บทถอดความฉบับเต็มของ Solution Spotlight - AI Enterprise Solutions & Technology Independence | unDavos 2026

anybody else. 00:04:20.720 --> 00:04:27.040 So just I realize that humans are weird, but technology when it could be used well, could do fantastic things. 00:04:27.199 --> 00:04:31.759 Um, but right now then, so I do a variety of stuff, but I'll I'll share stuff as we go. 00:04:32.399 --> 00:04:32.560 Great. 00:04:32.720 --> 00:04:35.600 Well, so good, because Richard and I started this debate in the hallway. 00:04:35.839 --> 00:04:35.920 Okay. 00:04:36.079 --> 00:04:39.680 And I've got to continue this saga. 00:04:40.160 --> 00:05:01.600 Because uh, you know, we think about how AI applies to these large enterprise systems, and and when we're talking about energy use in particular, which is an area I'm interested in, um, we were talking about the whether or not this is a um energy consumption and really minimizing consumption at the use point is something that AI can help a lot with. 00:05:01.759 --> 00:05:04.800 We've seen that with demand response in the utilities. 00:05:05.120 --> 00:05:09.519 Utilities in the electricity sector are not known as early adopters. 00:05:09.600 --> 00:05:09.680 Right. 00:05:10.079 --> 00:05:26.800 And so, Richard, can you talk a little bit about your perspective on the use of AI in the industrial sector and where where technology such as AI can be helpful and what still remains to be done from an engineering and a science perspective? 00:05:27.279 --> 00:05:40.720 Well, I I just want to say that I have deep understanding of these issues because one of my investments was in an energy trading company in Australia, and I spend more hours than you want to know analyzing spot pricing of electricity. 00:05:41.040 --> 00:05:45.759 So, with that perspective, I think that there's two ways that AI can help. 00:05:46.240 --> 00:05:50.480 The first is that uh just load prediction helps a lot, right? 00:05:50.639 --> 00:05:58.160 Because the fact is uh these systems have to be sized, you know, size for peak load, uh, small modular reactors are another nice solution. 00:05:58.399 --> 00:06:06.399 But one of the things I think that AI can help a lot with is doing that demand prediction and then figuring out where the load is really going to be and when it's gonna be. 00:06:06.879 --> 00:06:16.480 But the second is actually way more fundamental, which is that uh today, almost I would say 99% of all the AI compute is done in the cloud. 00:06:16.720 --> 00:06:21.759 And that is some of the most expensive in terms of electricity performance per watt. 00:06:21.920 --> 00:06:25.199 And the reason is uh like a car, right? 00:06:25.519 --> 00:06:37.439 Since we have Formula One here, a Formula One car goes about three times faster and costs about$250 million to make, or you can go buy a small car for$8,000 or$10,000. 00:06:37.759 --> 00:06:46.720 So I think what's happening now with AI is this gradual migration of that compute from just data centers to PCs uh to phones. 00:06:47.040 --> 00:06:55.360 And this phone, for instance, is more

powerful than the most the fastest Cray computer that was doing defense nuclear warhead things in 1995. 00:06:55.600 --> 00:06:57.279 So I think you're gonna see two things happening. 00:06:57.439 --> 00:07:04.160 One is more load prediction and so forth, but the second is just moving the workload out to the devices people already have. 00:07:04.399 --> 00:07:09.120 Uh for instance, uh Apple just announced a feature I can't believe. 00:07:09.279 --> 00:07:10.399 Actual live translation. 00:07:10.480 --> 00:07:16.000 You put your AirPods on, and by the way, when it's doing that live translation, it's not going to the cloud. 00:07:16.319 --> 00:07:18.240 It's just running on the local machine. 00:07:18.480 --> 00:07:22.480 And these machines are drawing five watts, 15 watts. 00:07:22.800 --> 00:07:25.600 So that third that efficiency I think is going to help us a lot too. 00:07:26.240 --> 00:07:37.360 Mark, you do a lot of work with On Davos with venture capitalists like Richard, and really helping bring investment to startup companies that are bringing these innovations to market. 00:07:37.600 --> 00:07:52.800 And I'm curious from your perspective, when we're talking about things like what Richard's describing, this sort of systems change, where you've got to not only have your suppliers of your phones developing phones that can do these things, but there is that larger system approach. 00:07:53.040 --> 00:07:58.800 Like that type of investment doesn't happen on a project finance basis, one company at a time. 00:07:59.040 --> 00:08:08.319 So, where do you see the role of conversations like on Davos fitting into that building those big ecosystems for those transformative technologies? 00:08:08.800 --> 00:08:11.680 Well, and I think that so there are many, many different pieces. 00:08:11.839 --> 00:08:14.079 There's also many different countries involved. 00:08:14.560 --> 00:08:23.040 Now, and that's why the Davos conversation is very interesting, because to some extent it's fairly easy to take a US-centric view, even when we're in Europe. 00:08:23.279 --> 00:08:32.799 And at the same time, then uh almost like the thing that's absent from our sort of most of the world, at least sort of almost by choice, is the Chinese world and then the emerging Indian world as well. 00:08:33.120 --> 00:08:36.559 Because if you like, there is a let's look at whatever hyperscaling. 00:08:36.639 --> 00:08:40.879 So I was having conversations yesterday at the Belvedere uh with people doing hyperscaling. 00:08:41.039 --> 00:08:51.279 And the thing that gets me is that okay, uh I lease cars because I'm not an idiot, but if I were to buy a brand new car, it loses 50% of its value the day that I buy it. 00:08:51.919 --> 00:08:57.279 If you look at these chips, these chips that we have right now, will they really have a 10-year lifespan? 00:08:57.600 --> 00:09:07.360 Well, they might be used in 10 years' time, just like I use my iPad to pretty much stick it in the kitchen to open up uh recipes, but it's not going to be used for anything really, really cool. 00:09:07.759 --> 00:09:13.279 So one of the things that gets me is first of all, some of these technologies will be obsolete in two years. 00:09:13.919 --> 00:09:15.919 We're throwing massive amounts of money in. 00:09:16.080 --> 00:09:29.120 The next thing, and this was a Davos observation I had last year, is that last year during Davos, when it was AI, AI, AI everywhere, what then came out is a little company called DeepSeek had a completely different way of doing it with a completely different cost model. 00:09:29.200 --> 00:09:41.600 And I think what the Davos conversation does is that it brings in perspectives from different uh countries, mindsets, technology base, which could mean that even though there is a consensus, we're marching off in this direction, it's all good, goes whoosh. 00:09:41.919 --> 00:09:51.360 And I think we're very likely to have messy, many sort of whoosh moments through technology in the next months and years that will be dramatic. 00:09:52.080 --> 00:09:56.080 We'll have a quantum uh computing uh session that's going to come up sort of after this thing. 00:09:56.320 --> 00:10:02.559 Eventually, when Q Day happens, all of this data that we have that is um encrypted will basically be in the clear. 00:10:03.279 --> 00:10:13.360 There's no conversation talking about like all of your private pictures, all of your visa records, everything will be in the clear in is it five years, is it three years, is it ten? 00:10:14.080 --> 00:10:18.080 There is no, I mean, but Davos is the type of place we can at least start to have these conversations. 00:10:18.240 --> 00:10:26.159 Now, sure we should have had these conversations ten years ago, but quite often there's a consensus that no, we should but so uh right now, yes, I I do see this. 00:10:26.399 --> 00:10:33.600 Davos gives us the place to have this ecosystem conversation within the different domains of technology and the different countries as well. 00:10:34.080 --> 00:10:42.240 So, Richard, as a as a VC, how do you approach your investments in this kind of world where things can change so fast? 00:10:42.399 --> 00:10:44.080 Do you try to stay diversified? 00:10:44.240 --> 00:10:48.159 Are you looking for specific sectors where you think you can have the biggest impact? 00:10:48.720 --> 00:10:55.360 Well, this is where I'm glad I only spent 15 years as a venture capitalist and now spent the last five years as a startup person. 00:10:55.519 --> 00:10:57.919 You're you're asking the hardest question. 00:10:58.240 --> 00:11:00.879 Because it is very, very hard to predict the future. 00:11:01.120 --> 00:11:04.799 And the problem is uh it's called selection, choosing the right company. 00:11:04.879 --> 00:11:11.360 It's sort of like uh you go on a date and you get married for 15 years, and that's venture capital, right? 00:11:11.440 --> 00:11:26.399 Because you have a it's a great meeting, it's

wonderful, you have the first day, and then you realize it's the first day of the rest of your life, and you're gonna live with this investment uh typically through two or three CEO changes, typically through four or five market changes, six pivots. 00:11:26.639 --> 00:11:28.240 So I think it's a very, very hard thing. 00:11:28.320 --> 00:11:50.159 And I think particularly now, because um just to follow up on your on Davos comment, I think this is the first time that we are we have a technology where we were just talking about Zambia and Kenya, for instance, where every other technology thing I've worked on, you can buy a phone, but you can't make a phone. 00:11:50.399 --> 00:11:54.080 You can buy cloud services, but you can't make AWS. 00:11:54.320 --> 00:12:03.440 This is the case where you mentioned DeepSeek, you can actually download that model in Kenya, build your own system, and you have zero dependence on any trading partner. 00:12:03.759 --> 00:12:10.080 This is a truly revolutionary moment where everywhere around the world you can develop your own state-of-the-art company. 00:12:10.320 --> 00:12:21.440 So, in fact, um we we started in Seattle and we're in the middle of moving to Singapore because we see the difference in worldview being so great between the United States, Southeast Asia. 00:12:21.519 --> 00:12:26.559 Well, we're gonna start an office in the Middle East so we can access Africa, and of course, India is right in between. 00:12:26.639 --> 00:12:34.399 So I anyone who's doing these things, I just encourage you to realize that it is unlikely that the way AI is deployed is gonna be the same around the world anymore. 00:12:34.639 --> 00:12:38.960 In the old days, you just take Microsoft's model and just import it. 00:12:39.200 --> 00:12:45.039 Today it's not that clear because maybe you don't have to have gigantic power grids. 00:12:45.279 --> 00:12:52.080 Maybe you can be more focused on PC infrastructure and micro generation and not to find a not find a 50 gigawatt plant. 00:12:52.399 --> 00:12:54.480 Yeah, I can see I've got an extra thing though. 00:12:54.720 --> 00:12:57.840 So um being an angel investor is a stupid idea. 00:12:58.320 --> 00:12:58.960 I know I am one. 00:12:59.200 --> 00:12:59.679 Yeah, I know. 00:13:00.000 --> 00:13:02.399 Um being an early stage VC is a stupid idea. 00:13:02.799 --> 00:13:04.240 Even any VCs here. 00:13:04.559 --> 00:13:05.200 Could be even dumber. 00:13:05.360 --> 00:13:06.080 Yeah, yeah, absolutely. 00:13:06.879 --> 00:13:09.279 Even late stage has got a phenomenal amount of risk as well. 00:13:09.519 --> 00:13:11.279 I've done that too, that's not so funny there. 00:13:12.399 --> 00:13:16.080 One of the things that so I did an IPO for my first software company. 00:13:16.240 --> 00:13:20.799 And I did an IPO because I wanted 50,000 euros to renovate my kitchen. 00:13:21.120 --> 00:13:34.960 And not a single investor, even though I had a company that was doing something, but not a single

investor would basically buy a little piece of my stock so I could so I had spent$340,000 and 18 months to do an IPO so I could have divisibility, divisibility of shares. 00:13:35.120 --> 00:13:36.159 As a founder, it sucks. 00:13:36.639 --> 00:13:37.600 Liquidity is hard. 00:13:37.679 --> 00:13:37.840 Yeah. 00:13:38.080 --> 00:13:48.320 Well so one of the things that I'm obsessed about, and I started a couple of years here in Davos, last year really got into, but this year is big, is secondary markets and pre-exit liquidity. 00:13:48.639 --> 00:13:49.679 Yeah, it's a big market. 00:13:50.159 --> 00:13:53.360 Because, I mean, if we look at this right now, what you're doing is stupid. 00:13:53.840 --> 00:13:59.759 It's just who on earth would say, like, I'm gonna bury the money in the ground and basically keep it there and I'm gonna hope it's okay. 00:13:59.919 --> 00:14:01.840 It makes particularly when there are instruments. 00:14:01.919 --> 00:14:09.840 So as I've been working, so my what I would love to do is free up hundreds and hundreds of billions of dollars of early stage capital. 00:14:10.080 --> 00:14:13.440 I had a few friends that put money into perplexity at an early stage. 00:14:13.519 --> 00:14:16.000 They're looking at what, 10 or 20x already. 00:14:16.320 --> 00:14:19.039 They are very active early stage investors. 00:14:19.200 --> 00:14:24.720 If they could exit half of their stake, they would have an extra 10 companies that they could invest in. 00:14:24.879 --> 00:14:34.639 And if they could invest globally, which means you don't have to pay San Francisco prices, you can get the Kenyan engineers doing the Kenyan model, this could free up hundreds and hundreds of billions. 00:14:34.879 --> 00:14:40.639 But you just have to have a few people think slightly differently on how they manage a cap table and what document they sign. 00:14:40.960 --> 00:14:56.960 So but but talk about that again from the perspective of these uh capital intensive improvements that we're talking about when OpenIAI talks about the the billions of dollars that they want to spend on data center build-out, which somebody else has to do for them. 00:14:57.200 --> 00:15:05.200 When you think about uh the energy system and like scaling these startups is is a whole nother animal, right? 00:15:05.759 --> 00:15:07.440 And so how does that fit into this model? 00:15:07.840 --> 00:15:14.320 Well, I think there are um I think AI right now is such a classic tale of two cities, right? 00:15:14.639 --> 00:15:23.600 There is the highly capital-intensive, we're gonna put, you know, for instance, the US, uh I think I read statistics like 80% of their capital expenditure is now data centers. 00:15:24.320 --> 00:15:25.440 So kind of amazing. 00:15:25.679 --> 00:15:28.480 Huge percentage of GDP growth is just data centers. 00:15:28.720 --> 00:15:31.360 It's not clear that is the model that's gonna work worldwide. 00:15:31.759 --> 00:15:33.440 So let me just construct a story.

00:15:34.320 --> 00:15:40.159 It's as though America decided, like in the in the age of it feels like it's a difference between canals and railroads. 00:15:40.320 --> 00:15:44.320 But we're just at the cusp where the first railroads kind of there, but they're kind of crappy. 00:15:44.399 --> 00:15:47.360 So it's much, much better to raise capital to make canals everywhere. 00:15:47.679 --> 00:15:49.039 Uh I actually have a different analogy. 00:15:49.600 --> 00:15:53.440 I have a different analogy because I just went to the defense thing. 00:15:53.679 --> 00:16:01.840 So for those of you who are history buffs, uh when World War II happened, everyone was building these gigantic things called battleships. 00:16:02.000 --> 00:16:02.159 Right? 00:16:02.320 --> 00:16:05.120 And they were amazing, they cost hundreds of millions of dollars. 00:16:05.360 --> 00:16:09.679 And then this uh, and by the way, a typical battleship then cost a million US dollars. 00:16:09.919 --> 00:16:12.399 An airplane costs about 8,000 US dollars. 00:16:12.720 --> 00:16:16.639 Turns out that an$8,000 airplane can sink a battleship in about 10 minutes. 00:16:17.360 --> 00:16:27.679 So I think it is much more analogous to say that the current way that America is building things has to do with the fact there's a huge capital base and you've got to use it somehow. 00:16:27.840 --> 00:16:32.480 It's not clear to me that that capital intensity is really appropriate for most AI applications. 00:16:32.960 --> 00:16:34.000 Let me just talk about one of ours. 00:16:34.159 --> 00:16:40.639 So we build advanced know-your-customer applications, anti-money laundering applications. 00:16:40.799 --> 00:16:48.240 Uh, we use very small models, we can run them on laptops, we don't have to, we don't have to buy anything from OpenAI. 00:16:48.399 --> 00:16:50.720 We typically can deploy them even on a phone. 00:16:50.879 --> 00:16:55.360 And uh it turns out you can have one or two software developers, AI architects, write the whole thing for you. 00:16:55.679 --> 00:16:57.200 And you can do that at scale. 00:16:57.519 --> 00:16:58.720 Uh that's what we do. 00:16:58.879 --> 00:17:04.079 So we actually have, uh because the university education system isn't there, we have our own internship program. 00:17:04.400 --> 00:17:07.920 And anyone here who's got a smart, who's once smart can apply. 00:17:08.000 --> 00:17:16.240 But basically we have an internship program that puts people through a six-month boot camp that teaches you all the business skills and technology skills. 00:17:16.319 --> 00:17:21.759 So you can literally, because this is the way our system works, you can literally write a business plan and then write the application. 00:17:21.920 --> 00:17:31.200 So you don't, you're not writing code, you're just saying, I need an application that can do this, this, and this, because it turns out most software projects fail because actually the business never made sense. 00:17:31.440 --> 00:17:44.640 So I think this is the kind of radical thinking that can happen in an undavo's world, because I can build a company in Kenya with four people and a laptop for 100,000 quid instead of 400 billion dollars. 00:17:44.960 --> 00:17:50.640 So I think that the unpart, you know, the uncola part of it is yeah, you can really think differently about building companies. 00:17:50.799 --> 00:17:54.559 I'll tell you, our company, you know, we'll be cash flow positive this year. 00:17:54.720 --> 00:17:58.240 We we've just done a small amount of angel funding, it just works. 00:17:58.400 --> 00:18:03.200 Because the thing is you can take the efficiency of this AI development and apply it to a company. 00:18:03.359 --> 00:18:05.599 So I think they're really two different models. 00:18:05.680 --> 00:18:14.079 So everyone time every time you hear someone saying we have to put a trillion dollars in, I just think about those big old battleships that people were building, and it could be the wrong move. 00:18:16.000 --> 00:18:22.079 So you see a future where these things are decentralized, they're pushed out across enterprises? 00:18:22.480 --> 00:18:22.880 Absolutely. 00:18:23.200 --> 00:18:29.920 I think that that is going to happen in big enterprises, but to talk about the and Davos part, uh, you know, I'm going to the African house later. 00:18:30.079 --> 00:18:33.519 It can happen anywhere, it can happen in Leeds, it can happen in Manchester. 00:18:33.599 --> 00:18:39.440 You know, this old world where you had to lift up your boots and go somewhere, that sort of ended now. 00:18:39.599 --> 00:18:44.880 Because you can actually, and for instance, uh in Wales, we're doing a whole training program for a big university there. 00:18:45.039 --> 00:18:46.559 We'll probably do it in Greece. 00:18:46.720 --> 00:18:49.920 And so this is much more approachable than ever. 00:18:50.400 --> 00:19:02.799 So, what would you either of you say to the critics who look at OpenAI, uh a chat GPT inquiry, ver the energy intensity of a chat GPT inquiry versus a Google search? 00:19:03.279 --> 00:19:07.599 Oh, I think that um I just give you an example. 00:19:07.759 --> 00:19:11.039 So we've built a sales, uh it's a sales engine, right? 00:19:11.119 --> 00:19:13.839 So it figures out which customers you want and so forth. 00:19:14.000 --> 00:19:17.920 And we first deployed this in the cloud model, and then we started shrinking. 00:19:18.000 --> 00:19:22.960 And we have a technology that you can take something that works for big and just keep making it smaller and smaller and smaller. 00:19:23.039 --> 00:19:27.839 And we thought we could hopefully get just technically speaking, have it run in a PC. 00:19:28.079 --> 00:19:33.599 So just to give you the numbers, uh OpenAI is probably one trillion parameters, it's vast, right? 00:19:33.839 --> 00:19:37.759 A PC model is about a hundred times less, about thirty billion parameters. 00:19:37.920 --> 00:19:45.519 We got to run in

1.7 billion parameters, so almost a thousand times smaller and a thousand times cheaper, and it will run on a thousand dollar computer. 00:19:46.000 --> 00:19:46.319 That's great. 00:19:46.640 --> 00:19:50.240 So it has no and and by the way, that computer burns twenty-five watts. 00:19:51.039 --> 00:19:58.079 So you've we've taken something that probably cost, you know, I'd estimate 10 to 20 kilowatts, and now you're 25 watts. 00:19:58.240 --> 00:19:59.599 So I think that's the engineering that's important. 00:20:00.000 --> 00:20:10.960 And you're pushing the the processing out of the data centers and down to the It turns out that it's in the computer science world, uh the expense goes like this, nonlinear. 00:20:11.119 --> 00:20:13.279 So if you only need a little bit of compute, it's very cheap. 00:20:13.599 --> 00:20:17.279 If you need a lot, it's very much, much more expensive than it should be. 00:20:17.519 --> 00:20:24.799 So you always want to move down as far as you can on the curve, and that's one of the things our our company TNE.ai does is try to figure out where the right part on the curve is. 00:20:24.960 --> 00:20:27.440 And when you're in deployment, that's really important. 00:20:27.920 --> 00:20:36.000 And it's a huge, it's a huge human behavior change for big enterprise systems that are used to being really centralized and understanding how those systems work. 00:20:36.400 --> 00:20:37.279 Well, here's the funny thing. 00:20:37.440 --> 00:20:47.279 If you look at uh if anyone who used their web browser today on their phone, what percentage of the computation for that web browser page do you think was in the cloud versus on the phone? 00:20:48.079 --> 00:20:52.480 Turns out when you're scrolling back and forth up and down, that's all on the phone. 00:20:52.799 --> 00:20:53.039 On the phone. 00:20:53.279 --> 00:20:55.119 All it's doing is squirting a bunch of data down. 00:20:55.200 --> 00:20:59.440 So if you ever wonder why any of these apps are so fast, it's because they're all running on the phone. 00:20:59.599 --> 00:21:07.759 So actually, our uh one of the ways to think about it is there's about 20 million servers in the world, and there's about six to seven billion dollar end devices. 00:21:08.000 --> 00:21:10.319 So all the computing is out there. 00:21:10.720 --> 00:21:11.920 I know we're almost out of time. 00:21:12.079 --> 00:21:13.599 Mark, did you want to add one more thing? 00:21:13.920 --> 00:21:14.720 I can always add extra time. 00:21:15.039 --> 00:21:15.519 Okay, all right. 00:21:16.079 --> 00:21:17.359 I I know he's in charge, man. 00:21:17.519 --> 00:21:18.000 I know we're going. 00:21:18.319 --> 00:21:18.880 He's in charge. 00:21:19.680 --> 00:21:22.240 I try I'm trying to be the good moderator, timekeeper. 00:21:22.559 --> 00:21:23.200 You're doing very well. 00:21:24.160 --> 00:21:30.559 So the um Last year we had a logo which is called It All Changes. 00:21:30.720 --> 00:21:33.680 Now I would have done the

same again, but it would have been a little bit lazy. 00:21:33.839 --> 00:21:52.480 So right now, I think because compute is relatively cheap, because the knowledge of making this stuff is basically everywhere, because there are so many module pieces, because money is not is is quite available, because there are lots of different countries, it means all of the enabling conditions to allow us to invent all kinds of stuff are all here. 00:21:52.720 --> 00:21:59.599 There are also some technologies like whatever nano atom by atom building of materials that might have been 20 years in research. 00:22:00.160 --> 00:22:01.039 It's also coming out. 00:22:01.200 --> 00:22:03.920 You've then got the quantum stuff that is also coming out. 00:22:04.000 --> 00:22:07.119 We've then got robotics, humanoid robots, everything else. 00:22:07.359 --> 00:22:14.960 I actually think the tech stack or the tech platform that we have is going to radically change almost every single business and organization. 00:22:15.359 --> 00:22:23.119 And what I'm looking forward to is there will be whatever, some VC brave enough or sovereign wealth fund, whatever, will say, you know what? 00:22:23.200 --> 00:22:33.599 I'm going to take somebody that used to run a Denon or used to run a Bosch or a Ford, and I'll give you 50 million to destroy that industry and rebuild it with all of the new tools. 00:22:33.680 --> 00:22:37.599 Not just like a little bit, like we're going to get rid of our SAP system and use something. 00:22:37.680 --> 00:22:38.160 No, no, no. 00:22:38.559 --> 00:22:44.160 But imagine that right now you have uh well you'll build the factory with humanoid robots at night. 00:22:44.400 --> 00:22:49.359 You'll still use the humans during the day, but at night you can get the human because they sit, they they can see in the dark. 00:22:50.480 --> 00:22:53.039 So right now, but that'll just be one piece. 00:22:53.279 --> 00:23:03.599 I think right now, I think me sort of my hope or expectation from a place like Davos is there'll be very forward-thinking people that'll realize that all pieces of the stack can be changed at the same time. 00:23:04.160 --> 00:23:08.400 And that means you don't need as much capital, or you can get much more expansion. 00:23:08.559 --> 00:23:11.119 So I think this is an incredible renaissance time. 00:23:11.599 --> 00:23:16.720 Now, I do think it's going to be a little bit tragic for the people that aren't going to be following along. 00:23:16.880 --> 00:23:21.839 However, from my view, on our phones that we have, we have the gift of knowledge. 00:23:22.000 --> 00:23:26.160 People could learn Korean, they could read Cervantes, they could do anything. 00:23:26.400 --> 00:23:29.839 The fact that they choose to look at Instagram videos is their fault. 00:23:30.079 --> 00:23:30.799 Just my view. 00:23:31.279 --> 00:23:34.640 They have the ability to do anything and they choose not to use it. 00:23:34.880 --> 00:23:41.519 So I do see though that those people who sort of can sort of rethink and rewire will just this is a phenomenal time to be alive. 00:23:41.920 --> 00:23:43.920 As long as you're on the top of the wave and not underneath it. 00:23:44.240 --> 00:23:58.000 Yeah, I was gonna say I'm gonna have to play devil's advocate because while it's exciting to see all of this technology get pushed out to something as small as a phone, the the robots that you're talking about, all of these things require electricity. 00:23:58.079 --> 00:24:00.400 They require energy in some form. 00:24:00.720 --> 00:24:04.960 And when it comes down to it, we're still boiling water to turn turbines. 00:24:05.200 --> 00:24:14.640 So to keep these machines running, we have some incumbent industries that require really um massive change. 00:24:14.880 --> 00:24:15.200 Absolutely. 00:24:15.519 --> 00:24:24.559 And and so that is, I think, one of the bottlenecks that we have to contend with to make this reality, this vision that you've described a reality. 00:24:24.960 --> 00:24:29.599 So at the um just sort of from the defense panel beforehand and connecting these things together. 00:24:29.839 --> 00:24:36.960 So I was having a conversation with actually with Bronte, and we're having playing a little game about um will you ever have city defense systems? 00:24:37.920 --> 00:24:41.119 So um Ukraine sent-cause we did in the Middle Ages. 00:24:41.519 --> 00:24:46.880 Well, and we're gonna we're we're moving to the dark ages back again, cities states, and everything else. 00:24:47.039 --> 00:25:04.640 But the logic is that so uh last year uh Ukraine Special Ops sent a truck full of drones, they drove the truck 2,000 kilometers, and then the drones just had to open up its roof, travel five kilometers, and it destroyed whatever, a quarter of a trillion a billion dollars worth of military hardware. 00:25:04.880 --> 00:25:08.880 If you think about it, all of our infrastructure is incredibly critical. 00:25:09.599 --> 00:25:12.559 There is no defense for any piece of infrastructure, basically. 00:25:12.799 --> 00:25:16.160 Particularly when you could just have a truck and drive it through. 00:25:16.480 --> 00:25:21.440 So right now I've been sort of playing the game, and this I mean, this might be when energy could change. 00:25:21.680 --> 00:25:32.319 Now let's imagine an actor decides, probably for a budget of whatever, one or two million bucks, to go out and take out pieces of the electric electricity infrastructure of the United States. 00:25:32.559 --> 00:25:34.720 How much would it cost to take out a dam? 00:25:35.599 --> 00:25:37.200 Two million bucks, maybe four? 00:25:37.519 --> 00:25:38.720 Go after the power lines. 00:25:38.960 --> 00:25:40.799 And power lines are cheaper to get out. 00:25:41.119 --> 00:25:45.359 Well, it's the okay, so so right now then, at the moment sorry, I really love military history. 00:25:45.680 --> 00:25:52.799 Well, and we are living, I mean, right now, the fact that maybe having

tariffs will allow a country to take over Greenland. 00:25:52.960 --> 00:25:53.920 Maybe it won't. 00:25:54.079 --> 00:25:59.119 But we're living in a war, like I think warfare is going to be the next topic for this next Davos. 00:25:59.279 --> 00:26:06.880 But then it means then that our in energy infrastructure, if we look at let's say, how we then begin to have changes, war is a way that you have a lot of changes. 00:26:07.039 --> 00:26:08.559 Ukraine is already seeing that now. 00:26:08.799 --> 00:26:13.920 At the moment, there's massives of complacency that everything will just be the same and maybe 2% different. 00:26:14.079 --> 00:26:20.240 Right now, when we see pieces of these infrastructures dramatically changing, it will force through changes, kicking and screaming. 00:26:20.480 --> 00:26:28.559 And if not, quantum computing will take the board of directors, it will find out all of their emails, visa, card transactions, and everything else, and that will happen. 00:26:28.720 --> 00:26:30.000 This it'll be crazy shit. 00:26:30.160 --> 00:26:31.279 Like great function. 00:26:31.759 --> 00:26:32.799 Can I make one prediction for you? 00:26:33.039 --> 00:26:33.440 Yeah, sure. 00:26:35.039 --> 00:26:38.160 Um I think now just to follow up on your point. 00:26:38.240 --> 00:26:42.640 I think everyone now knows that food security, energy security are vital. 00:26:42.799 --> 00:26:48.960 I think that every country and every place needs to think a little bit about their AI sovereignty now. 00:26:49.279 --> 00:26:53.839 Because the, you know, the easiest way to And a tech technology sovereignty. 00:26:53.920 --> 00:26:56.640 I mean, I wouldn't limit it just to AI, but technology. 00:26:57.119 --> 00:27:07.599 Because today there are countries that can just be completely shut down with an executive order from somewhere that just says you can't use data center X or you can't buy this critical piece of hardware. 00:27:07.759 --> 00:27:09.759 So to me, these are the big issues now. 00:27:10.079 --> 00:27:17.759 It's not just uh defense or war, uh, but it is also about do you how are you self-sustaining on how you educate people? 00:27:18.000 --> 00:27:18.240 Right? 00:27:18.319 --> 00:27:19.200 That's one big thing. 00:27:19.519 --> 00:27:27.599 Where you get the power, and being in, I do think, just so you know, I think this multilateral uh cooperation is gonna be very interesting. 00:27:27.680 --> 00:27:51.359 I'm going this afternoon to the you know the the new renaissance of Europe summit, which is all about how Europe, where are its dependencies, should it develop its own uh I'm gonna use the the new US term war technology since we no longer have defense technology, war technology, should you use your own, should you have your own data technology, semiconductor technology, AI technology, how independent are you? 00:27:51.440 --> 00:27:53.039 Because from independence comes strength. 00:27:53.200 --> 00:27:53.359 Yeah. 00:27:53.599 --> 00:27:56.559 So these are I think the issues that I'd love to see next year when I'm here. 00:27:56.960 --> 00:27:57.359 Totally, absolutely. 00:27:58.960 --> 00:28:16.960 If we need to sum this up, we're talking about if we're s if we're as we're doing that, um, the solution spotlight for AI enterprise solutions to really work, we need to consider education and resilience, is I think what you uh where we're concluding this conversation. 00:28:17.440 --> 00:28:22.880 I mean I would say the message is you need to have an you know, this is where small modular reactors are great, right? 00:28:22.960 --> 00:28:25.599 You need to have enough energy independence so you don't have to worry. 00:28:25.759 --> 00:28:32.319 You have to have enough a strong enough education system so you can move people together, and then you have to have enough um uh technology. 00:28:33.200 --> 00:28:34.079 Technology sovereignty. 00:28:34.160 --> 00:28:42.480 So are you using a stack that is primarily one where no one can say, I'm not gonna give this to you anymore, and your entire country falls apart. 00:28:42.960 --> 00:28:43.039 Okay. 00:28:43.359 --> 00:28:47.039 I mean another thing as well is that unfortunately there are no three things that will help. 00:28:47.200 --> 00:28:48.160 There's 50 things. 00:28:48.319 --> 00:28:55.759 And I think right now there is the we have a habit of simplifying things so in our poor, stupid human brains we can handle stuff. 00:28:55.920 --> 00:29:03.359 Now already AIs can do it better than us, because even like your basic chat GPT or even your phone version can handle 50 things at the same time, which we can't. 00:29:03.599 --> 00:29:09.599 The thing is that if you look at all of these challenges that we're working on, they all require at least 50 things to be worked on. 00:29:10.240 --> 00:29:10.799 At least. 00:29:11.039 --> 00:29:16.559 So right now, whilst I would love it to say, oh, here are the three secrets, it's obviously the first thing is that there is no three secrets. 00:29:16.640 --> 00:29:17.920 Yeah, it's complicated. 00:29:18.559 --> 00:29:28.480 The human behavior aspect of it does require, though, we find some ways to simplify it and keep it focused on a solution set that people can wrap their minds around. 00:29:28.880 --> 00:29:31.200 When the machines take over, we'll find out if we were right. 00:29:34.160 --> 00:29:35.759 Thank you very much for hosting this conversation. 00:29:36.160 --> 00:29:36.240 Great. 00:29:36.480 --> 00:29:37.279 Are we taking questions? 00:29:37.519 --> 00:29:38.160 Um I guess so. 00:29:38.240 --> 00:29:38.880 It's uh up to you. 00:29:41.519 --> 00:29:43.920 You can just get machine guns, you don't have to get submachine guns. 00:29:44.079 --> 00:29:45.519 Just go for the full assault rifle. 00:29:48.079 --> 00:29:50.000 Well, so I live in Berlin. 00:29:50.240 --> 00:29:59.200 Berlin had no power for two days because a bunch of left-wing activists decided to uh basically take out uh the sort of power substation. 00:29:59.359 --> 00:30:02.480 So right now, our infrastructure is completely fragile. 00:30:02.960 --> 00:30:05.519 It has zero defense, zero. 00:30:05.920 --> 00:30:10.640 And for some reason, the people that are in charge have decided not to do anything about it. 00:30:10.799 --> 00:30:12.079 It is incompetent. 00:30:12.319 --> 00:30:13.599 Generally, it's incompetent. 00:30:13.759 --> 00:30:22.880 But anyway, but it's the I'll we have a couple of uh by the way, um for those people who are expecting to speak still, if you put up your hand, Max, you want to Okay, yep. 00:30:23.359 --> 00:30:26.079 And then uh I think we had three more people to uh to fit in. 00:30:26.640 --> 00:30:26.960 Excellent. 00:30:27.200 --> 00:30:27.440 Very good. 00:30:27.680 --> 00:30:33.119 And and by the way, and I realize that some people have problems traveling in, so that we have to be a little bit flexible for closing in. 00:30:33.200 --> 00:30:35.440 So it's the I guess very good. 00:30:35.680 --> 00:30:37.920 Well, uh with that, I think we're wrapping up. 00:30:38.079 --> 00:30:38.799 Thank you, Richard. 00:30:38.880 --> 00:30:39.440 Thank you, Mark. 00:30:39.599 --> 00:30:40.319 This was a great conversation. 00:30:40.400 --> 00:30:40.799 Thank you very much. 00:30:41.359 --> 00:30:42.160 Thank you, thank you. 00:30:42.480 --> 00:30:43.440 Thanks for wondering. 00:30:44.000 --> 00:30:45.440 Good job, good job.