4 Waves of AI (We're on Wave 2)

Are we entering the most transformative era of work in history? Technology is evolving weekly, and culture takes time to adapt. In this episode, AI futurist and award-winning innovation leader Maurice Conti explains how rapid technological change, especially in artificial intelligence, will shape our world, the economy, and the future of jobs, and what AI leadership means for navigating these shifts. Drawing on his experience with companies like Amazon, Nike, Mercedes, and Lululemon, Maurice shares insights from Fortune 100 companies and explains how generative AI, automation, and human-AI collaboration are transforming the workplace. The key insight is simple. The future of AI is not big, flashy, or “sexy.” It is quiet, invisible, and massively impactful. Maurice explains why we have entered the “Augmented Age,” where humans and AI work together, and outlines the major shifts known as the 4 Waves of AI defining this transformation, including the grassroots AI revolution happening inside organizations and the rise of AI agents and ecosystems. If you want to understand where AI is really going beyond the hype, this is a must-watch episode. Like and follow for weekly episodes. In this episode: 00:00 Intro 01:04 The “Augmented Age”: Humans + AI working together 02:10 Sci-Fi to reality: Talking to computers like Star Trek 04:11 Have we arrived at intuitive AI? 05:15 Why AI progress isn’t linear 06:05 Can anyone predict the future of AI? 07:05 The Nvidia story & “more AI in the future” principle 08:57 Amara’s Law: Short-term hype vs long-term impact 10:08 Why AI will be bigger (and slower) than expected 10:54 The 4 Waves of AI explained 11:00 Wave 1: “Interesting but not useful” AI 12:31 Wave 2: Grassroots AI (hidden ROI inside companies) 13:16 Why CEOs can’t see AI ROI 14:09 Tool users → tool makers (the real shift) 16:17 The invisible productivity explosion 20:10 Wave 3: AI agents & ecosystems 21:48 Wave 4: AI as electricity (invisible & everywhere) 23:18 The dark side of “interesting AI” 26:15 Personal AI use cases (daily life & work) 27:09 Why most organizations are still “Grassroots” 28:38 The explosion of tools (new risks for IT) 30:15 CIO/CTO challenges: Control vs innovation 33:09 Building the right AI infrastructure layer 34:45 Where should companies invest in AI? 37:14 Start with the problem, not the technology 40:04 Toolset vs skillset vs mindset 42:45 Why mindset & culture matter most 44:28 The race to the bottom vs innovation 45:15 New workplace norms & ethical questions 49:49 Why culture changes slowly 51:33 Tech vs culture: A fundamental tension 52:30 Can organizations adapt to constant change? 56:06 The myth of big, sexy AI 57:01 Small AI, massive impact 58:18 Where real innovation is happening 59:13 Why young workers lead AI adoption 01:01:22 Advice for leaders 01:02:59 AI is a “Lord of the Rings” journey 01:05:15 Will AI replace jobs? 01:10:23 Lessons from past tech revolutions 01:13:47 Why humans need ~25 years to adapt 01:16:02 Why expectations rise with technology 01:17:38 Why there will always be more work to do 01:18:55 Final thoughts Connect with Maurice: LinkedIn: https://www.linkedin.com/in/mauriceconti/ Our links: Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast Follow us on YouTube: https://www.youtube.com/@InfoTechRG

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Speaker 00

  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.

• 0:21Geoff Nielson?

  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 longstanding AI futurist, working with organizations like Amazon, Nike, Lululemon, and Mercedes to take their businesses to the next level. Insights and brain power 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.

• 1:01Geoff Nielson?

  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 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 given that you've been talking about this for over a decade, and we seem to have recently crossed that threshold from imagination and reality, how is it shaking out relative to what you expected? What's the same? What's different? And what do you have top of mind right now?

• 1:35Speaker 00

  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.

• 2:12Speaker 00

  Which I still believe is sort of the the golden promise of 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 we were thinking about this technology and sort of how 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 it's the fourth Star Trek four, 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

• 2:57Speaker 00

  this engineer from from the past some formulas and basically tell them 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 in a conversation and basically a design and engineering conversation to build to build this 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 like hands him the 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

• 3:41Speaker 00

  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 this technology and is kind of the part of the vision that has totally come true.

• 4:11Geoff Nielson?

  The word I've heard you use to describe that, which I think is, you know, pretty encapsulated in that story is intuitive. Right, that it should just be intuitive to us versus, you know, just, you know, having to to write lines of code. Any interaction is just, you know, you can say, computer, and tell it what you want.

• 4:28Geoff Nielson?

  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?

• 4:43Speaker 00

  Yeah, I mean, we've arrived at today, uh, by definition, but and and the journey into the future will be a journey. We haven't arrived there yet. So, um,

• 4:55Speaker 00

  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, um, you know, when OpenAI opened ChatGPT to the general public was certainly a moment in November twenty two.

• 5:16Speaker 00

  And maybe that moment was when the majority of the people on the planet were able to to experience this this intuitive interaction.

• 5:26Speaker 00

  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 and it's 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, um, 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 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? And kind of where you want to place your bets.

• 6:11Speaker 00

  But you know, there there are ways to to think about the future of AI. One, I'll share this frustrating moment for me. So this is two or three years ago, um, we, you know, we're having our annual call with our financial planners. And that that sounds way fancier than it actually is. But, um, and, uh, and so like they're responsible for investing a, you know, small, um, 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 now three, three, three and a half years ago.

• 6:55Speaker 00

  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 talk, are the ones building this stuff. Like, we kind of have our finger on the pulse, but you, you know, you got a very convincing speech about all the analysts they have and, and, and how smart they are. And so I was like, okay, um, maybe it is risky and, and we want to, um, not be too risky with our, our little nest egg and fine. Year goes by, Nvidia starts to do what it's doing. And we have a, uh, the, you know, the, the check in. And I go, so how much Nvidia do we have? Well, we still don't

• 7:36Speaker 00

  really think that it's a buy, you know, yada, yada, yada. Um, and, uh, and then I think six months later, we had another check in and there was the same story. They were starting to change flavor on it. This is like a big, you know, I don't want to throw anybody under the bus. It was a big bank, right? Like they, they, um, 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, uh, 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

• 8:21Speaker 00

  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, uh, versus a 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, um, but I think that's an interesting, just kind of background, um, uh, reality to, to, 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. Um,

• 8:57Speaker 00

  This other way, and I'm sure you know it, 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, you know, six months,

• 9:34Speaker 00

  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. I think that's another one of the sort of fundamental.

• 10:11Speaker 00

  I think we're not doing a good job of proving Amara wrong, 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 am engaged with kind of the daily life of a team. And, and I had an aha moment. And, 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 gonna come through.

• 10:53Speaker 00

  The first wave of AI, I'm calling IBNU. I B N U. And it's, it's all the things we've gotten very, very excited about. Sort of the sexy output of, of humans using AI. So one example might be, I recently saw a music video.

• 11:15Speaker 00

  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, 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.

• 12:00Speaker 00

  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, um, that's not where the, the interesting stuff is coming.

• 12:26Speaker 00

  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.

• 12:47Speaker 00

  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 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, this kind of aha moment.

• 13:30Speaker 00

  But intuitively, it was like, yeah, I feel like there is value being generated here. But for some reason, it's not making its way to the, to the boardroom or to the, you know, to the executive staff. And, and then something happened. I was working with this team at a, 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.

• 14:15Speaker 00

  And, you know, we, we'd sit down and say, okay, we have to put together a narrative around this new thing. Like, we have to, we're working on this new thing, this new idea, and we gotta start to build a narrative around that. First thing one of these young folks does is they spool up a Claude Code instance. And you go, we're working on nerd ideas, strategy, you know, things like this. And you're pulling up Claude Code. Very, 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. They 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, this was a big week for me.

• 14:58Speaker 00

  That same week, somebody said, oh, you should go check out Replit. And this was not like Replit V1, this was maybe like 2, 2.5. Still early, it was new to me. So I sat down with Replit.

• 15:12Speaker 00

  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, you know, L cars interface visualization of this data set that was fully interactive. I could like it have sounds as the 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

• 15:56Speaker 00

  know, big deal. Like you have a data set and it's all Star Trek looking. Who cares? But the 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 I don't know how to code any modern languages. So I couldn't have coded that the old way. And and yet I was able to be I was able to build a tool that could do exactly what I needed to in the moment. I 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

• 16:41Speaker 00

  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 tool user to a tool maker is that you back to this question like where where's the ROI? Where's the ROI? If you look in an organization, let's say this particular organization has 400,000 employees, but that's that's huge. Even organization with 1000 or 10,000 people.

• 17:11Speaker 00

  And each of those people now kind of overnight has the 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, 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 might, I now have the ability to build a tool that

• 17:56Speaker 00

  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 it looked, 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. Uh, but

• 18:23Speaker 00

  what, uh, 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. Um, and they, they, they don't see how I did it. Um, and individual, uh, the, the individual impact is, it's maybe not ibnou, but it's certainly not groundbreaking. All I did was deliver this, you know, TPS report, um, 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.

• 19:04Speaker 00

  There's no way for them to, 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, the impact was relatively small. Um, but in aggregate, the thing is, is profound. So that's kind of this grassroots. Wave where the value, the ROI of AI is coming from the bottom up. It's coming from these, you know, young folks that are radically changing the way that they do their work and producing better, faster, more efficient, more creative, more insightful output.

• 19:41Speaker 00

  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.

• 19:53Speaker 00

  The next wave, kind of the third wave in the future of AI is, and not a lot of people, sorry, not, not, 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 agentic, 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.

• 20:29Speaker 00

  Not to belittle the grassroots wave, but this is, this is where AI gets even bigger and more impactful.

• 20:35Speaker 00

  I think it's super early days. I'm starting to hear people talk about, you know, sort of agentic 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 the agentic ecosystem. I think by definition, you know, 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, 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.

• 21:17Speaker 00

  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.

• 21:32Speaker 00

  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 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.

• 22:13Speaker 00

  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, four waves that are that are coming.

• 22:49Geoff Nielson?

  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,

• 23:28Geoff Nielson?

  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 like, I don't want this. Like, like, this is gross to me. If this is what AI is all about, like, count me out. Like, this is not the future I sign up for. But to your point, it's really interesting to contextualize that as, you know, a single layer. Are you seeing the same thing there?

• 24:04Speaker 00

  I totally agree. It had some downsides, this Ibnu wave. Maybe the the upside is that it got, because it's interesting. The I 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 toolmakers 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.

• 24:44Speaker 00

  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.

• 25:31Speaker 00

  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 Again, it's a personal, maybe because that exchange is more human-like, more intuitive. The use cases are also more personal and intimate, right?

• 25:59Speaker 00

  Largely, everyone kind of uses Excel more or less the same way to do more or less the same things.

• 26:06Speaker 00

  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, uh, different ways, right?

• 26:18Speaker 00

  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. You know, help me out here and then, um, you know, and then I switched to a medical situation and getting, you know, diagnosis and, and, and treatment recommendations that are really good.

• 26:46Geoff Nielson?

  So, you know, with that, and we've, we've kind of honed in pretty precisely on this grassroots phase. And that, that makes a lot of sense to me and I'm, you know, I'm curious on your opinion, but 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, you know, we'll cross the hump over the next handful of months or, you know, next year or two, but it feels to me like most organizations are in that grassroots layer. And it's, it's interesting to me and I, I see there's a lot to unpack about this specific moment. I mean, you mentioned the fact that, um, it makes productivity gains or value really hard to capture.

• 27:28Geoff Nielson?

  And that, that really resonated with me. And the other piece I was thinking about is like, 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 you know, if you Maurice are now able to do something, you know, 20% faster and 20% better. You know, you don't tell your boss and you just hand it in at the same time. And you know, congratulations, you've just gotten a day. Every week you have one day, you know, to, 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's in it for them, the employees, if it's all just sucked into expectation.

• 28:13Geoff Nielson?

  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, that's an explosion of tools in the organization that you would think in some way, um, you know, corporate IT has their arms around, which I'm positive they don't. And suddenly like, 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, um, there's a lot of consequences of being in this, in this layer

• 28:54Geoff Nielson?

  that maybe not, if you've just kind of had it sneak into your organization at a grassroots level, you may not be ready for. So I'm curious, I mean, first of all, do you agree with that, you know, kind of, you know, state of the nation? And then what advice would you give people who are trying to, I guess, operationalize this, this wave a little bit more effectively?

• 29:16Speaker 00

  Yeah, I mean, broad strokes, I totally agree. I, I, I think you're, um, you're absolutely right in, in shining light on that, that nuance. So again, one of the fundamental qualities of this grassroots thing is that it's, it's invisible to the organization. And so, um, it's not even, companies are not gathering that data. They can't. It's, it's just, there's a, there's a, um, hard blind spot, um, there.

• 29:45Speaker 00

  And so what they can do is, you know, listen to folks that are saying like, hey, this is happening.

• 29:52Speaker 00

  There are some risks that you absolutely need to get on top of. At the same time, it's probably worthwhile leaning into it

• 30:01Speaker 00

  in order to get more, more of that value. And, 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, you know, they're hearing, well, this is, this is where the value is. So make it happen. And at the same time, it's, it's a proliferation. I've been thinking about it, um, kind of like the, 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, um, number of different species exploded. There were, there were many more different,

• 30:45Speaker 00

  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, to your point. So what does the, um, 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, and reducing risk. And it's a tough, um, you know, what I, what I haven't seen work is, um, well, here you have Copilot. Nothing against Copilot, but like, this is what you have. Copilot's 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

• 31:31Speaker 00

  new or slightly different, slightly better. And the those 400,000 individuals down in the ranks want to use the best tool available. Um, and so the ones that are savvy and, 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.

• 31:57Speaker 00

  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, um, you know, right, and, you know, on on the teams called some of all replay. And it's like, well, I don't have replay. And somebody will be like, I do. I mean, you know, I'll do it.

• 32:19Speaker 00

  And so, uh, 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 and some CIOs like totally get it. Other CIOs like, yeah, no, we're not, you know, we're not allowing, um, that particular, uh, model. And it's like, dude, um, you, it is, it is allowed, like, it's, it's, it's being used broadly. So the, the challenge is,

• 32:50Speaker 00

  building this foundation that is flexible, smart in the way it manages risk, smart in the way it, it collects and manages data. And I'm sure we'll talk about data.

• 33:02Speaker 00

  I think that's the, the big hero value that, that, um, the IT organizations can bring, um, today. Not an easy task because it's kind of antithetical to the whole,

• 33:14Speaker 00

  you know, principles and responsibility of these organizations, uh, 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, um, before. So it's like more risky, more scary, more disparate and and less able to be managed. And you're being told to do it anyway, in, you know, in a responsible way. So,

• 33:39Speaker 00

  absolutely. I mean, I think that's the, that's the kind of the big action scene for, for, um, these teams. That's the big, the big challenge. And I've seen a few organizations get it right. Not many.

• 33:53Speaker 00

  I can really only think of one. And they're actually in your neighborhood. Um, and, uh, 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, um, 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. A new model comes out, takes them a couple of days to get it all, uh, you know, into their sort of pipeline and it's available to anybody in the company. They spent, they invested a lot in that early on.

• 34:25Geoff Nielson?

  Yeah.

• 34:25Speaker 00

  A lot of time and work in that early on. You can imagine how it's paying off now.

• 34:31Geoff Nielson?

  All right. Well, 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, 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, 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, you know, end up farther ahead?

• 35:16Geoff Nielson?

  And whether that's, you know, tools that are more grassroots, whether that's agentic, whether that's, you know, trying to use it to create new products and services or experiences. You know, what, how do you, how do you answer that question?

• 35:33Speaker 00

  Yeah, that's, uh, in many ways, that's a big question for, for leadership teams.

• 35:40Speaker 00

  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, it's still an issue. But a lot of

• 36:06Speaker 00

  leadership teams, a lot of companies are thinking about AI in, 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.

• 36:48Speaker 00

  And, and 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 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 gonna transform our business, blah, blah. I was like, that's not even a problem statement, right? Let alone the right one. So, um, yeah, I'm thinking of one CEO, this is a big, um, consumer apparel brand, that's

• 37:30Speaker 00

  a household name, and, and this conversation started with, you're 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's kind of like water, you know, uh, it was answers like, we're gonna reinvent this and that. I said, well, that's not even a problem.

• 37:49Speaker 00

  And over the course of the next six months, like, he actually got pretty annoyed at times, like, okay, now you're gonna ask me, what's the problem we're trying to solve?

• 37:58Speaker 00

  And I just wouldn't, you know, uh, wouldn't let up on that. And finally, um, about six months later, uh, and maybe it took that long because of my own shortcomings, but, um, he, you know, he said, that, now I get it, um, 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, um, 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 of getting clarity on the problem

• 38:38Speaker 00

  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 these tools. Because then, first of all, you 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 you're trying to solve. I mean, it's kind of like we 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 pounds of stuff on it.

• 39:21Speaker 00

  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 we've just wasted a lot of time and excitement and 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 in of sort of in that like where to invest and 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,

• 40:06Speaker 00

  you know, I've been, I've been using this framework for a while now, more than a decade,

• 40:13Speaker 00

  when trying to to to break all of this down. I'm sure you've heard it. It's it's toolset, skillset and mindset. And the toolset part is the technology, like, our technology is simply our toolset. Like, there's a lot of excitement and hubbub about it, but really, it's just our toolset. And it's where like 90, a year ago, it would have been 99%. Now, now it's probably 90% of the energy and focus and money is on the toolset.

• 40:45Speaker 00

  Skill set is about sort of bridging the gap between the humans and the technologies, like getting the humans to adopt the technology.

• 40:55Speaker 00

  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 thing that is like always screws things up. Do you know what it is?

• 41:14Geoff Nielson?

  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.

• 41:22Speaker 00

  It's the humans, right?

• 41:23Geoff Nielson?

  Yeah.

• 41:24Speaker 00

  It's, it's the humans for whatever, you know, depending for whatever reason.

• 41:29Speaker 00

  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.

• 41:43Speaker 00

  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, 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.

• 42:20Speaker 00

  You could think about mindset as culture. You could think about it as a leadership vision.

• 42:26Speaker 00

  And to me, that is the most important, again, only in the, in the context of, of a technology deployment and 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, and remarkably more difficult than the, than the other two. So like everyone's working on the technology, which is hard. It's the easiest thing that we have in front of us.

• 42:54Speaker 00

  That mindset is this, is the transformation piece. Like it's the responsibility that leadership has to think about the future of their organization in a, in a, in a place where we have this disruptive.

• 43:09Speaker 00

  Massively powerful, highly democratized technology that 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 are become possible? Um, you know, there's a, there's a bit of a debate now going on between, do we focus on these technologies to, um, to get efficiency to take cost out of the business? Um, or do we use them to do things that we've never been able to do before? Um, and I think if you focus on the, I mean, you know, there are the realities of doing business and sometimes

• 43:52Speaker 00

  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. Um, again, everybody has access to this stuff, so you're not special. Um, and so if you use these groundbreaking things to take cost out, then the next year you're taking more cost out, less of it. And then the next year you're taking a little less cost out and you're just racing to, um, you know, trying to get a quarter of a percent, um, out of, out of a process, which I, you know, is not a race I would want to win. Um, whereas the, the value side, the upside is, you know, reimagining, it's like, you know,

• 44:33Speaker 00

  Amazon going from selling books on a website, um, you know, groundbreaking, um, but now, you know, being responsible for some large percentage of the, of the internet traffic and 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, um, 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 three in the afternoon instead of five and not tell anybody?

• 45:17Speaker 00

  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 in 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 coworkers? Or should I keep it and be more competitive? The young foe, not even young folks, but folks who are in organizations using it like, don't know the answer, very uncomfortable for for people, very, very stressful. So like investing in that and figuring it out and having a clear vision and a clear voice

• 46:03Speaker 00

  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

• 46:13Speaker 00

  is an area for investment and and focus that is largely overlooked. Right now, we'll pay off in spades in the long run.

• 46:20Geoff Nielson?

  Well, the culture pieces, 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 to throw money at, right? Like, it's not investment in that sense. It's an investment of brain power 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.

• 47:03Geoff Nielson?

  Whether it's about, you know, we need to make you more efficient or, you know, that this is all about just cost cutting or, you know, whether whether it's a lack of vision, there's 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?

• 47:36Speaker 00

  Yeah, I mean, when it comes to culture and and building the culture for the future, for an AI future. I, you know, like I said earlier, like I'm a fan of keeping things very simple. Kind of Occam's razor, your question.

• 47:52Speaker 00

  Just pay attention. Pay attention to culture, like actually, you know, turn the spotlight on that and and figure out what you need to do to.

• 48:03Speaker 00

  That's the low hanging fruit, like companies are not thinking that they're not even. That's not even a thing like 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 that 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 to organization.

• 48:48Speaker 00

  I think that, you know, culture is a

• 48:51Speaker 00

  by its very nature, a, a, you know, an individual thing, something that is, that is unique to, to

• 48:59Speaker 00

  each organization. I will say some of the common threads are this apprehension and and stress that this particular kind of change is is bringing about. And there are probably some quick wins that a leadership team could, could deliver, which is, which, you know, among them are, here's what we think about AI. Here's our expectation. Here's, here's what I do.

• 49:26Speaker 00

  Personally, and just be super clear about that and and keep delivering that that message over and over.

• 49:33Speaker 00

  Again, pretty basic, not seeing it happen a lot with the level of clarity and intensity that that's required to shift.

• 49:43Speaker 00

  Culture.

• 49:45Speaker 00

  And by the way, here's, have you ever heard of the paste layers?

• 49:49Geoff Nielson?

  No, I don't think so.

• 49:50Speaker 00

  This is a 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 were talking about two years in the future. They're like 10,000 years from now. And so they have this framework called the paste 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.

• 50:33Speaker 00

  And then you have things like, you know, governance, like 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.

• 51:01Speaker 00

  Humans don't, don't like change and we're in a period of a lot of change, very multi-dimensional change. And that's part of the reason I think people are broadly stressed out.

• 51:11Speaker 00

  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.

• 51:29Speaker 00

  And yet they are on radically different time scales. So I think that's, you know, broadly applicable to leaders that are trying to focus on, on culture. They just need to have this in mind is that the, the technology is changing weekly. Culture takes years to, to shape.

• 51:48Geoff Nielson?

  Well, and I think, you know, in some ways, it just comes back to building an adaptive culture, right? Like, can you build into the DNA of your company or your organization? Yes, change is part of who we are versus, you know, we do the same thing forever, the end, right?

• 52:08Speaker 00

  Yeah, I mean, trying to, trying to bake in tolerance for change is, um, would certainly be an amazing solve to this problem. Um, when I was at Frog Design a couple decades ago during kind of its golden age, um, Hart Modesling, or the founder, had a, um, actually made a bumper sticker and put it on his cars, and 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. Uh, and guiding, you know, his clients, uh, like Apple and Hewlett

• 52:48Speaker 00

  Packard, um, uh, through, through these moments of change. And internally, like, it was a lot. So not only were these designers designing things that were new and so embodied change, but, um, the way that they were doing their work was constantly challenged and constantly, uh, put under stress of change. And, um, that was a big, big dynamic. It was very tough for people to, uh, to live through. And so like, no, no, changes. He was, he was trying to build change into the culture. Um, really tough to do. That is a fundamentally,

• 53:27Speaker 00

  you know, hu- hu- human, we're evolved to, um, how would I say this? I think hu- humans are evolved to not deal with change well. There must be a survival, uh, uh, mirror to that. Um, but I, you know, I don't know what that is, but I, I do know that we are, we, deep down, we, we just don't deal with change well. We're just not wired for it. So it's very stressful for us. Um, so I think a strategy that tries to build, um,

• 54:01Speaker 00

  A tolerance for change into the culture would be would be really tough. That would be a tough challenge. So 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, 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.

• 54:43Speaker 00

  But 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.

• 54:56Geoff Nielson?

  I really like that. That's a it's 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 like that. That's a yeah, yeah, deal with that. Exactly. Um, I 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 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.

• 55:38Speaker 00

  Yeah, so like, I get asked, like, 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 Ibnu 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's asking. So.

• 56:02Geoff Nielson?

  So let's reframe and talk about impact, I guess, rather than the cool factor.

• 56:06Speaker 00

  Yeah, and.

• 56:10Speaker 00

  Because the cool factor, like, I think a lot of people think that the future of AI is gonna be like Biggie Smalls, right? Notorious B I G. Like, AI is gonna be big and sexy. And I don't think it's gonna be like that at all. I think it's gonna 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 I think in the fullness of time, the real value will be from lots of things that are,

• 56:49Speaker 00

  again, the result of a Cambrian explosion that are many and disparate, running in the background, doing their thing, kind of like electricity.

• 57:01Speaker 00

  But to answer your question, like, where 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

• 57:14Speaker 00

  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 interesting bit. Again, like, you know, folks spooling up Claude 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, you know, a generative code tool to work through some ideas? And then you watch them do it and you're like, wow, that's a very different approach to solving problems. I think that's the most.

• 57:56Speaker 00

  So I guess my short answer to your question is, like, what's the, you know, exciting,

• 58:02Speaker 00

  you know, deployment of the technology? It's actually in its use, not in the end result. It's the way people are

• 58:14Speaker 00

  are internalizing this toolset.

• 58:17Geoff Nielson?

  There's a few implicit, maybe even explicit lessons in there that I want to 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 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.

• 58:57Geoff Nielson?

  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?

• 59:13Speaker 00

  Yeah. And first of all, I don't want to hate on the old folks like me, but 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 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. Like 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.

• 59:56Speaker 00

  How am I going to go do that? And so they just grab the best tools that they can in order to do that. So I'm not sure I was going to go with that. So the lessons, lessons that we could learn. So 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 draw value out of them because they need it. They actually have a problem. Again, the beauty of this actually having a problem to solve is quite powerful. So.

• 1:00:37Speaker 00

  And the lesson learned, sort of how we could,

• 1:00:43Speaker 00

  yeah, I'm,

• 1:00:47Speaker 00

  I'm not sure what the takeaway,

• 1:00:50Speaker 00

  sort of the deep learning, the profound learning is.

• 1:00:54Geoff Nielson?

  Well, let's, um, 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?

• 1:01:27Speaker 00

  So I think, I think the, you know, if I had to synthesize this all down to kind of a few delectable nuggets.

• 1:01:34Speaker 00

  I said, first of all, this is not about technology. Like everyone's talking about technology. We all have technology in our titles, technology, technology. It's not about technology. It's about reinventing your business for a future that happens to be powered by AI. And, and thinking about this future business or the future state of your business,

• 1:01:58Speaker 00

  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.

• 1:02:20Speaker 00

  It's, it's opening these doors that have previously been, been closed to us. And so, um, you know, taking advantage of this, this amazing new toolset.

• 1:02:30Speaker 00

  And getting away from this purely technological, techno-centric 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, to, 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.

• 1:02:56Speaker 00

  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 it's this journey. You can't even see the destination. You kind of can. There's like the Eye of Sauron off in the

• 1:03:37Speaker 00

  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 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 triumphs and

• 1:04:21Speaker 00

  so forth, then I think you, you know, you actually stand a chance of succeeding.

• 1:04:28Geoff Nielson?

  I love that. Very poetic, very, you know, imaginative. And, you know, on your note, something that I think we're lacking here. So I really appreciate that. So with this framing, Maurice, of, you know, augmentation, the augmented age, to me, that's one of the narratives we're hearing about the future of AI, right? 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

• 1:05:13Geoff Nielson?

  organizations look at that.

• 1:05:15Speaker 00

  Yeah, I mean, when it comes to jobs, Jeff, I will 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.

• 1:05:54Speaker 00

  And so technology, like one technology 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

• 1:06:16Speaker 00

  automated inside of jobs. Now, if your job involves only one task, then it might be ripe for a complete automation.

• 1:06:28Speaker 00

  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. There, 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 of different things going on, just think about slightly adjacently.

• 1:07:15Speaker 00

  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 fell into that description to 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.

• 1:07:55Speaker 00

  If you thought about, you know, automating school teachers, we have a shortage of 400,000 school teachers

• 1:08:04Speaker 00

  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.

• 1:08:22Speaker 00

  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,

• 1:08:48Speaker 00

  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?

• 1:09:05Speaker 00

  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 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, the, the time axis, right? In, in making these arguments.

• 1:09:35Speaker 00

  The other is related to this time thing. So I was trying to

• 1:09:41Speaker 00

  quantify a little bit, uh, how long it takes, like we're talking about culture and it being slow and taking a long time. Well, how, you know, is, could we look historically, is there, you know, can we get some sense around how long it takes for, for folks

• 1:09:57Speaker 00

  to change, for things to change. And so I thought about past revolutions.

• 1:10:02Speaker 00

  And I thought about the industrial revolution. So the industrial revolution took about seventy 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, 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 a hundred million people?

• 1:10:38Speaker 00

  And so the duration of the industrial revolution, about seventy years at its peak. We were generating something like thirty thousand horsepower from, from steam. That's kind of a measure of, of productivity. And the time to impacting a hundred million people was the full duration of the, of that, of the industrial revolution. It took about seventy years for, for it to be broadly distributed, kind of the, the norm. Then there was the personal computing revolution.

• 1:11:08Speaker 00

  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, um, 1977, big year, you might have owned, uh, was it a Commodore 64, uh, Apple II, or a Atari 4400? There was like a trinity of, of PCs that came out, started this revolution. It lasted about 23 years to its, its conclusion. There were, um, 500 million personal computers, um, at kind of the peak of this, uh, of this revolution. And the time to impact, uh, to a hundred million people was, um, 18 years.

• 1:11:51Speaker 00

  So we went from 60, 70 years down to 18 years in order to have that, that impact, right? Then, uh, the internet revolution, um, which we're still, um, kind of, we're not in the revolution part, but we're still, um, certainly very fond of, of these technologies. It took 25 years, start to finish. So the, the, um, personal computing, 23 years, internet revolution, 25 years.

• 1:12:21Speaker 00

  At its peak, um, by 2015, I would say, all humans impacted. So, um, at the peak of the thing, everyone on the planet, because banking systems, communication systems, were all, um, on that backbone, everybody on the planet's impacted. It took about three years to touch 100 million people. AI revolution, um, we're in the middle of it. We're about 10, 10 years in, uh, just, uh, arbitrary or date, um, Google publishes transformer, um, 2017, right? So we're about 10 years into it. We're still in it, um, time to impact to 100 million people. A month and a half, right? After ChatGPT released.

• 1:13:08Speaker 00

  And sort of degree of impact, I'd say today, there's probably about a billion people directly like using AI and about three billion indirectly impacted every day. So, but you know, number of people impacted has been going up. The speed to impact of a hundred million people has gone rat from seventy years to eighteen years to, let's say about a decade to a month and a half. So that's speeding up. What hasn't changed is that first number. The industrial revolution took sixty years, but the three revolutions after, including AI, take about twenty five years because I think we're in the middle of the AI thing. We got another decade to go.

• 1:13:49Speaker 00

  So it seems to me it takes about a quarter century for humans to absorb a new disruptive technology. Interestingly.

• 1:13:58Geoff Nielson?

  There's that upper limit to our metabolism for this stuff.

• 1:14:01Speaker 00

  Metabolism is a great way to put it. I hadn't, I hadn't thought of that. It just takes us twenty five years to metabolize these, these revolutionary technologies. And there's just no way, like everyone's talking about, oh, things are happening so fast, so fast, so fast, which they are, is the toolset is happening really fast, but the skillset 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 twenty five years. And so I thought, I thought that was, was interesting. And I'll, I'll say one more thing about jobs. This is a framework I came up with some years ago because I, I, I felt like there was this dimension that wasn't being addressed when we were talking about new technologies displacing

• 1:14:46Speaker 00

  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 a 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 Wally, where we just

• 1:15:30Speaker 00

  kind of go around in floating chairs, drinking huge soda pops.

• 1:15:35Speaker 00

  Or, or we have sort of massive unemployment and there's nothing left for the humans to do. I don't think that's gonna happen. Cause 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 toolset, let's say. Or we've developed a new skillset 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. Uh, ceremony. It's amazing. It's exciting.

• 1:16:16Speaker 00

  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, of capabilities. You can't. And the 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.

• 1:16:50Speaker 00

  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, what's the new competitive differentiation? Cause 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. Cause I don't want to wait for 45 minutes to, you know, figure out my canceled flight, whatever.

• 1:17:35Speaker 00

  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, 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 four o'clock on, on Friday. Yeah, I'm pretty much done with all my work for the week. I'm, I'm, 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.

• 1:18:18Speaker 00

  There's plenty of problems to solve in the world. That we are, you know, spending enough time working on. And, and so I think broadly speaking,

• 1:18:30Speaker 00

  there's more opportunity to do that than displacement of, of humans. Like there's plenty of stuff for the humans to do.

• 1:18:37Geoff Nielson?

  I think that's well said. I, 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, I, I'm inclined to agree with you and, uh, 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, uh, and I really appreciate your insight.

• 1:19:03Speaker 00

  It's my huge pleasure, Jeff. Uh, thank you so much.

• 1:19:08Geoff Nielson?

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