Global Value Creation Part 3, 3 Immutable Laws & Implementation Details

Hi everybody, welcome back to the 3rd and final episode of Global Value Creation. I will link the 2 other earlier episodes from earlier this year in the show notes here, but we're going to kick off straight away. And as we've said previously, we're trying to be the highest value per second of any podcast on the internet. And you know, it's limited supply. There's only 3 of them and there'll never be any more. So, uh, we'll see if we can meet some of those, uh, those targets today. So, Karl, how are you? Say hi to everybody.

Good morning, good afternoon, no matter where you're joining us from and where you're listening. Uh, yeah, I'm, I'm good, thanks, Sean. Looking forward to the 3rd episode and, uh, all the things that might be derived from there. So let's kick it off, shall we?

Great. Cool. All right. So I'm going to share my screen. I've got something to show today. And then Karl will do the same. All right. Can you see my screen?

Yes, I can.

All right, great. So I've been doing this intersection of capital and computing thing for 31 years now, which is crazy. I got started very young. Working on internet products and investing in technology companies in the mid-'90s. So here we are some decades later. And one of the primary things I've learned after many mistakes and fails and potholes and sinkholes and wrong turns is you don't want to bet against technology. And you want to bet against the major trends and macro waves, and then you've got to prioritize your bets. either on the technology you build to help enable the wave or on the companies you invest in with your time, attention, resource, capital, etc., on the leader or things that help that leader. So with that said, I've come to the conclusion over the years that there's really only 3 immutable laws as we sit here in the mid-2020s. and look out into the future of emerging technology and brand power. So the first one, I'll walk through this, is the internet. The second one, machines. And the third, money. And what you'll see is that each one kind of compounds on the last. And these shouldn't sound too foreign to you if you've been paying attention at all to the technology or investment landscape over the last few years. So the first one, the internet will not stop. At this stage, I think most people would agree with that. In the early— well, in the mid-'90s, they thought it was troublesome, especially around the dot-com boom at the turn of the century. But at this stage, we've got 5.5 billion humans who use it. It's critical infrastructure for everything, and it's sort of like oxygen. You don't miss it until it's gone, and then it's the only thing you care about. So I won't spend too much time on there. The enabling opportunity there is compute. You need some energy to power chips and then connecting those chips together. So let's spend some more time on the second one, which is machines. The premise, hypothesis, finding there is that machine use of the internet will dwarf human use. We are beginning to see that. It's been a growth vector for many years, and now with the rise of AI and agents and computer use tools, IoT, etc. We are at the precipice stage where almost half of the internet use is from machines already. Most people don't understand that, but as we look to the future, right, where agents and other technologies operate on our behalf, and some already do, then creating and handling hundreds of millions, even billions of decisions, transactions, computations per second is out of the realm of possibility of what is capable by a human being. So obviously the trend there is artificial intelligence. You may have heard about it, which is really just a rebrand of the word mathematics. So what you're really betting on in this second wave is mathematics that kind of automates some things for humans using the backbone of the internet. So if you don't agree with number 1, we're probably not a good fit. If you don't agree with number 2, we're probably not a good fit as well. And then number 3, we'll call it money. You can call it trade, you can call it transactions, you could call it exchange of value, you could call it kind of whatever concept you want. But really, the idea is that on the internet and between machines, really the order of value is computation enabled by energy. that transfers something from a single state to another state. So it's kind of a weird thing to say that digital energy is the transaction layer, but that, you know, has reached a sense of scale already anyways. I mean, the total value of all digital currencies is almost $3.5 trillion. As we sit here today, Bitcoin's about $2 trillion of that. And then recent legislation has opened up the stable with the Genius Act for stablecoins. Which are doing something like— I'm not even gonna say it, but it's a lot of money being transacted over digital wires. Even ACH payments, while not a digital currency, goes over an internet-connected vehicle or transformation medium. And so all taxes that companies and humans pay for actually goes over the internet. So already money is moving over the internet. Every day. And so really Bitcoin is the primary enabler of that, which is it took energy, it transferred it into digital bits and bytes, and is used for an exchange of values. So obviously the opportunity there, which is the settlement layer, the slowest-paced layer, is Bitcoin. And recently the latest Taproot update enabled pretty much any other stablecoin or digital currency to run on top of the back of Bitcoin, which runs on the tap— on the back of machine use. It runs on the back of the internet and software. So ultimately what you're betting on is mathematics. And I think in the future, the companies and the sovereign individuals who win, as well as the machines that win, will ultimately be the ones that invest along some of these paradigms. And yeah, that's kind of where we're headed. So I will pause there. We're a few minutes in. Any questions or thoughts from Karl? Otherwise, I'll hand the mic over to him.

Well, I would add just briefly what I'm gonna dig into a little bit more for our audience is the, of course, as I agree, Sean, with the general trends and the 3 laws you pictured, is the requirements, of course, to have a solid implementation. This is like, I still remember the dot-com boom when I was in my late high school years and early college years where everybody was going crazy on the internet and stuff like that. People investing money in tons of companies they didn't barely understand the business model. And then a couple of years later, Boom, or crash, better said. And I think the potential beneficiaries from AI and all these new technology-driven disruptions is massive and huge, but the tidal wave will want to be ridden, otherwise it will crash you. And I think this is one of the most important things that you can learn from all the revolutionary disruptive aspects since the beginning of the Industrial Revolution, almost a quarter of a millennium ago, that the ones who were able to ride the wave, they benefited tremendously. And the others, yeah, they've— their competition fall apart. So, so much for a brief comment on that. And then, yeah, happy to—

Grab your surfboard.

So let me share my screen briefly as well. So because, uh, thinking of AI as one of the, or the momentary key game changer of our times, is that many people still think that we're talking about the usage of a new tool. But to our understanding and the way how we are using it and how we are supporting organizations in the implementation of usage, it's a new mindset. As I said, a tidal wave is coming. Sean digged into that. When you think of the internet being used more than 50% by machines already, this is likely with the more and more sophisticated implementation of AI to go up to 80, 90, even 95-99% over time. So it will also bring a radical decentralization of intelligence in organizations, like automation, of course, wherever possible, but it requires autonomy on different layers and different levels. And from the very bottom to the top of especially bigger scale organizations. In the old mindset, we were much talking about efficiency, centralized control, and task-based work. Today it's about real-time optimization, distributed intelligence, and decision-oriented collaboration. Things that were already, let's say, laid out in different strategic business approaches that I was taught 15 years ago at business schools, maybe some at the tech school I went to 25 years ago, but with this new kind of blending all together with the toolbox of AI with every possibility that's sometimes already on the horizon, sometimes still not. Many, many more things are coming. And let's say the key message I want to focus here is that this transformation that is coming, it must be designed and not improvised because then, and this is the, this is the good news I can share. Of course, there will always be kind of hide and seek and trial and error, but the better you manage and transform this approach, the more beneficiary it will become for your organization. We're talking in the support on that and to ride that business transformation wave, usually about a typical 20% of cost reduction for the implementation, 20% reduced implementation time effort. And after that, besides other tasks, I'm happy to share some more After that, 20% increased performance on general outputs. And we at the SAP, we've built a proven toolbox to help organizations navigate this shift because, as I said, transformation is complex, but it can be structured. And the very first step on that is to recognize that the new strategic approach is needed, like things, the disruptive things of just 2025, tariffs, climate policies, digital disruptions, old strategies basically don't scale any longer. And business models must now integrate sustainability, agility, and intelligence as core design elements, and not in a kind of superficial, barely scratching the surface way of handling, but rather deeply embedded and implemented in the roots of an organization. And for leaders, and this is one of the most important things to my understanding, leaders must ask themselves and their respective organization, what value do we offer in the world where AI does 80+% of all operations? So who is eventually making decisions? And from customers, of course, to the organizations to design and to offer processes, products, services, whatever, and then, uh, to benefit from that. And so The second part is where I would like to show you a little bit on that. It's a case study of a company that we've consulted with over the last couple of months in regards to a roadmap for implementation, a multinational transport and service company. And in general, in the past, people were thinking of even digitization as a process-oriented model and tool. But when you think AI thoroughly, you end up with 4 different dimensions that are absolutely key for a successful implementation. We're talking about processes, we're talking about structures and steps, we're talking about knowledge and skills, and we're talking about the culture. And of course, when it's getting started, you need to take action and to ensure that you have analyzed and documented all the pain points of your organization of today. And then from that derive, and this is what my slide number 2 is showing a little bit in an overview, a kind of an impact effort matrix and a preliminary ranking and valuation of what benefits the organization most in the beginning. So to again differentiate between quick wins, midterm, and long-term implementation requirements. So, and this is just the example from this organization I would like to share. The most effort, but also the most impact was coming in the regards of empowered responsibilities. So this is what, what I spoke about before in decentralizing decisions and put the responsibilities from the top more to the bottom, wherever it is required to make a kind of decision. The coming culture shift and decentralization that is coming with it. And then of course it's about culture. So an organization that was in that way in the past sometimes led a little bit more in a top-down, sometimes already bottom-up approach, of course needs to change in the regards of how leadership is executed as well as how people are encouraged to, to speak up, to step up To get budgets, to get the decision-making tools at hand so that they don't have to run always to the top to get a decision 6 months later. Because the higher the speed level becomes with AI, the more likely it is that within a decision of 6 months, you might be even out of business depending on the size of the organization and the severity of the change and transformation that is coming. Of course, things like the process alignment and the organizational steps and new structure are also the basics and key to success. But the effort for the quick wins, and this is what is important here, outweigh the impact. So this kind of impact effort matrix shall give you a basic insight on what is required to then set up the roadmap and the prioritization on what to do. Because when you think it that way, transformation isn't about boiling the ocean. It's about doing the right things in the right order. And that's where the matrix comes in. So maybe from my side, as a kind of a bottom line, is this age of AI that doesn't wait, and transformation that doesn't start with technology. It starts with leadership. We're not here to— and I think Sean, who will step in just after that, might agree on that— we're not here to sell hype. We're here to equip you with tools, insights, and roadmaps to lead your organization into an AI-ready future. And Start with the biggest friction points. It's about encouragement. This is basically the only thing I would like to emphasize for the closure. Map it to the matrixes. Let's build your AI future, not in theory, but in real transformation.

So then is the 1, 2, 3, 4, 5, is that in the right priority order? Or is that the, like, order that was for this case study? Or do you—

Correct.

That's right.

The 1, 2, 3, 4, 5. This was basically the way how this case study and how We together with the client came up with the coming from the, uh, yeah, from the old, uh, ways of working, how he prioritized in the beginning the things we first talked about processes and then went on. And so this doesn't have an order saying 1 is more important or less important than 2 or 3 or 4 or 5, but it was rather the summarization. of the key elements we were talking about.

Yeah, it makes sense. Okay, so a different company, depending on their sophistication level, their place in the transformation effort, the order of operations could be different. I mean, everyone wants to start with low effort, high impact, but it may be technically difficult to get it over the line. So you kind of almost have to like have a 3rd dimension here, which is like feasibility in each company, because the order, even though you may have like, here's the right order in theory, like the order for each organization could totally be different. Have you found that to be true?

I would definitely agree on that. You might, you might turn the phrase in that way, saying this is a global toolbox that works for every organization. But you need to align and tailor-make it for every different approach and every different need and requirement. So the general toolbox is good, but you need to take a look at what the current pain points and the most important pain points are, because why I emphasize the quick wins a little bit before. Is to celebrate success, because if people think that this won't work, then AI and this whole transformation, which is an absolutely long-run effort, will start to wind down otherwise. But if you celebrate quick wins and you see the impacts coming, then people usually start aligning to new models and then continuing down the road.

Makes sense. All right, great. So let's, uh, let's wrap up, do some conclusion statements, and, um, then we will bid you adieu and set, set the audience sail on the seas of the unknown. And, uh, hope you got a good ship, strong ship made of steel. Okay. Yeah, if you want to pull this off, Karl, we'll do takeaways.

Yeah, well, maybe the takeaways from my perspective is I would like to quote Hemingway in a certain way. It's, if you want people to build a boat, don't tell them how to build a boat, but rather to tell them about their vast interest of the ocean, and people will follow en suite within a short period of time. And even more, the risks are massive, but the benefits that an organization can derive from that far outweigh, to my understanding, the risks of it. And even we as a consulting company, we use AI tools in many, many different occasions for us internally as well as together with our clients and for our clients and everybody benefiting from that. So I can only encourage our audience, it takes courage, but it's worth doing.

Great, love it.

Okay.

You want to stop share quick?

Yeah.

Okay, great. Final statements. I've been doing and commercializing emerging technology for pretty much my entire life. This stuff is incredibly difficult. I have a technical degree in mathematics. I barely scratched the surface on the depth and breadth of these technologies after 3 decades spending pretty much every waking moment thinking about it, researching it, building it, investing in it. So if this is non-core to your business, if you're not a tech company, you need help. Just Flat out, period. Even if you are a tech company, because we've had tech companies come to us and ask us a week before their major hardware announcement, like big tech companies, like what should our products do that they spent years building? It just proves to me that very few people in the world have the answer. And I think everyone's trying to figure it out at the moment. But there are principles. Here and unifying constructs that help you be right much higher than 50% of the time. And now with tools like AI that are pattern matching and compression engines that can consolidate a lot of information and draw parallels and connections between disparate topics that you can't see, it means blind spots are getting reduced given you seek them. So there is a way through the darkness, but you need a lamp. So I would caution everybody from DIY. This is the, the sheer opportunity is too great. And if you haven't spent a career understanding where the right levers are, you're gonna waste time, money, and the window. That's open may close before you figure it out. And we're at a precipice here where the monetary premium on assets like stocks can completely evaporate and get sucked up by Bitcoin. You're seeing already changes in organizational structures and dynamics, and I'm having conversations with private equity investment committees on whether a business will still be viable at the end of 5 years that may be growing substantially already. So people are taking this incredibly seriously, and I would just say, don't DIY. You're gonna give yourself a ton of pain. That's it for me. Yeah, I think there's a lot of opportunity there, but you need the right tools in your tool belt in order to capture it. That's it for me. We're mid-2025. The back half of this year should be very interesting, especially through the final hold period of the 2020s onto 2030. And yeah, we'll still be here pushing the ball down the field, as it were. So, Karl, any last words before we break?

Yeah, thank you on that. Thank you again on the opportunity, Sean. And anybody who would be interested in learning more and getting some support on that, including a little bit extended version of parts of the slide deck that I was showing today, just reach out to us through our channels that you all know, and then we're happy to assist in the way of, like Sean said, not DIYing your process and the transformation on that. Thank you. It's been a pleasure.

Great. All right, everybody, have a good one.