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Technology is changing Weekly culture takes years to shape. It's not a sprint, it's not even a marathon. It's like the Lord of the Rings. It's this massive transformational journey that's going to be exciting and full of opportunities, full of danger and pitfalls.
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This is a show about the future of tech and the future of work. I'm Jeff Nielsen and today my guest is Maurice Conti. He's a long standing AI futurist, working with organizations like Amazon, Nike, Lululemon and Mercedes to take their businesses to the next level. Insights and brainpower aside, Maurice has walked the walk and has a pulse on what's going on inside Fortune 100 boardrooms. The good, the bad and the ugly. He thinks we're looking at AI all wrong and that the future of the technology isn't big or sexy. I want to ask him how he sees this technology playing out, what we should be paying attention to and what we need to do to get ready. Let's find out. Maurice, super excited to have you on today. Thanks so much for joining. Maybe to jump right into it, I mean, you've been talking about AI and you know how we leverage AI for a long time. You've talked about things like us entering into an augmented age, how we use generative AI, humans and robots sort of coexisting. And, you know, given that you've been talking about this for over a decade and we've, we seem to have recently crossed that threshold from kind of imagination and reality. How is it shaking out relative to what you expected? What's the same, what's different? And you know, what do you have kind of top of mind right now?
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Well, Jeff, first of all, thank you so much for having me. It's an honor. And yeah, I mean, we started thinking about generative AI more than 10 years ago. I had the privilege of being part of one of the teams that invented the technology at Autodesk, focused more on design. At the time, I'd say largely so. This idea of the augmented age is humans and technology, in this case AI and robotics working together to achieve things that neither could do on their own before, which I still believe is sort of the golden promise of these technologies. And I think largely it has played out the way that we imagined it. I'll share a little story with you. So back then we were thinking about this technology and sort of how to describe it because it was just in our imaginations at the time. And there's this clip from a Star Trek movie. It's the fourth Star Trek 4 the Voyage Home they go back in time to save the whales. It's one of my favorite films. And in it, there's the scene where Scotty is in a manufacturing plant here in the Bay Area and has to use a computer. Happens to be an old Macintosh, in order to show this, this engineer from, from the past, some formulas, and basically tell him how to make transparent aluminum. And so he walks up to the computer and. And he says, computer, and expects the computer to engage in a conversation, in a. Basically a design and engineering conversation to build this thing. And the computer obviously doesn't do anything. It's a Macintosh se. And so he goes, computer. Computer. Doesn't do anything. And so Bones is there with him and he hands him the corded mouse and he goes, ah. And he goes, computer. And the computer doesn't do anything. And what's going on in Scotty's head? That, that expectation is exactly what I've been most excited about with Generative AI and is what's happening. It's now you. You can actually have a conversation with a computer. So like, Sci fi is now totally real. And it's that ability to have a conversation in, in human terms, within a human interactive context that I think is one of the most amazing parts of this technology and is kind of the part of the vision that has totally come true.
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The word I've heard you use to describe that, which I think is pre encapsulated in that story, is intuitive, right? That it should just be intuitive to us versus just having to write lines of code. Any interaction is just, you can say computer and tell it what you want. We've obviously come a long way in the last handful of years. But I'm curious, like, have we arrived? Is this now just. We're here at the age of, you know, intuitive, you know, AI intuitive, you know, computation, or, you know, what does the road ahead look like?
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Yeah, I mean, we've arrived at today by definition, but. And the journey into the future will be a journey. We haven't arrived there yet. So I don't, you know, I. I don't think about the future or the, the sort of progress of this technological journey as these moments of arrival. I mean, certainly there are some highlights, like, you know, when OpenAI opened, ChatGPT to the general public was certainly a moment in November 22nd. And maybe that moment was when the majority of the people on the planet were able to experience this intuitive interaction. But I tend to not think about the progress as being a sort of series of arrivals or These major milestones, it's much more diffuse than that and just sort of progress and evolution and it's more complicated and messy than you think it's going to be and where things are going. I, you know, I think no one really knows, number one. You know, futurists are not actually supposed to predict the future. We're sort of supposed to model the possibility space and the probability space and the desirable space and kind of think through what those would look like and then help. Like what I do is help my clients make strategic decisions based on those, those models. Right. Kind of where you want to place your bets. But, you know, there are ways to think about the future of AI. 1. I'll share this frustrating moment for me. So this is two or three years ago. We, you know, we're having our annual call with our financial planners, and that sounds way fancier than it actually is. But, um, and, and, and so it's like they're responsible for investing a, you know, small sum of money for us. And we're having the conversation and, and I say, so how do we have any Nvidia in the portfolio? Because I think this is going somewhere. And they're like, yeah, no, our analysts are not looking to buy Nvidia. We don't think that, you know, blah, blah, blah, blah, blah. This was maybe, no, three, three, three and a half years ago. I was like, okay. I mean, like, we, this is what I do for a living. We live here in, you know, the Silicon Valley. Like, you go to Chipotle and order a burrito. And the people on this side are talking about AI. The people on this side are talking, are the ones building this stuff. Like, we kind of have our finger on the pulse. But you, you know, you had a very convincing speech about all the analysts they have and, and how smart they are. And so I was like, okay, maybe it is risky and we want to not be too risky with our little nest egg. And fine. Year goes by, Nvidia starts to do what it's doing, and we have the, you know, the, the check in. And he goes, so how much Nvidia do we have? Well, we still don't really think that it's a buy, you know, yada, yada, yada. And, and then I think six months later we had another check in and it was the same story. They were starting to change flavor on it. This is like a big, I don't want to throw anybody under the bus. It was a big bank, right? Like they, they supposed to be clever. And I was like, okay, how about I put it to you this way? Occam's Razor. In the future, is there going to be less AI than there is today or more AI than there is today? And I think, you know, kind of light bulbs went off for them, but also for me, like, even though I think about this stuff every day, when I heard myself say that, I was like, oh, this is a fundamental kind of principle that, to me, is not about really predicting the future. I think this is a tautology today that in the future there will be more AI than there is versus less AI. There will never be less AI than there is in the world today. And I think, you know, that you could say, well, that's sort of trite and obvious, but I think that's an interesting. Just kind of background reality to ponder. I'm fond of Occam's Razor. You just kind of cut all the. All the extra fat and discourse out, and you just kind of get to the. To the core of it. So that. That's one thing this other way. And I'm sure you know it. Amara's not Amara's Law. If you don't know it in name, you certainly know it in content, which is. We tend to overestimate the impact of a new technology in the short term and radically underestimate the impact of that technology in the long run. And I think we're super guilty of that when it comes to AI right now. I think there's so much heat and discourse and attention and money and so forth focused on this narrative of what's going to happen with AI from today to 6 months, 12 months, 18 months. And I think some of those claims and enthusiasms are overblown. But at the same time, I think there are very few people thinking about AI in a much longer term. So in terms of degree, like in how far in the future and how fundamentally important and impactful it's going to be, I think people underestimate. I think it's going to be much bigger than anyone thinks or most people think, and it's going to take a lot longer than most people think. Um, I think that's another one of those sort of fundamental. I. I think we're not doing a good job of. Of proving Amara wrong or, Or. Or being aware of Amara's Law. And then recently, I've. I've. I kind of had a. An experience working with a team. I have a client, I work very closely with and, and am engaged with kind of the daily life of. Of a team. And I had an Aha. Moment. And it made me think about the future of AI in terms of these four waves. Like, there's these four waves of AI that are going to come through the first wave of AI. I'm calling ibnu. Ibnu. And it's all the things we've gotten very, very excited about, sort of the sexy output of humans using AI. So one examp example might be, I recently saw a music video, the entirety of which was created with a series of different AIs. So the song was written, the lyrics were written by an AI, the music was written by an AI. The music was then produced and performed by an AI. And then the characters in the video were produced by another AI. And then the whole thing was put together in an amazing video. It looks like, you know, a. A pretty darn good music video. And it's impressive. Like, you see and you go, wow, this is. This is amazing. But to me, it's Ibnu. It's interesting, but not useful, right? And so we've. We've gone through this whole last couple of years where a lot of the discourse, a lot of the attention, a lot of the excitement has been around things that are genuinely interesting, no question, in lots of ways. Socially, creatively, technologically, very interesting, but in the end, not that useful, not that impactful, not a lot of ROI and so forth. I think we're broadly tapering off of that now. I think, you know, a year ago I was saying these things and people were like, no, no, like there's so much going on. I think, you know, generally people are getting that that's not where the, the interesting stuff is coming. Where we are right now is the second wave that I'm calling grassroots. And this is where I had that light bulb go off. And this grassroots wave, this grassroots movement kind of came to me as a result of a series of conversations that involved this question that CEOs, CIOs, CTOs were asking in these boardrooms that I sometimes get to hang out in and going, well, where's the roi? Like, I'm getting this bill, the subscription bill, or this, you know, you know, all these tokens I'm spending or, or what have you every month. And it's huge and growing and accelerating. Where's the roi? Where's the roi? And folks were having a really hard time, like, pulling up the spreadsheets and going, well, we don't, we don't see it. There's, like, the line item is not there. And intuitively, you know, I didn't, I didn't Sometimes feel confident enough to push back then until I had this kind of aha moment. But intuitive is like, yeah, I feel like there is value being generated here, but for some reason it's not making its way to the boardroom or to the executive staff. And then something happened. I was working with this team at a client closely enough that I was sort of observing the, the work processes of these folks. Like I was, I was seeing how they were doing the work that was being asked of them. This team is young, mostly people under 30, very bright folks, work fast, like you know, high performance team. And we'd sit down and say, okay, we have to put together a narrative around this new thing. Like we're working on this new thing, this new idea, and we got to start to build a narrative around that. First thing one of these young folks does is they spool up a Claude code instance. You go, we're working on nerd ideas, strategy, things like this. And you're pulling up Claude code variant. And I go, wait, wait a minute. Like we're not, we're not building anything yet. We gotta like figure out what we have to go build first and go, yeah, yeah, I know, but this, like, this is the way I'm gonna gather my thoughts and so forth. And to me that was a huge aha moment. Like that same week, it was a big week for me. That same week somebody said, oh, you should go check out Replit. And this was not like Replit V1, it was maybe like two, two and a half, still early. It was new to me. So I sat down with Relet later that week. I had to communicate this sort of concept around a data set that I had in a particular way of using that that was kind of creative. And so I needed to visualize that data in a way that was unique. And so I sat down with Replit. I worked for maybe an hour and had a fully functional, beautifully designed. It was like Star Trek themed LCARS interface visualization of this data set that was fully interactive. I could like, it had sounds as the radar plots change shape and so forth. Took me an hour. And on the one hand you could say, well, yeah, that sounds like IBNU to me. Like, you know, big deal. Like you have a data set and it's all Star Trek looking, who cares? But, but the click, for me, the kind of eye opening moment was I went, oh, suddenly I've gone from being a tool user to a tool maker. So I don't know how to code any modern languages. So I couldn't have coded that the old way. And yet I was able to build a tool that could do exactly what I needed to in the moment. I'll probably never reuse that tool. Took me an hour. Absolutely did its job. Blew away the, wasn't about blowing them away. The folks I presented it to understood deeply what I was trying to get across. And that idea then went on to have legs. So, you know, mission accomplished there. Now the big takeaway for me on this, on this transition from being a, a tool user to a tool maker is that back to this question, like where, where's the roi? Where's the roi? If you look at an organization, let's say this particular organization has 400,000 employees. But that's, that's huge. Even an organization with a thousand or ten thousand people. And each of those people now kind of overnight has the, has, has been empowered to be a tool maker. So each of those unique people, they're all different and they're all doing, if they're not doing different jobs, they're certainly doing them differently. Like you and I might be on the same team kind of doing the same stuff, but we probably have very different intimate work processes like in inside of how I, you know, you might, you know, our boss might come and ask us to like each deliver one of these things and she's kind of a, kind of a hard ass. And so we're stressed out and you're going to go about it one way, I'm going to go about it a slightly different way. And I now have the ability to build a tool that works the way I want to work. In order for me to deliver this thing maybe 20% faster and it's 20% better looking. Let's say when I hand this to our boss, she's going to look at it and go, oh well, you got that done faster than last time. That's good, Good job. And this looks great. You know, I haven't seen this level of quality. Good job, right? Fortunately, Jeff, not using AI, not using replit, he's not doing as well. But what is invisible to the organization is the fact that I built a tool to do this better. All they see is a slightly better result and they don't see how I did it. And the individual impact is it's maybe not ibnu, but it's certainly not groundbreaking. All I did was deliver this, you know, TPS report slightly faster and it's a slightly better report. But in aggregate, if you multiply that times a thousand employees, 15,000 employees, 400,000 employees, the impact is profound. It's huge, almost incalculable. Right? And at the same time it's completely opaque to the organization. There's no way for them to measure, to even see this happening unless you. Because every instance is different from the next. And so the data set is ginormous. Each cell in that data set is not very big because the impact was relatively small. But in aggregate the thing is profound. So that's kind of this grassroots wave where the value, the ROI of the AI is coming from the bottom up. It's coming from these young folks that are radically changing the way that they do their work and producing better, faster, more efficient, more creative, more insightful output in ways that are again in aggregate hugely impactful, but at the same time invisible to the, to the organization. So I thought that was an interesting sort of duality. The next wave, kind of the third wave in the future of AI is. And not a lot of people, sorry, not a lot of people are talking about this grassroots thing happening. I think that's, that's not as, as observed out in the, in, in the discourse sphere. The third wave is what everyone is talking about. And that's agentix kind of this agentic wave of AI. I'm totally on board with it. I think the, the value, the impact, the ROI is going to be huge compared to the grassroots wave. Not to belittle the grassroots way, but this is, this is where AI gets even bigger and more impactful. I think it's super early days. I'm starting to hear people talk about, you know, sort of agentix and so forth as here now. I think it's, it's super early days. I think where a lot of the energy should be is on not just agents, not just agentic enterprises, but agentic ecosystem. I think by definition for me, I like simple things. An agent is simply a machine system that has agency, can take action on a system outside of itself, whether that's another agent or a non agentic system. It can go do stuff that isn't inside of it. Simple. And in order to do that effectively you have to have an ecosystem. At the same time. As soon as you have two agents that can do stuff, you by definition have an ecosystem, small, low population, but it's the beginning. And, and that's one of the things I'm most excited about is this, is thinking about this as an ecosystem and then the last wave, which is kind of. So the agentic wave where in the middle of it, where it's the beginning, there's still a ways to Go. But it's potentially going to have a lot of impact, both positive and negative. The final wave I'm calling electricity, and it's where AI in the future is going to feel a lot like electricity feels to us today. So it's ubiquitous, it's everywhere, broadly speaking. There are some exceptions, but it's everywhere. You pretty much can't do anything without it. Like, you and I can't have this conversation. I couldn't have heated my tea this morning. Like, pretty much anything for most of the world can't happen without electricity, and no one really much cares. Like, there's a relatively small group of people that are involved in generating and distributing that electricity. Like, they care a lot. But the rest of us, you know, you don't wake up this morning, like, super stressed out or intensely thinking about electricity. It's just there. It powers everything that you do. And I think that's kind of the final stage of this AI revolution. So if you ask me how I think about this future, that's kind of the four waves that are coming.
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If you work in it. Infotech Research Group is a name you need to know no matter what your needs are. Infotech has you covered. AI strategy, covered. Disaster recovery, covered. Vendor negotiation, covered. Infotech supports you with the best practice research and a team of analysts standing by ready to help you tackle your toughest challenges. Check it out at the link below. And don't forget to like and subscribe. I really like that model. And I've got, you know, no shortage of questions or things I want to unpack, you know, about each of them. And I was kind of processing them as you went through it. And I mean, I'm thinking about, I guess broadly the ROI question and the value question, because that also seems to be on a lot of people's minds. And it's interesting to me because, you know, your IBNU level, where it's like, interesting but not useful to me in some ways. That layer is almost destructive in the sense that I think it can turn people off AI because they say, I don't want this. This is gross to me. If this is what AI is all about, count me out. This is not the future I sign up for. But to your point, it's really interesting to contextualize that as a single layer. Are you seeing the same thing there?
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I totally agree. It had some downsides, this IBNU wave. Maybe the. The upside is that it got. Because it's interesting. The eye in IBNU is. It's interesting. It does draw you in, right? And it drew people in enough to start using, to start experimenting and it enabled the transition to the grassroots thing where they started, you know, folks became tool makers and they started making like my data visualization tool is not sexy, right? Not nearly as sexy as the music video, but did add value to my, you know, to my, to my work. So yeah, the dark side of it was not delivering on the promise, this big vision of AI. At the same time, I think it drew in the masses with this excitement and people started to experiment. And, and that's really where AI, to your earlier point, like this intuitive exchange, like that's, that's the beauty. And so when people, even now, but certainly like over the last couple of years, you know, I would talk a lot about AI to folks and you sort of get head nodding and at the end I would say, look, if there's one thing I could tell you, go use it, go to ChatGPT and just use it for 15 minutes. It will be, it will be way more valuable than the 45 minute talk I just gave. That'll be like garbage compared to the 15 minutes that you would spend actually interacting with it. Nowadays people are like, yeah, you know, people have, have sort of adopted the use of these things and, and again, it's a personal maybe because that exchange is more human, like more, more, more intuitive. The use cases are also more personal and, and intimate. Right. Largely everyone kind of uses Excel more or less the same way to do more or less the same things. AI is really different. And even day to day, the way I, you know, I, as the same individual, day to day, use it in radically different, different ways. Right. The same way that I would talk to different people and get their opinions on different things. Like you're a magician in the kitchen. And suddenly I have people coming over tonight and I'm like, Jeff, this is what I have in the fridge. What do I do? Like, they're foodies, they're super snobby. Help me out here. And then I switch to a medical situation and getting a diagnosis and treatment recommendations that are really good.
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So with that, and we've kind of honed in pretty precisely on this grassroots phase. And that makes a lot of sense to me and I'm curious on your opinion. To me, I think most organizations, if we're being honest with ourselves, are probably still in that grassroots layer. Maybe they aspire to get to the agentic wave and maybe we'll cross the hump over the next handful of months or next year or two. But it feels to me like most Organizations are in that grassroots layer. And it's interesting to me, and I see there's a lot to unpack about this specific moment. I mean, you mentioned the fact that it makes productivity gains or value really hard to capture, and that really resonated with me. And the other piece I was thinking about is that's if they capture it at all. Right? Because there's a world, to me, where the only people who capture it are the employees. And if you, Maurice, are now able to do something 20% faster and 20% better, you don't tell your boss, and you just hand it in at the same time and congratulations, you've just gotten a day. Every week, you have one day to, you know, go to the beach or do whatever you want. And so, you know, organizations have to wrestle with that. And by the way, that's also creating some conflict between employees and employers as we think about, you know, what. What's in it for them, the employees, if it's all just sucked into expectation. And then the other piece I was thinking about there, and this is one that's given me a lot of heartburn lately with a lot of the CIOs, CTOs, and tech folks I work with, is if everybody is not just a tool user, but a tool maker, now that's an explosion of tools in the organization that you would think in some way corporate it has their arms around, which I'm positive they don't. And suddenly, what data are you feeding into that tool? Is it the wrong data? What does it have access to? What if it has some level of agency and starts not playing nice with other tools? So, to me, there's a lot of. There's a lot of consequences of being in this layer that maybe not. If you've just kind of had it sneak into your organization at a grassroot level you may not be ready for. So I'm curious, I mean, first of all, do you agree with that kind of state of the nation? And then what advice would you give people who are trying to, I guess, operationalize this wave a little bit more effectively?
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Yeah, I mean, broad strokes. I totally agree. I think you're absolutely right in shining light on that nuance. So, again, one of the fundamental qualities of this grassroots thing is that it's invisible to the organization. And so it's not even companies are not gathering that data. They can't. It's just there's a hard blind spot there. And so what they can do is, you know, listen to folks that are saying, like, hey, this is happening. There are some risks that you absolutely need to get on top of. At the same time, it's probably worthwhile leaning into it in order to get more, more of that value. And what I think that looks like is this, I think that this, the CIO CTOs that I work with are put in a hard position just as you described. They're hearing, well, this is where the value is, so make it happen. And at the same time it's a proliferation. I've been thinking about it kind of like the Cambrian explosion. So like 530 million years ago there was this explosion of life on Earth. And it wasn't just the quantity of life, but it was the number of different species exploded. There were, there were many more different species suddenly on the planet. And I think that's with this kind of tool user to tool maker transition. That's, that's one of the things that's happening to your point. So what does the, the IT department do in, in a big organization? Well, in order to empower that, they, they need to figure out the security, the safety, the access. I think it's about removing friction and reducing risk. And it's a tough. You know, what I haven't seen work is, well, here you have copilot. Nothing against copilot, but like this is what you have. Copilot is great, but it's one of the many tools. And then one week it's the best and then the next week there's something else that's better. Like, you know, every week we get something that's new or slightly different, slightly better. And the, those 400,000 individuals down in the ranks want to use the best tool available. And so the ones that are savvy and are using the tools are like, yeah, but I need replit. And it is like, well, we, that's not, you can't. For good reason. I mean they're not just being crabby for very good reason. And what happens is they go figure out a way to get replit on their machines or they bring their personal machine to work and, and they're using replit. And I've, I mean that's, I've seen that, you know, right. You know, on, on the team's call, some, oh, replit. And it's like, well, I don't have replit. And somebody will be like, I do, I mean, you know, I'll do it. And so because this technology is like highly democratized, these aren't these like massively deployed managed systems. Right. I can just go to the website and Download it. That's the reality. And it's a tough reality. And some CIOs like, totally get it. Other CIOs like, yeah, no, we're not, you know, we're not allowing that particular model. And it's like, dude, it is. It is allowed. Like, it's, It's. It's being used broadly. So the, the challenge is building this foundation that is flexible, smart in the way it manages risk, smart in the way collects and manages data. And I'm sure we'll talk about data. I think that's the big hero value that, that the IT organizations can bring today. Not an easy task, because it's kind of antithetical to the whole, you know, principles and responsibility of these organizations in the past. And not only is it sort of in the opposite direction, but some of the challenge, like, the risk is even bigger than it was before. So it's like more risky, more scary, more disparate, and less able to be managed. And you're being told to do it anyway in, you know, in a responsible way. So. Absolutely. I mean, I think that's the. That's the kind of the big action scene for these teams. That's the biggest. The big challenge. And I've seen a few organizations get it right. Not many. I can really only think of one. And they're actually in your neighborhood. And it's brilliant because they're like, yeah, no, we have. We've got a layer, and whatever people want to use, it just goes through that layer. And. And we're good. And. And we turn around. I mean, it's not. There is a delay. It's like a handful of days. New model comes out. Takes them a couple days to get it all, you know, into their sort of pipeline. And it's available to anybody in the company. They. They spent. They invested a lot in that early on. They put a lot of time and work in that early on. You can imagine how it's paying off now. Right.
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Well, that's what. That's what I was thinking. I mean, it's very easy for you and I to say, you know, oh, you put anything in the layer and, you know, magic comes out. And I think we. We've both been in the game long enough to know that you have to do an awful lot of work to make something seem that simple. But that's really exciting, and it's nice to have even that sort of direction, I guess, in terms of how you should be conceiving of this at that sort of operationalized layer. I do Want to maybe take us a step up, I guess, of the organization and just ask more broadly based on what you've seen in the organizations you're working with, if you have any guidance in terms of where to invest in AI versus where to not invest in AI, where are you going to get the most bang for your buck, where are you going to end up farther ahead? And whether that's tools that are more grassroots, whether that's agentic, whether that's trying to use it to create new products and services or experiences. How do you answer that question?
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Yeah, in many ways that's the big question for leadership teams. So the way I would think about where to invest, first of all, I would start with not investing in sprinkles. And what I mean by that is, again, as time goes by, leaders and companies are getting better at this. A couple of years ago, this was rampant. I think it's still an issue. But a lot of leadership teams, a lot of companies are thinking about AI in, in this way they want to sprinkle AI on the organization. Different parts of the organization just kind of this magic AI dust and then sit back and expect radical change, radical new value to come out. And that's not, as you know, that's not the way AI works. Like, AI is not this magic general purpose dust that you can somehow magically deploy onto our product development folks or our supply chain. Like we're gonna, we're gonna put AI in our supply chain. Like that doesn't actually make any sense. And in a lot of companies were kind of approaching AI that way. They were just sort of spending on AI without having clarity on the problem that they, that they were trying to solve. And so two years ago, one year ago, I would be spending an inordinate amount of time with my clients just trying to get to the problem they're trying to solve. You know, I would be having a conversation with a CEO and saying, okay, that, like, I understand you want to do AI, but what's the problem you're trying to solve? So, well, we're going to transform our business, blah, blah, blah. I was like, that's not even a problem statement, right, Let alone the right one. So yeah, I'm thinking of one CEO. This is a big consumer apparel brand that's a household name. And, and this conversation started with CEO, like we, we need to do AI. I said that that's great, you probably do need to do that. What's the problem you're trying to solve? And it was kind of like water, you know, it was answers like, we're going to reinvent this and that. And I said, but that's not even a problem. And over the course of the next six months, like, he actually got pretty annoyed at times, like, okay, now you're going to ask me what's the problem we're trying to solve? And I just wouldn't, you know, wouldn't let up on that. And finally, about six months later, and maybe it took that long because of my own shortcomings, but he, you know, he said that. Now I get it. You've changed the way I run my business, right? And it's this discipline of having real clarity. It's not just kind of the marketing problem, kind of the marketing corporate speak, but it's true clarity on the problem you're trying to solve. Then we can go figure out what the best tools are to solve that problem and whether AI would be good at doing that or not good at doing that. And I think the companies that have done a good job of getting clarity on the problem that they're trying to solve, these are big strategic level things, are the ones that have gotten or will get the most value out of deploying these tools. Because then, first of all, you're much more likely to get to get a good tool solution or a problem tool fit, and then much more likely to, once you deploy the tool, to actually apply it at the thing that you're trying to solve. I mean, it's kind of like we go into Home Depot and we're like going down the tool aisle and just like, you know, putting stuff in the cart. It's so exciting. And we're going to spend so much money and we walk out of there with this, like, super. One of those big flat carts that, you know, has like £6,000 of stuff on it. And there's power tools and electric, you know, electrical stuff on it. And we get out to the parking lot and we, oh, we're working on gardening, you know, where we've just wasted a lot of time and excitement and energy and we're totally unequipped to, to solve the actual problem, which was we're out in the garden and we have to, you know, plant a lawn or something so that, you know, that, that over the last couple of years has been, you know, the primary, the primary piece of, of sort of like where to invest and so forth. Then of course it gets more specific given, like, once you have clarity on the problem, then we can get super specific and go, oh, then, you know, you probably need to do this or that the other thing, you know, I've been, I've been using this framework for a while now, more than a decade, when trying to break all of this down. I'm sure you've heard it. It's tool set, skill set and mindset. And the tool set part is the technology. Like our technology is simply our tool set. There's a lot of excitement and hubbub about it, but really it's just our tool set. And it's where like 90, a year ago would have been 99% now. Now it's probably 90% of the energy and focus and money is on the tool set. Skill set is about sort of bridging the gap between the humans and the technologies, like getting the humans to adopt the technology. You know, you've been in it for a long, long time. What's the, what is the like ultimate rate limiting step on the deployment of a new technology in an organization? There's like this one thing, like you go to, you've been working months and months, you go to turn the switch, you deploy the thing. There's this one that is like always screws things up. Do you know what it is?
- [41:15]
B
Yeah, well, it's, I assume it's the, what some people affectionately call the PIBCAC problem. The, the, the person. Right, the. The user.
- [41:22]
A
It's the humans, right? Yeah, it's. It's the humans for whatever reason, you know, depending. For whatever reason. And so in many ways the humans are more important, even just in sort of an IT headspace than the technology, because you could do whatever you want on the technology. If you don't get the humans right, it's going to go nowhere. And so that's the, the second piece. And I would say. So technology, yes, super important. Like without the technology, you can't do technology stuff. Okay. The humans, I think more important just in the, I mean, not even in a broader societal context, but just in the context of getting value out of technology. The humans are more difficult and more important than the technology as a, as a thing in your, in your kind of pipeline. And then finally there's the mindset, which you, you know, you could translate to culture. You could think about mindset as culture. You could think about it as leadership, vision. And to me, that is the most important. Again, only in the context of a technology deployment. I'm not talking about the broader picture, but just in terms of getting value out of a new technology. That mindset is more important, more valuable, and remarkably more difficult than the other two. So everyone's working on the technology, which is hard. It's the easiest thing that we have in front of us. That mindset is the transformation piece. It's the responsibility that leadership has to think about the future of their organization in a place where we have this disruptive, massively powerful, highly democratized technology that makes possible things that were impossible before. And so you need to think about your business like, well, what do I do? Where do I take this business in this future where things that are not possible today become possible? You know, there's a, there's a bit of a debate now going on between do we focus on these technologies to, to get efficiency to take cost out of the business, or do we use them to do things that we've never been able to do before? And I think if you focus on the, I mean, you know, they're the realities of doing business and sometimes you need to take cost out, that's fine. But if the, if the focus of taking advantage of AI is to reduce cost, I think you're in a race to the bottom. Again, everybody has access to this stuff, so you're not special. And so if you use these groundbreaking things to take cost out, then the next year you're taking more cost out, except less of it, and then the next year taking a little less cost out, and you're just racing to trying to get a quarter of a percent out of a process which I, you know, is not a race I would want to win. Whereas the, the value side, the upside is, you know, reimagining. It's like, you know, Amazon going from selling books on a website, you know, ground groundbreaking, but now, you know, being responsible for some large percentage of the, of the Internet traffic in the compute on the planet. That's reimagining. Like, as new technologies come, you just reimagine the whole business. And I think that's one of the, the big opportunities and responsibilities that's facing leadership teams and where I would, would invest. That's the mindset part. It's also like we're saying culture, like, and you, you, you touched on it, which was, you know, as employees have access to these tools, do I just get my work done faster and you know, stop working at 3 in the afternoon instead of 5 and not tell anybody? There's all of these new norms which is a big part of culture that are still being explored. Like, should I tell my boss that I used AI and get a kudos because I'm clever and using the new technologies, or should I gloss over that fact and just get A kudos because I, I did good work. Should I share, you know, should I share my, my little, you know, the tool that I created with my co workers or should I keep it and be more competitive? The young folk, not even young folks, but folks who are in organizations using ed, like don't know the answer. Very uncomfortable for people. Very, very stressful. So like investing in that and figuring it out and having a clear vision and a clear voice for, you know, for, for your workforce to guide them through this, you know, period of change, that's going to be rough. I think is, is an area for investment and focus that is largely overlooked right now. Will pay off in spades in the long run.
- [46:20]
B
Well, the culture piece is, it's really interesting to me and it's interesting not just because it's so important, but because it's one of these things that it's not, it's not just a problem to be throw money at. Right. Like it's not investment in that sense. It's an investment of brainpower and of, you know, organizational effort to be able to overcome. And you know, the, to me, the subtext, Maurice, of what you're saying is that an awful lot of organizations are getting this piece wrong and there's an awful lot of different ways to get it wrong. Right. Like you can, there's so many different minds you can step on here. Whether it's, you know, just focusing on, you know, everybody has to learn AI and this is about AI versus about an outcome. Whether it's about, you know, we need to make you more efficient or, you know, this is all about just cost cutting or, you know, whether it's a lack of vision. There's so many unique angles here. And I'm curious, you know, in your experience if you have maybe recipe is too strong a word, but any, any guidance for getting this right. What, what is the culture that's going to thrive with this technology? What are some of the messages and I guess what are some of the more, you know, common pitfalls that you recommend people avoid?
- [47:36]
A
Yeah, I mean, when it comes to culture and building the culture for the future, for an AI future, like I said earlier, I'm a fan of keeping things very simple. Kind of Occam's razor. Your question. Just pay attention, pay attention to culture, actually turn the spotlight on that and figure out what you need to do to, that's the low hanging fruit. Like companies are not thinking, they're not even, that's not even a thing. Like we're, we're so far broadly Speaking we're so far from making progress against that that already just paying attention and starting to deploy some resources and, and some brains and some creative people to, to thinking about what the problem actually is or what the series of problems is going to be. This is a system of systems. There are going to be many interconnected challenges. What those are and then how we might, how we might prioritize them and then go about solving them. Like that's, that's like we don't even need to get into the details. If we just do that. That's, that's already a huge, a huge win. Also. It will differ greatly from organization. Organization. I think that, you know, culture is a, by its very nature, a, A, you know, an individual thing, something that is, that is unique to, to each organization. I will say some of the common threads are this apprehension and stress that this particular kind of change is bringing about. And there are probably some quick wins that a leadership team could deliver. Which among them are, here's what we think about AI, here's our expectation, here's what I do personally and just be super clear about that and keep delivering that message over and over again. Pretty basic, not seeing it happen a lot with the level of clarity and intensity that's required to shift culture. And by the way, have you ever heard of the pace layers?
- [49:49]
B
No, I don't think so.
- [49:50]
A
This is super interesting model that was developed by the Long Now Foundation. This is a group of folks based here in San Francisco that thinks about big societal stuff on an extremely long timeline. Sort of 10,000 year, you know, we're talking about two years in the future. They're like 10,000 years from now. And so they have this framework called the pace layers, which looks like a, you know, concentric circles and it's the, the rate of change. So the outside circle is the thing that changes the fastest. And so fashion is one of the things that in, in society changes most quickly. I would sort of put technology in that outer ring and then you have things like, you know, governance like, you know, governments are slower to change and so forth. And the second most inner ring is culture. Culture is one of the, after that it's, it's evolution and nature, right? So like nature changed very slowly, but just outside of that is, is human culture. Culture changes super slowly. Very difficult to change. Humans don't, don't like change. And we're in a period of a lot of change, very multidimensional change. And that's part of the reason I think people are broadly stressed out what's interesting. The reason I like this model is we're dealing with two things in that diagram at the same time, and they are technology, which is among the fastest, and culture, which is among the slowest. And we have to manage these two things concurrently, and yet they are on radically different timescales. So I think that's broadly applicable to leaders that are trying to focus on culture. They just need to have this in mind, is that the technology is changing weekly. Culture takes years to shape.
- [51:49]
B
Well, and I think in some ways it just comes back to building an adaptive culture. Can you build into the DNA of your company or your organization? Yes. Change is part of who we are versus we do the same thing forever. The end. Right?
- [52:08]
A
Yeah. I mean, trying to, Trying to bake in tolerance for changes would certainly be an amazing solve to this problem. When I was at Frog Design a couple decades ago, during kind of its golden age hard mode, Essling or the founder, had a. Actually made a bumper sticker and put it on his cars that said change is fun. Because a lot of the, I think his big challenge with clients, sort of the challenge he took on was all about change and guiding his clients like Apple and Hewlett Packard through these moments of change. And internally, it was a lot. So not only were these designers designing things that were new and so embodied change, but the way that they were doing their work was constantly challenged and constantly put under stress of change. And that was a big, big dynamic. It was very tough for people to, to live through. And so, like, no. No changes. He was trying to build change into the culture. Really tough to do. That is a fundamentally, you know, human were evolved to. How would I say this? I think humans are evolved to not deal with change. Well, there must be a survival mirror to that. But I, you know, I don't know what that is, but I do know that we are. We deep down, we just don't deal with change well. We're just not wired for it. So it's very stressful for us. So I think a strategy that tries to build a tolerance for change into the culture would be. Would be really tough. That would be a tough challenge. So I, I might instead try and take the, the variables, take the unknown, take the change out of the experience for, for the workforce and, and give them as much as possible. Well, yeah, we don't know a lot of what's going to happen. We know it's going to be really different. But at least we do know these. We, you know, two or three pillars, these touchstones, like at Least we have that. And, and we can use those as our north stars to, to guide us. It's going to be stormy weather on the way and we're, you know, we're going to have some detours and so forth. But it broadly we're going, we're all going in this direction now because of AI. I think again that that's so simple but broadly absent. I think in, in organizations.
- [54:56]
B
I really like that. That's a, it's an interesting and I guess different approach than I had in mind to like, you know, try to maximize the certainty and minimize the feeling of change versus just say, you know, love change, damn it. So I deal with that. That's a. Yeah, yeah, deal with that. Exactly. I want to switch gears a little bit from culture back to the tech piece. And you know, as you said, you've had a lot of experience, you know, in boardrooms with the hands on keyboards teams as well, dealing with a lot of these initiatives. And whether it's cool projects or cool problems that you're solving, I was wondering if you could share, you know, maybe some of those that have caught your attention in the last handful of months.
- [55:39]
A
Yeah. So like I get asked like what, what are the cool projects you're seeing? And I, I think that makes us guilty of Ibnu or like pushes us towards the, the IBN thing. So if you ask me to come up with like the cool sexy thing, that's exciting. I'm going to start thinking about these things that are interesting but my focus will not be on generally on their use, depending who, who's asking. So,
- [56:03]
B
so let's reframe and talk about impact, I guess, rather than the cool factor.
- [56:07]
A
Yeah. And because the cool factor, like, I think a lot of people think that the future of AI is going to be like Biggie Smalls. Right? Notorious Big. Like AI is going to be big and sexy. And I don't think it's going to be like that at all. I think it's going to be lots of little, small, humble AIs doing not very sexy, not very visible things. But in aggregate the impact is going to be phenomenal. So people, you know, in asking that question are looking for the big, you know, superstar thing. And, and I think in the fullness of time the, the real value will be from lots of things that are again the result of a Cambrian explosion that are, that are many and disparate, running in the background, doing their thing, kind of like electricity. But to answer your question, like what are the most impactful things I would go Back to these young folks that I've had the privilege of watching work kind of standing over their shoulders. If you want to see the most interesting things up to the minute, go watch these folks work and pay attention to the delta between kind of the old way of doing it and the new way that they're doing it and tease that delta out. That's the, the interesting bit again, like, you know, folks spooling up cloud code to work on a narrative or the content for a deck. So not the design of the deck, but really the ideas, which, you know, at first sounds pretty dissonant, like, why would I spool up a, you know, a generative code tool to work through some, some ideas and then you watch them do it and you're like, wow, that's, that's a very different approach to solving problems. I think that's the most. So I guess my short answer to your question is like, what's the, you know, exciting, you know, deployment of the technology? It's actually in, in its use, not in the end result. It's, it's the way people are, are, are internalizing this, this tool set.
- [58:18]
B
There's a few, you know, implicit, maybe even explicit lessons in there that I want to, you know, push on a little bit more. Maurice. One of them is that it seems like that grassroots piece that, like not being top down and just saying this is how you use AI, but actually watching people listening, figuring out where they're getting value, is a more useful approach to, you know, actually getting real change out of this and real impact. The second piece that you've come back to a couple of times now, and I wanted to push on, is you've mentioned that these are young people that you've been most impressed with using it. And that caught my attention. Do you think that there's a special role here for young people and that we should be bringing them into the fold or looking to them to help us craft our new way of doing business.
- [59:14]
A
Yeah, and first of all, I don't want to hate on the old folks like me, but it just, in my experience, it happens to be this team made up of more junior folks. So I don't think it's really necessarily age related, although I'm sure there's a curve where more young folks are doing more cutting edge stuff also because they're still learning. And so they're still learning their craft, their profession. And so I think much more likely to, because they don't know how to get it done. They're being, you know, the mean Boss is asking them to deliver this thing again. And they don't, they've never done that before. So like, oh, shoot, I gotta, I gotta crank this out. How am I gonna go do that? And so they, they just grab the, the best tools that they can in order to do that. So I'm not sure where I was gonna go with that so that, the lessons, lessons that we could learn. So, um, yeah, so I don't, I don't think it's just about young people necessarily, but they're, they're sort of primed because of their situation to be more likely to experiment with these tools and, and draw value out of them because they need it, that they, they actually have a problem. Again, the beauty of this actually having a problem to solve, it's quite powerful. So, and, and the lesson learned, sort of how we could. Yeah, I'm, I'm not sure what the takeaway sort of the deep learning, the profound learning is.
- [60:54]
B
Well, let's, let's, let's maybe zoom out a little bit and you know, reframe a little bit because we've, we've covered a lot of really, really interesting insights here that have spanned, you know, technology to culture leaders, to frontline staff. And so let me frame it this way. For any business leaders or technology leaders who are listening to this, what's kind of the capstone piece of advice you'd want them to walk away with having listened to this?
- [61:27]
A
So I think if I had to synthesize this all down to kind of a few delectable nuggets, I'd say that first of all, this is not about technology. Like everyone's talking about technology. We all have technology in our title. Technology, technology. It's not about technology. It's about reinventing your business for a future that happens to be powered by AI and thinking about this future business or the future state of your business to take advantage of the things that today aren't possible, that will be made possible by this technology in the future. Like what are the doors that AI unlocks for your business that today are locked or yesterday were locked? Like these, these unlocks, I think, are the most interesting space. It's the opposite of going for the, you know, cutting, taking out cost. It's opening these doors that have previously been, been closed to us. And so, you know, taking advantage of this, this amazing new tool set and getting away from this purely technological, technocentric vision. You know, I don't think we have a shortage of technology. I think we have a shortage of imagination when it comes to leadership. That's where I would focus. And then the other, the only other thing I would say is a lot of people, a lot of leaders, a lot of leadership teams think that this is a sprint. We've got to deploy X or Y in the next six months. We've got to, you know, achieve this in the next 18 months. It's not a sprint. It's not a sprint. Some more clever folks are saying this is a marathon. We've got to dig in. We've got to have a clear strategic vision and plan for where we're going to be going over the next handful of years. You know, three to five years. It's not a marathon. It's not even a marathon. I've been thinking about it. It's like the Lord of the Rings, right? It's like nine hours of movies. And that's this journey. You can't even see the destination. You kind of can. There's like the Eye of Sauron off in the distance. But there's all these adventures that happen on the way. Some of them are good, some of them are dangerous and scary. You're going to make new friends along the way. You're going to learn new things, establish new alliances and so forth. That's the way I think that we need to be thinking about it. It's this massive transformational journey that's going to be exciting and full of opportunities, full of danger and, and pitfalls. It's messy and complex, difficult to predict. But again, at least if, you know, like, we are going to Mordor, like we, hey, we're all going in this direction and get ready for epic battles and, and, and triumphs and so forth, then I think you, you know, you actually stand a chance of, of, of succeeding.
- [64:28]
B
I love that. Very poetic, very imaginative. And on your note, something that I think we're lacking here. So I really appreciate that. So with this framing, Maurice, of augmentation, the augmented age, to me, that's one of the narratives we're hearing about the future of AI, the other one being more, I guess, automation and the way I kind of process the difference is, is it people and we're doing things better, or is it instead of people and, you know, depending on where we end up in either of those camps, it could have very dramatically different impacts on jobs, on the job market, on the economy. I'm curious what your take is in terms of where you see this landing and how you recommend organizations. Look at that.
- [65:16]
A
Yeah. I mean, when it comes to jobs, Jeff. Well, I'll give you A shortcut answer. I'm generally pretty optimistic, but let me, let me explain why because, you know, this is a serious, a serious question that, that I actually have a lot of hand wringing over. You talked about automation. I think one of the things that we started talking about this maybe almost 20 years ago is that technology is good at automating tasks, not jobs. And most of us, most people are involved in jobs that, that require lots of different tasks, often very disparate tasks. And so technology, like one technology tends, tends to be very bad or it tends to be very difficult for a technology to automate multiple tasks, even for something as powerful as, as generative AI. And so I think a more nuanced approach is to think about which tasks are going to be automated inside of jobs. Now if your job involves only one task, then it might be ripe for complete automation. That's kind of the first thing. The second thing is people tend to think about the future one dimensionally. And the reality is that the future is n dimensions. And by dimensions I mean like different facets to it, different things going on. Think about all of the things going on for you today and then multiply that by, you know, eight plus billion people around the planet that there's a lot going on. And people don't tend to think about the future that way. They pick one detail, one facet and project that one out into the future. Also happens when people are thinking about the future of jobs. They sort of pick one or two details and kind of forget the rest. But there you don't even need to think about the trillions of different things going on. Just think about slightly adjacently, you know, if your job involves one task only, it's pretty easy to automate. If I had to think about a job that, that, that fell into that description, it'd be like truck drivers. Truck drivers do one thing, they bring the big rig from point A to point B. And we know we have like, you know, waymos all over the place here that that is a small version of a truck that can go from point A to point B all by itself. But if you zoom out just a tiny bit. So turns out that in the US there's today a shortage of 100,000 truck drivers. So just today, like we need 100,000 more truck drivers than we have. If you thought about, you know, automating school teachers, we have a shortage of 400,000 school teachers in the US today. Actually, I'm not certain it's us. It might be global, but there's a, there's a deficit of that particular thing. So those are just two examples. But just zooming out a little bit makes the conversation much more complicated and for me, much more, much more interesting. The other thing is, so the future is N dimensional, not one dimensional. And, and guess what? The future happens over time. Like the future doesn't happen on Tuesday 2031, you know, November 16th. It happens, you know, every day. So in all of those dimensions transform into that future. So, you know, what does that all mean? While we are automating tasks, time goes by. And because of Amara's law, it tends to be more time than we think takes longer. Right. What happens? Well, people age out of the workforce, they retire naturally. So suddenly all these people that we, you know, all these people that we thought were threatened are, are no longer in the job market. People change careers organically, new workforces coming in, they get trained differently because they see the stuff happening and so forth. So sometimes people ignore the, the time axis, right, in making these arguments. The other is related to this time thing. So I was trying to quantify a little bit how long it takes. Like we were talking about culture and it being slow and taking a long time. Well, how, you know, could we look historically is there, you know, can we get some sense around how long it takes for, for folks to change, for things to change? And so I thought about past revolutions and I thought about the Industrial Revolution. So the Industrial revolution took about 70 years to, to, you know, kind of follow its course. I thought about three things. The duration of the revolution, the sort of the penetration of the technology, kind of its at its peak, what was going on, and then time to impact. And I made this one up. You could totally poke holes in it. I'm not a statistician, but sort of how long did it take for the technology to impact 100 million people? And so the duration of the industrial revolution, about 70 years. At its peak, we were generating something like 30,000 horsepower from, from steam. That's kind of a measure of, of productivity. And the time to impacting 100 million people was the full duration of the re. Of that of the Industrial revolution took about 70 years for, for it to be broadly distributed, kind of the, the norm. Then there was the personal computing revolution. It took, I don't remember the numbers. I have to look at my cheat sheet, but I can't see with my glasses on. So 1977, big year you might have owned. Was it a Commodore 64, Apple II or a Atari 4400? There's like a trinity of PCs that came out started this revolution. It lasted about 23 years to its conclusion. There were 500 million personal computers at kind of the peak of this, of this revolution. And the time to impact to 100 million people was 18 years. So we went from 60, 70 years down to 18 years in order to have that, that impact. Right. Then the Internet revolution, which we're still kind of, we're not in the revolution part, but we're still certainly very fond of these technologies. It took 25 years start to finish. So the personal computing, 23 years inner revolution, 25 years at its peak. By 2015, I would say all humans impacted. So at the peak of the thing, everyone on the planet, because banking systems, communication systems were all on that backbone. Everybody on the planet's impacted. It took about three years to touch 100 million people. AI revolution. We're in the middle of it. We're about 10, 10 years in just arbitrary date. Google publishes Transformer 2017, right? So we're about 10 years into it. We're still in it. Time to impact to 100 million people, about a month and a half right after ChatGPT released. And sort of degree of impact, I'd say today there's probably about a billion people directly using AI and about 3 billion indirectly impacted every day. So the number of people impacted has been going up. The speed to impact of 100 million people has gone rat from 70 years to 18 years to, let's say about a decade to a month and a half. So that's. I'm speeding up. What hasn't changed is that first number, the industrial revolution took 60 years, but the three revolutions after, including AI, take about 25 years. Because I think we're in the middle of the AI thing. We got another decade to go. So it seems to me it takes about a quarter century for humans to absorb a new disruptive technology.
- [73:57]
B
Interestingly, there's an upper limit to our metabolism for this stuff.
- [74:01]
A
Metabolism is a great way to put it. I hadn't thought of that. It just takes us 25 years to metabolize these revolutionary technologies. And there's just no way. Everyone's talking about, oh, things are happening so fast, so fast, so fast. Which they are, is the tool set is happening really fast, but the skill set and mindset were just programmed. And maybe there's no way around it, and maybe we don't want to find a way around it. It's about, it's about 25 years. So I thought that was, was interesting. And I'll say one more thing. About jobs. This is a framework I came up with some years ago because I felt like there was this dimension that wasn't being addressed when we were talking about new technologies, displacing things, jobs among, among other things. And it's, it's a model that I call the curves of opportunity. And so if we think about technological capabilities as this first curve, our technological capabilities have been going up since three and a half million years ago. We invented our first tool. It was the stone hand axe. We've been, we've been building better and better tools. And better tools equals better capabilities. Right. And that curve has been getting steeper and steeper. And you might think that when it gets vertical, when our tools are so good they can kind of do anything, that all kinds of weird stuff happens. Maybe we become like the humans in the movie Wall E where we just kind of go around in floating chairs drinking huge soda pops or, or we have sort of massive unemployment and, and there's nothing left for the, the humans to do. I don't think that's going to happen because there's this other curve that I haven't really heard anyone else talk about that is the curve of expectations. So as soon as we are capable of something new because we have a new tool set, let's say, or we've developed a new skill set that uses tools in a new way, whatever it is, we're able to do something new immediately. Society, humanity, expects us to do more. And it's just like hardwired in us. So like, the world's tallest building gets built. There's a ribbon cutting ceremony. It's amazing, it's exciting. We cut the ribbon. World's tallest building. What's going on down the street? They're building a taller building. Right? It's just kind of this mechanical thing. And so this other curve, this, this curve of expectations, or really the curve of opportunity, is in lockstep with the curve of capabilities. You can't sort of a tautology, you can't unlock these two things. And so the day that we're able to deflect 60% of our tier one customer service calls because we've deployed an agentic AI that becomes the expectation. And the result is that as a, as a customer, I'm not waiting for 45 minutes, I'm waiting for zero minutes. That's now the expectation, like, that's table stakes. Everybody should be doing that. So the first companies to do that have a, have a chance to differentiate for a certain amount of time. And then everybody's Going to be catching up and then that's table stakes. And so the headroom, sort of, the new abilities made possible by these technologies are about, well, what's the new competitive differentiation? Because a zero minute wait time is not competitive differentiation. It's now the new expectation. Right. And if you're not, if I am waiting 45 minutes, I'm not doing business with you, I'm going to fly with someone else because I don't want to wait for 45 minutes to, you know, figure out my canceled flight, whatever. So I think, I think that's also a useful model to think about. Well, what's going to happen with jobs is, yes, we're going to be able to automate some things and therefore do new things, do things more efficiently and so forth. But the market is going to expect more of you the next day. And that's why we have to continue to like, there's no end to, you know, the innovation. That's why, like there's no one I've talked to that's like today, forget AI, like no AI today. They're like, it's 4 o' clock on Friday. Yeah, I'm pretty much done with all my work for the week. You know, I'm going home. Like everyone I talk to, regardless of what they do for a living, there's always stuff that they don't get a chance to get to. There's always stuff that they'd rather be working on than the stuff that worked. Like there's plenty of problems to solve in the world that we aren't spending enough time working on. And so I think broadly speaking, there's more opportunity to do that than displacement of humans. There's plenty of stuff for the humans to do.
- [78:37]
B
I think that's well said. I really like the framework around expectation and the nature of competition there. Basically just driving us to do more and better versus just, you know, less and less and less. It's interesting. I'm inclined to agree with you and I mean, it's a more optimistic perspective than you often hear in the media. So, Maurice, on that note, I want to say a big thank you for joining today. You've given me lots to think about and I really appreciate your insight.
- [79:04]
A
It's my huge pleasure. Jeff, thank you so much.
- [79:08]
B
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