top of page

Speaker Details

Geoffrey Moore

Author, Speaker, Advisor

Session Transcription

Welcome, everyone, to this session at SaaS Metrics Palooza 2023. I am so honored and pleased to introduce a second time presenter here at our second annual Palooza, Geoffrey Moore, the famous and leading author of not only Crossing the Chasm Zone to Win. Hey, Geoff, welcome back. Well, thanks to be glad to be here, Ray. Thanks for having me come back. Yeah, you know, last year, you had one of the highest rated sessions where you really introduced the zone framework that came out of your Zone to Win book. But man, have things changed this year with the introduction of chat GPT and generative AI and large language models. The entire SaaS Cloud industry is thinking about what's next. How do I compete and differentiate myself against this new technology? So I thought today's discussion about how Zone to Win applies to adoption of AI would be a great topic. What do you think, Hugh? I think it'd be great. I'd love to have a chance to do that. I'll recap the framework, and then we'll apply it to this new world. OK, well, let's go right to that, and we'll start with the framework. Go ahead, Geoff. OK. Well, so the idea behind this framework is if you're a startup, you actually are a disruptor. And you would actually start in this framework in the lower left hand corner, like with a venture capitalist, and you get seed capital. You'd be in your incubation phase. And your transformation phase as a startup is probably crossing the chasm, where you become a real business. And you have some success, at least with some use cases in some vertical markets. And then the performance zone is when you really scale to your maximum potential. You're now winning business in many segments, perhaps many geographies. You're scaling to different size. At some point, you may get acquired, or you may acquire as a way of getting bigger and bigger. But you're now delivering your value to the world at scale. And that really is the function of the performance zone. And then as more and more of that scale happens, you need more and more help from the productivity zone to be able to deliver at scale efficiently and effectively. So all the processes behind the scenes that allow a global enterprise to have a financial infrastructure, HR infrastructure, and IT, marketing facility, everything that supports the performance zone becomes the productivity zone. Many of us here on this call today are in companies right now who've been through this cycle or somewhere in the middle of this cycle. But as you pointed out, what generative AI and the chat GPT and the large language module is all doing to us is it's starting another cycle. It's saying, hey, guys, we're going to incubate. And you're, well, I'm not through the last one. So basically, a bunch of the tensions that happens is we call it you have to zone your enterprise. Because what you learn about this model is that each of these four undertakings, the trial balloons of the incubation zone, the big bets in the transformation zone, running your product line and your sales theaters in the performance zone, running shared services in the productivity zone, each one has a different operating model. And so when you're going through it the first time, you kind of add them on one at a time. And so you don't experience a lot of conflict. But when you try to do it the second time, now each of those zones says, well, I need my own resources. I can't give resources to another zone. And yet the other zone demands those resources. So that's where the whole tension behind Zone to Win came from. And working with the team at Microsoft with Satya Nadella and the team at Salesforce with Mark Benioff, how would you solve for that problem? And as we'll see in the next slide, there's kind of a solution for it. But before we answer that, go ahead. Let's talk a little bit about the framework. Go ahead. I was going to say, Geoff, before, because I know we're going to talk quite a bit about how this introduction of AI, depending on what zone you're in, how they can take advantage of the framework. But let's talk about, kind of, the different work that's required in each zone first. Yeah, and I think the accountability metrics will kind of give you a flavor for that. So let's start with the performance zone, because if you're at this conference now, you probably have a business that's underway. So start with the performance zone. And that's the zone that your investors look at. It's your partners and customers also look at how you're doing there, because they want to know you're a viable, successful franchise. And so when you look at that zone, the purpose of that zone is deliver your value to the world. But we typically measure it financially, because financials cut across every industry and every line of business anywhere in the world. So there's a kind of a common language of bookings versus plan, revenue versus plan, contribution margins. But the truth is, that's not what the world's buying from you. The world is buying outcomes that your company delivers that makes the world a better world. And so the performance zone is all about doing that today with what you have today, even though what you have today is not the coolest thing in the world, but you have customers, you have products, you have services, you have obligations, and you have a quarter to make and an annual plan to make. And all of that is the job of the performance zone. And that's the zone that the world experiences through you. So in one sense, everything we do always ends up in the performance zone if it's going to make a difference one way or another. So that's great. And people understand that, and we've been doing, that's 200 years worth of business knowledge, not radical. The productivity zone is increasingly becoming important because, and this is one place where Gen AI is going to have a big impact, because scaling this cost effectively on a global basis in a digitally transforming world requires a whole new set of processes, a whole new set of infrastructure, a whole new set of operating procedures, et cetera. And getting to those new states as fast as you can reliably and scalably and cost effectively is a big, big, big challenge. And so the productivity zone right now is under enormous pressure to adopt all these digital capabilities and the AI, et cetera, et cetera. And so the way you do that is it's important that you bring them into your organization at the right pace. If you go too fast, you'll lose your adoption and you'll create a big mess. If you go too slow, which is typically more frequently the problem, you'll just get marginalized, you'll get left behind and the startups will take away the world from you. So finding the efficient frontier of how fast can we go and then running programs on that frontier to move us and keep that sense of progress and urgency, it's really, really, really important, all the while maintaining compliance standards, which is kind of your obligation as a larger enterprise. So that's what's going on. And the guys who run the productivity zone are not trying to make the quarter and they don't necessarily even deal with your direct customers, but boy, are they the cartilage that holds everything together. And so they really, really, really, really matter, but their issues are more internal. They're less quarter to quarter, they're more year to year, and they're very process oriented as opposed to event oriented. Performance zone is all about events, including the sale event that comes on the last minute of the last day of the quarter, that kind of stuff. Okay, that's the core business, that's probably 90% of most people's resources are on the right hand side of this diagram. But now that incubation zone, and this is where I think it's really important with companies who are looking at the Gen AI product mark, Gen AI, a chat GPT stuff, how are we gonna use this? And when we're talking about product market fit here, we're really, first of all, talking about product market fit with ourselves. Like, how does this fit into our operating model? Is it gonna evolutionary or revolutionary to our operating model? We need to find that out. And the incubation zone lets you run the experiments faster and outboard of the rest of the company so that you can go take chances, go quickly, you have plausible deniability, you can shut things down if you have to. But what you're looking to do in the startup world, you talk about having a marquee early adopter and then cross the chasm with repeatable use cases, you're doing the same thing inside your company. You're looking for that first big win with AI inside your own company. Where would that come from? How would that work? And there's always somebody inside your company says, I'll go first, I'll try it, I wanna do this. And so that you work with them and whatever they're trying to do, you try to make them successful. But the real question is, where's that repeatable use case which really pays off for us, given the business we're in and given the things that we're trying to do. So you don't wanna leave the incubation zone until you know what those repeatable use cases are. Most people leave the incubation zone too soon. They throw it out into the performance zone, they throw it out into the productivity zone. Those zones got a lot on their plate already. They can't explore, that's not fair. So it's really important that you complete the exploration while you're in the incubation zone and you deliver a completed opportunity, not a possibility. And this is where a lot of people really lose value because they don't discipline the incubation zone to those metrics. And then finally, if it is gonna be revolutionary, if it's gonna actually change your world, then the transformation zone is necessary because you've now got to deconstruct part of your inertial momentum in the performance zone and the productivity zone is in resistance to your future. And you have to shift your resources from the current business to a future business. It's very painful. You go through what we call a J curve where your performance goes down before it goes up, but it's part of the cost of getting to the new world. And so it takes an enormous amount of commitment. It has to be the board of directors and the CEO and everybody has to be 100% on board doing it because once you get past the tipping point, now you're in a whole new world and now you're swimming with the tide instead of against the tide and life can be very, very exciting and very, very productive, but you've got to get over that hump. So that's what the transformation zone's for. So that's kind of a whirlwind tour of the four zones, if you will. Okay, so Geoff, you kind of did two things there. One is to gain internal productivity using new generative AI tools. You really need to be considerate of your employees, what zone they're in and then how you rolled out to be broad-based. But then the second thing for a lot of our audience members who are B2B SaaS executives is how do I leverage AI to actually make my product differentiated for my customers to maybe stay off those small startups who are AI native but don't have access to our customers, to our data. Which way do you want to go first? Internal productivity or external product differentiation? Well, we'll do both. We'll do, since you brought up external, let's do external first and we'll come back to productivity in a minute. So I think, as you know, I've been doing a ton of work with Salesforce over the last decade and their dream force is coming up next week and they're going to announce, and by the way, we started at the beginning of this last year Salesforce started an entire internal productivity zone initiative around becoming a more profitable, more efficient company. They were under pressure from activists, shareholders, et cetera, et cetera. Right in the middle of that, ChatGPT comes out of nowhere, just like a tornado. And so they pivot, a $34 billion company pivoting, that's like watching an elephant pirouette, right? But they pivot to say, we're going to implement GenAI GPT in every one of our products because we've had a large language module for five years. They had been working on it in the background, which was amazing. So what they're doing with their performance zone is saying, how do you, but it's not, it's evolutionary, not revolutionary from the point of view of, I think both them and their customers. So if you're a salesperson, how do you use ChatGPT? Well, it summarizes my sales calls. It helps me draft emails to my customers. So it's like, and they've had these Einstein, you know, Salesforce, sales with Einstein. This is now Einstein more talkative. ChatGPT is just a talkative Einstein, right? So if you're doing the service thing, you know, that whole thing about helping the service, the contact center person be much more knowledgeable faster or even substitute for the contact center person and say, look, talk to the AI and we'll escalate if you have trouble. But as you and I have both experienced with this ChatGPT stuff, it is remarkably effective even in its current state. It's obviously gonna get better, but they're doing that. And then the big point that I would make there for them, and this is also hold for people on this call, is to the degree that you're using your own data, you overcome one of the biggest concerns about this gen AI, which is hallucinations and bad actors and whatever. But if you think about, well, we have a corpus of data the large language modules is organized around and it's our data or our customer's data, then that makes it much, much, much less anxiety producing when it goes into the performance zone going forward. So that's how they're doing it. I think every one of their products is gonna have a gen AI component to it to take out what I would call the second order busy work of that function. Every function has a primary value added moment and then a whole bunch of stuff you got. Doctors, when they're with a patient, that's their primary value add. When they're entering all that stuff into the Epic system, that's the secondary stuff. Everybody has that sort of wealth managers and finance, same problem. When you're with your client, that's great, but what about all the other stuff you gotta do in the background? Sales reporting, services, logging all service incidents. I mean, it just goes on and on and on and on and on. So I think that the ability to bring, and if you don't have, I mean, look, if you're a $20 million company, you don't have a large language module, right? But Microsoft and Google and Amazon, all three of them are, and Meta now, they're for aggressively marketing these opportunities. And I think for those folks, if you don't have your own corpus, I think the productivity zone play is gonna be better initially. So the productivity zone just says, hey, we're gonna use it internally, but there'll always be a human filter on top of it. So all that hallucination stuff, I'm sure we'll encounter it, we'll just filter it out. That'll be part of the human's job, but we will free our humans to spend more time at the efficient frontier of the real issues and less time doing all the other stuff. So Geoff, let me ask you a question because you're working with a lot of the largest companies who are kind of in the performance zone right now. I know that you could be in a different zone than any four different ones at the same time in a big company, but let's talk about when Salesforce is rolling out generative AI, is it a just natural add-on to the current usage of the product? And how do they get their customers to understand that there's more value now in the current product? Or do they differentiate it as a new release, new features, and you have to go through a little bit of a training and learning curve for the users? You know, it's a good question. I mean, we're gonna find out. Basically, I'm going to Dreamforce next week and I'll sort of see the first proof of the pudding. My bet is it's a little bit of everything. The thing that is kind of amazing about it, at least of the stuff that I've been experiencing, and I haven't been using Salesforce's products, I've just been using kind of the chat GPT stuff that everybody can get a look at, is how empowering it is out of the box because you really do feel like you're having a conversational exchange with a human being. And you feel like the thing at the other end is trying to understand you. And you can use prompting to get, and once you prompt them with something, you can redirect the conversation. The thing that always frustrates you with automated solutions is when they get you wrong, you can't change them. It's like, you know, I'm not interested in those books, Amazon, goddammit, but you can't tell them that. Well, with Gen AI, you can. And once you tell it once, it remembers. So I think that the amount of learning curve for the end users is gonna be relatively modest. Now, the learning curve for the product designers, on the other hand, I think that's where we're gonna hit the, because they've got to rethink their craft. They have to rethink the architecture of their product, particularly the user experience designers. They're the ones that are gonna have to figure out, so how do I architect the user experience in this new world? And we're gonna invent it. We're gonna make mistakes. We're gonna iterate. We're gonna go forward. But boy, it's an awfully exciting time. Yeah, you know, one of the things that we do is we benchmark. So we collect data from thousands and thousands of companies and we provide benchmarks. And I think about the similarities to generative AI. A company like Salesforce has sales performance data from hundreds of thousands of customers. So they could apply these models against that and make everyone more effective. But then you've got a Wells Fargo that's like, no, no, no, no, no. If you're gonna apply that against my data, I only want it to be using my data and no one else can get it. Do you think it's a major point of kind of decision that companies need to decide, am I using a single customer's data versus my aggregate customer portfolio data to get these benefits? Yeah, and I think the default's gotta be it's the customer status first. So in other words, I think where you can get consortium behavior, I think everybody benefits, but that's gotta be an opt-in. You know, you can't, you have to be very respectful of that. So I would say in general, and therefore to the degree that they don't opt-in, you're gonna have a more limited, less articulate, less inventive model than you would if you had, you know, we know that these things get better with more data. But I think that's the customers, you got the customer's gate there. I do think, however, like if you look at places like all the case information that help desks have, or any customer support or customer success organization has an enormous amount of log history. So I think there is kind of a no regrets move. You can, and that, by the way, that does cross many different clients, but it's more about the vendor's product than it is about the data that the client has about their customers. That's the stuff you got to be very careful. Yeah, in fact, I've talked to a lot of companies over the last few months that are building AI native products. So they're in the incubation zone, Geoff. And they're saying, we have to put in our contracts that we have the right to use your data on an anonymized and aggregated basis to apply our large language models against. I don't know if that's something you've seen, if you've worked with young companies that. Yeah, I mean, that is the number one hurdle for a startup because these things gain value with mass and startups don't have mass, right? They have enormous differentiation, incredible future potential, but they have a very, very limited performance zone impact. And so the advantage of the incumbent is can I pivot my historic resources and use the power I have today in this new way? If I can, I can probably keep the startups at bay. If you're a startup, I think what you got to say is I've got to figure out a market development strategy where at any given point, the collection of data I can get my hands on solves the problem that I've committed to solve. And initially it'll be small fish, small pond, but it'll be a high value use case that frankly is very hard for traditional vendors to deal with. And we'll crack that nut first and then cross chasms and all that kind of stuff. But we have this thing we used to call the bowling alley, we still call it the bowling alley, which is use case by use case and vertical market by vertical market where the established vendors are going too squirrelly, too small a segment, we're not gonna go the extra mile there. And the startup says, well, I'll go the extra mile, I will. And so it's really important when you're like a $20 million company, you got to be really thoughtful about, I don't want to play in a pond that's bigger than $100 million because I'll just be a minnow. But by the way, the pond I play in, I want to be the biggest fish in the pond. So I need to have something that would dramatically cause that pond to convert to me who's somebody they've never heard of. And the only reason they'll do that is because they have an urgent use case that nobody else is solving for them. And you're gonna say, I'm gonna crack that nut wide open. That's interesting because we do have a pretty big cohort of the audience here in that, I'll say 10 million to $25 million range, Geoff. So they went out of the incubation zone, they're in that transformation zone, right? But maybe it's for one market segment, maybe it's the SMBs and they want to go up market. So I'm thinking, the question is, if I'm number one, kind of going into the bowling alley, at the same time having to deal with this new AI reality, that seems very dangerous because you still don't have real markets share, but you're trying to introduce a new technology at the same time. Any advice there? Yeah, I mean, that's a very scary place to be. I think the game I would play is, define your product commitments backward from the problem that you've committed to solve. So if I'm a 10 to $50 million company, I wanna find a 50 to $100 million market opportunity where there's a lot of trap value, meaning if I actually can solve this problem, people in that market are gonna weep with joy, okay? So that's where I'm gonna go. Then my second question would be, do I need to have Gen AI to crack that nut? And if I don't, I would say my first priority is to crack the nut, not Gen AI. But then the more interesting question was gonna be very quickly, but as soon as I could add Gen AI, how does that change the dynamics of my solution? I think what these companies would likely discover is, it doesn't make your solution more powerful, but it makes it a hell of a lot more efficient. And that's gonna become important as you wanna scale. But you don't have to race to Gen AI. If you're solving, what you have to race to is a true solution to that use case that you're claimed to fame. Yeah, that's interesting, because one of the things in this B2B cloud industry, people really wanna talk about verified outcomes. What's the business value to their users? So it sounds like to me in that transformation zone, the more you can get customer validation of their outcomes and then able to use that to go down the bowling alley, or bowling alley, so to speak, really important is validated outcomes. Yeah, and the way you make that work well because validated outcomes can often be a rather a blurred landscape, you know, like, well, how do you get the data? How do you do it? So the key to that is you pick a use case, which is horribly, horribly, horribly broken. So you don't need like validations, like the damn thing is broken, it's lying on the ground for Christ's sake. And so just having people have a testimonial, I was saved, that's the validation. And that's what makes crossing the chasm work. Because once one person figures out how to solve this problem, they tell their friends and that anybody who has that use case goes, wow, there is a solution to it, this is what you do. And so that if you think about that, that's just a validated business outcome. That's all that really is, but that's not how they're talking about it. They're talking about it, you've changed my life, you know, that kind of thing. Got you. Okay, do you mind if we move to the internal use case of generative AI in your framework? Is that okay? Yeah. And the reason I wanted to do that was one of our speakers, Janelle Tang from Bessemer Ventures, helped author the State of the Cloud 2023 report. And she's providing an update on that six months after the original publish date. And one of their five predictions for 2023 at the beginning of the year was the benefit of generative AI will first accrue to the individual. It's that marketing employee says, I'm going to be able to write better ad copy or that podcast producer that says, I'm going to be able to produce 20 mini clips to promote the podcast much more efficiently. And you mentioned earlier, find that one person who's going to really be the person who tests it, validates it, et cetera. So do you think that's kind of the way AI is going to roll out for internal productivity is the individual who then shares it with his or her team and then to a department? Well, I think it could even happen faster than that if you have a department manager or a functional manager who says, you know, for the last five years, I've been trying to hire people into this function. First of all, it's kind of a boring function. And second of all, you know, budgets are tight and I'm not a revenue generating function. I'm a compliance function, I'm a cost function. I'll give you an example. I'm on the board of a company called WorkFusion. So WorkFusion has taken AI solutions and applied it to financial compliance regulatory processes like adverse media events, money laundering, fraud detection, you know, know your customer, all these kinds of stuff that if you're a financial institution, you got to do it. Well, today, basically people have to do that work and you go offshore to get low cost people, but it's still a people driven thing. So they've actually created digital workers. Well, so from the point of view of the internal group, a digital worker is not that disruptive. And by the way, the digital worker works 24 hours a day. They don't get bored, you know, they don't want to be promoted, you know, and what they're doing is they're taking a bunch of work that requires discretion and judgment, but there's enough history around these issues that you can extract that judgment from the corpus of the large language module and not have to apply, you know, a live human being to that problem. Of course, what it causes it to do is as you've already implied with the marketing example is it frees the human being up to deal with the efficient frontier where frankly there is creativity required, there is judgment required, there is new stuff to require. And people worry about, oh my God, you know, we're gonna run out of jobs. Well, yeah, we're gonna run out of the old jobs. Yes, we are. But do you think the world's gonna run out of problems or challenges? I mean, what world do you live in? But what we have to do, what Darwin has told us for the last, you know, 900,000 years or whatever it is, we have to evolve with the problems, but there's always an efficient frontier that needs a creative response. We just have to make sure that we can get our bodies over there and not stuck in the mud doing the old stuff. Right, so since it's interesting, you talked about the digital worker because Marc Andreessen has been talking a lot about your AI assistant, that you're gonna train your own personal assistant and it's gonna be along there with you to help you make decisions, to make better decisions, help do the work. And it kind of sounds like what you're talking about is every employee, knowledge worker could have that assistant to help him or her free up their time to think more high value, creative ways. I think the next step is you to digital colleague. This is actually, this is a digital colleague. I mean, they're not my assistant, they're actually better at what they do than I am. And basically that's their job. Now, I know they're a specialist. I'm not gonna refer to them, I'm not gonna refer work to them that they're not qualified to do, but if this is in their bailiwick, yeah, they're my colleague. Yes, I love it. You know, we're coming up to the end of this session, but I know you've been working with so many companies and you've shared both the WorkFusion and the Salesforce examples. Can you share another example of how they're applying this zone framework to how they're thinking about rolling out AI, whether it's internal or external? Don't need to share the name, but just, I think these examples are really helpful to the audience. Well, you know, think about, so I've spent some time with Cisco, I've spent some time with SAP, I mean, Intel. I mean, great, great, great companies, iconic companies, right? Every one of them, let's take SAP. SAP is kind of like the manufacturing backbone of the world. I mean, let's face it. And so they look at something like Gen AI and they're going, you know, we want to create marketplaces where people can have more resilient supply chains. Well, how do you do that? Supply chain is an interactive dialogue-based process. It's not, you don't automate a supply chain, you facilitate a supply chain. And so you look at a technology like Gen AI and you say, if we can introduce the Gen AI into that process, so that we can talk through things like, you know, specification requirements or allocation issues with certain products or vendor qualification, there's all these processes that are, that have an enormous amount of inertial mass to them in order to do them at scale. This is what SAP has been so good at. They have operated at scale amazingly, but they've had to do it originally with rather rigid rule-based discipline. And there's no accident, by the way, it's a German company, right? So you want to do it this way, but you don't know how to do it, right? But everybody realizes in a more federated world, that doesn't, you increasingly have to have a more collaborative negotiated interaction. So the excitement in that company is all about, whoa, this chat GPT can, I mean, obviously people have to do some of this, but the more we can offload that negotiated rapport to the software, the faster we can get, you know, to the, and by the way, SAP has always been very successful at the top of the pyramid, but the world, a lot of the world's in the middle of the pyramid. And so that's a place where you need more standardized, more leverage than we've had in the past. So that's, so they're going to be inserting that into their performance zone products. But again, because when the bigger you are, the more you have to be thoughtful about, how do I roll this out? How fast do I roll this out? Where do I roll it out first? And that's going to be on their agenda for the rest of this decade. Well, unfortunately the session is coming to an end Geoff. So what I'd like to do, if you don't mind is maybe just kind of summarize how the zone to win framework really applies in today's period of disruption. And then any closing comments you'd like to make. Sure. Would you want to summarize or shall I summarize? Oh, I'd like for you to Geoff. Okay, that's good. Okay. So I think the main thing is when you're running an established enterprise, you're no longer, let's say running on venture capital or private capital. You're basically working with your own operating income and that's how you're funding yourself. Then you have a natural set of priorities. Your performance zone comes first, A, because it's delivering your mission to the world and B, it's funding your entire operation. So that's number one. Number two is in order to do that efficiently and to free up some capitals for spending on future things, you need a productivity zone, which can allow you to optimize your existing business and also run programs to transition to new models when you need to sort of intervene and move the needle from an old place to a new place. And those two zones are really most of the game most of the time. You always wanna be funding an incubation zone because particularly with things like the Gen AI, but it's not just Gen AI. It's every industry has changes that are coming up that are very exciting. When you go to the conference, you have lots of envy for the startup, but the reality is they're not proven and they're not integrated. And there's a lot of work to do. And so an incubation zone is your company's opportunity to say, can we steal a march on this opportunity by really treating it like a real business opportunity, not a lab project, not corporate entertainment, not cool demos, but really creating real options for the future of my company, whether they be using it internally or externally. And so the incubation zone is a place where I'm actually spending a lot of my time because most corporations frankly don't run their incubation zones very well. And then the transformation zone is that one that, I mean, we talk about a ton of it. And when mostly when we talk about it, we're usually talking about Geoff Bezos or Elon Musk or Reed Hastings or Steve Jobs or some amazing leader who voluntarily went out in front of everybody and just blew them away. But most of us on this call, that's not who we are. We're not the disruptor. We're much more likely to be the disruptee. And our job with the transformation zone is, do I have to take my company through a transformation in order to participate in the next generation of my industry? Or can I, on a more evolutionary basis, work my way into the new technology as I go along? Any board member would tell you we prefer plan B. I mean, just for sure. But sometimes it requires plan A. Particularly when I did the work with Microsoft, Microsoft looked at the cloud computing model that said, that's an existential threat to our back office business. So they needed a transformation. And the whole Azure, the whole movement to Azure was run as a transformational initiative. Dramatically, in fact, I think a canonical example of a great transformation. Perfect. Well, Geoff, I will tell you, one of the great honors of being in this industry is having a chance to meet people like you, work with you on and off for the last 25 plus years. So from the bottom of my heart, thank you for being a speaker here at SaaS Metrics, Palooza 23. Well, my pleasure. Thank you, Ray. And recommend everyone to follow Geoff. He does a lot of great work, not just in technology, but he's got a new book that talks a little bit more about the philosophical side of life that I've been a big fan of. Name of that book, Geoff, is? The, I'm sorry, I almost said The Invisible Staircase. The Infinite Staircase. The Infinite Staircase, which is wonderful. Geoff, have a great rest of the year. Thank you so much. Thank you for coming. Take care, Ray. Thank you.

bottom of page