The Godfather of Modern GTM: This Is How To Measure AI Impact | Mark Roberge, Co-Founder @ Stage 2 Capital

Speaker A: Mark Roberge is the founding CRO at HubSpot, co founder and managing director at Stage 2 Capital, and the author of the Science of Scaling. And he's seeing a new problem across go to market teams. Everyone is telling him good news.

Speaker B: We are like, so AI enabled in our sales team. And I'm like, how do you know? So it's like, we got to measure it.

Speaker A: In today's episode, we dig into quantifying the impact of AI on your go to market efforts. And Mark reveals why adding more AI tooling often fails to close deals any faster.

Speaker B: The vendors are highly motivated to accelerate sales action with AI. The buyers are.

Speaker A: We also explore the concept that every board wants to hear, but almost every operator gets wrong.

Speaker B: How?

Speaker A: Uh, Mark is currently disappointed by the state of AI and go to market and the hyper specific lever that can literally double a team's sales results. Welcome to Topline. Hey everybody, it's Sam Jacobs. Welcome to Topline. I'm joined by my wonderful co host AJ Bruno. The CEO quotapath Asit Zaman is off doing adventures. He's not joining us today, but we are joined by a very special co host and guest, Mark Roberge, friend of the pod, general partner and founder at Stage 2 Capital, founding CRO at HubSpot, author of the sales acceleration formula, and also author of the new release the Science of Scaling, which I can attest that I have read start to finish. Mark, welcome back to Topline.

Speaker B: Wow, Sam, gotta bring you around. Thank you. Appreciate, uh, the opportunity to fill in Assad.

Speaker A: Here's my takeaway to prove that I read the book the. My favorite part is the leading indicator of retention. You make the good point that oftentimes retention data comes too late. And if we're trying to calculate unit economics and we don't know what retention is, we need to work backwards, especially if we're trying to scale from a leading indicator of retention and drive the results of our onboarding and our customer success organization towards that. And I thought that's a. I think about that all the time that I talk about unit economics because I get people asking me, you know, how reliable is ltv? I'm like, well, Mark would say it might not be very reliable at all. We need to focus on what the leading indicators are.

Speaker B: A plus, Sam, on that one kind of B minus on the proof that you read it cover to cover. Because that's in the first part. It's like the first. I think it's the second chapter. So if you could quote me the last chapter.

Speaker A: The concluding scene was A woman. She. It was a woman that had, like, gone public or something. And I will tell you another detail. Some of the, like, some of the, um, the descriptions and the metaphors you use are extremely, like, literary in a way. It was. It. I can't remember all of them, but I like, I wanted to text you a few times because they're like. He felt as sad as the darkening clouds over the fall sky.

Speaker B: You know, I was like, okay, I don't know if I should say this.

Speaker C: No, you have to say this.

Speaker A: Those are all the AI that's all chat.

Speaker B: Well, it, like, obviously those are my original thoughts coming from actual board meetings, and everybody reads those fictitious. I, uh, put those things in there because it just makes, I think, the book more interesting. It makes it more accessible. And I've been. You guys have all been in, you know, I sent 75 board meetings. Of course, like, you know, all these, like, stories, all those thoughts are original, but I'm not like a professional writer. And I may have relied on, just like writers in all the history of the world have relied on help. I may have used some help from amazing literary authors. Now, I did. I didn't use, like a human helper. I didn't. I wrote the book, the whole thing, every word, right. But like, yeah, AI is very powerful right now, and it can help someone who scored less than 500 on a verbal sat sound like an actual amazing novelist. So it's great.

Speaker C: As Mark knows, I mentioned this, I think the last time, which is we did a book club as a management team and read, uh, Science of Scaling. My favorite part. This is not how I'm m earning my A is I find it interesting, the text size between chapter to chapter. Sometimes you'll go from, uh, it'll go from size like 24 and then it'll go down to size 10.

Speaker B: Oh, my gosh. Really?

Speaker C: Yeah, I'm. It's.

Speaker B: Hey, Wiley. Wiley, what's up?

Speaker C: It keeps you on your toes. Wiley does a good job of keeping you on your toes, chapter to chapter.

Speaker B: All these publishers charge a lot of money. You think? Come on, let's go.

Speaker C: Let's get that. The leading indicators retention was also our favorite part in the beginning. I Vibe coded an LIR for every single organization that is going to be part of Atlas and a guided flow very, very soon coming, uh, upcoming. I love that.

Speaker A: Well, let's go.

Speaker B: It was sick. We tested it at a loss off site. AJ Vibe coded a good amount of the science scaling book and it is good. Like we did an entire hour aj, I don't even know. We split the team up into 12. We all went into a different room for 30 minutes and played with one of your tools, came back with like what we love about it and what like the enhancements we would write. It was like it was sick.

Speaker C: I took all uh, the enhancements for. So I picked one which was ilr, which is the best one to like that I had furthest developed I would say as a framework. Took all of the feedback, put the feedback into it and then I uh. That was from Repple. I ported it to Claude Code, uh, myself, our cpo and Cole, my co founder then uh, imported it and it's almost production ready to get sent over to Aaron, uh, runs marketing at Stage two. So I am super excited for that.

Speaker B: That's great.

Speaker A: That's awesome.

Speaker C: Yeah, super excited.

Speaker B: And I do want to talk to you a minute A ah, cool story from this week about the lead indicator attention which obviously one of the principles here is like our goal as an organization is not to create revenue, it's to create value for the customer. And revenue is one of the benefits we extract from that. And I had this amazing conversation with this gentleman, Fred Re Held who I don't know if you know him, you probably wouldn't. He has more followers on LinkedIn than any of us.

Speaker C: Oh combined.

Speaker A: Thinking about how I don't see popular.

Speaker B: Okay, so he invented NPS.

Speaker A: Oh, uh, that's so cool.

Speaker B: Yeah. So he's this HBS grad from the 70s, right?

Speaker A: It's an HBS.

Speaker B: But also he's a partner. He's like, I think he's worked at. He's been a partner at Bain Capital I think for 50 years. I'm turning 50 next week. He's been a partner at Bain capital for 50 years and so obviously he's like, you know, Bain's printed money on that, the mps and we had an awesome conversation because obviously like we connect very deeply about that first principle of like we lose our way as a business of obsessing with revenue generation as opposed to value generation. And he's, he's just like a, ah, date the. One of the best in the world. And right now his new work is he's um, he's trying to do a lot of work on referrals and quantifying referrals and in the same way that mps quantified product value or service value, he's like really working building off of the viral coefficient I think movement of the PLG time, um, which, which is cool. And you Guys reminded me of.

Speaker C: Is funny to me that nps. Everyone likes to hate on NPS in some way. There's just some like. But, uh, if Austin was here, he'd be railing on employee nps. He doesn't believe in it. Like taking just. It's like a very no on it. But we do an ENPS and we do an NPS and we do an onboarding nps. We measure it three different ways. It is a very, very hard metric. It is just. Is brutally hard on because you get one passive, let alone a detractor. And it just tanks the score. And people don't realize it goes from minus 100 to 100. So you have this scale. So if you're at like 30, you're like, ugh, uh, this feels awful.

Speaker B: Which I think is prob. Depending on what your benchmark is. That's pretty up there.

Speaker C: Yeah.

Speaker B: Top 20% at least. You know, like, wasn't like the iPhone like 75 or something? I don't know. Like, um. Yeah. There's two things, um, I'll share there. The first one was, um. And I shared the story with him. It was really impactful at HubSpot with our product back in the first 10 years, what we did when we were fairly transactional, so we had thousands and thousands of customers. So we could do this. We split the customers into six cohorts, all of which were representative of the entire base. Right. So a certain amount of big, certain amount of small, certain amount of non tech, tech, Europe, Asia, whatever. And that allowed us to run an MPS study every month and only have to, um, survey every customer twice a year. You follow me? Which was like, so every month we got to get a new score with a new set of patterns in the promoters and a new set of patterns in the truck because each cohort was representative of the entire base.

Speaker C: Yeah.

Speaker B: And that allows us to learn so fast because we're like, oh, my God, this is the top three reasons for detractors. That's going right into the rock.

Speaker C: New statistical dynamic data.

Speaker B: Did we move that? Did we move it? Did we. Did we bounce that out of the top three?

Speaker C: That's so interesting.

Speaker B: Yeah, that was pretty cool. I'm trying to think of the other one on him. Oh, yeah. This is an interesting debate because we. I was telling about my work and I was telling him how, like, it's. It's crazy how many people will say you're ready to scale when you have product market fit, which I agree with. But then I've done this in like 50 audiences this year, I think, A.J. you were in one of those audiences where I'll be like, okay, cool, that's a good answer. And I'll run out in the audience and I'll have someone on the right side of the audience whisper in my ear what they think product market fit is. And I'll go to the left side of the audience and have someone else whisper in my ear what product market fit. No one has ever matched. Uh, well, yeah, and that's the, that's the absurd thing is like you have this term that everyone's like, it's like such like every board meeting. We have strong product market. We lost product market fit. We're pivoting to find product market fit. Good news. We have product market fit. We're going to. What is product market fit? Like, no one's in agreement on it. And so, so Sam, um, before, I'll let you chime in because our debate was I, I obviously root it in retention in the long term and then use the Leanicator to measure it. He was arguing that referrals is a better measure of product market f. But go ahead, Sam.

Speaker A: On yours, I.

Speaker C: Well, this is. I'll close my ears and then I'll whisper it. I'll whisper it back to you.

Speaker A: Okay. I think the fundamental driver is retention, but it's articulated in your unit economics. I mean, payback period. And LTV to cac. It determines product market fit. Like referrals you could get. You could be. If you're an AI native company with 30% gross margins, you could get lots of referrals but be out of business because you can't afford to run your company. So I think product market fit is the relationship between customer acquisition cost and retention and gross margin, you know, and average arpu.

Speaker B: Good. AJ uh, yeah, this is. Do you have one, AJ or do you want.

Speaker C: No, I'll go, I'll go. Because it's. I, I think it's. I might. And even my own definition has changed. I would think about it in a more simplistic manner of. Are your customers expans expanding? Do you have well over 100% NRR? Um, year over year. And the, the challenge we have. And like GPUs is a good example or anything that has extreme product market fit right now of. Because everyone's a little bit of a snowflake. So it's easy for a SaaS era company say, yeah. If 120% of our, uh, customers are expanding, NRR is 120%. That's an easy measure. And all of the other measures on ltv, cac, things that are top of the funnel, I think they take care of themselves. If your word of mouth and your referral is showing that you're expanding as a business. That's my. That would be my.

Speaker B: Okay. Yeah.

Speaker A: I like them in the modern world based on your gross margin, though, because modern AI businesses don't have 90% gross margins like you have to make sure

Speaker C: you can build product market fit. So I don't think. I don't think you can put gross margin in a product market fit. But I'm interested. What?

Speaker B: No, but those are. Those are very good answers. It's one of those things where it's like, where do you cut the scope of it? Because you're. Both of you are precisely correct. Depending on that. And so let me explain what I mean. And I think the other thing in my seat as a professor, I try to come up with things that, uh, I mean, you. We mostly live in our B2B, you know, software world, but I try to also make it applicable to someone selling sweatshirts and tractors and pharmaceuticals.

Speaker A: Right.

Speaker B: Where that gets a little more challenging. And, um, to your point, Sam,

Speaker A: all

Speaker B: the way up to Harvard Business School would agree. Harvard Business School has this thing called the diamond square to define a business model that includes how you sell, how you generate the service, your cash flow formula. And they define product market fit as all that moving together, which is good. I think that works beautifully. Um, I break those up in the book, right? I say it's product market fit, then go to market fit, where product market fit I talk about as creating the value that you promise for the market as measured by retention in the long term and leading care retention in the short term with no regard to unit economics. Ignoring unit economics. Then once you check that off, introducing the term go to market fit, which means now we have shown that we can consistently create the value we promise the market, but now we have to prove that we can acquire and serve those customers profitably. And so, like, again, both are right. I personally root that in my methodology just because I really like to see founders focus on product market fit in an unscalable manner and not obsessed with, like, setting quotas and optimal price and all that kind of stuff. And I pick those apart. And that's probably driven by just where I am as a vc. If I was a partner at Bain, I probably would do it differently because you have to operate them together.

Speaker C: Yeah.

Speaker A: Well, I think we could keep talking about this forever.

Speaker B: Uh, most of the Audience is like, dude, this is usually a good podcast. I didn't know this nerd was going to come on.

Speaker C: Okay, so let's, let's head to topic number one. Um, which I will kick us off. So, Mark, I have a board meeting in three weeks which Liz will be represented.

Speaker B: I'm very aware of that, A.J.

Speaker C: yeah, you're very aware of our board meeting. And I, as I'm thinking, because we're starting to talk about it, and as I think about it, uh, this is the first board meeting where I'm going to go into it and I need to measure AI productivity across the organization and every part of the organization. Engineering, we've traditionally been doing it and I would say our team is. It's a. That's a whole topic by itself. I think we're two and a half times faster than we were a year ago, but there's a lot of nuance and pieces to that. But with Go to Market teams and how executives and the boards are starting to measure them and how we should be measuring them, I'm kind of curious on, on some of your opinions here. And to help like get this jump started, I pulled iconic State of the Go to Go to Market. This was in January of 2026. And they released a few numbers that are worth just discussing or thinking about as we talk about this. So they said when AI is fully embedded in go to market, 67% of ramped AES hit quota versus 59% when it isn't at the SMB. That that number is more dramatic. It's actually 160% of quota attainment versus 80% if it's not. That makes sense. Um, AI native companies convert trial to POCs to close one at 56% versus 32% for everyone else. And AI clearly lifts top of funnel, uh, 11% on the uh, lead to MQL and MQL to SQL, another 8% but barely moves active deal cycles. So it's showing up across the org, but it's. Every company is adopting it at different cycles, different levels. You have AI native first companies and you have different B2B. So I guess maybe for me, as a traditional SaaS, that's pivoting to it. How are you looking and going into these board meetings and thinking about what slides need to show up and what should they look like for us as operators?

Speaker B: Yeah, this is great framing. I love that iconic study. It's actually already triggered some additional thoughts. And this is cool to jam with you guys because I think this is something I've been just thinking about just in the last three weeks. So it's fresh, growing conviction. Test it out with many audiences and boards and let's jam on it and keep pushing it. So I think I'd root it to your point. AJ is like, I just got fed up of going to these board meetings this year and everyone's like, good news. We are like so AI enabled in our sales team. And I'm like, how do you know? Like, you're writing emails with ChatGPT, like, what? So it's like we gotta measure it. And that's what I've been thinking a lot about. And um, the, it's. I think it's fairly obvious what the lagging indicator output is. And I, the, the iconic study made me think a little bit about it because I was rooting it in. Like you've got these, you know, aj, you've got certain reps who've been with you number of years and there's certain reps that are just consistent hitters. Like they just, they always produce 250k a quarter within a delta of 5%. Right. And I think when they don't, it, they.

Speaker C: Your, your whole org feels it because you, you essentially miss.

Speaker B: And it's, it's a beautiful first off that's a little totally tangential hidden gem of like, if you're trying to evaluate, um, and diagnose whether you have a sales quality issue or something else like demand gen or macro or competitive or whatever, you look at your tenured consistent performers to see what's happening. It's one of the best indicators. And so few boards look at that. Okay, so like, so you got these people and like you've made breakthroughs in AI when you see those folks have a step function in productivity. If, like, if, if Mary had 250k a quarter for the last 12 quarters and now all of a sudden she's at 400. And you've been making a lot of investments in AI. I would argue there's something happening there. So there's a general PPR measurement. I, I love the look at the

Speaker C: top cohort of sellers and tenured sellers. Then.

Speaker B: I don't know. That's what, that's what your comment made me think about. I, I might want to shift that and you guys can. After I finish this narrative, maybe you guys can debate and comment what it is. But like, maybe it should be percentage of reps hitting quota. It's a better consistent thing because I might want to change that. Um, I just don't. So anyway, let's think about that. Regardless, there are some fairly clear lagging indicators, outputs in sales, which we have a huge benefit over engineering of. Like how the hell do we know if we're actually going doing better? It's like we're producing now. Of course there's uh, there's another piece of how do you bring LTV into that? Okay, so we can, we can talk about that. But there's like a clear like output that we're trying to generate. Let's extract that one level back. Now, Iconic is focused on the close rates, which is interesting. I'm interested in that. But I, I have a different view that I probably like a little better. And we have to debate it. And that is the revenue velocity formula, which is a beautiful first principles formula that is rarely talked about in the industry. And you guys might be familiar with it. I'll just kind of walk the audience through it. It's the. To the number of active opportunities that our rep is working times the ACV times the close rate divided by the sales cycle. Okay, so let's say your rep is working 20 opportunities. They close 10%. So that's two for $100,000 each. So that's $200,000 with a sales cycle of two quarters. So you divide that two. So that's $100,000 a quarter of productivity. That's essentially what it measures. Okay, so now you've got this output of productivity that's extracted back algebraically to four variables. And so the question I have is which variable is easiest to 2x? Okay. So I go through those numbers and I'm like, okay, ACV 2 exit. No, if I 2x ACV, it'll float me up into a different market potentially. So be careful.

Speaker A: Right.

Speaker B: Close rate and sales cycle are highly dependent on buyer behavior. Yeah, like smaller number. What's that?

Speaker C: They're also smaller numbers.

Speaker B: They're smaller. Yeah. But like, I think that's what something I'm feeling very much right now is. The vendors are highly motivated to accelerate sales actions with AI. The buyers are not feeling that same pressure. So I think over time buying processes will be dramatically enhanced with AI, but they're just not at the right speed right now and we just can't do anything about it with anything like that going faster.

Speaker C: M. Let me just see if I unpack that a second because I think that's a really interesting point. Just around that the A. The buyers aren't feeling urgency around either. I don't know if it's buying a. Well, maybe buying AI separately But the, if the seller's using AI, that's a seller, that's a company journey, that's not a customer journey. They're not like, they're not seeing that. And at the end of the day where I see this is still where buyers are still, uh, still buying from people they still want to buy and have a conversation at the end of the day. Yeah. And so no amount of AI is accelerating that close. Like the actual negotiation, the closed process,

Speaker B: let's say like, uh, you're, you're, you know, you're a large restaurant, um, you know, brand and you're looking for a new POS system. You're still buying like it was 10 years ago. Like you're putting together a committee, you're putting an RFP, you're like, you're still

Speaker C: probably buying the POS from 10 years ago. You're still buying toast or whatever.

Speaker A: Let's go to the, to the point. I mean, we're skipping over how are organizations using AI that would deliver this increased productivity. Tell me if this is where you're

Speaker B: going to get there. Yeah, we're going to get there. Okay, so let me just like, let me, you guys can like, let me paint that out so that, and you guys can like pick up all the different parts. So the end output is either a PPR or a percent of reps hidden quota. The extraction back is the revenue velocity formula, which has four variables to it. And the one that's easiest to double, I think is the number of opportunities that a rep is adequately working. Because ACV will push you into a different market and sales cycle. And close rate is dependent on buyer behavior, which we can't change easily, but we can easily 100% in control change the number of opportunities our salespeople are working adequately. Now extract that back one more. And I think that's where you get to selling time. And today's AI can dramatically improve selling time. And just for the folks that know selling time is in a given week, what percent of that time is a rep face to face with a prospect or existing customer. And in the industry, best in class has historically been about 30%. And I see a lot of evidence that uh, today's AI can push that to 60% plus. Now just if you just isolate that same rep, same quota, same territory, same product, same price, same ICP. And all you do is go from 30% to 60% selling time. Just algebraically you double productivity. So then you extract that there. Then you're like, okay, that triggers a whole bunch of thoughts Number one, are you even measuring selling time which AI can do for you so we can benchmark and I want to see that in the boardroom. And then number two, let's list out the 25 AI initiatives we can do and which ones are most likely to move selling time and let's go start doing them and see if we can move selling time.

Speaker A: So what I hear you saying is the AI use case in Go to market is the administrative work. It's recording. You know, it's like granola connected to Claude connected to your Salesforce instance connected to Slack. Every call is recorded, all notes are recorded. Maybe AI drafts your follow ups instantly and to your point as a result like all of that pre call prep after call follow up can be much quicker. Maybe doubled in term or the speed of which can be doubled or the halved. And so the main use case is sort of like all of the promise of uh, Scratchpad and Dooley from like six years ago of let the rep work in zoom or in person or on a call and all of the other stuff magically happens. That's one use case and that makes m sense to me what you just said and I like that. And then the other place that people are using AI but again pick it apart is uh, I could you could make the argument that it might show up in close rate because it would be. But it would be first SMB to mid market businesses where you have better ICP definition and more personalized. Uh, but maybe that's still just personalized outrage and maybe that still just contributes to opportunity creation and not necessarily to close rate.

Speaker C: I mean there's one part of that Sam. Or just to add to it which is where the company is in life cycle because icp I think about that as well of just like if you can get much more personalized and tighter using AI, you're going to. Your messaging is going to resonate well. Um, and what your value prop is actually going to hit the right customer at the right timing. So you'd actually see it in the retention rates as well on the other side of it.

Speaker B: Okay, so all these points are great. I think um, I should expand it because the way you guys are talking about it is let's attack all those numbers because Sam, I totally agree with you that there's the. The thing I outlined was really just increase in selling time which increases the number of opportunities. I just say I do believe that that is the easiest one to double right now. And that's why and I don't think people are Even measuring it, selling time, whatever. So that, that spins off a whole discussion of like what are the most effective use cases. And Sam, you did a really good job of rallying off the ones like prep meeting, post mortem, CRM updates, et cetera. Um, you do quote and AJ, you as you know, you as well, like, yeah, AIs made some significant advancements in refinement optimization of ICP, which ICP in one way is if I have, if my team has like the capacity to call 25 new accounts this week, which ones do we call? And as an ecosystem we've put that in the hands of a 22 year SGR, which is stupid, right? So like, and that's not going to necessarily increase selling time, that's going to increase probably ltv, honestly. And it might increase close rates, whatever, but like it really should drive ltv and that's not even in the math. So we have to figure that out. Um, and then there's like a whole bunch of other awesome powerful use cases. Like one of our um, high flying companies is Letter AI, which is like basically allowing, you know, it's, it's replacing a lot of the work of the sales manager on coaching and just like literally devising a, you know, diagnosing deeply which skill a rep should work on and personalizing training for that. And that doesn't increase selling time, it increases close rate because of skill assessment. And AI is really good at that right now. So yeah, it can be more comprehensive, but I think it's fairly easy. Your points are fairly easy to encompass into the revenue velocity formula because you're attacking each variable. Now there's also like Iconic is rightly calling on close. Well, they called on close rate, so. It does, aj, it does. I don't think there's anything new there from Iconic because they just, they were very focused on PPR percent of reps heading quota and then they were quoting funnel velocity, which is encompassed in the revenue velocity.

Speaker C: Well, it's also interesting because this was a January 2026, so I was looking at 20 and the acceleration of the tooling and what we're using to do some of the things that Sam's talked about in the last six months has exponentially changed. So I think there's probably an updated. But to hear. Read that back to you, uh, Mark. And like if I, if quotapath shows up, our board meeting and Ryan Milligan, our CRO, he has a scorecard and his scorecard is a slide. Maybe it's on. Well not maybe if he has revenue velocity. Yeah, Selling time, coach diagnosing and then revenue per head. Like those four things seem like they triangulate the scope.

Speaker B: Yeah. If I would talk about this a board meeting I would just extract it back to the impact. I start off with like what is the end goal? And I uh think we need to I appreciate your guys advice and like do we focus on PPR or do we focus on percent of reps heading goal goal. So it's kind of like or you could do both but I don't want to like overcomplicate it. So I would start there and be like hey, we feel like there's a massive impact to use a to drive these and we're not going to like hide behind our AI investments. We're going to hold ourselves accountable and this is the way we're holding ourselves accountable. Now as I look at that number I'm going to extract it back to the revenue velocity formula which is these four variables and we're going to attack three of them. We're not going to attack ACV because we just don't think right now that. But we are going to attack um number ah of opportunities actively worked close rate and sales cycle. We're going to attack those and then I'm extract that for the number of opportunities. I'm going to attack that by increasing selling time. And by the way we just started measuring selling time and we have to talk about how do you measure selling time. I think we're working on some AI skills to do that. That would be a cool one for you to mess around with AJ But I think every company should be measuring selling time right now as a benchmark in a way to influence and then, and then now that I've got that framed, these are the 25 things on the roadmap M of AI and go to market and these are the five that we're going to do this quarter. And this is and we'll tell you in a quarter what the impact is. Something like that.

Speaker C: I the the point around revenue per head or the percentage of reps hitting quota, it's a super interesting one and I would almost think about it in the size of the sales team because uh, as sales teams have slimmed down your efficiency so your revenue per head should be going up naturally but the percentage of reps getting quota, I mean you're also probably managing out low performers or ones that aren't going to be adopting should also be going up. I almost think that those two together tell a compelling narrative and story around it because they, they ultimately Tell where the go to market team is headed and the type of business that the team wants to build. Um, where the other metrics that you're mentioning. This selling time piece of it is, is, is really interesting. It's like the one that I starred coming out of this. And how do you measure that? And in two and a half weeks I will have answered it on our board slide. So I'll send it directly to you.

Speaker B: We should kick it around.

Speaker C: But the, the lagging indicator is of course the, the latter two and I just, I think where it's. It's a conversation that I would come into a board meeting like look now and this was last year so I'll give you our stats. We were $140,000 of new business per rep. Uh first new business on sales team. We are about 170k today. Is all of that AI? No. Is some of that AI? Yes, for sure. Our reps hitting quota however is uh. We're probably like 70 to 80% of our reps hitting quota on any given quarter which I would say is probably close to best.

Speaker B: It's really good. Oh it's awesome. Yeah, your quotas are too loud. Sorry sales team.

Speaker C: Well, it's kidding but Mark, we've raised our quotas.

Speaker B: I know.

Speaker C: No, you're doing year over year. Yeah, we're going to continue to raise it. So that's why that the ARR per uh rep has, has continued to climb I think is a function of that. So it's a, it's a complicated conversation because you're also adding quota capacity and the financial plan as a mix of this and you have to get that right variable um, incentives as well as a part of this too.

Speaker B: So before we go to Sam, I think the thing I'd pull out of that that's really critical AJ is like that lagging indicator of PPR percent of reps is externally hard to attribute directly to AI and selling time probably is as like even though it is that's a big one. So I think that's why we have to like extract this back to the mini metrics that will be more directly impacted. But we're always going to suffer with attribution here in this whole analysis. Go ahead Sam.

Speaker A: No, it's a question. I have one comment and one question. My comment is it almost feels like we have maybe another 12 months of this conversation before AI is just baked into our expectations about how companies operate. Um, my question to you Mark and A.J. since you sit in a lot of board meetings too is have you seen companies that were clearly the inflection point of their growth was their aggressive adoption of AI in their go to market strategy. And they attribute, you know, they were this before, I don't know, last year and now they're this. And the reason that they say is because of their AI adoption. Like, do you see across the 70 or so board meetings you've been in, do you see a few examples of companies that were tier B that became tier a because of AI adoption?

Speaker B: Not unlike a 2x but on a 10 to 20%. Um, and I still think we're so early. I m think we had fairly amazing breakthroughs in R and D and coding just largely because, I don't know, I don't really think AI was particularly. I mean it is, there's certain arguments of like, coding is very deterministic and it's like very, it's a very. I think the most important thing is just like the people that enabled that coding revolution were the builders of the product. You know, it kind of goes back to like one of the best selling, um, uh, products in the Salesforce app exchange was DocuSign, because the salespeople used it and loved it. You know, so it's like I feel like that was a big driver and now salespeople are just a little less connected to it and it's coming. But I, I've been disappointed, Sam, with your, your question. It's a disappointment of. Across our entire portfolio. And it's not, I think it's not our portfolio, it's just the entire ecosystem. And even like, you know, I'll go when Kyle Norton puts something out or, or um, Gene at Versal, I'll call him up and be like, show me that. And it's interesting. But even they admit like some of the stuff they're talking about is still in an aspirational state. So we're getting there. I think by the end of this year we'll see more of that, but it's just we're in the moment right now.

Speaker A: This episode of Topline is brought to you by Pavilion. The pace of change in go to market has never been faster. AI is reshaping how teams operate. Buyers are changing how they research and purchase. The Playbooks that worked five years ago are dead. They're gone. Get out of here. That's why more than. Go ahead.

Speaker C: What was like the best. So you were just in Jackson Hole. I'm just interrupting the ad.

Speaker A: It's great. I love it. Bring it.

Speaker C: I was, I actually really. I haven't asked you this. I. I'm truly interested. Like Pavilion Gold, Jackson Hole.

Speaker A: Okay, Deets.

Speaker C: How was it? Oh, okay.

Speaker A: I love it. I have a few superpowers. Not clear that one of them is running a company, but, but one of them is creating the conditions for transformation in, in person experiences. And we got, um, about 25 people in Jackson Hole, Wyoming, and we started off Tuesday evening in this campfire barbecue outside on this plane with the mountains. And it was absolutely stunning. And people were just getting to know each other, but we had CEOs like Dan Lee from Nooks and Josh Perk from Vector, and, uh, Nico Chenault, the co founder of Dust. And then we had investors like Brett Queener. And then we had a bunch of amazing operators and we did a bunch of great stuff. We had all these conversations. Some people went fly fishing, some people went hiking. By Thursday night, we ride horses for an hour, uh, through the country along the banks of the Snake river. And we come to a tent, a campground that we've set up, and we're sipping Moscow, um, mules or, um, um, bees knees, I guess they were. And we're sitting around the campfire and the fire is rolling and everybody's friends, everybody's friends at this point. And we know that they're friends because Matt Thompson, the president of so Cure, texts, uh, Everybody on the WhatsApp group a picture of himself shirtless and saying, hey, there's a hot tub on the roof. And they serve beer and shout out

Speaker C: to Matt, by the way, for that one.

Speaker A: It was just a moment where it became more than work. You know, it became more than work. It became something deeper. It came and it gave people literally. The campground was along the rushing banks of the Snake River. And you would see people walk away from the campfire and just stand by the banks of the river and stare at it 30 seconds or a minute. And it's because people need that. They need a reset. They need an ability to reset their brain. I came out of that, uh, very, very enthusiastic. The feedback we've gotten is absolutely amazing. It's one of those experiences that if you're not there, uh, you know, you did. You missed something. You missed something. There's lots of people that can do stuff like that, but I do happen to be one of them.

Speaker C: Where's the next Pavilion Gold at, Ben? And when?

Speaker A: Well, it's going to be at gtm. We're going to do this incredible dinner, but you want to hear something great? So this is a long ad read. Sorry, Neil, but. So I had this big Debate about Presidents Club. And Steve Roland, the president of Klaviyo was there and we were. And he's like CRO of Okta. He's like a well known go to market guy. And I was like, it just doesn't make sense. People are spending millions of dollars on President's Club. He's like, you are wrong. You are absolutely wrong. And um, once people go. And he's like, and especially the spouses, the spouses that get to go on President's Club, you know, I give a speech and I'm pointing at the spouses and I'm like, I hope I get to see you next year. And on the plane ride back home, the wife or the husband is looking over at their partner and saying, you better be in President's Club next year. He's like, I don't have to do a forecast call. I don't have to do anything.

Speaker B: So here's what's going to happen.

Speaker A: This is Pavilion Gold President's Club. So here's a, uh, final observation. Uh, a lot of people want to ride in private jets. It's an experience that people desire to have. So if you're a Pavilion Gold member and you refer three other Gold members, they have to join. I don't care about, I don't need an email address. They have to join. If you join. If by a certain date, I'm picking it, I'm planning it. Um, let's say December 1st. If you have referred three or more gold members, you get to join the Pavilion Gold President's Club. We meet at Teterboro Airport and we hop in a PJ and we fly down. It's an airport that.

Speaker C: Are you sure it's just not my six seater airplane, twin engine.

Speaker A: It's not, there's no propellers on this plane. It is

Speaker C: plane.

Speaker A: And we're gonna fly to like Hilton Head and do like a uh, clam bake and a uh, dinner on the beach. And then we're gonna either stay there the night or fly back in the next morning to give people that experience so that every one of those people can say Austin.

Speaker C: And I qualify for this because I want to refer three people.

Speaker A: I, you know, if they join, you know, you guys are good friends. We'll see. There's a lot.

Speaker C: Can I just fly stuff?

Speaker A: There's GPUs. You know, you scratch my back, I will scratch your back.

Speaker B: Five minute ad.

Speaker C: Best ad we've ever done.

Speaker A: Join Pavilion, everybody. It's a lot of fun and we uh, get to hang out and ride on PJs. Okay, bye. This episode of Topblind is brought to you by. Nooks Outbound is broken because reps spend more time preparing to sell than selling. The new Nooks AI assistant fixes that one chat window that runs deep account research, surfaces the context actually in the buying window and launches personalized sequences on command.

Speaker C: As it's.

Speaker A: Ask it what's working and it'll turn your best plays into automated ones. Set it overnight and your team walks into a prioritized digest. That's AI, baby. This is the AI SDR conversation done right. See it at Nooks AI us. Uh, at aj We Nooks in gonna Nooks a little bit.

Speaker C: Nooks. And is that. Is that the real verb? Is that what the CEO. It's what the kids.

Speaker A: It's what the kids say. They say like six, seven, you nooks in at the end of the day, Sam.

Speaker C: Um, all we are really trying to do is get everyone prepped and prepared for at the beginning of the day, getting our sellers prepared. And if Nooks takes care of it of that, then that's gold.

Speaker A: That's it. It's truly gold. Austin, you're feeling on Nooks a positive. Your positive feeling on Nooks.

Speaker C: Big fan. I think people should buy it. I think this is good for society.

Speaker A: That's great. I was with Dan Lee, the CEO of Nooks, uh, in Jackson Hole, Wyoming. A great company, a great leader, and, uh, and really impressive. Uh, their growth trajectory is really inspiring and also very, uh, high NPS on their power dialer. You know, they started off as a dialer and, uh, sort of like a new version of Connect and Sell.

Speaker C: If.

Speaker A: Do you remember Connect and Sell? You guys remember?

Speaker C: I remember connected cell.

Speaker A: So it's like you only. You only get connected when somebody picks up the phone so you don't have to like, redial. And that's pretty.

Speaker C: You guys ever salespeople on when you had to use that?

Speaker A: It was.

Speaker C: We had a sidecar and Connect and Sell. You could dial from different, uh, area codes.

Speaker B: I was used to, like, I had

Speaker A: to dial the number.

Speaker C: And once in a while I'll dial the wrong number and know that always goes to voicemail.

Speaker B: Get a break.

Speaker C: This was like, nonstop, like, all the time. There was a person on the other. I was exhausting.

Speaker A: Yeah.

Speaker C: Really efficient.

Speaker A: The funny thing about not to we love you, Nicks. But also with like Flintstones, because it wasn't like computers. It was just there was a team of people that were dialing like somewhere. It was just so hilarious. It's like you know, and you take the picture and it's the bird drawing it on the stone.

Speaker C: Anyway Nooks just like going for it.

Speaker A: Thank you Nooks. Thank you Dan Lee Nooks that I go check it out folks. People really do like the product. AJ I think this is probably a good time to go to our next segment.

Speaker C: Our next segment.

Speaker B: Wait, can I, can I just like can I steal if it's okay? Because I think this is really right on the edge and I think I really would respect your guys opinions here on this of like can we just brainstorm for three minutes how you would actually measure selling time?

Speaker C: Yeah, so I started.

Speaker B: Sorry Sam.

Speaker C: I started. No no, no, it's good. I actually started to do spot checking and this isn't the right way to do it because I should just set up, I should just cloud code an app for this. I started to spot check the go to market team. So it's not just new business sellers but am our solutions team and their, their number of customer calls that they've had. They have and I've been doing that for a couple months. And what I have noticed is a trend. The trend is that the top performers are getting more and more, this is again qualitative, uh, more and more selling time as a percentage of their calendar and the low performers are getting less and less. And there's a capacity conversation that I'm going to have uh, with the team post this quarter. Uh because we're, we're doing, we have an opportunity and inflection point but we're like working on the optimization of efficiency and then we want to grow 100% next year. And that's not uh, hyperbole. I like literally think we can grow 100% next year and to do that we have to make sure we are as efficient and fit as an organization. And so looking at their calendars and just literally doing the analysis and having Claude export and download all of it I think is an easy start that you should be doing with the internal. Now don't take that to the board right away. I would start to uh, look at that with my croat. We have a meeting next week, uh, two weeks from now before the board meeting to work through that and see if that is a metric that we want to start to track and bring it to the board as like listen, we're not doing anything, we're not making any decisions quite yet with this but this is how we're thinking about AI adoption As it relates to selling time. That's where we're starting.

Speaker B: Yeah, that's what I was thinking was either like, whatever you're using is your call recorder. You, you know, whatever, you know, otter fixer, like Zoom Grill or whatever. Just like you would just run the analysis against each rep and how long that's running. And that would be the numerator. Now, of course, like devil's in the details. How do you know if they're talking to a customer or not? Right.

Speaker C: So we don't. We don't record with Gong. You don't record internal meetings.

Speaker A: So I look at the calendar and take out any internal email addresses and

Speaker B: just look at external calendar could be good. What if it gets canceled? But I agree, like, I think these are all the devil in the details debates. Right. I could. So sure, you can run a cool model. Be like, okay, use the calendar to figure out who it was. Then use gone, um, to figure out if the median occurred. Um, and then that's the numerator and the denominator. Do you just use 40 or 50 hours a week? Do you like, look at when they log in denominator?

Speaker A: Why do you need a denominator? You can just compare gross talk time.

Speaker B: That's fair.

Speaker C: I. Yeah, I don't think you.

Speaker B: That's fair because you have to get down on percentage.

Speaker C: The thing that's Mark's thinking about, I think is. Is a ratio, which.

Speaker B: No, that's good though. You don't need it.

Speaker C: Well, I, I would just say it's like, is there a way to look at nine hours normalized?

Speaker A: Go ahead, A.J.

Speaker C: no, if there's a way to look at it, it is normalized data. Maybe it is just the gross number. It's just the hours.

Speaker B: Yeah. Instead of saying 30 selling time, we just say 25 hours of selling time.

Speaker C: VCs, love ratios and percentages, though, Sam. So.

Speaker B: No, that's. That's a good point. That's a good point though, Sam. It's just like how many hours of selling time per week?

Speaker A: Yeah, we all work in the same quantum field of time. So that is normalized. You know, like everybody.

Speaker B: How do you do it with your outside reps? And especially given that I think like we're outside is kind of in vogue right now.

Speaker C: Well, Enterprise. Yeah, it is in vogue. Enterprise is a little difficult. I mean, I think you. You can definitely still do the same thing.

Speaker B: Yeah, they still have those recorders like people do have on their phone. And they do have those new co. Like things. You. The cards, you can bring. I don't know if that's cool. Like, people are doing that.

Speaker C: But the question I have for you, Mark, is do you make this a part of their comp? Do you incentivize this, or is this too easy to game?

Speaker B: Probably my. I. That's a really great question. Haven't thought about it. I love it when I get a fresh question. Um, my instinct tells me no. And it goes back to, like, I remember hearing stories that, like, all the way back to, like, I hired a bunch of Monster.com reps in, like, 2007, way back in the early days, and they told me these horror stories about how they were comped on, uh, the phone. The phone was just like a new. Like, we were closing on the phone, right? The sort of comped on their phone time, and they literally were passing around 1-800-Numbers to call just to get your phone time up, even though it wasn't even a customer. So I just feel like. I think it's very dangerous to comp on lead. I. I'd run sales contests on it potentially, but I think I. That would be. My instinct is like, it's dangerous to. To put comp on it.

Speaker C: Yeah. All right, well, you will get a board slide from me. And yeah, July 15th is our board meeting, and it will be on selling time. And I can't wait to work with Ryan. I'm like, ryan, figure this out like any good CEO. Okay, we do. Yes. It's time for our next segment. Sam, I get to host it, which is my favorite. And this is quiz Pro quo.

Speaker A: All right, good.

Speaker C: We did it. We crushed it. Neil's got it.

Speaker A: I'm not, uh, the kids would call that cringe.

Speaker C: AJ Every week it's cringe. Um, so I had a conversation with a friend of the podcast, David Spitz of Bench sites. And David has a ton of great data on companies and public companies. And I was digging into it with him yesterday, and I was like, oh, this is going to be great, because I am going to completely stump market and Sam tomorrow with this data. And so I am, uh, I'm ready to go with it. So first question is on the rule of 40. And there's been a lot, a lot of conversations, specifically at the M M and A on rule of 40, uh, for companies. And what does that need to look like? And I am going to ask you, which of these companies has the best rule of 40? They're public SAS companies. Okay, ready? You have Snowflake, Palantir, or Crowdstrike.

Speaker A: I think it's either you guys have

Speaker C: to answer it, not me.

Speaker B: Yeah, I got. I'll go first or you go first, Sam.

Speaker A: One unified answer, Mark, but we typically defer to the guest.

Speaker C: Oh, good.

Speaker B: I would guess Palantir.

Speaker A: Yeah, I would guess Palantir, too.

Speaker C: Palantir is correct. See, I get easy questions.

Speaker B: M. No, that was. That was hard. I mean, the way I thought about it was. I mean, government defense is just like a whole. I mean, these are huge contracts that are super sticky. Um, yeah, I don't. And I, you know, just like, tremendous respect for the business. I think it's pushed.

Speaker C: Well, any guesses on what their rule of 40 number is?

Speaker A: 72.

Speaker B: I would say 55.

Speaker C: 55. It's 137. 137%.

Speaker B: How's it break down the. Between EBITDA and growth? Do you know?

Speaker C: I mean, uh, but the funny thing is it's up 81% from a year ago. 81% two years ago is 33%. So the AI wave for Palantir has been amazing.

Speaker B: That's crazy.

Speaker A: That is crazy.

Speaker C: Wow.

Speaker A: What a business.

Speaker C: What a business. Okay, question number two.

Speaker B: I wonder what open eyes is negative 5070?

Speaker C: Well, that was one of the topics to talk about their negative. So which of these companies has the highest ARR per employee among traditional public SaaS players? Is it A, Adobe B, Salesforce, or C, Twilio?

Speaker B: I would say Twilio.

Speaker A: I'm going to say Twilio too, man.

Speaker C: Just. I'm giving you guys softballs.

Speaker B: I mean, the breakdown there is like plg, you know, very, very product led. I feel like with Adobe, I'm like, okay, now I'm dealing with like 40 years of like.

Speaker A: Yeah, uh, exactly. Legacy, craft, archaic organization.

Speaker B: Salesforce is known to be a body shop. Like hire a thousand reps, fire 200. Just like. Right. Twilio's product led.

Speaker C: Well, okay, so same same.

Speaker B: A lot of human revenue. You know what I mean?

Speaker C: What do you think? What do you think that revenue is Pre. Uh, per FTE is, um, 430k, 600k. This is going to blow your mind. And I got to double check this. 5.6 million.

Speaker A: Oh, my God.

Speaker C: That's direct from David.

Speaker B: Crazy.

Speaker C: Isn't that insane? Another good crazy.

Speaker B: Well, I think Anthropics is higher and, uh, maybe level.

Speaker C: Is Nvidia higher than that? Nvidia can't be higher than that.

Speaker B: I don't know. They've. They've scaled up a lot of people, but still they're printing money like crazy. Um, yeah, that's that's crazy.

Speaker C: Yeah, that's crazy. What a business. Okay, the last one's on go to market spend, which has changed quite a bit since the 2020. 2021 Zerp days. What's spending now as a percentage of revenue versus back then. So is it A, 31% now versus 40% in 2021, B, 27% versus 35%, or is it C, 40% versus 20%?

Speaker B: Is the first number now and the second number?

Speaker A: Yeah, the first number is now.

Speaker B: And then, um, who are we measuring against? Like, what's the.

Speaker C: This is all public SaaS companies. The median SaaS companies, I would say

Speaker B: it has gone down. And so was. Was there two options to have gone down or just A and B?

Speaker C: Yeah, A and B have gone down. So 31 versus 40% and then, uh, 27% versus 45% in 2021.

Speaker A: I'm gonna say that one.

Speaker B: Yeah, Yeah, I was gonna. I'll go a. Just real quick.

Speaker C: Yeah. Oh, you're going A. Okay, so A is the answer. Yeah, got it. Sam. Sam, you were wrong.

Speaker B: I mean, who knows? That was a crap shoot.

Speaker C: That was a crap shoot. That was an Austin type, uh, question. So 31 versus 40 in 2021, which isn't gone down as much as you would expect with all this talk of optimization around efficiency and AI and I don't know, like, is. Is that including, like, where does AI fit into the cogs?

Speaker B: Uh, I personally don't think it's big. Like, when you talk to a lot of these, and I have, like, um, you know, as you guys know, like, I have a thousand LPs, and they're working at all these companies, you know, all the new ones, Anthropic and OpenAI, and all the old ones like Adobe and, you know, serversnow, et cetera. And they're just. General narrative is like, we're cutting like crazy with the premise that AI offsets it, but it's not offsetting it. We're just, like, driving our people like crazy. You know what I mean? Like, we're just. There's a little bit of fat cutting, but there's also just a lot of, like, you just. The AI hasn't caught up with it.

Speaker C: Yeah, there's a momentum.

Speaker B: That would be the general anecdotal narrative, AJ But.

Speaker C: All right, topic two, which, again, we threw all of Austin's topics out the door. And so, Mark, we were talking, um, right before this, and you made this, like, general statement around, is it. Is it better to be a fast follower today? Than an incumbent. And could you be two. Two guys out of mit? I just picked a random university, of course, Mark, mit, uh, to build something and fast follow on the. The coattails of a company that is. Is already blitz scaling or scaling like crazy. I won't say blitzscaling because that means headcount a little bit, but I think

Speaker B: it's a good word for here. So like that, uh, yeah, that's uh. So if I could just spend two minutes framing this both sides.

Speaker C: Yeah, that's.

Speaker B: You guys like chime in on like, hey, maybe you forgot about this perspective. Or like what about this? Or I feel like this is more weighted.

Speaker A: So.

Speaker B: First mover versus fast follower. Copycat. Um, first off, I, I personally like kind of invest into this because as an investor it's important to note where you have conviction against the consensus. And the consensus across most of the. Especially Our World, the B2B software ecosystem, both in venture as well as the founders, is that the first mover always wins, has a massive advantage. And I think the blitzscaling work is a good one. AJ, uh, Reid Hoffman and Chris Yee, um, where they kind of make that argument. Um, now unfortunately, or just noting if you spend some time in Perplexity or chatgpt or wherever and just say, hey, what are all the rigorous studies around first mover versus fast follower? I can't find one that says first mover wins most of the time. Like it all, they all say fast follower wins most of the time. I would say if I were to average it, I would say it's about a third first move or 2/3 fast follower. Okay, so this is like some rooting there. Now let's, let's break down the advantages and disadvantages of both models. Um, first mover and building off the blitzscaling work, you get to the headline first, you name the category, you're associating your vendor and brand with the category. You get the first big logos, the first major customer studies, all the top engineers, um, go after join you and the tier ones give you the biggest check that scares everyone else off. Okay, so that allows you to propel.

Speaker C: Yep.

Speaker B: Uh, the disadvantages are relative. The fast follower, you have to burn a ton of money figuring out the product. Sometimes you're ahead of the market, you have to evangelize the market, and sometimes you raise at a massive valuation that you are under pressure to grow into, which puts you in what they call ACV jail, where you can't optimize your price because you just have to maximize the crap out of it to grow into that valuation copycat exact opposite. It's going to be hard for you to raise from a tier one because everyone's afraid of the tier the person that's already backed. You're kind of like the also ran second person but in a. If you run a pure copycat and study what Rocket Internet did in Germany 15 years ago, they made a lot of money doing this where they just literally copied the US winners in the German market. Okay, so that would be the play here. And this is all driven from like I did this like speech to one of those open claw, um, like last week at MIT and literally like one kid was in high school, you know, I mean like everybody else was in college and they're super smart and they're like, they're kind of running this and it's like I'm literally just going to. There's a company in name a category that built a product and they raised, you know, they raised 400 million. Their last round is $5 billion. They're doing about a hundred million in revenue. So they raised at whatever 50x and they're uh, Everybody says they're way too expensive. It's not a hard co product to copy, especially with R and D today it makes it a big why now? I'm just going to copy the product and sell it for 70% off. And my only advertisement is Acme Co. At 70% off the same thing. And so like that's kind of the play. And they're, they're arguing that there's a massive why now around it today that didn't exist five years ago that I think is three or four dimensions to it. The first one is product development cycles are 10x faster because of, you know, of AI. So how it copying product is just a lot easier. Number two, we've never seen higher valuation multiples for the first movers which puts them in ACB jail. Uh, and then number three, even these companies that were started 18 or 24 months ago, they built their product on an architecture that is already outdated.

Speaker C: Right.

Speaker B: You know what I mean? So, so there's that whole piece and then the final thread is like, well if they do that then will someone just copycat them too? Right. So like there's that, that whole thing. Um, but yeah, that's the, that's the debate.

Speaker A: Let's, let's go through some examples. So first of all you could say that Claude and Anthropic are the fastest follow to ChatGPT and OpenAI and they've now surpassed OpenAI I think in terms of revenue and certainly in terms of B2B usage, Logora and Harvey is a good one. I don't know, they're both big but I know that Lagora is very growing very quickly. What are other good examples recently versus you know Salesforce I would say is the first mover when it comes to CRM and I would say there's one,

Speaker B: there are other clouds out there, I'd have to study that one. But I don't think, I mean obviously the easy ones like historically Slack and Zoom were nowhere near the first in that category. Um, on the B2C side like you know, on the browser side, the search engine side, the iPhone side like none of those were like first movers. And I even think like if you study deeply like Workday, Salesforce, um, you know even ServiceNow, I don't think those were the first major cloud attempts that were funded. Um, but more recently I don't know, I don't know if we've really, there's a, there's a reasonable argument that some have tried and failed more recently. Um, but I don't, I don't know.

Speaker C: It's a really interesting premise and it's also why you're going to see the rise of one of the reasons you're going to see rise of AI tech enabled services because in the absence of product all being equal if you're delivering a solution. Let's take comp for example. If uh, I told our customers like listen, I'll just take comp right off of your plate altogether. You don't have to think about it, you don't have to deal with it. It's just we, we, we handle everything 100%. I don't they uh, wouldn't, not every, not 100% are going to opt into that but that product market fit in demand because I know what people think about the ICM space would be quite high, will be quite high I should say, knock on wood. Yeah. And I think you can't have a fast follower that's following that.

Speaker B: Yeah.

Speaker C: So the traditional SaaS, companies that have put hundreds of millions of dollars like Gainsight into cs, into evangelizing the category, we have seen lots of fast followers in that category but not, I don't think there's been a great example of anyone that's like risen above Gainsight. Now you could argue that the TAM on that uh, category or just the category in general wasn't interesting enough to create a fast follow type ah of organization. The one category that's playing out. That will be interesting to watch. It's just kind of a sleepy one. But a pretty big deal is ERP and NetSuite. Sure. And there are two companies, um, that we deal with in our spaces from a partnership standpoint, Campfire and Real IT that have gotten a lot of money poured into them for AI. Erp. Yeah. And it's not in the CRM because everyone wants to be in the CRM and the ERP arguably is just as big of a space. I'm sure the TAM for CRM is much bigger. But it's still ERP is quite a behemoth, which is invoicing. You're running all of these contract, um, how do you keep track of all of your contracts at scale for organizations? And, uh, it'll be interesting to see that.

Speaker B: I want to make sure that we frame in it. We can go in any direction. But I wanted to like make sure I was clear about the intention here and make sure. I don't think you're saying this, A.J. but I want to make sure like I didn't intend for us to evaluate traditional last generation incumbent versus new attacker, which is a whole different debate. And we've had that debate a lot. My intention here was to evaluate the recent movers, the re like who's been funded in the last two or three years as an AI native new company with big valuation and how vulnerable are they to a copycat?

Speaker C: Right.

Speaker B: So not necessarily a gainsite being disrupted by replacement service now being, you know, disrupted by a replacement, but more of like these big brands that you're hearing about that were refunded in the last two or three years. Are they vulnerable or are they the winners? And I think like so far. But both of your dialogues, something I didn't mention in my old initial commentary, which is also another interesting conversation, is it tests the durability or moat of that incident company.

Speaker C: Right.

Speaker B: So I'd agree with you, like I would be way more bullish on the moat of Harvey and Lagora, even though it's somewhat being tested, because I believe that the end market law firm cares a little less about price optimization and feature optimization and cares more about what Morgan and Morgan is using. And if Morgan and Morgan thought Harvey was good enough for them, that goes a long way. And that is if you extract that back down to Michael, um, Porter's five forces Barrett entering his work on M.O. he classifies that as almost like a brand moat where you buy it because of the brand. And I think today, like Pat Grady sequoia talks about as trust. He's one of the lead investors in Harvey. And so they're kind of the same. Thing is like your M.O. is that there's this recognition of Harvey as being a trusted product for all the big law firms. So if I'm a law firm, I'm going to use them irregardless of the competition, feature set and price. Okay. And I think that translates well to a lot of end markets that are not technical. Right. So I think that's part of what you guys are saying is there's certain ones that have a high MO and I wouldn't want to copycat them.

Speaker C: Well, that hackathon that you. Was it a hackathon that you uh. Okay, yeah. So they, they did they present what they built from, from any of these categories?

Speaker B: I mean literally like I went and talked to them on Wednesday and they had started building this stuff on Monday. I mean they open claw, which is cool, right? Because like there's a whole nother set of like interesting vulnerabilities uh, that are like is the LLM even a uh, utility or does it have a moat? And if you build on like especially the obsession last six weeks on token optimization and compute optimization, like do you that that's an opportunity for a copycat to just really generate a lot of efficiency, you know that the current, the folks that built two years ago don't have.

Speaker C: Are you. Do you think you'll. So they'll present this at some point and I'm sure you'll talk to some of these students uh, and they'll become founders of this. I guess my question to you is are you looking at any companies right now? Have you had.

Speaker B: I've already made one.

Speaker C: You made one?

Speaker B: Not gonna say one yet because I don't want to poke the bear yet.

Speaker C: Well that's also interesting as well because ah, when, when I think about it it's like when I started Trend Kite, I uh, was competitive directly to Meltwater. And the question I always got asked is like oh, are you afraid of them? Like they wouldn't like I'm a startup, I'm moving much, much faster velocity wise. They couldn't even like keep up on a product road roadmap. But that's changed a little bit because you have these incumbents that are just last year the Harvey Lagoa, whatever. But pick that for any other industry. They, if they, if they do feel like they're getting poked, they should be able to, to change course pretty quickly as well. So even that like startup mode is A little bit that's disintegrated.

Speaker B: It's a good point, but I think that's where there's some vulnerability. And guys, let's just so you know, like, I'm not. This isn't my entire investment thesis, nor is it like, I don't even know. No, it's an interesting discussion, but like, yeah, my counter to that would be I would take advantage that they're in ACV jail. Meaning because they had to spend so much money figuring the product out, so much money evangelizing the category, and, uh, because they had raised at a hundred X valuation multiple, it would be very difficult for them to drop their ACVs by 70%. I mean, you're talking about potentially a company that has a hundred million dollars in revenue with an AC of a hundred thousand and two kids from Caltech are now offering a very similar product for $15,000. I just think that's very difficult. Difficult to pivot to.

Speaker A: You know, that's interesting. This is what Adam Robinson's doing with Molt sets. He's trying to dramatically undercut Zoom Info, Apollo and all of the contact database information, all the data information for 30 bucks a month and just say, I don't care.

Speaker C: Yeah, yeah.

Speaker B: Ah. I mean, that's interesting. I would say, like, let's take that one apart. What doesn't necessarily apply in this case is Zoom Info and Apollo don't fall into the 100x valuation trap.

Speaker C: Right.

Speaker B: They were. They're more of like. I think that's more. Probably a little more of an incumbent, like a. A 2015 incumbent to now, which is. That's a whole other opportunity. Um, and I also like the moat durability. Everyone says proprietary data, proprietary data. You would think Zoom Info would fall into that. And it's, um, you know, if he. If they're having success and obviously the market isn't really bullish on it, given how. Poor Henry, good friend, but like, he'll. He'll pull it off. He's an executor, that's for sure.

Speaker A: AJ is it. Should we go to Bulls versus Okay,

Speaker B: so I'll disclose who I'm going after on the. Yes, it's quotapath.

Speaker C: Oh, my gosh.

Speaker B: I'm just.

Speaker C: Insight is already invested.

Speaker B: Think. Yeah, I think you guys pretty well. Okay.

Speaker C: Speaking of your. The Zoom Info comment on you. You mentioned. And this is a little bit of a Bulls versus bearish on that regard. And so I first want to make the disclaimer that this is not financial advice. And. And Mark might abstain from answering some of this. But I'm going to ask it anyway. So in this segment, ask, uh, question. I'm going to ask one question because we're running short of time and both of you have to say whether you're bullish or bearish on it. So yesterday, Jason Lemkin had a tweet. You all might know what this is.

Speaker B: Sorry.

Speaker C: That HubSpot is at 3 1/2 billion in ARR, which I also think they have like 2 billion in cash as

Speaker A: a part of that.

Speaker B: Right.

Speaker C: There's a lot of numbers in there. Growing 23% and it's trading at 8.8 billion in ARR. That's. Yeah, 2.4x, 2.5x.

Speaker B: Clay is trading higher than that.

Speaker C: Yeah. So this is either the most mispriced software stock of all time or AI really will be the death of everything software. So the question is specific to HubSpot. Are we bearish or bullish on HubSpot long term?

Speaker B: Yeah. So because of my connection, I'll say it again here, I'm just on the advisory board. I have no inside scoop. I haven't had Inside Scoop in 12 years. And what happens in the board meeting or any insider numbers. And I have someone else besides myself manages my entire financial portfolio. But I would be very bullish on that as well as some of the other peers.

Speaker C: Sam, what do you.

Speaker A: I agree with Mark and I think

Speaker B: this is all the time frame. Like I think it's a buy right now with an evaluation of a cell, whatever, six months a year from now, two years from now, over a 10 year period. That's a different question.

Speaker C: Yeah, I, well it's. That's a different question.

Speaker B: But in terms of just the stock price right now I'm going to take

Speaker C: a little bit of a counter and I look similar to Mark. I know a lot about HubSpot as a HubSpot Ventures company. The bearish piece of it is more on what they've said publicly and more around the vision and more around executive changes. Uh, those two have been in flux and they've been very public about that, uh, over the last year. And so my sense objectively looking at it is it's a company that is still really trying to nail down what does, what does it smart, what does a customer really want and how can they articulate it best? They change the name from Inbound to Unbound. They had this marketing loop thing that Unbound or Inbound did release last year. I don't know if that's gone anywhere. I Haven't heard anything. Kip Boldner was, like, really big on it last year, and so I think it has a little bit of a vision crisis, personally. And so that's the. Bearish.

Speaker A: I would agree 100% with that, but I. The core business seems mispriced for.

Speaker C: For the performance that it's generating.

Speaker A: Okay, last question. Bull or bear for both of you on Halligan coming back as CEO in the next 18 months?

Speaker B: Yeah, I mean, I would. I would be bearish. I know him, obviously, very well. I know Yamini well. Um, I don't know. I just think, like, when he puts trust in someone, he puts trust in them. He had that accident that he almost died, you know, and I, uh, don't know if he'd want to take it on.

Speaker A: Fair enough. Well, he also might not want to take it on because, you know, it's like the reputation is on.

Speaker C: It's like he's already done this thing.

Speaker B: Right. Yeah.

Speaker C: I won't talk to Halligan and HubSpot specifically. Um, given I know both of. Not anywhere near as well as Mark does, but know both of them. But. But I am bullish on founders coming back. Look at Finn and Intercom. Sure.

Speaker B: And.

Speaker A: Sure.

Speaker C: And that. And setting the vision and. And doing it in a way and just kind of transforming the business. It's.

Speaker B: Yeah.

Speaker C: Founder mode is a thing. Um, and so that if that's a part of it and the vision's a part of it, I'm sure there's some calculus.

Speaker B: I love that. And I just want to say. I just want to, like, go, like, just put something out there to help the ecosystem. I thought. Is Paul Graham. Right? His MEM mode.

Speaker C: Yeah.

Speaker B: That was brilliant. Wonderful. I think in some cases it was misinterpreted. That could hurt the ecosystem where, like, I agree with the Intercom situation or even, like, what's happened in any of these companies. Zoom Info, HubSpot. Like, you need to go back into founder mode, which means, like, you ex. You know, you're no longer stretching the spreadsheet. You're going back to, like, experimentation and pivoting and that kind of. Of stuff. Okay, beautiful. I think there are a lot of founders that, um, interpreted it as. I'm never going to hire an executive team and delegate that function to them. I am always going to run this very micromanaged command and control organization, and I think that's a problem.

Speaker C: Yeah.

Speaker B: So just like, be care. Like, be careful of the application of the memo.

Speaker C: Yeah, for sure. Mark, you want to take us out?

Speaker A: Yeah, I just want to say, Mark, thank you, uh, for being a friend of quotapath, of pavilion, of sta. And of Top Line. We appreciate you and we love you, aj. We feel the same about you.

Speaker C: We're gonna set. We need to send swag. I need to get the A plus accolades next time from Mark. Given, uh, that Sam got all the A plus accolades. So I'm, um.

Speaker A: Oh, what A plus accolades did I get?

Speaker B: You talking about?

Speaker C: I know, I know, I know. I'm only kidding. But seriously, do need to send you some swag, Mark. Thanks.

Speaker B: Yeah, no, thanks. It's always a lot of fun to jam with you. Yeah, thanks, Mark.

Speaker A: If you are looking for more Top Line, check out the Topline newsletter@topline media.substack

Speaker C: um.com that's toplinemedia.substack.com or if you're a

Speaker A: video person, check us out on YouTube. Topline Media is what you want there. Have a delightful day, everybody. Ra.