me in January from Mamoon at Kleiner Perkins, who's been a great investor early investor in Slack and Figma, Box, etc. And the narrative is that there could be a very close parallel to how the internet disrupted society and B2B software and how AI will. And we're seeing a lot of parallels to that. And if we were to say what year this is in the internet rollout, it's probably around 1998. Okay, so if you remember, if you were around back then, 1998, it was the new economy. Do you remember the new economy? All the Goldman Sachs bankers and McKinsey people quit their jobs and became CEOs of these supposedly disruptor businesses that went public with no revenue. And it was like iterative ideas. It was like putting the news online or putting your company brochure online. No one could see Snowflake. No one could see Uber yet. And even if they did, The society wasn't ready. It was iterative stuff. Like, the early adopters, the first movers didn't win either. In the first wave of the internet, we used AOL to dial up to it, opened up a Netscape browser, and searched with AltaVista. Where are those companies? That's where we are right now, potentially. And then obviously it came crashing down, and the joke was that B2C was back to consulting, and B2B was back to banking. And it was like, I remember I had enrolled at MIT at about the trough for business school, at the trough of that curve. There were 600 students in my class, 4 of them were starting companies and I was one of them. Everybody's like, "What are you doing? Starting a company? That's so 1999." And sure enough, like within those walls, companies like HubSpot were created and we had this beautiful disruption and innovation. That was an amazing time for entrepreneurship. Perhaps that's what we're going to see with AI, and we're seeing early signals of it in terms of crazy valuations, and certainly iterative technologies, just embedding AI in the workflows we do today. That's not the answer. That's not the disruption. In addition to just rethinking your vision, you got to think really big about it because Even if you feel like you have your AR strategy, is it really just an iteration that's going to die in the hype cycle? Or is it truly a disruption on what's going to sustain for the next few decades and produce unicorns? All right, so how do we come up with that? I kind of think right now an important piece of work is the innovator's dilemma. And I'll get to that. But what we're trying to do is we're trying to think like, okay, if Web 1.0 is static content and the user-generated content produced a bunch of unicorns, what is it that's going to be the hype versus the displacement? And that's where I like to hone in on innovator's dilemma. I don't know if you know innovator's dilemma. It's a long time work, long time work. We got a fan here in front of it. Clay Christensen, the late Clay Christensen, we lost him I think last year. Jason M. Lemkin, professor at Harvard Business School, author of this work. And after reams and years of research, he proved that the innovator's dilemma causes big companies to get disrupted. It's an important key for entrepreneurship. And the whole premise there is that big companies ride that, like, that iterative disruptive wave of that technology. And they get so big that they have to sell big products for big profits to their current market. But along comes a new disruptive technology that in the beginning doesn't represent a big market, and they ignore it, partially because they're too small for them and partially because it will disrupt their business. Okay? So how many— raise your hand if you use CRM. Okay, let me, let me say this. In 1995, Siebel and ACT owned 90% of the CRM business. Raise your hand if you use a CRM. Keep your hand up if you're using Siebel or ACT. Case in point. Why didn't they go? Innovator's dilemma. So like they have a beautiful on-premise client-server application And here comes the internet. And I'm sure there was a strategic conversation of like, "Should we make this cloud-based? Let's look at it." Well, first off, it would cause years of work for us to re-architect this whole thing, a huge cost. And for what gain? There's no market. What CTO is going to put their data in the cloud? That was the message of the time. Every CTO wants their data and the server farm in their basement with protection of 3 badges to get in to get access to it. No one's gonna put it in the cloud. But Salesforce takes the gamble. Their first logo was software with a red X through it. And it didn't get sold to the big companies. It got sold to the small companies first for a little bit of money. But they worked on it year after year after year. And then the big companies were ready for it and Siebel and ACT weren't. And it caused cannibalization. And disruption. So what is the innovator's dilemma today as you rethink your startup in an AI-first world, knowing that we're in a hype cycle? How do you not get hyped? How do you become a true disruptor and find the innovator's dilemma? I don't know, but the gentleman that told me about this one is in the audience here. His name's Omer. He's the co-founder of Tatango, and he's working on one of these. He basically said, "Think about it. Let's just use sales tech as an example. Most sales tech unicorns, the foundation of their pricing model is seat-based. That's how it works." Just imagine if an engineer approaches the board or CEO and says, "I'm working on technology that's going to get rid of all SDRs." No way. That will destroy our business. Our whole business is seat-based. And God forbid we could even transition from a seat-based to a consumption model. There's no way we can demand the ACVs that we get from them. It'll cannibalize our entire business. And yet the potential of AI and the maximum efficiency it can create will be the displacement technology. Is that an innovator's dilemma? If you look at the language from all of the incumbents today, it's copilot, copilot, copilot. Is displace their cannibalization? Is that the opportunity for entrepreneurship? Is that the difference between hype and displacement in this huge tech disruption that we're going into? What's the innovator's dilemma for your business? Your sector. Now I do have to say, like, this is scary stuff. Alright, like, not just the fact that geniuses that work on this stuff are scared of the future of the human race, which, that's very scary. But what's probably easier to see is the economic impact that this could have. And I'm not like, I'm not advocating it. I'm just saying this is what people are talking about and this is the potential of the technology. Solving this is not for Saster. This is for Congress, United Nations, I don't know, the halls of— the socioeconomic halls of Yale and Stanford and Harvard, I don't know. But I do know that I want to just mention it. I recognize the economic impact this could have. This technology is coming no matter what. I appreciate petitions to sign to try to stop it, but I I'm not sure that that's the solution. It just seems like bad actors will be able to get ahead, a leg on us. And I'd rather this technology be run by the people in this room. I trust them more. And so the only point I'll make is if this were to happen— and I will say a caveat that, like, as I debate this in the halls of Harvard Business School with some of the best economists in the world, they have gone off and done research and come back to me and said, hey, The same narrative was happening at the brink of mass production at the beginning of last century, and the same narrative was happening at the brink of internet and e-commerce and disruption of jobs, that that would happen. And it didn't happen. In fact, it created more jobs. So that's their prediction. I hope they're right. It's hard for me to see it, but that's what they're saying. But God forbid this does create tremendous job displacement. If you happen to be one of the benefactors Just make sure you find a way to progress rather than regress society. I don't know what the answer is, whether it's a UBI tax. I don't know. But just let's make sure we understand this as we embark on this crazy technology. OK. So go to the mountain, rethink your business in a post-IA world. Hard to do. And by the way, we're in a hype cycle. So think bigger than you actually are. Unfortunately, more complexity and more bad news. If you are able to come up with a big vision and figure out a true displacement business model and technology that is going to be the unicorn in 10 years, the market's not ready for it right now. You will have no customers. I'm sure someone came up with Uber in 1997 and it failed miserably. Probably wasn't even technically feasible. So we're not ready for it. So this is really hard because it's like, "OK, if I keep working on what I'm doing, I'm going to get disrupted in 10 years, but if I work on the true disruption in 10 years, I'm not going to have any customers." And so this is where the old adage of "design big but start small" is a very good narrative and operating system to follow. And in fact, this is— forget about AI— this is a root cause of what I see as a lot of entrepreneurial failure this year anyway. And it's caused because of the obsession with venture capital. I mean, it's surprising to people that more wealth is generated in startups, in non-VC-backed startups, than VC-backed startups. That's surprising to people. But it doesn't make the headlines. The VC-backed startup makes the headlines. So everyone wants to raise VC. And like, I'm a VC. I'm telling— more wealth is created in not VC. I'm just telling you that. But when you have to go after VC, like our job for our investors is to generate huge returns, like go after unicorns. So you have to tell a big market story, like a billion-dollar market story. The problem is, yeah, tell the story, but most entrepreneurs try to prove the story right out of the gate. They build a huge product for everybody and try to sell it to everyone, and that leads to failure. You just can't do that. We don't need to see it all working in the first year. We need to believe the vision. So yeah, tell the big story, but build the MVP that could potentially get you to $10 million. And once you test it, exploit it and scale that one product in that single market, get it to $10 million. It takes 2 or 3 years. If you're cruising, And then that gives you time to test the next market or test the next product or both. And then use that in the next 3 years to get to $50 million. And that gives you more time to test the next product, the next product market, and you're a platform and now you're going IPO. That's the big story. And that is really critical in this time where I'm telling you, or I'm pontificating, that the ideas we have today are either going to get disruptive or they're iterative. We need the big idea that the market's not ready for today. So we have to find that beachhead that's safe. And that happened during the internet. Bezos and Jobs, I can give you a story about both of them. Jeff Bezos did not set out to be a book salesperson, but that was the first version of Amazon. Bezos was, I think, a quant hedge fund person. And he was like, "This internet thing's crazy. All of commerce is going to be disrupted. All of brick and mortar is going to be disrupted. We're going to order everything from clothes to food to everything online. And I want to sell that." But he didn't start there. He actually made a list of 50 things to sell and looked at it through a lens. And he chose books first because we weren't ready. For the rest. Books had high margin, books had millions of unique products every single year, and most importantly, no one at that time trusted putting their credit card in, and no one at that time trusted buying a product without touching it in a store. But who cares? It's a book. So that's the beachhead, and he exploited it. And not only was it a beautiful beachhead, but it created a moat for the long-term vision. Because it was years of selling books that helped him with his operational rigor, all the warehouses, all the delivery mechanisms, and Walmart couldn't catch up. And here is his vision 20 years later. The same thing happened with Jobs. I don't know if you know, but Jobs got fired from Apple in the '80s, and he got rehired as CEO when they were months away from bankruptcy, and he turned the company around with the reinvention of the Mac. Then when the internet came about, he had the big vision. He's like, "Apple is going to be the hub for the consumer on their digital media experience, everything." But he didn't start there. He started with music, which was ironically number 2 on Bezos's list of a best product to start with. He started with the iPod and the iTunes Store and used that moat to transition to the iPhone. And the app marketplace, and the rest is history. We're talking about some of the most valuable companies in the world on the advent of a huge technological disruption. And we are back there right now. And that's the opportunity ahead of us. Okay? All right. I have to go to the mountain and reimagine my business. But we're in a hype cycle, so I have to think bigger. And when I think big, they're not gonna be ready for it, so I have to choose a good beachhead. And we got a couple examples on how to do that. Another example on choosing that opening beachhead that's unique to AI is we need good data to train the model. We didn't need that with mobile, we didn't need that with the internet, but we need good data. And so if you want to stack the deck in your favor, choose use cases as an entrepreneur where the big company incumbents don't have the data. That'll stack the deck in your favor. If we go back to sales tech as an example, everybody's starting SDR copilots. They have all the data. The incumbents have all the data, the messaging, the cold calls, the conversion to opportunities. Everyone's starting forecasting AI models. They have all the data. They have all the opportunity conversions and the sales cycles. Sales AI coaching, they don't have the data. No one has videos of sales managers coaching reps and then that turning into performance improvements. No one has that. It's just an example. But if you're going to go after this huge technological disruption, consider whether the incumbents have the data or not. Now at the same time, like even if you are doing an SDR copilot, there is a chance you can win because of talent. There's tremendous research that shows that the top engineering talent prefers startups over big companies. Part of it's because like they actually don't care as much about maximizing their salary income. They care more about working on cool technology, with little red tape to deploy that technology. And that's what happens at startups. It doesn't happen at big companies. Their stock can actually be worth a lot too, and that draws them in. So now we're faced with a bake-off. You got startups who have the best AI tech talent, but they don't have the data. They have limited data. You got the incumbents that have like average, AI tech talent with all the data? Who wins? I personally thought the most data would win, but Sam Altman and Peter Thiel disagree, and I will go with their vote. They, I think if I'm quoting them correctly, feel like there's diminishing returns to the data at some point in the model development. So if you had to choose between having an A+ AI tech talent team with millions of records of data and you are going against a mediocre AI tech talent team with trillions of data, that the startup wins. And that starts to make sense with the diminishing returns of that data. So there's a feather in our cap as we go head to head. But regardless, what does happen in these companies is the acquisition of data becomes an important function, probably within the business development domain. 'Cause we don't have any data. And we see some early signs of techniques here. We see some companies who are out there using the publicly available data to build a model, which is pretty commoditized. But they're actually using your company data to tailor the model to you. That's cool. I come in, I use your data, I tailor it. I think there might be some limitations to how much data a single company has to build a model, but that's cool. There are other people who are striking up relationships with people who have data but have no shot at building a tech team. Maybe the credit bureaus is an example. Maybe grocery stores is an example. I don't know. Who has tons of data and has no chance of building a tech team? That's cool if we can partner up with them. The problem is that the credit bureaus think their data is more important than the tech talent, and the startup thinks the tech talent's more important than the data. So you have a little bake-off. I mean, maybe if you can actually find 8 different players that have data and start to build exclusive partnerships with them, you kind of chess move them into having to join your movement or being left out of the future. But regardless, we've never really talked about data acquisition as a function. The narrative is that that's going to be an important function for startups as we attack this opportunity. So that would be a framework to think about a beachhead. We have to think bigger. We have to go to the mountain. We're probably in a hype curve, so we can't get stuck up in something iterative. We have to find a— but yet they're not ready for the big idea. So we have to come up with a beachhead. And a good beachhead is where they don't The big incumbents don't have the data. We can get access to the data and that beachhead creates a moat for the long-term vision. Something to think about. So we ran an experiment at Stage 2 Capital that'll be instructive to the next point in this journey to building a unicorn in a post-AI world. And so Stage 2 Capital, if you haven't heard of us, we're the first VC firm run and backed by go-to-market professionals. So we have the CRO and the CMO and the CCO. We have 500 of them as our investors from Snowflake and Zoom and Atlassian and GitHub and all those companies. Some of them are sitting in the room here and speaking at the conference. So what I did, and actually I wonder if this is true for you all, how many of you 2 years ago in 2021 had a board mandate to grow at all cost and now your board mandate is to be very efficient. Is anyone going through that change? Okay, that was happening to a lot of our LPs as well. How many of you are looking at AI as a main driver to do that? Yeah, there's not really many hands, which is a little confusing to me 'cause it seems like that would be a big opportunity to adopt that. And so that's what I said at the LP meeting in May. I was like, hey, If you're under a lot of pressure from your board to do more with less and you want to try to use AI in your go-to-market motion, like for your SDRs, your account executives, your support people, your marketers, whatever, we're seeing a lot of go-to-market AI tech. So I can just email them to you every week and you can take meetings with the ones that we see. And people are psyched about that. 100 LPs said, "I want to see that for my company, to use in my company." And so I promoted that. I went online and said, "Hey, listen, I have 100 CROs and CMOs from places like Snowflake and Atlassian and Zoom and all these places that wanna see the tech we're seeing. So if you're starting a company in go-to-market AI, fill out this landing page and I'll promote it to them. And I had 200 people fill that out in like a week. So we got a pretty good glimpse at like where the innovation was happening specifically in go-to-market AI. And it was actually kind of boring to be honest with you. It was all the iterative stuff. Like a quarter of them were SDR Copilots, a quarter of them were forecast things, whatever. And we had like, I don't know, we had 50 meeting requests, I think. And I'm exaggerating here, it was pretty much zero pilots. That's crazy. And when I went back to the LPs and I was like, what's going on? They're like, we love the technology, it's just the lawyers and the IT people won't let us use it. We don't have a legal policy on IP for AI in our data. And the security team doesn't know how to deal with it. So we have to wait. Okay, well, that's pretty instructive. We're starting a company, where do we have to sell? Well, first off, I think that's good news for startups. Because that means that your incumbents that you want to try to disrupt are going to wait to get more operationally efficient. And you can get ahead of the game and get there faster. The other thing that I saw was they're just suffering, especially in go-to-market tech, from too many apps. And they were like, "I just want to wait to find out what my current vendor comes out with." And I think that helps us as startups too, because I don't know if the current vendors are going to come out with the best stuff. I think the startups will. And if the big companies are going to wait to adopt the new innovations, It's just an advantage for us as startups. So I think that's quite instructive to when we build that MVP is where do we start? And I, I kind of feel like there were like two seemingly unrelated things that happened in the last couple years. One being that ChatGPT came out and we got a really good glimpse of what AI could do. And the other one is that we moved, we had the bullish market in tech dissolve and we're back to an efficient market. So, now what you're left with is, if I had to choose which industry to roll out a new tech into, I would not choose manufacturing, I would not choose healthcare, I would not choose finance, I would choose tech. They're innovators. They know how to run experiments. They know how to deploy this stuff. And the biggest meme in the boardroom at the tech companies is, "Do more with less." That's kind of the summary of AI. So we have this beautiful, like, the opposite of a perfect storm, this opportunity where our beautiful beachhead market has a strong desire for our technology. It's just the big folks don't want to adopt it. So that's how I would think about it, is if I'm going to build my MVP, I'm going to build it with a startup tech community. That's scary for me to say because The startup tech by itself, that has a high failure rate, so you're going to have a lot of churn, but it's just where you're going to have the quickest bang for your buck to build the models. Then quickly move up to the SMB mid-market tech, which is a pretty common best practice. Over the years, the big tech companies, I think, will get their legal and IT stuff together and also see that the AI stuff that the large incumbents come up with, is sub-optimal to what you as entrepreneurs come up with. So that's it. I got one more for you, but like, we could be on the brink of the most important technology disruption of our lifetime. And that means that a lot of the stuff we're working on is it's gonna be irrelevant in 6 years. So rethink it and understand that we're in the hype curve. So you gotta think really big and appreciate innovator's dilemmas as a way to craft that story. Knowing that the market's not gonna be ready. So you have to be very strategic in choosing that first use case like Bezos did, where the market is ready and it creates a moat for you. And selling it to the right market. Okay. I got one more for you. But, um, and by the way, um, I'll take questions over there. It's just that like, uh, AI is so controversial right now. Like there's protesters outside of this conference right now. I just feel like a politician taking questions on AI in front of cameras. So if it's cool with you, just go over there and I'll do the questions. Sorry for chickening out. But anyway, I had mentioned Stage 2 Capital. I want to share some content because I always come up here and speak and I try to give you really tactical stuff that you can use tomorrow and I love getting the LinkedIn messages that say like, "I went to SaaStr. I implemented the thing and it accelerated revenue." I love that. And this is more strategic and I wanted to do this today because again, I'm really paranoid that what we're working on is irrelevant. So that's why I talked about that today. But I want to give you some tactical stuff too. So first off, Mandy, who's going to be speaking on Friday, one of our partners. If you're in annual planning season, you should be in annual planning season. Don't wait till January to build your bottoms-up plan because you'll realize that you should have hired 4 reps in Q4 to hit your 2024 plan. Please start your annual planning process on October 1st so you can hit your 2024 number. Mandy's got a great template that you can download. That helps you build a bottoms-up plan that you can actually hit in 2024. Our other— another partner of ours, Liz, uh, Christo, um, she's been running every Saturday an Ask Stage 2, um, blog. We basically sit as a group and say, what are the biggest questions coming from our entrepreneurs every week? And she writes about them, whether it's standing up a partnership program or setting up your first pricing model or putting together an MSA or whatever it is. She writes about those things from the trenches that we're feeling. If you haven't seen my work on Science of Scaling, I personally believe the number one failure reason for startups is they choose the wrong time to scale and they choose to scale at the wrong pace. They basically copy what Snowflake did and that's not right for you necessarily. And so what the Science of Scaling work does is it helps you and your board calculate using your data when you should scale and calculate using your data how fast you should scale. So if you're thinking about moving to scale mode, you can check that out. And last, for Mandy again, she's actually speaking on Friday on this. There's a Brain Date on building your first go-to-market playbook if you have to be in that realm. So these, I have one more thought for you, but these slides are posted on the Stage 2 Capital LinkedIn account right now. So if you want to download the slides, it has all the links from the downloadable material. You can go to Stage 2 Capital LinkedIn, download them. Feel free to follow us if you want to track some of the tactical things that we put out. Okay. The last point I'll make is as entrepreneurs, history is on your side. Of the Fortune 500 companies that were on that list in 1950, 90% are no longer on the list. Because of entrepreneurship and innovation and disruption, because of the things we talked about. The longevity on the list has been cut in half. The average time in the Fortune 500 list for a company in the 1970s or so was 30 years. Now it's 15. AI is expected to do an even— make an even bigger dent in that. History is on your side. And I just want you to remember that, because entrepreneurship is hard. And it's so hard to think about how can we compete with the billion-dollar marketing budgets, with the 10,000 employees, with the billboards, with the 20 years of engineering development. But you have to remember that those people are driving aircraft carriers, that takes so much energy just to turn it 5 degrees, and you are flying an F-15 fighter. And there couldn't be a better time to do that than now, with arguably the biggest technology advancement of our lifetime. History is on your side, so go make history. Thank you.