A (4:47)

You mentioned AI being in some ways the driving force, but not a singular force here. Hence the very nature of a convergence. What do you see as being the biggest factors at play here?

B (5:04)

Well, the biggest, biggest factor is that we're not talking about a single technology or even a cluster of tech trends anymore. What we're talking about are systems level change. So convergences don't really pile trends one on top of the other. They operate across different domains. So artificial. Anybody who's listening to this knows that AI is not like an overnight thing, depending on who you talk to. You know, AI has been in some form of development for literally centuries. I mean, you can go Back to the mid-1800s to Ada Lovelace and the guy that she worked with who were starting to think about algorithms and computation in new ways. You know, and there was a lot of development in the 60s. There was development in the 40s, again, the 60s. It was 1965 that the term artificial intelligence was coined. I'm sorry, 1960, 56, not 65. At Dartmouth University. You know, lot of development, 70s and 80s. Then there was a crash like an AI winter. We've all forgotten this now, but like there was such a thing as a generative adversarial network. That was all the rage some time ago that, that, that led to where we are now. The point being this has been a long, long transition. At the same time other things have happened, like COMPUTE has scaled up, like the enabling factors for AI also started to improve at the same time. So we have this system level change rather than somebody made a cool thing with an AI. So that's one. A convergence results in a net new reality. So, so you know, you can now sort of demarcate the world before and the world after generative AI, which has kind of become shorthand for AI. But it's, it's, it's one of many, many facets of artificial intelligence, which itself is kind of a, a word that includes many other things. Convergence tends to redistribute power and value. So you know, I think right now it might be easy for us to all think that OpenAI is going to become the de facto winner or some combination of OpenAI and Microsoft or Google or whatever. To be truthful, I don't know yet. It's still pretty early days and who has leverage shifts every now and then. But all of that will wind up rewriting competitive dynamics in a way that didn't like. It wouldn't map back to what we were doing before. And then these convergences are hard to reverse. So again, like if I think back to social networks in the early 2000 teens, so foursquare Gowala, the like mobile social networks that conferred like cool digital merit badges for doing stuff, all of that is gone. Um, that, that was sort of trendy for the moment. So a convergence is, is the type of, it sets off a systems level change that just becomes very, very hard to reverse once it's done. So it's pretty clear that, that, that we have entered this cycle now and AI underpins it, but it's linked to all of these other things.

A (8:30)

I want to talk in just a minute about some of those other things and these specific convergences. But maybe before I do, Amy, this notion has come up a couple of times now of, of winners and losers and the competitive landscape changing, whether that's for individual firms, whether that's a kind of a nation state level. When you zoom out and you look at the, the landscape of convergences, the, the cycle that we're in right now, what do you see as being the key themes that are going to differentiate the winners from the losers right now.

B (9:02)

Yeah. And I think it's important to point out that artificial intelligence underpins several convergences that are underway. So it itself, like AI, is just technology. It's a suite of technologies. It's not anything beyond that. But there are some characteristics that will foretell who's going to be where on the other side of this. The biggest one is, are you. Are organizations digging in their heels, putting their heads in the sand, trying to go backwards in time to the way things used to be? Or are they willing to, in a way, destroy the things that have worked best because they see that on the other side of this, business will be done differently or governing again. This is systems level change stuff. So on the other side, what is different? That is a very difficult question. Question to ask. And most of the time, what we see are leaders who are like, anytime there's change trying to go in reverse or we see leaders who are, you know, they're sort of guided by two principles, fear and fomo. So they're either afraid of what's happening, they do nothing, or there's this fear of missing out and they make rash decisions quickly. It's a very small group in that third category, which is taking a surgical clinical look at what's happening and really mapping out, you know, what are we doing going forward and what's our vision for the future. I think that there are definitely. Man, you know, the tech industry used to be much better at this. And I was just trying to think of, like, which tech company, and I don't have one for you right now. I just don't. I don't think any of the technology companies are doing that. We work with most of them. So if they are, I haven't seen anything yet. I don't think that's entirely their fault. I think that there's been this whiplash effect with policy in the US that has made doing business pretty challenging on the. So anyhow, I can't think of a lot of businesses or sectors right now that would fit into that category of we're willing to disrupt things in order to move forward. I do see some companies getting back to the core, like, what's the one thing that only we can do? Let's do that better than everybody else while we are building toward a future. So there are some companies doing that in terms of, like, governments. Government's tough, you know, because government's tough in every country right now. We are seeing a lot of leaders going backwards the ones who are trying to push forwards, I don't think have the long term view. Yeah. And the one thing I think everybody ought to be doing right now in, in government is rethinking what regulation is, because regulation is a look back, it's not a plan for the future. And I don't see anybody truly innovating on the regulatory side either. So I don't mean to sound so dour, but that's kind of the, that's the boat we're rowing in right now.

A (12:28)

No, it's, it's completely fair and you know, for what it's worth, it's. This is, this is not meant to be a geopolitical podcast, but we often end up in geopolitics anyway because to your point of how intertwined it is with, with tech and work and everything else there. And I mean, it's hard to get away from the fact that it feels like there's an awful lot of noise in the geopolitical realm and the policy realm these days. That's serving as a distraction.

B (12:52)

Yeah, And I was going to say, look, I happen to be deeply interested in geopolitics, but you can't have a conversation about tech in the year 2026 without also having a conversation about policy and regulation and economics because they all go together. Now. There is, I don't care what anybody in Silicon Valley says. For a long time those leaders thought that whatever, move fast, break things, ask forgiveness later, they have all the power. And Wall street thought it had all the power and they didn't care what anybody else said. And of course DC always thinks it has all the power. So it set up this three sided prisoner's dilemma where everybody thought that they were fully in charge, when in fact it was shared power. And the companies, the people, whatever, the financiers who got that, were much better at figuring out how to work through those dynamics. Most weren't. That has changed. So you are now seeing these strange new bedfellows. You see tech leaders and politicians around the world, not just in the US getting together and trying to have, doing deals, doing different kinds of, you know, having different kinds of conversations. I think there is a sense that, you know, what no one part of that triangle anymore is, is, you know, has accumulated all the power.

A (14:24)

So 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 just find myself coming back to the comment you made earlier about the big tech companies used to be much better at sort of this future proofing and long term planning and that, you know, even they have sort of lost their way there, which to me is like really alarming at this point in time given the magnitude of what's going on in kind of every aspect of our society. You mentioned policy as being a driver of that. Is that a singular driver? Is there more to it than that? Like, have they gotten too big and cumbersome? Have they gotten too complacent? Is the technology moving too fast? Like, what's going on? And is there, is there a road? I'm chuckling because I was going to ask is there a road backwards? And I feel like that's a theme we've already been talking about saying there isn't or is like, how do you get this train back on track?

B (15:40)

Sure. I mean, I think there's a recalibration which I will tell you at first. Let me go back to something you said. So you use the word future proof that the companies used to be really good at future proofing. What I would say is they used to be very good at future developing. By that I mean having a strong perspective on what the future could be and then developing toward that and then just as like a side quest conversation very quickly. I hate the term future proofing because it's nonsense. There is no way to future proof. In order for you to be able to future proof anything, you'd have to be Dr. Manhattan. Are you a comic book Alan Moore kind of guy?

A (16:20)

I'm with you on Dr. Manhattan. Yeah.

B (16:21)

So like you're not and neither am I. You cannot stop. You can't go to another planet, like stop time and contemplate and feel brutish, you know, and have, and have all the data and know both the past, present and future. Like that's not any of us. So if that's true, then there are always like the world is multivariate. Like things are always happening. Nobody's ever going to have control over, over everything. So there's. You cannot future proof anything at all. I'll come back now to the main, the main road after the side quest. So yes, to your point about policy and regulation, I think there are probably some tech companies that out there that might be arguing, well, regulation, you know, policy, it's made it really tough for us to innovate or there's so much political uncertainty or economic uncertainty, whatever, like, you know, just. It's really tough. Here's where I think, here's what I think happened. I think it has nothing to do with any of that. I think it has to do with greed. We are seeing. The amount of capital right now that is flowing into AI is alarming. OpenAI has been around for a decade. You know, this is really tough, complicated technology. Very challenging. I don't know how much they raised before 2022 when ChatGPT became public. However, they've raised a metric, I was gonna say an expletive there. They've raised a lot.

A (17:55)

You know, you can swear on here if you want.

B (17:56)

They raised a metric. Fuck ton of capital, you know, 2023 onward. And it's debt. Like there is so much debt that at this stage I'm not sure, you know, I'm not sure what's going to happen once investors start, start getting jittery and they will. So the problem here is that if you are in a situation where you have taken that much capital or you are spending that much money and your run rate is absolutely insane, then, no, you are no longer thinking about the long term. I mean, you may go to your yurt somewhere and smoke a bowl or do ayahuasca or whatever it is the cool tech guys, the cool tech bros do on the weekend and have a vision quest, but at the end of the day, you come back to like operations. And when you are, when there's that much money moving around, you are, you have a fiduciary responsibility to do something about it. You know what I mean? And that's not just the story of OpenAI, Microsoft, Google, I think Amazon too, they're all spending 200 billion plus dollars this year on data centers. I mean, these are extraordinary amounts of money. And don't forget the market used to punish tech companies for R and D. So what we're seeing right now is a flood of capital moving in, a lot of money being spent, business models having yet to be proven in the enterprise. And so no, there's nobody out there thinking through not just like, what could the future be, but what are the potential consequences of what we're building if we're not really thinking all of this through? And that is problematic.

A (19:42)

I completely agree with you and you know, I have a few different concerns about that. And just to unpack a couple of different things you said there, Amy, one of them is, and I'll continue your analogy A little bit. But it really feels like the tech companies are starting to get high on their own supply, like because they're raising this much money. The hype and the narratives around this stuff and the certainty with which they're describing a very specific future because there's so much money tied up in that it makes it, from my perspective, very difficult to figure out what do we actually predict is going to happen versus what do some of these fundraisers need to convince us to believe is going to happen, which is one side of it. And I mean, and that's not even to speak about, just like the fog of war that's created by, you know, how many convergences are happening at once. But I mean, what's your outlook on this in the, I guess the shorter term and the longer term? Do you think we're, there's an implication in what you're saying that we're kind of dealing with a house of cards here and that we've extended this farther than it can reasonably be expected, or that we can't possibly get the gains that are being promised now is that fair? And where do we go from here? And what does that mean for tech and business in the next handful of years?

B (21:06)

We just need time. This is the thing, like, are we going to get to a place where we have tools that perform? Like right now there's a whole bunch of tool, most of the tools perform between, you know, like they can do the same level as a person or. Right. So the definition of artificial narrow intelligence was can you do a singular task or narrowly focused task at the same level or better than a person? And the answer for a long time in lots of places has been yes. I mean, that's gps, that's your anti lock brake system in your car. So yes, the, the movement toward artificial general intelligence is to do not just multimodal, but like lots of different tasks, again at or at or above. And that was always the standard definition we have right now are tools that are more than multimodal that can do what we, what we can do at like, you know, 70%, maybe 80%. But it, but that has become a satisfactory level of doing things. And again, like in business, part of the reason for that is a whole bunch of people decided 80%'s pretty great if we can eliminate a whole bunch of headcount. And the temptation to improve, bottom line through attrition was so great that 80% has kind of been like palatable. You know, that would never have been true years ago, or maybe it was. And that's kind of how outsourcing started. We can do it cheaper. Maybe it's not going to be as good, but it's so cheap and we don't have to pay salaries and all the rest that goes with it that will offshore everything. We're offshoring work now to AI systems that they're okay enough but we also don't know how decisions are being made and a lot of other things. So. So to answer your question in this very long winded way, we just need time. You know if you. We need time and we need good decisions to be made. But right now there are all of these incentives that are sort of pushing against patience. And so part of that is. And let one of the things that's pushing the tension, the things that's causing the tension. It's not just investment. It goes back to what you said at the beginning about predictions. You know the thing that's true today that was not true five years ago is that everybody's got a hot take on AI, right artificial and like I've spent 20 years researching. I wrote a book on this seven years ago that you know was a bestseller but it also took. It bled me of my two years of my life. I would, I would never claim to be an AI expert. Never. And you now there are all of these people who are. Feel like they're qualified. If you look at LinkedIn recently you will see like everybody is some form of an AI expert all of a sudden overnight fractual fra. My favorite one is the fractional Caio Chief Artificial Intelligence Officer. Like tell me you're full of bullshit and don't know what you're doing without telling me you're full of bullshit. Shouldn't, don't know what you're doing. But part of where this is coming from is you can use generative AI to now have a hot take, right? So you just use that to give you an idea of what to talk about or write about and then you do that. So part of it is that part of it is there's a lot of. There's a lot of demand for increasingly salacious stories. So like for those who aren't. Aren't aware like Mult Books is part of this incredibly dangerous never down download it to a machine open system Anyways so it's, it's a. It's a social network for AI bots. Okay, whatever. And then like all of these posts were going around about, you know, like one of them wanted to invent a secret language so his human couldn't understand what he was saying. And there was another one about like a manifesto on how to destroy humanity. All of this made the news. Most of that was written by people for the purpose of gaining views. And somebody posted like a 5,000 word screed linked to it on Twitter and It got like 65 million hits about like an existential threat posed by AI. If you go back over the past few years, the level of existential threat has. I'm not sure where else we go from here. How much more dead can we be? You know what I mean? Like, so there's an incentive at the companies to make decisions because there's a lot of money on the line. I think there's also incentive for people to talk in increasingly dystopian, apocalyptic terms about AI because there's attention on the line also bad.

A (26:07)

Well, that's it right in the attention economy. Like, you know, whether it's rage bait or whether it's just saying the most outlandish thing. And like I always have to put up my hand and say, yes, in some ways we're complicit in this because we put our stuff on YouTube and on platforms and if you say like, you know, Amy has a reasonable take on AI, people like boring. Like, when is she going to say that it's going to kill us all? Right. Like, it's just, it's. Which, you know, is, is neither here nor there. But, but I'm curious, Amy, coming back to this notion of AI in general and not knowing exactly what it's going to do and people making these grandiose promises and it's been around for a while and 80% is okay. There is this, this issue too, where AI is so broad it's almost meaningless. And people talk about it as though it's like you can almost interchange AI with magic because it's used so broadly that it's going to be able to solve all these problems and do all these things. And I'm curious, from your perspective and coming back to some of the convergences, what are some of the, the spots where you think AI can actually have a material difference on our societies and the way we do business and what are some of the ways where you think, okay, this is overblown, this is another mold book. It's not actually going to be a sea change here.

B (27:28)

Yeah, Again, I think this. Look, the thing I like to remind everybody is that a fork can be a lethal weapon if you stab somebody in the neck with it hard enough. Right. So like, otherwise it's just a Fork. It's not that exciting. Artif. Most of artificial intelligence, like the, the things that make up AI are not exciting. They're. You know what I mean? So it's, it's how we implement AI. And right now the thing we're doing with AI is telling. Telling stories about it more than we are actually doing things with it. But there is. So I just want to like, put that out there. I don't mean to shit talk AI, but like, but that's, like, that's where we are now. What can you do with like, what are some actual things that are happening? So programmable biology is one space that is incredibly interesting and promising. So up until very recently, you know, the laws of physics, chemistry and biology, they kind of just were. Because there was no way for us to intervene in a real meaningful way. That has been changing. So artificial intelligence and biology have intersected. And because we know a lot about biology, we know the language of biology, rna, DNA molecules, proteins, it's possible to now combine and shift those materials in different ways that we just were not able to before. And we do this computationally, believe it or not. DeepMind, which is the big robust research arm of Google that is doing, I think, some of the most impressive AI work out there, they happen to be the ones also applying some of that work to biology. So just as there's a chatbot, you go to ChatGPT or Claude or whatever and put in a prompt and get out some stuff on the other end. There is something called Evo 2, which is a model where you can kind of do the same thing, but the prompt that you write in is for the purpose of getting something biological out on the other end. This unlocks unbelievable opportunity for us ranging from new materials. So, you know, in the near future, I've seen some, I've seen some really interesting research on wood that is clear, you know, and totally different types of enzymes that can break down materials in other ways that don't have negative side effects. It unlocks opportunities for us to deal with climate change. As much as it would be awesome for everybody on the planet to take an eye toward the future and make better decisions, you know, that's not gonna happen in total alignment for everybody on the planet. So we can keep working on that and. Or we can also mitigate the situation that we're in. So. So imagine engineering almond trees to require like 18 of the water and can withstand like drought and things like that. So it just, it gives us totally different opportunities we've never had before. And that's before we even talk about human health and new drug discovery. You know, I obviously I could go on and on and on, but like the fact that biology has become programmable in my lifetime and we're at the cusp, we're not like, this is not mature yet. We're at the beginning. You know, that to me is super, but so much more exciting and interesting than talking about the robots killing us in our sleep.

A (31:12)

And you know what's interesting and in some ways depressing about that? And in this world where everything is so narrative driven is whether it's robots in your sleep, whether it's just generative AI and slop and all of that, it feels like so much of that has taken the oxygen out of the room for things like programmable biology. Right. Like, I don't know, it seems like in any other period you and I could be talking about programmable biology and like, that's it. Like, you don't even need to talk about other trends or convergences because there's so much wrapped up in there.

B (31:42)

Yeah, but here's, here's the rub. We need to talk about it because this is not the same thing as asking a generative AI to like write your fiance a little note. You know, like, that may be ethically good or bad, or maybe you don't care, I don't know. But like, that's not gonna make a national headline. People forget this. But it was in the 1990s, early days of cloning, which is pretty common now, cloning animals or material. Dolly the sheep was cloned. And the purpose of this was not to create a new race of demon spawned sheep. It was for medical research. But the world collectively lost its mind. The Pope came out and had a edict at the time. President Clinton had to have a spe, like the President of the United States had to have a special press conference on cloning sheep. Like sit with that for five seconds, you know what I mean? So that was 30 years ago and we didn't have anywhere close to the technologies we've got today. There is so much happening in the sort of broader generative biology, synthetic biology umbrella that has everything to do. I mean, look, there is. In Japan, researchers have figured out how to do something called induced pluripotent stem cell combination. So basically you can start with a skin cell and coax that cell into becoming any other type of cell, lung tissue, heart tissue. And they are doing this as a way of creating embryos. So again, not about demon spawn, but it's about ways of helping to Manage fertility, which is a tough nut to crack. But we should be having the conversations about this right now because people are going to have thoughts and feelings and if we, you know, because unlike AI, the life stuff is very much tethered to religion. So if we don't have those conversations now and the tech pushes forward at the same clip that it is, I promise you that the. What seem like silly freakouts that everybody had about Dolly the sheep is going to pale into compare. Pale in comparison to the, to the feelings people are going to have. And you know, that's, that's not good because, because of the way that medical information misinformation is already spreading. You know, it's just going to make it that much harder and then it's potentially going to take away life altering in a positive way, technology. So like, that's, that's, it's a, it's a great point that you raise. I mean, we're, we're burning all of our mental time and energy and everything else on, you know, dystopian sci fi and we're not like pushing up our sleeves and having the harder conversations that I wish we would all be having with information though, sorry. Cat asterisk, not hot. Taste informed.

A (34:36)

Yeah, well, and I have a concern about that and I'm curious on your perspective because you almost convinced me of the opposite thing you were arguing, which is that when you use Dolly the sheep as an example and you say, okay, well, we need to have more open forum discussions about this new technology, you know, what programmable biology, like, let's stick there as a basket of technologies, like, do we really need the Pope and the President commenting on this technology? Are those going to help it, like drive it forward or push it back? Because I'm almost worried, like, have we gotten to a point where like, we, we are too stupid to sort this out? Maybe we're better off distracted by like the big shiny things and let the people who know what they're doing actually create this in the back. Or, or do we just need to go through this, some sort of creative destruction to actually get there?

B (35:22)

So I have a. I want to talk about creative destruction. I'm going to get up from my desk for one second. I want to show you something because it perfectly answers your question. Hang on just a sec. I will be right over here. So the question that you're asking, the question you're asking is, should we just have these conversations with people who don't know what they're talking about? Everybody's gonna have a Hot take, blah, blah, blah. So in 1975, there was a group of scientists asking the same question you just asked about. Early days genetic engineering, all right? And academics, researchers, they always kept things behind closed doors because they did not want to invite unnecessary scrutiny from people who didn't know what they were talking about and they didn't want to have any issues. But knowing what was on the line, they took a different route. So in 1975, a handful of these amazing world class researchers met in a similar. I can never say this right, A similar met in California, near Pebble Beach. I'm terrible at names. They met. Anyhow, they met. They decided to invite journalists. They invited, they, they invited journalists. They invited think tank people. Excuse me. And they did what they were gonna do anyways, but they had a bunch of people there with them. It resulted in the COVID This is the original. Trying to back this up so you can see it. This is Rolling Stone. This is Stevie Wonder on the COVID of Rolling Stone and it says, genetic scientists facing the mutant microbe. These guys are covered like literal rock stars, all right? They are in the same magazine as Elton John and the Kinks. So the result of this was better information and helping people gain knowledge and understanding. And it did not result in what we currently have today. So my point is there's like an argument to be made for just invite people in and have a normal conversation. I think the very fact that I get irritated every time, every time a name in AI comes out and is like, oh, we're building something, it's dark, we can't talk about it. We just. I, what I know what I've seen, I can't talk about that is doing nobody any good. There, there is a totally. There is like an alternate universe in which there's a different approach and that approach is better. And, and anyhow, but that was literally about biology and it, it set things on a good course for the future.

A (38:26)

So the, the, the point you're making is that transparency around this stuff is, is necessary. And even if it's unpleasant or leads to, you know, discourse that's, you know, going to be bumpy. We have to go through that to be able to ultimately get people on board with this.

B (38:43)

We do look at what happened with messenger rna. So that's also incredibly powerful technology that nobody talked about. You know, we then had vaccines for the, for the, for Covid. And to be fair, that was a tough time when there was a lot of information flying around and confusion. But wouldn't it have been nice to know in advance. Right. And then we might have had less consternation and we would have more pathways into using that technology now for additional purposes.

A (39:15)

Yeah, that's fair. That's fair. In terms of the convergences that you're keeping an eye on, there's an awful lot and I want to be mindful of not trying to do a deep dive on all of them, but if I can. We've talked about biotech in some capacity. We've talked a little bit about computer other sort of buckets. You talk about is you talk about agentic AI, which we haven't really talked about at all. You talk about data and surveillance being used by AI and kind of that sensor piece. And you talk about AI relationships and people's individual relationships with AI and how it helps them gain information or even just connect with people who formerly would have been people that they connect with. There's a lot of space there. Where should we go next? What area within that do you think needs to be top of mind maybe for business leaders to better get a sense of what's coming in this convergence landscape?

B (40:15)

Yeah, I think that there's a couple. So again, everybody's very excited about artificial intelligence and agentic AI. And that's wonderful if you're in business. You know, most businesses drag their heels on digital transformation and then it took a very long time. Some we literally have a client that is on year 14, not joking of their digital transformation. I, you know, there's a lot of consultants who have made a lot of money because this company is willing to go that slowly. The problem is that there are other forms of computing that are also in development that will also be used by business at some point. So there is the classical compute architecture that we've got now and like all that stuff and AI sort of fits in there. Quantum computing does not yet have commercial applications, at least not at scale, but they are coming. And so the story around quantum is always quantum encryption and like passwords, you know, encryption no longer works and all this stuff, horrible, horrible stuff. The other side of quantum computing is being able to take just like massive amounts of data and being able to model it in either near real time or real time to be able to get information on the other end, you know, it requires a different type of compute computing system we have today. There are huge implications there, positive and negative, but for everything from weather prediction to supply chain management, to like financial services insurance, like suddenly, once it starts to be functional for a business, quantum is like a set of keys that unlocks the ability for companies to do things they would love to right now, but there's no way they could. There's a third area. So like there's a. So there's AI, there's agentic, there's classical computing, you know, AI, there's quantum. There's actually a third area that's a little bit further away, but it does exist now of computing and that's biological computing. So there is such a thing as collecting brain cells, creating an organoid, which is like a tiny brain and attaching that to a computer and having that power the computer. So it is literally a brain computer. Now there's. I know that sounds ridiculous and silly and whatever, but there are a couple of companies that already exist. There is such a thing as a. I'm looking down at the cpu. There's a. Imagine like a old school C. I'm trying to describe what it looks like. It's like a imagine just a CPU that has a transparent top, like a gaming computer almost without all the cool crazy lights, that has tissue, human tissue, brain tissue running it. There's also a cloud based version and you can actually watch that operating in real time. Why would you want a brain computer? Because it requires nowhere close to the amount of energy that a traditional computer does. Because it's just like our brains, much more efficient. It can also be used to do different types of computation. So the implication here is sometime in the very near future a company is not just going to need AI or have access to AI, but you'll have different types of compute for different circumstances. You'll want to run quantum for some things and AI for some things, but not the same things. You may need biological computing for other things. And businesses are not set up that way. You know, our society or government, the enterprise, it's all set up for like we have this type of machine deployed in this type of way and we've got like managed services, you know, whatever. That that's not what the future is going to be. So that's a pretty big convergence, the poly compute convergence. And so that's coming. Let me keep going.

A (44:34)

I guess you're talking about organoids. Like suddenly I'm realizing, oh, I think maybe we do have to have a societal conversation about this.

B (44:41)

Because if you didn't like, totally.

A (44:42)

If you didn't like Dolly the Sheep, you're not going to like these like organ computer farms that are giving me like stranger things in the Matrix.

B (44:51)

Yeah. And so again I think it does beg different types of questions than we're used to asking. So Could I. You know, people right now are trying to train AI systems on their personality to like, upload who they are, but it's at best a terrible facsimile. Like, it's nothing like who they really are. You know, if I'm able to grow so, like, if I had an external, a literal exo brain of my own brain cells, it's not going to have the lived experience. We don't know yet about memory because we're not sure how some of this stuff functions. But, like, you know, we don't know what that looks like in the future. Or maybe I would license my brain to somebody's company to, like, use my brain cells, you know, that's also plausible in the future. We have agents for sports right now. Maybe we're going to have agents for intellectuals who broker deals for, you know, for sells. So these are interesting things to think about. Those are not the existential questions business leaders are going to have to wrestle with in the next five years, but in the next decade, yeah, probably, you know, that is coming. So again, some of this is kind of fun to think about or scary, depending on your perspective, but this has to do with, like, scalability throughout your. If we enter a poly compute world, that means you have to, like, reorganize your business a little bit, you know, and that type of thing takes time. The larger that your business in is, you know, the larger the business is and the type of industry that you're in.

A (46:30)

I want to come back to something you just mentioned, which is that, you know, these aren't necessarily the existential business questions that leaders need to answer in the next five years, maybe 10. What are the existential questions that business leaders need to be thinking about right now? And how should the convergences help them do that?

B (46:48)

Yeah, so this kind of goes back to what we circled around a touch not too long ago, and that is creative destruction. So in the past year, in the past couple of years, there have been just innumerable changes, and that is causing disruption throughout every single industry. And I think for most leaders, you are trained to not get distracted, follow your inst, be very methodical, stick with what works. But what we're starting to see is that the greatest threat to any organization is no longer disruption from the outside only. It's the refusal to destroy things from within. And that concept came from an Austrian economist named Joseph Schupere, and he called this creative destruction. And that is when it's a process where you've got new innovations because of these convergences that are relentlessly displacing what exists. So older technologies, companies, business models, power dynamics, geopolitical relationships even, and that resets economic structures. So the issue is this is not about evolving. So I think a lot of companies are looking at AI right now or any of these things, right? And they're like, well, we just have to go faster. We have to do what we're doing. But more, right. It's an evolution. And leaders are assuming that past success or the way of doing things before will guarantee their position, their dominance, whatever it is, in the future. And that is not going to be true anymore, at least not during this cycle. What will wind up. It's not like an overnight failure, but those. Those organizations are going to calcify and they're going to wind up being irrelevant. So this is the moment in time to ask very difficult questions that. That are. They're almost like dangerous to be asking because they're questions like, what do we need? What is. What do we need to allow to die so that we can, you know, get. Get to the next piece of what's happening so that something more valuable can live. And that. That is a tough, tough thing. That is an existential question for businesses because, you know, I think if. If a lot of leaders take a good, hard look, you're going to have to look. It's possible that, like, the thing that is propelling the business to success right now, if you're not willing to make a change, could be the thing that kills you not 50 years from now, but like five years from now. And so that to me would be, you know, if I was a leader, I would be. Keep. That would be keeping me up at night much more than whatever's happening in AI right now. AI is certainly an input to that, but there's so many dynamics, there's so many convergences in play right now that every leader of every business should really be asking that question, where is the world going? Where will value be created? How will we participate? And do we have to kill something off right now that we are doing in order to create something that generates more value for us, for everybody, for society? Whatever it is in the future,

A (50:40)

it. It's reminding me of the. The Mark Carney quote. I don't know if you heard it from the World Economic Forum about nostalgia is not a strategy that.

B (50:49)

Yeah, I did not hear that. But, yeah, 100% that is like it.

A (50:53)

It just feels very on theme for, you know, as you said, letting things die to move forward.

B (50:57)

I think of all the things he said In Davos this year, that might not have been the specific highlight.

A (51:03)

Yeah, fair. Fair enough. And there we go, drifting back into geopolitics again.

B (51:06)

I can't help it.

A (51:08)

No, no, I can't. I can't either. So you started our conversation by saying that there like maybe almost alarmingly few business leaders are doing this right now. And it sounds like, if I'm interpreting you correctly, like even fewer than in the past. And so I'm curious, you know, is it harder than ever before to have these conversations? And like, what are some of the biggest barriers? Is it just like a matter of courage? Like, what are the biggest barriers preventing people from having these difficult and dangerous conversations and how do we enable more of it?

B (51:47)

The reason. So there's a couple of reasons why it's hard to have these dangerous conversations right now. It is partially because leaders are incentivized to be hyper focused on right now. You know, you are punished for taking your eye off the next quarter. That has not always been the case, but that is definitely the case. And at least in the United States, the market is so up right now that, you know, it's just a lot of pressure if you don't have a informed, articulated vision of the future. And I'm not talking like aspirational unicorns and balloons or you know, a true strategic vision is something that results from developing scenarios, building out quantitative models. And it's not just financial. There's work that goes into it. If you've done that legwork and you have a true strategic vision that answers those questions, where the world's going, where value be created, how will we participate? And it's not fluff with that. You can then build out strategic pathways and then people know where they're going. If you have something like that, then it is much easier to have a conversation that includes those dangerous questions because there is certainty. How we get there, I don't know, but there is certainty because. And we feel, you know, it's data backed, right. So we know where we're going. So now we can have those conversations. That and meaningful debates asking things like what do we need to kill so that we can grow? Or do we need to lay, you know, do we, do we need to lay different groundwork with other, you know, try getting partners we've never had for all this stuff. Right. It's much easier to have those conversations. If you have not done that work, however, and most leaders have not, then you're not going to have those conversations because they, they do feel threatening. You're not going to have the confidence to have them. And from my point of view, that's an abdication of your responsibility as a business leader. And I don't, you know, I, look, I, I deal with CEOs from companies all around the world. And my, you know, it's not a good statistical sample because just about everybody that we deal with is dealing with us because they, they want to figure out what the, what that strategic vision is and how to get there. But the vast majority of leaders don't do that. Now, some of that is because CEO tenure is. The turnaround is very fast, much faster than it was. And there's a lot of promotion from within. So a lot of CEOs used to be CFOs, and that's the safe bet if you're bored. It also means that you're missing out on some perspectives and skill sets that would have been helpful to sort of figure out the next 20 years versus the next two. So some of it is that. Some of it is. It just feels like there are other pressing matters. So there's like a discipline piece that's missing, but it's possible, but you have to be willing. Part of being a leader is telling everybody where you're going. You know, I mean, that's, that's part of it. I suppose there's such a thing as a captain of a ship that just stays at dock all the time, but that seems like a weird thing to do, right? So you have to chart the course, tell people where they're going and give them autonomy to, to help you get there. It doesn't matter if you're publicly traded or not. And honestly, that's the same. The same is true of a government leader. But you have to, you have to like, buckle up and do the work, and it's hard work and you have to be willing to take some strategic risks, you know, but that's, that's your job.

A (55:48)

I, I think that's extremely well said. And I'm. It's such a juxtaposition from some. So many of these, like, press releases you read now that are just saying, like, AI is going to improve productivity. Like we're going to lay off a whole bunch of people I don't know, like it. It feels like it's a completely different mindset for how you think about where the world is going.

B (56:10)

Look, I was around during the last AI winter. I'm not saying we're going to have one now, but my job is to look long term. I work in this very specific area of strategy called strategic foresight. The point of strategic foresight to some degree is to make predictions using data, but also acknowledging that the predictions that we make today are the best that we can do. And they could all be wrong five seconds from now because we're going to have new data. So the point is to do enough of a rigorous job that point in the future can be defined and articulated, and the strategy can be attached to that so that decisions can be made today. So it's, it's not, you know, it's not rocket science. It's tough work, but it has to be done. I think navel gazing about AI is just procrastination. Either navel gazing or, you know, the increasingly apocalyptic stories is, is a, is a diversion from doing the hard work of leadership.

A (57:23)

I love that. And on that note, Amy, I wanted to say a big thank you for coming onto the show today. This has been really interesting, really insightful and I've super appreciated the great conversation as well as your insights.

B (57:33)

Thank you.

A (57:35)

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