Dr. Christian Guttmann on the Coming Tech Tsunami – and How Leaders Should Ride It

\[00:00:00\] **Johan:** Christian Guttman. Welcome. One of the things that really interests me with, uh, with your role is that you're, you're kind of a hybrid player in the, in the, uh, AI space, and I'm really looking forward to speaking to you because you have a super deep academic background in AI with like PhDs from way back when AI wasn't the gen AI that we know today.

You're within the startup and, and kind of investors scene, and you've been in, in that scene in, uh, Silicon Valley as well and in the US and you've been part of, of like global leadership roles. So you've seen kind of all of the faces of ai. Can you tell us a little bit how come you ended up in this space and what's kind of interested you there?

\[00:00:40\] **Christian:** Yeah. Um, the space of AI is very fascinating, has always been very fascinating from my young age. You know, when I started programming AI systems, you know, I was like 1340. My first systems were like. Doing in intelligent databases and, and searches and so on. So, very fascinated. Also basically inspired by science, sci-fi, science fiction, you know, like, uh, everything from Star Trek and Knight Rider and, and those types of movies, which many, uh, in the community, in the AI community in the early days.

I think were very inspired by having a intelligent system that you could interact with, you know, that would be your companion, that could give advice that is smart and has an awareness about the world. So that, that really brought me into it. And that has been sort of my line, like first, um, initially really just understanding it and that's why research, uh, attracted me a lot.

'cause it's a systematic way of understanding how the world works and how you can build and engineer AI systems. Uh, and then. Over the last 15, 20 years, more and more into, okay, how does it impact the world? How does it shape society? How do you create value? How does it, uh, give real business impact and, and value to individuals?

So that, that's really what is a, is a red line that, uh, threats through all those experiences. Uh.

\[00:01:58\] **Johan:** Coming back now to Europe from, from the States. What, what are the, the kind of big differences in mindsets around ai, do you feel?

\[00:02:05\] **Christian:** Um, I guess. Two or three of the main differences is, uh, that the Americans have been part of that game for a very long time. Uh, they have invested and have been building companies back in the days, everything from Hewlett Packer to Microsoft in the seventies, eighties, uh, during which Europe was not developing as fast in the space.

And there might be historical reasons for that, but we haven't been part of that initial root movement, if you want to call it. Hmm. Even though we had pioneers like touring and others. But unfortunately, we sort of were moving away from that initial part. And then of course, with an exponential development in the computer industry, uh, by large, you know, we are, uh, now not quite on par, which we see in practical, in a practical sense with all the tools and the services that we currently, um, buy, you know?

Yeah. Uh, and there we have the, the biggie that come out of the, the us So that's one. Another one is I think the American and the American, the US American culture is, um, is. Very far away ahead. I'm very mature when it comes to entrepreneurship, which for me means you have a very daring, risky, disruptive positioning of your mind.

Like you think you can build big things with big impact, and therefore you deserve to have a big investment. Hmm. That's sort of the mindset that I've experienced, um, from entrepreneurs, investors, and I had so many discussions also in the Valley as to why the valley is what it is today. So in that sense, that's certainly very unique feature and the development that the Americans have, have built up.

Um, and um, I think that's sort of probably the two main ones. It's, it's the, the risk and the openness to innovation. Hmm. Uh, I think that's a, that's a big difference.

\[00:03:52\] **Johan:** And it's interesting because we, uh, when we record this is, it's just a day or two after they released, uh, this year's, uh, Nobel Prize and this year's price for, for economics goes.

I haven't fully understood it, to be completely transparent, but the, the, um, disruptive, uh, innovations, right? The what do you know about that?

\[00:04:14\] **Christian:** Yeah. Uh, it's, I think, uh, creative destruction. Creative

\[00:04:19\] **Johan:** destruction.

\[00:04:19\] **Christian:** That's right. Yes. Yes. And, um, yes, so. In fact, I have been myself and my approach towards innovation, uh, has been shaped by, uh, by let's say research and experiences from the innovative Styler by Henry Christensen, for example, and also Joseph Schumpeter, whom in fact has influenced now the current Nobel Prize Hmm.

Uh, winners, uh, in their thinking of how can you build an ecosystem that's innovative? And, uh, Joseph Schumpeter, for example, basically suggested, oh, we need to creative destruction. We need to remove the old in order to make space for the new. Hmm. Um, and that is something that, uh, you know, um, societies, uh, have in the US have basically really absorbed Yeah.

And have been taken, um, uh, seriously. Uh, one could say that in Europe we have a bit more of the attitude of keeping the status quo, so keep stability basically. Yeah. And uh, that's something where we haven't been, uh, really forward looking in that sense.

\[00:05:23\] **Johan:** And I think it ties into how do you typically approach AI as a, let's call it a non-technical European company. Mm. That you could kind of see, like the first level of, of AI implementations are, are really just about kind of slapping AI onto existing processes. And it's, it's really a bet on, on automation and, and you might see that five, 10, 20% improvement in productivity and it, and it shows up in the short term, which is really good for the CFO.

We don't really take that step back and really think of what's fundamentally possible now, that wasn't possible, uh, a year or two years ago, where I think. I will see that the, the players that get the most value out of ai, they'll probably think more in, in creative destructive terms.

\[00:06:13\] **Christian:** So, I mean, you're right.

There are basically two ways you can apply AI to business. Um, one is you, uh, look at your current operations and make them more efficient, cheaper, um, faster and less risky. And that's very valid. It's absolutely fine and everyone is looking at it. That's great. The other one is to look at the, um, how do you improve the top line?

How do you build new products? How do you, um, change the market? Right? And generally speaking, um, there has been more emphasis on the top line, the second, uh, part in other places, uh, in, in the world right now. Uh, um, so that's one. However, I mean, speaking of all the strength and the momentum we are building up in Europe, uh, uh, we have, and I believe we are now entering a more industrial. AI space, which means the absorption of ai. And I think it is perfectly fine to start with internal optimizations, making sure that you don't forget what you can do the on the external side. Hmm. And then of course, most many businesses in Europe have a certain core product. They have a, a certain core offering.

And focusing on that as being your core business, uh, and improving the product quality, the speed at which you develop it, it make, makes perfect sense. So that's, that's fine. And I think, and believe even Joseph, uh, Schumpeter suggested and many of the Nobel Prize, uh, winners now also that the startups are essential to the ecosystem.

So we need to give those new startups in Europe also space to grow. Mm-hmm. Um. Not too many rules to follow, to be honest. Uh, uh, of course it have has to be regulation. It needs to be all, um, um, well organized. Um, but there needs to be also enough breathing space. Mm. Um, 'cause we see an exodus of, uh, startups, uh, to the us.

Um, and that is not something we want to see more of here.

\[00:08:02\] **Johan:** And No, it's interesting. So, so if we look at the Swedish example of, of lovable for example, they obviously get 200 million US dollars from an Silicon Valley investment fund. Excel. Yeah. Yeah. And obviously they'll start to, to recruit a lot of US talent, put more operations there, even though I, I don't know the specifics.

They might keep the head office in, in Stockholm, but

\[00:08:22\] **Christian:** Yeah. Yeah, exactly. So, um, yes, it's true. And I think just to be honest and, and, and factual, it's also true, uh, that. Lovable and many Swedish companies are in fact American companies. Right. In what way? In what? Because they're registered in Delaware, the headquarters are in Delaware.

Hmm. So, which makes them technically American companies, and I don't want to, I I'm, it's just fantastic. Right. I met Antoine, so thumbs up. Good for, you know, great for doing, and obviously we have other fantastic entrepreneurs like Daniel Egg and Klarna, Sebastian and, and others. Right. But let's also be honest, just factual, right.

Uh, we would want to. Uh, the decisions that were made also by the investors is to register and move to the us, which is, which is a very natural way of looking at what are the best living conditions for a company. Hmm. So if the answer is us or another place in the world or in Europe, then that's the answer, right?

Which only means to me is that we have to have better living conditions in Europe, in Sweden, for companies to stay here.

\[00:09:24\] **Johan:** And how much of that is like regulation? How much of that is, uh, access to capital, to talent?

\[00:09:29\] **Christian:** Yeah. I, I think one big part is just we need one big market. Yeah. In Europe, if you have that, we have solved a lot of my humble opinion, which is in very practical terms, you have a business registered in Sweden or in Lisbon or in Zurich.

That business can operate. Switzerland is, of course, not only eu, but you know, but that business can operate across Europe. Uh, it doesn't have to go through hurdles in every country to, you know, register again, get certifications, licenses, uh, regulatory frameworks. So that's one. Um, and also for talent, talent movement, right?

As it stands, if you are getting a visa in the Netherlands, you cannot move to another country quickly. So that's certainly something that's, um. That's something that we can really improve on. Uh, of course the other part to some extent is also the investment, the ticket sizes. Yeah. When the level will got 200 million now, um, that's exceptional.

That would be an exceptional investment for a, uh, European, uh, VC fund. Um, these are big numbers. I speak to many VCs. Some of them as, uh, say money isn't everything, but it's certainly, um, a useful ingredient, you know, of success and, uh, but, but I think we would do all, all of us a big service by creating one, um, bigger European market.

Hmm. Both for investment, both for operations and for, uh, deployment of products basically go to market.

Yeah. Is a very important,

\[00:10:53\] **Johan:** I know you speak a lot towards investor groups towards C-Suite type audience. What are the kind of main things that you think that especially European or Northern European CEOs or investors are kind of missing around a ai

\[00:11:12\] **Christian:** I think one aspect that also struck me as perhaps something that certainly is remarkable from a cultural perspective is the technology knowledge that's expected from a business leader in the us. Hmm. Uh, I speak in general terms, right? Yeah. But I would, I think it is very beneficial for a leader, uh, A CEO or at least a C-Suite and the board to have a very technologically business savvy, um.

Experts. Yeah, because, uh, I mean, I myself have experienced it heavily being a vice president at a large enterprise AI company in the us for example, uh, the expectation of me to lead. Hundreds of my employees is one thing. Uh, the business executive function of building close relationships to, uh, to customers is another.

But for me to also have a very clear understanding of how the technology works Mm uh, is extremely important. 'cause otherwise it will become so intangible. Yeah. Uh, to know what the technology can do, where its limitations are, yeah, how can you, what can you do? What can you not do? How fast is it? Uh, you want to, how to say, be not surprised about moments like the deep seek moment that we had about half a year ago, right?

Where suddenly people said, oh, do we even need data centers? Which is surely, uh, on the, on the mind of many more, um, people in Europe now, uh, whether it's politicians or leaders of companies, do we need a data center? How will that data center look like? Uh, how much should we invest in it? That's often on the top of the agenda of many, um, leaders that I'm speaking to.

And the question is also, will that be necessary? Hmm. So in fact, if there were, were new algorithms and you knew the technology and you, and you knew there would be more deep seat moments coming, uh, it, the answer might be no. Maybe our data centers need to be much smaller.

\[00:13:09\] **Johan:** No, I think it's super interesting that that point that you make, that it's almost.

'cause I shared the experience, it's almost impossible to have a strategic discussion in the management team around how AI impacts our business, unless you have a fundamental understanding around ai.

\[00:13:28\] **Christian:** Yeah, yeah. And they have great examples in, in Europe, by the way. Have, has a heads off, uh, uh, for example to, um, Ilma, who is the chairman of Nokia and, uh, CEO of F Secure.

Mm. Uh, so he is known, he wrote, by the way, a great book called Per, um, uh, paranoid Optimism. He did the, he bought, uh, he sold, um, Nokia part of Nokia to, to Microsoft. But, um, he's one of those people that indeed went to Stanford and did a two, three, uh, week course in machine learning. So went back to the, you know, back to the, uh, school bench, so to say.

Yeah. Learned AI from scratch. So I think that that's a really important. Hmm. Attitude. Hmm. Uh, to understand what the technology can, can do. And when you look back our heritage in Europe, when you look at the Siemens or Ericsson or other, uh, old companies, uh, the, the founders were engineers. Yeah. They were technologists, they were scientists, you know, and that's where the deep knowledge came from, where they were able to build products which were superior, competitive.

And, um, and, uh, so, and by the way, I'm, I just also want to contextualize, I think it's. Obviously we all need each, each other in the world. I think it's great that we have a strong Asian market in ai. We have a strong American market in ai. Uh, but both of those parties will also benefit of Europe being very strong.

Mm-hmm. So, I'm not saying, you know, that for me it's more like a, let's say a a, a friendly but very rigorous competition in which we want to be at the very forefront. We want to run really hard here to make sure that we are, uh, also at the forefront and we can have and share our voice and, and, and set the tone.

And set the speed.

\[00:15:09\] **Johan:** And I sp, uh, spoken to, to multiple. People on this podcast on, on what are really the hurdles for, for European AI to become better than it's, and, and there are multiple opinions ranging from regulatory access to capital, or not necessarily access to capital, but very fragmented capitals.

None of the really big bets that you have in the us mm-hmm. Access to data was a big one. Um, what is your kind of view on, on how come we, we aren't better at developing the, the kind of foundational models here in Europe?

\[00:15:40\] **Christian:** Um, so a couple of points here. I think one, um, we have. I would point to some companies that have made some very extraordinary and great investments.

Um, they built the European, um, ecosystem, for example, A SML, right? One of the big players that in fact are supplying the NVIDIA and other microchip, uh, uh, uh, producers with their technology. Mm. That as a European, it's a Dutch company, and they have made an investment in, uh, in opening in a mistrial, uh, which is a great sign of it being an European investment.

It's a very unusual investment also for a, uh, for A SML, which is, let's say, a little bit more conservative, right? So there's some good things happening here. Um. Um, I think overall I wanna, I would like to speak more of the many positive things because I think momentum is building up, but if you look at how, uh, uh, certain things are still maybe could be better, let's put it this way.

It is the concentration of, um, capital and the, um, the risk appetite, the also the knowledge and the experience. Uh, again, um, there are many examples of family businesses in Europe, which have made big investments, uh, but maybe they're not big enough and not enough. Let's say classic family businesses are investing more.

Uh, so examples are Schwarts in Germany, Berg and Sweden. Mm. Obviously great investments, but, um, many family business of that ca caliber, uh, still invest in, let's say brick and mortar. Mm. That's how they. Built their fortune. It is something that's much more predictable. Um, and it's very understandable too.

It's a little bit, if I made simplify it a little bit, it's a little bit more on the Warren Buffet style investment, which is perfectly reasonable. Obviously Warren Buffet really well. Uh, but of course that's something, uh, where I would like to see more investments in. And that also requires investors to be educated, uh, about what these new technologies can do.

\[00:17:52\] **Johan:** Yeah. 'cause they face the fundamental same problem as the CEO does, unless they understand the, the potential. They, they won't see the potential.

\[00:17:58\] **Christian:** Exactly. Right. Exactly right.

And another thing I want to say, and I haven't concluded that thought yet, but what I'm seeing also with the US and with, uh, Europe.

It's, it's obviously a game of, um, mathematical game of probability. So there is a tendency for, uh, Americans to really pick the winners and give them far more, they doubling down on the winners and ideas similar to those of Amazon or others. They, there would've, there were hundreds of companies, similar style, but of course, um, many of them weren't successful.

And that means many very unsuccessful entrepreneurs Yeah. Uh, have a much harder life now in, in the us uh, in Europe, we even it out a little bit more. Yeah. So we might be putting money in five companies, which become medium big if you're lucky, but they will also be less extreme losers. Right. So I'm not sure if we want to embrace it, but if we do not, it also means.

We may not have these very large companies, which are as, as associated to high risk and high return, uh, environments basically.

\[00:19:09\] **Johan:** And what do you think is the second order consequence of that? If we look at it and we assume that that's true. So, so the European or the American or, or the Chinese market, that they'll have the, the massive players that are funded at a scale that we can't compete with in Europe, or we choose to, uh, to not compete with in Europe.

\[00:19:27\] **Christian:** Yeah. Yeah. Okay. So I see like two things now. So one, um, many speak of, um, sort of AI bubble now. It's a very, uh, you know, uh, interesting topic that many people speak about because obviously the valuations of some of the big companies are huge. Mm. Question is, will the trailing pe um, um, you know, price earnings ratio in the future continue to grow?

Mm. Meaning will, uh, the Magnifi, uh, magnifi seven and other AI companies continue to. Basically double every, every year. Mm. Which is massive, right? Mm. It's already trillions of dollars a companies are worth. Yeah. If they continue to grow that way. We are speaking of companies that are massive, 20, 30 trillion.

Yeah.

Right. I think the likelihood, uh, there will be something happening in the next one or two years. That's number one. The, um, if they do succeed, then it'll, of course be a very, very superior, huge companies having a massive influence on everyone else. Hmm. I, I mean, in some sense you will then have companies which, you know, like, uh, BMW or, or, uh, Siemens or, uh, Ericsson, which will be very cheap in comparison.

Mm. They could either be gobbled up or they could be very controlled by each. I mean, this is a little bit like now thinking ahead. Yeah. If you continue with massive growth, that will be long-term consequences. Uh, the other one is of course, like an AI bubble. Happens, right? Like it's, it's brick, so it, it, what is it?

It bursts, right? Yeah. And so we have a deflation and then there's a chance for companies to reestablish themselves, European companies also to look at, you know, uh, buying, uh, potential AI companies that have deflated evaluation and then basically piggyback on that and, you know, uh, make those AI companies part of their own organization.

So that would be,

\[00:21:15\] **Johan:** it's interesting 'cause it all kind of ties together. One of the things that I've been been spending time thinking about is, I think there, there's probably no doubt that AI will create, if you just look at growth as a GDP number, we'll probably improve GDP. And we, we can talk about robotics and such, uh, as well in the future, uh, or in further along in the podcast.

But the question is also distribution of, of that wealth. Like how do we ensure, 'cause I, I don't think that we're, we really want to build towards a world where we just continue to, to concentrate wealth in, in the top. 0.1%. It's typically not good for a society, but that, that's kind of what I get from, from your extrapolation of the question of mm-hmm.

Of like this mega giants, they just continue and they gobble off the B BMWs of the world and all. Yeah, yeah. All of that. Do you think that's a, a likely future? And what can we do to, to kind of,

\[00:22:07\] **Christian:** and when you say, uh, top 1% concentration mean individuals or companies, or both?

\[00:22:12\] **Johan:** Well, if you look at Elon Musk, it's fundamentally the same, right?

\[00:22:15\] **Christian:** Yeah. Yeah, yeah, yeah.

And what was the question again? How I see this being like,

\[00:22:20\] **Johan:** is there an alternate route where we can get benefit of all of the growth mm-hmm. And, and the economic potential within ai, without necessarily just defaulting into, to a economic concentration of power. Hmm.

\[00:22:37\] **Christian:** That's a big question.

\[00:22:39\] **Johan:** Yeah. Isn't it? But it, but it's also very relevant. Like how can I, okay. So I'll, I'll give you my 2 cents on it and then we can go from there. Mm-hmm. So I think the choice in terms of how we implement, 'cause this podcast fundamentally is targeted towards leaders in, in businesses that are not probably AI businesses.

Mm-hmm. I think we have a choice right now in, in how we look at what do we do actually do with ai. So if we only limit what we see because we have limited education or insight or technical training or whatever in, in AI's potential. So we slap on AI onto legacy processes and we aim for five to 20% improvement of, of some type of EBITDA number, productivity number.

Yeah.

And then we kind of call it good.

\[00:23:23\] **Christian:** Yeah. Yeah.

\[00:23:24\] **Johan:** And back to, to your idea of, of, uh, like how. Fundamentally, do we think differently around innovation in Europe? We're, we're a little bit more risk averse. We, we don't think really big, but I, I really think that that's probably what leaders should do. Like a complete reimagination i, I know it's a big work to do, but fundamentally, how can we transform this business that we're currently in? Yeah, might be in a low margin business. We have high personnel cost. How can we fundamentally think differently on how we deliver value to customers and build the, the probably harder path. But to me that would enable more companies to fundamentally, you don't have to be the infrastructure builder to really capture a lot of the value in Canada, of the distributed way of, of European companies.

Right? So I think the choice is really down. To all of the leaders in all of the companies. Like, you really need to think about this now. Mm. And what's dangerous is like, it's a fast game and we, we have a tendency to not understand exponential curves. Like all of a sudden you'll have competitors that are, are out competing your own price out, competing your own speed.

Yeah. Out competing your own quality all at the same time. For sure. And that's kind of dangerous. And they'll just start popping up and, and like they're, they're building now while, while you're kind of deliberating. Where's the ri?

\[00:24:39\] **Christian:** Yes. Yes. A hundred percent. And I think, so a couple of thoughts go to my mind.

So, uh, one is when you look back of how technology was adopted and how it changed industries, um, dot com was a good example when the internet became prolific. Hmm. Uh, it were people like Jeff Bezos and others that understood that becoming and using the technology of the internet did not mean. You will be, you are a standard legacy company and you have a website and now you're part of the business.

You're done. Yeah. That's not how you transform. Mm. That's not how you read the benefits. Mm. Of a new technology Similarly. Uh, and by the way, so how do you do it then? Well, Amazon and other companies went many levels deeper. Mm. So obviously it's, yes, the internet and websites and email and so on are something you see on the forefront similar to Che, bt, et cetera.

But what really, what you really need to think about is supply chains, chains, uh, chains. You need to think about talent. You need to think about go-to market strategies. That all changes. And that's the depth of it. So the, the front end, if you like, the website or the Chet GPT, it's like the tip of the iceberg.

Yeah. And the, the real changes occur behind the scenes. If you really wanna reap the benefits and restructure your organization, help your workforce, uh, build new products, you need to go many levels deeper. Right. And take this really serious. And I, I remember I was, uh, back in those days in Tokyo, I was in Dubai.

And many companies, you know, they, they did not really, that was 2000, 2001. Some big music companies, I don't wanna mention their names, but they basically haven't really, they haven't really changed their thinking at all. I mean, they, there were many stories of people saying, Hey, have you guys used Google?

Right. That was like, obviously already an old technology for most people that were truly, uh, in the, in this new technology trend. So, so it's, I think it is extremely important to see how you move beyond the tip of the iceberg and start thinking what structures are, are changing and similar now with the LLMs and with chat PT and so on.

Yes, you can look at your existing workflows and processes in your organization as an example, and then pepper a little bit of AI on it. Um, maybe you make certain processes a little nicer and faster, but you will not see wic. Changes, uh, in the organization that get you to a level where you will, uh, stay competitive, uh, where you have a good chance to survive, uh, or where you will ideally become even more successful and and bigger.

Yeah. So I think you need to have a very serious angle at that right now, uh, in order to be at the forefront of this, uh, tech revolution.

\[00:27:36\] **Johan:** Yeah, absolutely. And it is so well laid out, and I completely agree. I do think part of the. Problem doing this in, in kinda a quarter economic space is that that is probably a multi-year investment before you really see the istic returns and it's so tempting to kind of go for the cheap automation place.

Yeah. 'cause you see them on your bottom line next month.

\[00:28:00\] **Christian:** Yes, exactly. And to be fair, I mean, look, I, I have been. In leadership. I've been a CEO and I speak to many CEOs. The, it's not, it's not an easy task. In fact, I know many CEOs, uh, that are very, very keen. They see the potential of, of ai, uh, often, and I've been talking about it a lot in the past.

I've worked with, companies have seen transformation, but it's often the culture. And it's a speed of adoption, right? So, uh, and that means people, you know, uh, it means, uh, the existing, uh, work processes. It means the love and the affinity you have with the existing products and the existing processes. Uh, that's something that, that you really, really need to work with if you want to introduce ai.

And that's, I think, a lesson learned already years ago Now, I think any serious business that just builds an AI team with, uh, only AI technologists, which by the way, I should also highlight. So I'm not suggesting to have pure technical teams. You need to think about change management. Hmm. You need to think about it as being a.

Startup within your business with having a go-to market inside your business, making sure that the, uh, consumers of the technology are constantly convinced and you're con continuously improving the inside product, if you like, so that you drive that, that value forward.

\[00:29:19\] **Johan:** Yeah. And I think you underestimate the, uh, the value of the storytelling around it.

Mm. Because if we, uh, again, if we only look at, so, so a lot of companies, they struggle with AI adoption. Mm. Um, and if you look at what we present from the C-Suite, for example, mm. Is fundamentally an automation play. You don't have to be that smart as the employee to say that adoption means, uh, I'm automating myself away.

Mm-hmm. So what I'm lacking a lot is that, okay, in five years time, how's this fundamentally a, a company where you can excel as a human? What's that story like? How, how does technology. Plus human makes something absolutely extraordinary. Mm-hmm. Yeah. And those type of visionaries, I'm, I'm like, those stories, I'm, I'm not hearing them.

\[00:30:07\] **Christian:** No. And that's exactly the point. Like, in my mind, it's not about, I don't think people will, uh, lose their job unless they don't change at all, and they want to stay precisely where they are and do exactly what they have done the last 10 years. That's always a dangerous move. Right. But I think what, uh, what makes a lot of sense to me is no doubt that if you, what, what happens is you can become five times more productive, or let's just say 50, you know, a hundred percent more productive.

That's already a humongous change. 20% more productive is a, is about, but the, the, the difference is, okay, you do certain tasks today as an employee or in any function you can imagine, and now you have a new tool, new processes, and your productivity should. Become much faster. So you, you, you have basically, I, I don't like these superlatives, but it is a little bit like a, a super worker.

Mm right. Someone who is able to produce code, um, much faster. Someone who is able to, uh, do uh, uh, pre-screening of customers. Much faster. Hmm. Uh, someone who, from an HR perspective, can screen onboard, um, uh, new employees much more efficient. Right. So, so you increase the quality just as with other technologies that we had in the past, whether it's the internet or electricity.

Yes. Of course. Immediately you might think, oh wow, you know, like these types of jobs will disappear. Yes, in their original form they might disappear, but the transition into a new type of work, um, is what you really should aim for. And I think that's really the responsibility for leadership and for the board and for, for the company really to look at that aspect.

And I agree, if some companies talk about it, um. I'm not sure. It's a little bit, if I may say a little bit distracting, if some companies say, Hey, we don't employ any more people, uh, we, we, we have a whole lot of AI doing the work for you. It's a little bit distracting. May partially be the case, but I think, uh, more important is how do you integrate it?

How do you make it a harmonic interplay between the humans that have unique skills and the, a ai that has other skillset basically acts as a magnifier of all the skills that a human has.

\[00:32:23\] **Johan:** Yeah, a hundred percent. And I, and I think from, from the humane perspective, it's not only about you can be. Twice as productive.

Mm-hmm. It's also how can we ensure that you have a working environment where what you do on a daily basis is fundamentally something that you enjoy doing. Mm. Like you're being more creative, you're being more in contact with other humans. And how do we, when we break roles up to tasks, how do we think about what tasks do I fundamentally don't enjoy today?

Mm-hmm. Mm-hmm. Is focus on, on getting those to the machines. Mm-hmm. And the things that I do love. Funny. I think it's so funny. Uh, like a, a year ago or so, it went round on, on LinkedIn. Like, uh, I was expecting when the, when the kinda machines took over that they would do the Yeah. Taking off, uh, of the dishes and doing the cleaning.

And I do the creative work and we're kind of in the opposite right now. A lot of the creative work of the writing and mm-hmm. And the, the kind of, uh, strategy processes, whatever, we delegate those to ai, but we still do the dishes, right? Mm-hmm. And I think it's, it's kind of up to, to all of us to think about, okay, what will my work look like in five years?

Because I think there's like tremendous agency, not only in the C-Suite, but also in on the entry level. Like, when we think about redesigning how our team works. Mm. Do we want to automate everything so that, say that we're in a sales team, for example. Mm. We wanna automate everything so that we never have to meet another human again.

Mm. So all the sales processes automated, all the onboarding is automated. All customer successes. Just chat bots everywhere. Yeah. Do we do the opposite things? Mm. We automate everything so we can spend as much time as possible in front of the customer.

\[00:33:57\] **Christian:** Exactly. Uh, I'm, I'm totally with you. I think the human element and the human condition should be celebrated and, uh, leveraged.

Leveraged here, right? Yeah. So with the, uh, with the event of ai, it means, number one, what are we already good at? What do we really enjoy? How can we improve and increase the human connection? I think that's where the, that's where our strength lies. I, I can say personally, some people remember they were surprised when I say this, but, you know, I certainly prefer face-to-face meetings, especially in the beginning, whether it's, uh, an, uh, investment case or whether it's, uh, you know, partners that I meet.

It's so important to meet someone 3D and real. Uh, you can read the room, you can, um, connect to people in a very different way. Yeah, you can move a little bit, right? You're not, uh, uh, looking at the screen and be forced to. Not move. And it's very, it's a very unnatural environment. It's not the environment where you build trust.

Yeah. Uh, with someone where you can read someone else. All these elements are super important. And also for, you mentioned the enjoyment of your work. Right? Like for years when I did my, uh, PhD back at Monash in Australia, it was at the faculty of IT and the faculty of medicine. So we built back then a whole lot of tools which would make the GP so the physicians' life easier.

Hmm. So that what they do enjoy is that connection with the patient. Yeah. They want to. Do their work. They want to test them, touch them, move, and they, they want to smile to them. So, so, and what has increased over the last couple of years is the time you spent on the computer in order to enter all the data about that patient.

Right. More requirements. And of course here we see some companies in Sweden, we have Tandem, for example, right. To automate exactly these types of, let's say more tedious, boring processes, right? They so tandem just shortly, you probably know, right? But they are, um, uh, taking all the notes in a discussion with the patient and then, uh, put that, put that into the journal and, and proper formatting with all the coding.

And so naturally saves, saves you a lot of time. Yeah. Uh, as a real winner. And there's a couple of other companies like that in the

\[00:36:06\] **Johan:** world, and I love that type of innovation where you can fundamentally think about what, what couldn't we do? Two, three years ago. That's, that's possible now. And I think those types of innovation will be the real winners.

Yeah.

\[00:36:20\] **Christian:** Uh, yeah. Yeah. And this is another very interesting point, like when I am. Speaking, especially with startups and innovation labs, when you are talking to customers, uh, one way is to ask them what they need. That's of course, important and it shows traction and interaction with the clients and customers and employees.

But the other one is you need to invent on their behalf. Yeah. Because you are now having a technology where people in their mind have almost put a blind spot on because you think, well, that's not possible. Clearly that's not possible because if we were to automate that or make it faster, you would need.

Like a million people. Yeah, exactly. Have a million people to do that. So therefore you can do it. So you have all sorts of these blind spots where you suddenly, uh, this knot is un knotted by using new technology. 'cause you can do it super fast. Mm. And, uh, that, that's not, it's just our nature. Right. It's, it's hard to remove blind spots in your vision.

Right?

\[00:37:19\] **Johan:** No, but I think you, you make an excellent point. And, and to any like, management team listening here, I think a really good conversation in the management team is that okay if we look at the way that we deliver value to our customers. Mm. If we assumed that we had infinite amount of people. What will we do to delight our customers?

Mm-hmm. And I think that's a great way to finding this type of innovation. That's a good question. Yeah. And it's kind of tangible in that sense. And I did it actually for, uh, for myself just a couple of weeks ago. Uh, but more in the experiment if I had unlimited. Staff roles around me, what would we, what would they do to make my life more delightful?

Mm. And that, out of that came actually three agents that was like super simple to, to implement. It's like, it's an easy way of, of kind of understanding and framing the problem.

\[00:38:08\] **Christian:** Yep. No, I was very, very good. And I, I have recently done, I do a lot of hands-on work too, so it's not just a strategy and leadership for me, but I'm doing a whole lot of hands-on implementations, which is even easier now, you know, when it comes both to, um, you know, building agents Yeah.

Or building quantum algorithms. Right. I'm very much into this and I built myself, uh, uh, basically a health clinic, you know, okay. With agents. Uh, so I'm a bit of a health nerd and health engineer looking at my blood values and microbiome and a whole sort of data sets, connecting it with, um, public data sources, for example.

And I have different agents, which are my experts, um, and they talk to each other and provide me with, uh, the latest. Advice and guidance on how I should live a healthy life. Hmm. So, uh, that's something that I have been building also with the thought. Wow, okay. I can go to a long levity clinic or some specialized health clinic.

Takes a lot of time. Yeah. Arguably the doctors, even if they're the best, I can still not absorb all the papers and knowledge around the world. So yeah. Now I have a team of 15 agents that provide me with the latest advice based on my specific, uh, data and background and all the outside data. So, for example, weather, Poland, all that, all that information Right.

Also influences.

\[00:39:20\] **Johan:** And it, it brings up a secondary point where I think, think there's a blind spot in the C-suites. Mm-hmm. So we, we assume that our customers will kind of stay the same that they are. So if you look at a, a health clinic, they kind of assume that patients will be patients. Mm-hmm. But you're changing your patient behavior a hundred percent.

Yeah. And I think that's gonna happen as well for all of the customers. Yeah. So I've been in, in the software as a service business forever. Mm-hmm. Mm-hmm. Fundamentally building software as we know it. Is that the right play? Hmm. Like do we use dashboards and all these kind of static views where we do have to do all of the analysis ourselves is, is that what we're building towards?

Is that the customer expectation in five years? Yeah. Or is something completely different? Yeah. Yeah.

I think that's so interesting.

\[00:40:03\] **Christian:** Yeah, exactly. I think you are aware, right, that the many say there might also be the moments where an Apple iPhone or Samsung phones matter may become obsolete because the way you interact with Yeah.

AI systems will be, you know, through, uh, maybe your virtual glasses or just, you know, the microphones, which by the way is an opportunity that you missed a couple of years ago. I know big, big players in Europe that I spoke to had, um. Had the ambition to build basically these virtual assistants. Mm. Uh, and doubled down on that.

Unfortunately, they took a few steps back, unfortunately, I don't know the exact reasons, but it was, I think, partially a little bit of the immaturity of the technology at the time, because we talked 2019, 2018, the LMS weren't that far. But the idea of having a different interface and interaction, as you suggested, I think is, is very, very, uh, viable.

And it's a very important part of that. Well, what's gonna be the future customer and experience a client expectations, basically.

\[00:41:01\] **Johan:** Yeah. You mentioned quantum algorithms before. Mm-hmm. Yeah. I've never heard the, the term before. What is that?

\[00:41:06\] **Christian:** Now. Quantum is basically a family of technologies. Uh, it includes quantum computing.

It also includes quantum sensing, uh, and the quantum communication to other examples. Okay. Which, which quantum sensing, for example, is quite mature. There's indeed com companies, even in Sweden and in Europe, that you can just say you have, uh, sensors which are magnitudes more sensitive. Okay. To measure anything, you know, eruptions or volcanoes, uh, turbulences in the air, whatever it might be.

Mm-hmm. So that's actually quite mature. Mm. Uh, super useful, you know, now with the rise of sore and, and military applications obviously very important there, uh, but in many other spaces. So, um. Quantum internet. Um, I can put some light on that too. But quantum computing is basically you have the ability to solve very, very, very complex problems much, much faster.

You can ask me a question about why it's relevant for the C-suite now. 'Cause I think it's extremely relevant right now. You need to, but with ai, that's like you're in the middle of the storm right now, right? With ai, you must act now. And I know lots of, uh, CXOs and leadership and boards are acting, uh, try to act. As aggressive as you can, it will be good. Right? I mean the RI of higher risk project is usually, yes, more risk, but will be a big impact.

But with the quantum part, we are like, we see the storm brewing.

\[00:42:31\] **Johan:** Can you, can you kind of roll out where are we currently in the technical capabilities of, of quant computers and, and what will, will we start seeing in more the commercial space over the next five, 10 years, do you think?

\[00:42:44\] **Christian:** So, super exciting area, right? Yeah. So, because I think so much to talk about, uh, let's start from some basics maybe. Yeah, that's, I tend to do that some anchoring, you know, where are we now? So good to note that the Nobel Prize winners in quantum and physics, I think it was, they received the Nobel Prize last week too, was two three quantum winners.

So, um, so we see obviously traction, uh, quantum computing, um, has become, um, very successful field. Also in the, uh, yeah, maybe also worth noting right now. Uh, 2025 is a year of quantum. Okay? So a hundred years ago, Schrodinger, you know, all of us know the Schrodinger's cat. He basically went to Duland and Ireland in Germany.

And he discovered, or let's say he wrote down the, the, uh, many of these fundamental mathematical formulas that are still used today for quantum computing, quantum mechanics, uh, and quantum mechanics is underlying quantum computing. We talk about concepts, and I don't wanna be too technical, like super positions, which suggests that you have states, uh, which are zero and one like you would have in classical computers, but at the same time, all the states in between.

Hmm. This is a confusing part, which literally no one understands in the world, including Einstein. It just happens to be an effect that we observe and it turns out to be an effect that we can use without actually understanding how it works, uh, in principle. Very practically, uh, it means that you have the ability to, uh, build quantum algorithms, circuits, uh, with certain qubits, and you can solve logistics problems much faster.

So in airlines or delivery of parcels or what it might be, uh, finding the optimal, uh, assignments of crew members and, uh, and, uh, you know, airplanes, et cetera. Or, um, logistics, you know, how do we use, send parcels in the most efficient way? Turn out to be extremely complex problems. Mm. Computationally and with quantum, you have the ability to solve them much faster.

Therefore, acting, providing better customer experience, faster shipments, uh, quicker turnarounds, if there's like a strike or machine at the airport does not work. You can recalculate the entire schedule in a, in seconds rather than in days. Mm. Which is a reality today. And by the way, this is very clearly linked to lots of costs.

So airlines, as an example, happen to know that space a little better. Uh, or they quite well due to friends that work in this space. But you need to hire hundreds of more people in order to make up for those gaps. Yeah. Which you would not have to do when you, if you are actually using quantum optimization.

So I'm not sure I, uh, if I have been to quickie, but, but the end, it is also worth noting that Quantum Gates Gate computing. Doesn't exist properly yet. The amount of quantum bits we have, the circuits we can run is very, very limited. And, uh, it's, and the machinery you need to build in order to have quantum computing is, is very complex.

So that's where we are today. Um, but the speed of investment has been very quick. You have, uh, lots of investors being very, um, aggressive again, especially in the US and also in Asia. Uh, we do some good things here. Happy to, uh, you know, elaborate on that and, um. And, uh, yeah, the applications are very strong.

Like I, uh, invested in several quantum um, stocks last year. When you look now at some of them, they have done really, really well, have gone up again. Another 300% I think the last two weeks, I assume, due to the Quantum Nobel Prize winner. Oh yeah. Right. So that has gone up, um, arguably. Very highly valued right now, you know?

But no doubt in my mind that that will continue to grow. And yes, if you speak to the normal quantum physicist, they will tell you, oof, this is ambitious. Like we have no control over quantum states. You guys are dreaming, right? But you, we have to be now very smart in Europe and make sure we invest in that new technology early on.

We cannot wait until we come to a point where, oh, it works. Yeah. So now maybe let's do it. Mm. That, that's too late. Mm. Like in five years, in 10 years when the thing is settled, uh, and we know that it works, we should not then think, oh, we should have invested back in 2025. No, now's the time.

\[00:47:13\] **Johan:** But it's interesting also, now that I gotta think about it, when, when you were talking. We have a bunch of different really big technological shifts on the horizon.

So obviously we, we've talked a lot about ai. We've talked now about quantum computing. You have stuff in the energy space, you have stuff in robotics, and if you just roll this out from the European per perspective, if you, if we're gonna act the same way Yeah. All throughout all of these technical revolutions, and we have a society that's fundamentally completely different in 20 years.

There's robots doing everything, uh, with like energy is just abundant. Yeah. But all of the found foundational technologies is not made in Europe.

\[00:47:51\] **Christian:** Mm-hmm. Yeah.

That's not the future I wanna see. No, I wanna see a future where we have a substantial, uh, contribution to this, this development. We have to, yeah.

Uh, I believe, and I think many others of the big players in the world believe that Europe has a, has a value to add in the, you know, to the world. I I'm sure the many countries in Asia, many country countries in the Americas do see Europe as, um, as a cult culture and society that can, that can contribute.

We need to, but we need to be economically strong. That is a absolutely important. Otherwise, we will just have much less say. So. That's the, obviously I'm saying. Something that is probably obvious for most, you know, just, yeah. But it is of course a very, uh, very important and, uh, and yes, there are many technologies which are now on the rise.

Many which we will not be able to discuss in the podcast. Uh, but I can mention them quickly, at least where I think we need to in Europe, uh, be aware of them that doesn't, mm, how to say. Mm. It doesn't necessarily touch all CEOs and industrial leaders that we have in Europe, but they will still be, uh, influencing our way forward.

It includes space tech. Okay. So I have been looking quite heavily into investments in space tech too. Uh, there are several companies that are now looking at sending satellites to, you know, to the, um, to outer space. And, um, they optimize that. Right? Looking at 10, 20,000 satellites to be showed up with rockets, right?

\[00:49:22\] **Johan:** Mostly like communication and stuff like that? Or is it, are, are we talking about, I don't know, space industrialization or, or like

\[00:49:30\] **Christian:** Yeah, space industrialization. There's basically, it was also great to hear, it was a German government, a minister suggesting there that the, uh, you know, those, that own space will own the power and earth, right?

Yeah. So it's absolutely important. It's everything to do with the, uh, minerals, the metals, the, the expansion to other planets. Yeah. I mean, it may sound still far away and it sort of is, but the, the, um, the roots, it starts now, right? Mm-hmm. So there's a lot happening that SpaceX, uh, area, uh, energy, clearly.

Mm. Right? I mean, that's something, uh, if it's not for the AI data centers, which may or may not come in the future, it is, um, also big space, right? You know, a fusion, uh, reaction. Also, uh, far away topic in some ways, but there are companies very, very deeply involved. So energy will always be, has always been a big, big topic.

Um, yes. And so I'm saying all those things because I've invested in many of those companies. Yeah. Uh, and if you like, it's a bit like the, uh, which is classically called the shovels and the, the, the picks, right? Yeah. Picks and shovels of the, uh, gold rush, right? Mm-hmm. The cooling systems. Mm-hmm. Uh, they also, the.

So cooling systems, neo clouds, new types of, uh, microprocessors. Uh, there's a whole lot of, um, companies that are supplying this entire ecosystem. Yeah, yeah, for sure. Which have a chance to expand in other areas other than AI too.

\[00:51:02\] **Johan:** Yeah, from the investment perspective, uh, I actually use a lot of, of like AI to, to kind of follow the, the magnificent seven in, in their value streams downwards.

Mm-hmm. And there are so many interesting companies doing like small little things, but are, are doing extremely well of the rush to ai.

\[00:51:19\] **Christian:** Yeah. Yeah. Yeah. Very, very well. Very much. And, and another big area which I think we wanna discuss is in humanoid, uh, robots. Yeah. So, I don't know if you wanna go into that.

\[00:51:29\] **Johan:** Now's let's, let's, yeah. It became a nerding out session on, on new technology. I love it. Yeah.

\[00:51:34\] **Christian:** Yeah, probably.

But all of this is very connected, like Yeah, yeah. Exactly. On your agenda. Yeah. Um, uh, and if the minimum you're doing as a, as a CXO, sv, you know, senior vice president or board member is to put a book next to you Yeah.

My table and read that. Right. Or listen to an audio book or whatever might,

\[00:51:50\] **Johan:** so i've read tons of books on, on AI by this point. I haven't read that many on robotics. Mm. Do you have any recommendations for, for like a good, uh, like a very, like, uh, so I love the books that, that kinda lay out what fundamentally changes in society as a consequence of this new technology coming out.

So I approach it from a organizational and leadership perspective primarily. Mm. Even though I also love sci-fi, so I think that's fun. But, but.

\[00:52:17\] **Christian:** Ooh. I need to think, I need to think a little bit about what's sort of the most recent books that talk about robotics in your humanoid robotics and how the, how the, how there's some practical impact on society.

Um, well, I'm, I can just, maybe, maybe it comes, some, some books come, we can put

\[00:52:34\] **Johan:** it in the show notes afterwards if we have

\[00:52:35\] **Christian:** some good ideas. Yeah. But I can say that it's probably one of the first areas that really interested me, and when I started my, my research and when I worked, uh, back in the nineties and nineties on robots.

Mm-hmm. Um, they were already very popular then. Mm-hmm. And I, I may. Describe that This has been around for a very long time. Right. The effect in the classic Russell Norwick book of ai, which everyone that, uh, does AI has surely read the, the, the Bible. I hate to use word, but you know, is, um, is, is talking about shaky the robot at SRI, Stanford Research Institute of 19 68, 19 72, something like that, where, um, shaky the robot would be moving with a logic implementation from one room to another.

And the reason why it's called shaky is because say a robot was built in a way that it was just. Very loose. Mm. So the thing was shaking all the time was, you know, going against the wall and so on. But these were the early day, early days of robotics. Uh, those initial successes led a whole lot of science for sci-fi authors and so on to, uh, to, um, write more about it.

Then the N nineties, I worked with a Sony IBO Robots, which were robotic doc Mm. And also Honda built Asimov. Mm uh, which are humanoid robots back in the nineties, which had the ambition to play soccer. And there's something called the RoboCup by cup, not cup, uh, which is, uh, that these robots would play soccer, uh, against other teams of robots.

And the idea, the ambition, is by 2050, they would win against the best human soccer team, right? That's the ambition. And this broad challenge has been around since about 1996. Uh, people like Manela Beso, who works, in fact, JP Morgan, and, uh, he, Ronald Kitano, uh, initiated that back in those days. And it's still very ongoing.

Hmm. So, uh, you can imagine, oh, it sounds like playing fun. But the idea that you have these robots being completely, uh, autonomous and independent, uh, they have their own world perception. You have to control, um, their. Arms and their, uh, robotics and they have sensors, uh, lighter, you know, perception, sensors and so on.

So it's a, number one is a much more complex domain than, for example, uh, go or chess or, or chat GBT or LLM much more complicated, right? 'cause you live in work in the real world,

\[00:55:00\] **Johan:** and especially if you want them to, to be multipurpose. Mm. Like the idea of actually having a robot here cleaning up the kitchen.

Yeah.

Yeah. And then, uh, going and folding sheets.

\[00:55:10\] **Christian:** EE exactly right. Exactly. And so there, there is a couple of companies now, uh, exactly. Looking at that. Of course. Right? And I was, uh, six years ago, I need to look up that company again, but they had indeed this robot arm installed in the kitchen, which would, you know, uh, make you food and cut your, uh, cut your meat and all that.

\[00:55:26\] **Johan:** But the tech demo are, are super interesting. I remember just a few years ago that they were not that cool, uh, or they were cool, but in a novelty sense, but they, it was quite clear that they fell over all the time and stuff like that. And quite quickly you see demos that are, are. Really rapidly improving.

\[00:55:42\] **Christian:** Yeah. Uh, I was with, uh, mark, the CEO of Boston, Boston Dynamics. Oh, okay. Yeah. We were Mark Rebert. We were both, um, in given an AI award in Dubai a couple of years back. And so we were having, uh, several sessions. I asked him, you know, obviously the robots made a lot of waves on, on YouTube and on Right. And how much AI they are using and how they see the, the future.

And he said that, uh, some people associate these robots, um, with sort of military applications. Yeah. But really where they are used mostly, and where he has sold them is to industrial domains. Right. Hmm. Uh, uh, more like robotic floors and those, those, uh, industrial companies. Yeah. So, um, so we had a very interesting discussion, uh, on that topic.

And of course, uh, robots, um, have, it's a big trend in China now where robots are developed for. Uh, children. Okay. So, you know, the whole idea that you can, uh, kids already talk to their doll and then take beers in the past, but now they, they talk back. Yeah. So you have the, that whole industry now, um, emerge,

\[00:56:52\] **Johan:** which is super interesting if you also add a bunch of, of, uh, science in, in like, how do we learn better?

And, and like it's, it's not just the entertainment and the doom scrolling that we teach them through the iPads, but actually we, we fundamentally stimulate them to learn more and, and explore the world and have that type of, um, relationship to, to the robots. Yeah, exactly. If you're a betting man, at what year do we actually have the, the kind of multipurpose make my life easy robot walking around here.

\[00:57:20\] **Christian:** Okay. I mean, in a robot that does all different things in the household.

\[00:57:24\] **Johan:** No, you might have two, three robots, but, but like fundamentally, when, when do I stop, uh, changing the sheets and, and doing the dishes?

\[00:57:30\] **Christian:** Yeah. I would say in about, uh, three to five years

\[00:57:34\] **Johan:** that close. Mm. Like it's, it's both like super, but also super ing in terms of, again, job. What are future proof jobs and we think about electricians and plumbers and whatnot. Yeah. That they're, they're not really that impacted by AI right now. Mm-hmm. But guess what? You have the robot revolution coming right after it.

\[00:57:59\] **Christian:** It's fair enough. I mean, you want to categorize, right?

Yeah. Like if you have certain still critical things, you know, you, it may not be like still hairdressers, plumbers might be a little bit more complex, but if you talk about normal household chores, possibly 80% of those could be covered by a robot in three to five years. Pretty, pretty sure. Everything from bed sheets and cleaning and you know, all that stuff, putting the dishes in, the dishwasher, all that thing.

Yeah. I think that that will be very, very feasible.

\[00:58:27\] **Johan:** And then moving to, to the next technological space. We also have like biohacking and the medicine exploration. Yeah. And uh, what's, what are they called? Alpha gene? What? No. Hmm. The gene folding thing that, oh yeah. Alpha fold. Alpha fold. Yeah.

\[00:58:42\] **Christian:** Deep mind. Yeah.

Yeah, for sure. Yeah. Yeah. Huge area. Life science. I love this area. I'm also here at the Enes Institute. Of course. Uh, uh, so, uh, what to say here, like, we are having a tremendous amount of data. Uh, it includes genetic data, microbiome data, blood results, you name it. At the same time, we also have more and more knowledge, more nature publications coming out, right?

So more and more, uh, uh, scientific knowledge. So combining it and start to personalize, that has been an ambition of that life science space for. At least 20 or 30 years. Right. So now we come closer and closer to making that actually possible. Uh, because we have the compute, we have the models, we have the ability to start making drugs faster and potentially also Deb, these drugs to who you are as an individual.

Mm-hmm. So there is, uh, many startups we have here, we have startups that are in Europe and in the US and they focus on exactly these types of things. Hmm. Drug discovery is one of those things where, uh, I have recently put more emphasis on personally because I both built prototypes in quantum computing.

Hmm. Um. Where you can little side note here, right? In quantum computing you can actually simulate the real natural conditions under which, um, uh, molecules and peptides, uh, connect to the biological wow counterpart and that you can do on the deepest possible level, therefore, improving the prediction as to whether these drugs are successful or not.

Yeah. As opposed to the classic way even DeepMind and ai Right. Or, or alpha fold. They do it in a certain classical way. They use all the data that has ever been created in this space and then they try to predict and create,

\[01:00:35\] **Johan:** but it's not true simulation. That's what you're saying. It's

\[01:00:38\] **Christian:** like all data. Uh, let's say all it's like past data and, um.

It gives you less Mm, flexibility. Mm. Like if you face new situations, you can't. But if you do those simulations, which are by the way, extremely complex Mm, massively, uh, complex, um, you can all only really simulate certain parts. But if you, when you do that, you simulate things on the most on one, the deepest possible level.

And that gives you huge prediction part that that value is enormous. So that's a little side side. It's so

\[01:01:10\] **Johan:** interesting and I think it's, it is obviously not just gonna happen within medicine. I, I think, just think about how we develop. Um, I don't know, engineering processes for, I saw when I was in Silicon Valley last week, uh, they just released a, a new, um, a new car that was from an engineering standpoint, a hundred percent.

They say it was a hundred percent. It probably wasn't, but developed a lot through ai and obviously they have completely different solutions for, for how does, uh, the suspension work and the brakes work and stuff like that. I think they have more than a thousand patents from it. Mm-hmm. But they just mm-hmm.

3D printed the, the, the car. Yeah. Which is so interesting. So, so this type of like fundamental change in being able to truly simulate and do something completely different versus what we do today is probably gonna touch every industry.

\[01:01:58\] **Christian:** Yeah. Exactly. Yeah. So you have, um, and it's also fair to say when you ask me earlier, you know, quantum and ai, ai, the machine learning AI that we have today, uh, is still very based on the idea that you need a lot of data.

Hmm. And the data will turn into something useful. Yeah. And that's totally valid. There's a lot of value in many of these application domains. Uh, now, and I think we should talk a little bit about agentic AI and multi-agent systems too. Yeah. Which I think is,

\[01:02:31\] **Johan:** it's funny that we're one hour 20 into the podcast and we haven't really talked about it's,

\[01:02:35\] **Christian:** but Yeah.

Yeah. You mentioned that that's probably the beginning. That's Yeah, exactly. Yeah. You should talk about, um, but the, but the, the quantum part is really that's looking at real simulation of existing scenarios, like real time. So it gives you a very, very different perspective of what compute can do and what value it can create.

Right. So the, the, the past data, turning it into model part is more well known. Uh, but it also has its limitations and it challenges, you know, the quantum part is, uh, will be a must have in three or five years. Oh. And with the quantum part, very important for, uh, the leaders that listen to the podcast and the senior management, it will take time.

To adopt quantum. The reason why I mentioned you need to be on the ball right now, is it take, it will take you minimum of two years for anyone to even learn how quantum computing works. Yeah. Because it's so different. What I briefly mentioned with the number of qubits and the quantum circuits. You talk, it's a different world.

Mm. It would be like you are, instead of driving a car, you start to fly a plane. Mm. You cannot just, oh, well it's about the same. I just go into No, you talk about very different concepts. Mm. And it'll take you a good amount of time and, and already. Now of course, many should know if they don't already. Uh, the companies are investing in encryption, quantum safe encryption.

Ah. Because right now, um, the non-encrypted or the normally encrypted, uh, data is something that can be corrected in the future by quantum. And that means that every communication we send around today Yeah. Can be read in five years time. Yeah. Potentially. Right? Yeah. So therefore it's probably in your interest to make sure, unless you really don't care what information has been sent around today to look at that quantum encryption part.

Yeah. So, um, so that's something I think is just one example of so many. You need to prepare the, the organization now for that new big, big wave of innovation coming. Hmm. And I, I, I'm almost very, very, I'm certain in latest in five years time, we talk a lot more about quantum. Quantum will be, will be the topic number one topic.

And we will also talk much more embedded, uh, uh, robotics and, you know, embedded ai, which is, that's actually what it is, robotic ai.

\[01:04:54\] **Johan:** It's interesting how many of, of these technologies kind of intersect over a period of five, 10 years.

Mm. And how massive the changes. Mm. And again, if we're assuming the position of a C-suite, who's kind of mm-hmm. Taking a a, yeah, let's make, let, let's make a, a fast follower bet on ai. Mm. How dangerous that is, because it's not just gonna be ai, it's gonna be the next thing and the next thing and the next thing.

So you fundamentally need to, uh, back to, to creative destruction. Yeah. Uh, back to that, you need to, from an organizational perspective, truly acquired the skills of innovation at this point

\[01:05:32\] **Christian:** in time. Yeah, yeah, yeah. Exactly. And I think, I think now we talked a lot technology. I have a feeling like maybe 70, 80% was technology, because I, I, I feel so embedded.

I feel so rooted in that. But of course, uh. For me at least. And if we had a more business-like discussions, when I sit together with CEOs, the question is of course, how does that turn into value? Yeah. How does that change, uh, turn into change in the business and how can we track that change? Hmm. Uh, these are the really important questions that you need to look from a business perspective.

Right. And, uh, I have been a board member at companies also. It's like setting the, setting the rhythm and setting the, the tone for that is extremely important. Now it's, I think it's the, it's important for the stakeholders of every company, and therefore the board that is responsible to set that pace and set the targets properly.

I put it a bit, uh, yeah, a bit blunt, but that's then also the executive team and the company needs to put that into action. That's a, that's really a very strong responsibility for, uh, for companies in, in Europe. I say it a little sharper here because I want us to be very successful at the. You know, the pace has to increase and I'm happy to help.

Right. I think you're also happy to help. So, you know, but I'm a, I'm ambitious, I'm co competitive and I'm also good spirited in an international global perspective. So I just think that, you know, we have a tremendous potential, huge talent and a great ambition. Right? We can absolutely make it happen.

\[01:07:06\] **Johan:** Well, uh, how does that look differently?

So say that we've converted somebody in the audience now. Mm, yes. I agree. We need to urgently do something different tomorrow versus yesterday. Mm. What do we actually do? What are the decisions that I make?

\[01:07:17\] **Christian:** I had actually the thought, how do we now turn this into some practical Yeah, exactly. I mean, the, the, it depends always a little bit where the company is right now, from whom we talk to.

At the moment, right? So let's assume, uh, uh, I try to give general guidance, but let's sit down. That's important. Like, where's your business now? How big is your business? Is it a small, medium sized company? Is it a large company? Uh, is it a company that already started doing automation? What's the general culture or when it comes to change and when it comes to, uh, technology?

I think there's a whole lot of important questions you need to look at before you give very generic advice, because now comes a bit of generic advice, which I have been giving for 10, 10 years roughly, and it adds, you know, there's more, more advice coming. But roughly speaking, of course, you need to start.

You need to, ideally you start with use cases in your company, which are not too ambitious. They need to be very meaningful for you, the company. So it's not like, oh, we change big, big things. It needs to be something where you start, where you show the benefit so that you get the people on board, you need to build momentum.

I think that's key. Um, and people need to see the traction. Mm. I think that's very important. Yeah. I think you also want to, uh, celebrate successes and the individuals that take risks. Uh, you want risk takers. You want a bit bit of the mavericks, right. To make these types of changes possible. Um, and that's important.

You also need backing off the leadership. Hmm. Very important. I mean, so many projects in the early days I have seen where people with great ambition have tried and their, their efforts haven't been, let's say, properly recognized. That's not good. Leadership is responsible to make sure that those are presented and celebrated.

Right. Um. So that's, that's a starting point. Uh, it's very important to have a business savvy technologist, which has a pragmatic attitude in the leadership team so that they can give proper advice, which is a trusted voice in these discussions to move the company forward. Hmm. And I advise and practical terms, often CEOs, CXOs to sit down with these people and have plenty of lunches and coffees to have these exchanges and see how you can turn that into, into action, into practical use cases.

So that's on that level,

\[01:09:35\] **Johan:** So you argue for, for a business savvy technologist to, to move into a, a more central position of power. Yeah. I think the opposite is probably equally true as well, that you need from the non technologist brain be more interested in technology.

Yeah. You, you need to make that leap now because the future is so shaped by it's, it's not gonna be enough by just having the understanding of the economics or the sales or the marketing or whatever basis you have. Exactly.

\[01:10:01\] **Christian:** Exactly. And I've recently, let's say, entertained the thought and suggested to companies in a pharmaceutical space to, they are, they tend to be a bit more conservative, right?

Mm-hmm. Uh, and naturally. So it's a long term business. Drug development takes 15, 20 years and very risky, et cetera. So. How do you get AI into that company? Hmm. Uh, and if you had an AI expert going into the space, you will start seeing some incompatibility. Hmm. Because the leadership in classical pharmaceutical spaces are usually people that did the PhD in biochemistry.

Yeah, exactly. Yeah. And so they, I can totally understand, and I've, uh, have been on these discussions where they look at this AI expert PhD in ai, let's say, and these two worlds are clashing. And now you're meant to be giving that person, that AI person much more mandate and power to change the organization.

There's question on, can I, um, can I guide that person? Hmm. Can I give that person some advice? 'cause I'm in a different space. And the other way around, how much can I trust them? Because what that person talks about, CNN's and machine learning, whoa. This is so, so my suggestion there would be, in practical terms, can you think of, um, co a, a double.

Head position. Okay. As we have also like co-CEOs type environments. Right. One that has, um, uh, business savvy or industry savvy, AI technologist and the other one being in the core business, but a very strong AI interest. That letter person would also need to be well integrated into the entire operational business.

Hmm. So that they can help translate those changes. It is, I think, not a good idea to have a only an AI technologist with all the background and all that, and then say, yep, here you are. You get a mandate. Change the change big things in the company. Yeah. It's a, it's a difficult, difficult,

\[01:11:51\] **Johan:** yeah. And the like, this is traditional change management that we've learned so much around the just general digitalization.

How many of these initiatives just failed due to the fact that we delegated it only to the engineers? If it were to be successful, the changes need to actually happen in operations, in marketing, in whatever. And if you support Yeah, exactly. So, so the kind of power dynamics of how you run the business is so central to the success.

\[01:12:17\] **Christian:** And as you probably know, right, like this MIT report that came out a month or two ago where it's suggests 95% of AI projects fail. And if you look deeper right, what does that mean? Or on the surface you say, ah, AI doesn't work. No. If you look deeper, it's the, it's things like integration, it's adoption, it's cultural changes.

Yeah. So that's where the bottlenecks are at some.

\[01:12:38\] **Johan:** Yeah. And I, and I also think, I mean, there, there's been plenty of takedown pieces on that piece as well, but I, I think for me, the, the most interesting one is how do you define success? Mm. Again, coming back to is success in a period of deep innovation? Mm.

Is it only measured by ROI in a period where you would expect the real return investment come into years' time? Mm. Is it by definition of failure? Hmm. I don't think necessarily this, and maybe there, there, there's probably a bunch of really bad AI implementation. I'm saying, not saying that it's not, but I'm saying it's such a, it came at an inopportune moment because there, there's a lot of, I think there's a lot of fear to be honest in these positions of power from, from the CXOs that we're talking about that they see that I don't really understand the technology, but I'm also.

Deeply afraid that I'm gonna be completely irrelevant in five years time if this actually happens. So that's so scary that I prefer actually not to look at it at all. Mm mm

I think that's a dynamic going on a lot.

\[01:13:35\] **Christian:** Yeah. It's, I, I understand, and I think we also had a discussion before the podcast that I think, uh, so the Nordic AI Institute is certainly helping in these types of discussions to number one, demystify.

Yeah. You know, what that means. And to create a story. I think that's mm-hmm. You know, how. How is the big picture looking like? Yeah. 'cause just let's say closing your eyes and turning away from it is probably not a long-term strategy to of success. And as I said earlier, the sometimes it takes a little bit to start suggesting, Hey, how can I, uh, see this as a really great opportunity?

Yeah. Like, I personally have always been very early adopter of new technologies. Yeah. I would imagine. You know, like we both have an aura ring, right? Yeah. Finished, finished product. And I mean, it's, it's, it's great. It's an opportunity to learn. It's an opportunity to turn this into impact. It's an opportunity for your business and the people that you have in your business be more successful.

Right. So, um, that's how I look at it, you know? Yeah. It's, it's great. Yes. Does it come with more risk? Yes. But it goes back to Schumpeter and the innovator dilemma. Um, we have to relearn, uh, and open up on how we destruct. Creatively and maybe constructively existing systems to make space for the new. Yeah, that's a, rather than only looking at what we need to preserve.

Yeah. The world is moving too fast, um, to only. To put too much emphasis on preservation. Mm. So it's, it's a, it's a, it's a very, it's a fine, we need to obviously also look at the whole spectrum of society in our, our workforce, uh, to see how we make that happen. So it becomes a good journey for all of us, most of us at least.

Right, because you Yeah. You obviously many different players in stakeholders in society, you know, which we need to take care of.

\[01:15:34\] **Johan:** Yeah, absolutely. Let's make sure that we. Don't end the podcast before we've actually dealt with the agents and multi-agent systems. Exactly. Let's make that the, the last big points.

Yeah. Yeah. Where to begin in that discussion? What's a good starting piece?

\[01:15:48\] **Christian:** Yeah, exactly. The most, uh, in many ways, the most important, uh, threshold now in both. It's an area that I did my PhD in and did most of my work in innovation and research. And so multi-agent systems and agent systems, uh, in very simple terms.

Uh, what's an agentic AI system? Uh, let's start with the, you know, when we have the chat GPTs and we have applications here, then you often have things like chat with your data or you, you talk and you discover things and you have deep research and you, you know, that's a typical rags and so on. So many people are familiar with that.

Hmm. The next one is agentic ai. So it takes it a level further, which is a bit like a workflow. Do you have a, you have certain, uh, steps which. Which an agent takes, or which in which a chat GPT takes. And it, you know, in the simplest of ways, it might say, oh, you do first understand my question. Then you go out and find information, and then you turn it into a report.

That's a typical agentic workflow. Yeah. And it has two or three, uh, qualities. That's say one is it becomes a more structured way of getting things done. And uh, secondly it is a, uh, you can, um, it's a, it's a, yeah, it's a very predictable. Workflow so it doesn't deviate, right? Mm. So that's, that's some very simple terms what, uh, an nagen age agent workflow is.

\[01:17:06\] **Johan:** And I had my first real experience outside of like playing around and prototyping this in my engineering departments just before summer. Mm mm So they develop enterprise code. Uh, so we couldn't really do the, uh, vibe coding stuff for, for a long time. Then we clawed code, the, the last update that came before summer, we actually managed to.

Connect the, the visual sketches. Mm. Uh, so it could, uh, actually interpret them as machine, like these are the changes versus what we currently have in our code base. And then we had a couple of agents, one product manager, one front end developer, one backend developer, one quality assurance, and they could actually develop the whole code, uh, the change.

Mm-hmm. So, so my first experience with this fully working was, it was a small functionality, but it would've taken an engineer maybe two weeks to develop. Mm. I got the sketch at lunchtime. Mm-hmm. And at one 30 I had working, uh, uh, upgrade in, in our production environment. Hmm. You talked before about 20, 30 even doubling a productivity.

Mm-hmm. That's like 10 x to productivity. So it's kind of here and now, which is fascinating to me. Mm-hmm. And a lot of, obviously an engineering process is quite nice because it's also follows the structure of, of what needs to be done. And not all processes are like that.

Yeah.

But yeah. Yeah, it is here now and I think that's a good thing to understand.

This is not. Especially in Europe, we, we don't really see that many implementations yet on multi agent systems.

\[01:18:41\] **Christian:** Yeah. And now I want to also make sure what, what I just mentioned is agent ai. So the next level is actually multi-agent systems. So it's important to distinguish, all right. The difference would be a chat two BT type interface on LLM interfaces.

You're just chatting and discovering information agent ai, so you give it more power to do stuff. Yeah. And obviously this is very, um, very useful. You know, you mentioned now one example can be in customer interaction, so what it is. So it's really, really good. Very powerful already, but it totally doesn't reap yet.

Now, now comes a big thing. Now comes a big thing. Go for it. It's not, these things are not multi-agent systems yet, and it has both a huge impact on what's required to build multi agent systems and what multi-agent systems can do. Difference being, we, we have barely any multi-agent systems out. You have autonomous agents that make their choices as to what the next steps are.

That's the difference. Mm-hmm. And they have the ability to interact with other agent systems. Yes. In a dynamic way. That's what multi-agent systems are, and they operate anomaly under open conditions. Now you may wonder, why would I wanna have such a thing? Uh, because it gives you basically this exponential power to do tasks in a.

In a much faster way. In a much bigger way. You have, um, the ability to let these agents figure out what they want, work with others, and that's true. Multi-agent agent AI doesn't allow you to do that at all. These are very deterministic Yeah. Workflows that the agents do some things and each step, which is probably very wise to do at this point in time.

Multi Asian system is also completely different to machine learning or LLM research. So the tradition and the core and the root of that is in a very different, uh, research field, which has existed for 40 years. So now, uh, what is it in practice when you have these systems? They need to have abilities to negotiate.

They need to have abilities to assess how to collaborate with each other, assess what the abilities of the other agents are. Uh, that happens to be much of my, my PhD research, which I spent many, many years doing.

\[01:20:52\] **Johan:** When you talk about negotiate, would you say that they negotiate with other agents or do they also negotiate with other humans as part of their Yes.

\[01:20:59\] **Christian:** Both. Both.

\[01:21:00\] **Johan:** Yeah. That's interesting.

\[01:21:01\] **Christian:** Mixed human, mixed AI teams, and you can use it classically. Uh, people like Nick Jennings, Woolrich back in the nineties have built systems for, for example, uh, hotel and hotel bookings and travel bookings and so on, right? Mm-hmm. So here, classical example, you have an agent which might work for a agency and they go out and negotiate rates, uh, with um.

Hotels with airplane airlines, right? So how do you do this negotiation process? And that's very non-trivial. Like there is, or like, let's say this is, has very little to do with Mach classic machine learning and data science. This has to do with things like Paris to Optimality n Equilibrium. Mm-hmm. You enter a space of, um, of, of ai, which is so far very underutilized.

So, uh, and which only means it's a fantastic opportunity. In fact, we now in Europe have a chance to move into that space on own that space of multi-agent systems. Uh, the big two questions you wanna ask yourself. When do agents compete with each other? When do they collaborate with each other? Both having huge, um.

Uh, impacts on what value you get outta it.

\[01:22:10\] **Johan:** Can you double click down on, on what that means? I'm not hundred percent sure that I followed. Like what, what's the distinction between compete and collaborate and, and how that roll out?

\[01:22:20\] **Christian:** It's great. I love it.

It's my second chapter of my PhD thesis and probably what's mostly discuss, and it distinguishes the entire multi-agent community.

So in competing environments, you have agents that have limited resources and need to, need to find, um, ways of negotiating how they can have more of those resources. So fundamentally, a zero sum game can be, can be, uh, doesn't have to be, but, but basically these agents would negotiate how much they would, would want to have of something and how much they can, can offer.

And, you know, they, they are in environments where some might. Just have much less and others have more resources. And so it's like a, yeah, zero sum. It's environments where, you know, Nash, equ, Libra, and negotiation principles come in, um, coalition questions, you know, how do you have the right coalitions of players or like agents that you succeed together with?

Do you need to assess how good they are, et cetera. So, so that's already super complex. And, uh, Vincent Connet under, uh, Andreas from Fin Finland, these are very big, um, players in this space. They have, by the way, also built big companies in the automated trading space already 15 years ago. Very, very successful in the us.

So that's one. Uh, the other one collaboration is, uh, people like Professor Mill Tambo, who is also part of the Nordic AI Institute, for example, uh, has, um, published papers and applied this. In, in the real world, uh, where he looks at flexible teamwork. You deal with uncertainties. Uh, you have, for example, if we were to collaborate with each other, there's still a lot of unclarities.

Like, I don't know, if you were to play soccer, how good are your skills? You need to form a model about how good you are, what you can do, how we would compliment each other as a team. Mm. Right? So that's collaboration. We might have no competing, um, aspect. Between ourselves, we just wanna win the game. Yeah.

We just wanna win the soccer game. So our primary goal would be just to start learning how we can communicate on the field. Mm-hmm. With limited, uh, communication skills. We need to learn how we, how good we are. Uh, and, uh, mill, uh, for example, applied that in, uh, at the Los Angeles, uh, airport for security, um, security spots that he's, uh, yeah.

That he chose. Uh, but I don't know, does that make it a bit clearer? Like competition versus collaboration? Collaboration is in environments where you would fully work as a team. Yeah. There's no nothing where you have limited resources, but you're dealing more with how can you make the best of, of the team where you have individual agents that are fully autonomous.

An agent, one agent does not have access to the knowledge of another agent. Yeah. And, um. Or the view of that other agent. They're fully autonomous, so they need to talk to each other. So if you had robots on Mars solving problems, building a space station, they need to, and they will have limited communication.

Robots will have different types of capabilities. Uh, and they need to basically, but they're in the same team.

\[01:25:24\] **Johan:** I think, like what? I'm not sure I a hundred percent understand, but what what's going on in, in my mind is mm-hmm. It would be so interesting if, if you follow any. Complex task of a manager. Hmm.

It's fundamentally, it's pretty similar principles. It's, uh, sometimes collaboration, sometimes competition. Like I've been in the field of strategy for so long, we talked about real simulations before. And then having the idea of, of we want to get to this position and can we truly just simulate all the permutations of, of how we act, what we release, how the competitors react and, and what, what we need then in terms of marketing messaging mm-hmm.

And, and what we need to do in, in engineering in order to make that happen. And making this really, really elaborate kind of, uh, simulations play out. Yeah. As a basis. Every everyday decision. Mm. That, that's kind of interesting because like these are gonna be just available if you have, pay the right licenses and whatnot.

Mm-hmm. Mm-hmm.

\[01:26:25\] **Christian:** And those are actually a great example of something called a micro simulations and agent, uh, agent, um, simulated systems. Okay. So what happens there is you have these agents or entities and you make them. Uh, act with each other. So in Sweden, in fact, there were lots of applications where you simulated every single Swedish person.

Yeah. Based on certain qualities as to where they live and how they, what they earn, how they travel, et cetera, et cetera. This is, by the way, already implemented 2019, 19 90, 90 in Stockholm. Right. Okay. And then you would, uh, simulate, uh, what would happen if a pandemic occurs? Ah, so, you know, lander, someone arrives.

How fast would the virus spread? Yeah. Given those factors that I mentioned, travel, you know, you apply theories of, uh, infectious disease, you know how the virus would spread. You look at calculations and likelihoods, and then you would see when you simulate this tool. When you simulate Sweden or in fact Europe, all the world.

Right. How fast the virus would, would spread. Mm. Uh, so that seemed more, let's say a passive, uh, way of simulating entities. Mm. Um, I, we, I actually worked on a project where I looked also at the active part when you now have agents in play Mm. Which would be proactively assessing that spread Yeah. Like a hospital or, or pharmaceutical company.

And then you would, um, make them act and plan what to do under those circumstances. Yeah. So these are in some sense two different spaces, right? Yeah. The first one is more simulating the interactions and the spread. The other one would be a proactive, uh, planning. Yeah.

\[01:27:56\] **Johan:** Can we

influence the outcomes?

Exactly. Yeah. It's interesting. And, and what are the, again, coming back to societal imp implications, if this is not just something that's kind of the spearhead of, um, modern medicine and we have one implementation of it, but this is an everyday occurrence. Mm mm Again, coming back to the idea of like superhuman and super workers.

Mm-hmm. Like if you had this kind of incredible. Analysis power and simulation power and Mm. I think that would correlate probably pretty positively with innovation as well.

Mm.

Uh, because you could take a lot of risk out the innovation.

\[01:28:29\] **Christian:** Mm. Yeah. And, uh, uh, ing Tim in Germany and professor, he has a very strong thesis where he suggests that we should have these simulations much more having these types of digital twins in order to understand what effects certain actions in the company or the product would do.

Uh, and he was referring, I mean, it's the same in, in, in, uh, automobile, right? You don't build a car and then you drive it against the wall a hundred times. It would be terribly expensive. Yeah. Instead, you have very sophisticated simulation environments to just save that amount of physical damage. Right.

Yeah. And so, uh, and that's very similar here in some sense, right? Yeah. You could, you could apply that. Yeah.

\[01:29:05\] **Johan:** Yeah. It's super interesting. Mm-hmm. Hey, this has been absolutely fascinating. It feels that this could be a three hour pod. Uh, thank you so much for coming and, and sharing. I, I would. I love to, to have a peak at your investment portfolio is one of the insights after, after this podcast for me.

Mm-hmm. Hmm. Whatcha most excited about to finish out?

\[01:29:27\] **Christian:** I think we are at a time where much of those ideas and dreams that the I and the ai, uh, pe my friends in the AI community 20, 30 years ago ahead now turn into real reality. Mm-hmm. So having humanoid robots that you can talk with, that you can interact with that can help people, uh, I think it's an exciting time.

It's a lot to do still, no doubt. Also, how do we solve problems like alignment problem? How do we make these robots something that we really attach to s on, but it's certainly super exciting. Mm-hmm. It allows humanity to be on the next level of exploration. So we can think of how can we start. Going outside on earth, right?

How we can, can we go to new planets with the help of ai, robotic ai and so on. Uh, how it gives us the ability to look deeper and discover more in more things about the world. I can learn. And perhaps a third one that makes me very excited. I'm very confident that AI has, has the ability to bring out more of that human condition and the human quality out of us.

Uh, meaning that we connect better. I think we have now been in a bubble over the last 20 years where our tension was taken away. Yeah. With many apps, with the technologies that take away our attention, that hijacks us. I think we have a chance now to go back and connect on a human level, therefore.

Bringing out that beautiful combination of an AI powered slash, you know, human, human element in, in, in the future society and business. Yeah,

\[01:31:12\] **Johan:** I completely agree with that last point. It's interesting how fundamentally a, a technological shift has, at least for me personally, forced me to think so deeply about the core of my humanity.

And that to me is fascinating. Yeah. Thank you so much Christian.

\[01:31:28\] **Christian:** Thanks for having me.