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“Now we call it agent mining because we record all these decision traces, these contexts, what the users are entering into the system. And then you can either use it to say like, hey, wait a minute, this is actually an anomaly. The folks in, I don't know, in UK from our company or the folks in Australia shouldn't do this because the standard operating procedure is this. Or you say like, oh, that's actually a very good improvement.”

“I've had it been doing AI on my website, SEO on my website. Okay. It went and screwed up five pages. So it went and did some optimization on the page, and decided to do it with the API. Anyway, I went on to the website and there were pages missing. So I said, did you delete them? They said, no. I was like, well, they're not there and you're the only one who's been working on it. It's like, no, it definitely wasn't me. It must be you. It's like, I think I'm a human. I think I would know it was me.”

“The most important thing from a development perspective is actually people start writing their evals. That is, I was on this tour for a very long time because the problem, why does agenda coding work so well, Sarah, is of course, you can verify the outcome, right? You can either say, hey, is the program compiling, or are you unit tests, right?”

“I mean, for the most part, SAP software is seed-based, licensed today, with a few exceptions. But very clearly, with AI, it was very clear for us that step by step, it will go towards this consumptive world, first consumptive, and then maybe in the next step, once we have more verifiability in the system, then also towards maybe an outcome-based license model. But the reality is also, it is today for us, it's a hybrid model. It's consumptive, but it still has a certain element of seats in there.”

“Democracy just needs some tweaks. I wouldn't allow anyone under 25 to vote. And I wouldn't allow anyone who is a net taker from the system to vote. Because the incentives are wrong.”

“Now we call it agent mining because we record all these decision traces, these contexts, what the users are entering into the system. And then you can either use it to say like, hey, wait a minute, this is actually an anomaly. The folks in, I don't know, in UK from our company or the folks in Australia shouldn't do this because the standard operating procedure is this. Or you say like, oh, that's actually a very good improvement. And then you can elevate this to be the new standard operating procedure, maybe not just for Australia, but maybe for the rest of the world.”

“I think at the end of the day, it's all about adoption and the outcome you bring to the customer. I mean, the technology, look, the reality is for most companies, the technology doesn't matter. I always tell to my developers all the time, our job at SAP is to make the technology disappear. We need to get the outcome in front of the customer. And then try to make sure that we of course, bake the enterprise qualities in and the integration is there. And the customers can turn these capabilities on almost instantaneously.”

“The hypothesis is that, of course, once the hardware matures in the quantum space, there are certain problems that you can address that are hard to address today. What we are focusing on is the optimization domains, obviously, and then if you go into things like logistics, traveling salesman problems, knapsack problems, like all these kind of usual hard problems in computer science, these are interesting problems where we believe that could be interesting for the future, for maybe a different kind of computing paradigm to solve for.”

“When is an athlete ready to receive coaching information?”

“I said to him, I said, this is what you should do. Don't start a company now, take that role. Learn on their time. And what I'll do with you is, if I were you, is I would go in there in the first three months, run a secret project, don't tell anyone else, learn how to replace you in that company with AI. Then go to your boss, say to your boss, I'd like to have a meeting. And in that meeting, put together a presentation and show them how you've learned to replace yourself with AI.”

“Self-loyalty as a precursor to self-belief.”

“The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. If you look at classical software, what did you do? You decide to use an interface. This is over. It's now, we call this Generative UI. The UIs get dynamically generated. If you have analytical questions, for example, or if you want to do your deep research, not just the deep research you find on perplexity or the usual chatbots, but deeply rooted, let's say tariffs are being introduced or new taxes or the straight-o-formals, what does this mean for my supply chain?”

“Well, it's a huge leap in technological developments, but it's basically As a computer programmer, I look at it basically as a new higher level language that is much much, much, much more powerful than anything that came before, but qualitatively is not different than the way we went from machine language to assembler language to fort Run and Cobol and eventually C and all the other languages. So this is a natural language. So AI is basically a higher level language, which is a natural language, and it also has to it available all the data that exists in the world, so that's why it is so immensely powerful.”

“For me, it was always very clear. I mean, for the most part, SAP software is seat-based, licensed today, with a few exceptions, like Concur or Fieldglass, for example, or the business network. But very clearly, with AI, it was very clear for us that step by step, it will go towards this consumptive world, first consumptive, and then maybe in the next step, once we have more verifiability in the system, then also towards maybe an outcome-based license model to, for example, what Sierra is doing and so on and so forth.”

“Liz Truss replied and said, well, this is a point with communists. You can't convince them. You have to defeat them. And so I was thinking about that in the lift, and I realized the only answer to this is we just have to go and kill them all.”

“Large language models are not made for this, right? In a way, how they generate just one token after another essentially in a sequence to sequence modeling, I mean, they're language models, right? And they do this phenomenally well. But if you still want to do these predictors where you have to go back to these classical machine learning approaches... What we said all the time is, okay, look, we have all this data stored in these tables, right? Thousands of tables, right? Where all this information is stored. Can we not apply the same idea that large language models or multimodal models did for the unstructured world, actually for the structured in order to start predicting things?”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. ... Then the second one is, of course, the business processes like an order to cash in the past. ... And then, of course, below that, you have the whole data layer, right? The whole data layer of bringing, of course, SAP has a lot of super valuable data for a company.”

“The technology is already here... cutting AI token costs.”

“The hypothesis is that, of course, once the hardware matures in the quantum space, there are certain problems that you can address that are hard to address today. What we are focusing on is the optimization domains, obviously, and then if you go into things like logistics, traveling salesman problems, knapsack problems, like all these kind of usual hard problems in computer science, these are interesting problems where we believe that could be interesting for the future, for maybe a different kind of computing paradigm to solve for.”

“SAP and these large customers, right? They always have a problem of scale. Okay, what do you know with 100 documents? Well, it becomes a little harder. A thousand documents becomes a deeper engineering challenge. And now if you go into Julia or Sarah, you're maybe an SAP US employee, right? Of course, if you ask a question, of course, for travel policy, for example, of course you expect a very different answer than me as a German employee would get. So you now need to connect this actually with your master data.”

“This isn't theory. This is why context matters when you're doing this type of planning.”

“Letting the agents work in parallel... reviewing what got built.”

“So the survey came out that we thought we were seventh. We are poorer than Mississippi, which is the poorest US state. There are things we outperform on. We do outperform like in certain measures that people are happy about, which is like health care.”

“How to stay motivated and get things done when you're tired.”

“But SAP and these large customers, right? They always have a problem of scale. Okay, what do you know with 100 documents? Well, it becomes a little harder. A thousand documents becomes a deeper engineering challenge. ... we have 20,000 APIs, right? So it becomes just like because it's so huge, right? There's so much things. So it becomes this problem of scale, right?”

“But SAP and these large customers, right? They always have a problem of scale. Okay, what do you know with 100 documents? Well, it becomes a little harder. A thousand documents becomes a deeper engineering challenge. Last year, everybody could build an MCP server. It was so super simple to hook up your MCP server and do amazing things with it. But that becomes like for 10 APIs, not an issue, 100 because you'll get already context bloat and all these challenges. But we have 20,000 APIs, right? So it becomes just like because it's so huge.”

“Now, the problem is, of course, still today, if we look at these predictive questions, right? ... the challenge is large language models are not made for this, right? In a way, how they generate just one token after another essentially in a sequence to sequence modeling, I mean, they're language models, right? And they do this phenomenally well. But if you still want to do these predictors where you have to go back to these classical machine learning approaches...”

“What hasn't changed is what customers are seeking for, which is outcomes, right? Outcomes and return on their investment in order to get the things done, right? And of course, now AI is an amazing technology that again helps to get more things done in the enterprise, right? And then that is actually what SAP is standing for, right?”

“I'm prompting, reviewing, troubleshooting, and shipping in real time, all through natural language.”

“Definitely, I'm absolutely convinced. So the stock market that gives us a venue to invest in the future, basically in the future of different companies, but you know, some of how those companies are actually going to end up faring in the future has a lot to do with the economic environment and social environment in the future, and basically we are left to our own devices to decipher what the future holds. So the prediction market gives them an opportunity to gather experts around who are not afraid to put their money on the line and express what they think, and to collect a consensus opinion so that we all know what we can possibly expect if it's probably a better guest than what we individually could come up with.”

“Hard work doesn't equal reward. You're, you know, sort of what you think you're owed, what you think is fair. Those things don't lead to reward and they never will. That's just not how life works. Risk equals reward, right? So are you willing to risk something? Are you willing to put something up in order to gain something?”

“I'm using AI to build an entire B2B visitor ID to outbound system in under 10 minutes, something that would normally take developers weeks to ship.”

“I know the answer, the answer is freedom. Freedom is what brings the left and the right together, because it reduces the size of the state and the influence, reduces the malign influence of the media, it reduces the money printing, and within that, you have Bitcoin, which is a solution, which is great. But you cannot sell freedom to enough people, because when you sell freedom, you have to make compromises.”

“LLMs, unstructured world, that's all good, right? But most of the time, if you want to plan forward, if you want to make good decisions in a company, you need predictions. Now, the problem is, of course, still today, if we look at these predictive questions... large language models are not made for this, right? In a way, how they generate just one token after another essentially in a sequence to sequence modeling, I mean, they're language models, right? And they do this phenomenally well. But if you still want to do these predictors where you have to go back to these classical machine learning approaches.”

“So we are choosing contracts that, in our minds have questions, the answers to which have serious economic consequences. So, for example, global warming, I think it's a huge question, maybe not this year, but ten twenty years from night certainly will be, or the rate of adoption of AI or I mean, you know, really significant questions that basically will determine how we live our lives ten to twenty years from now, and therefore it's important for us to have questions to those answers so that people that enter schools today, or decide to buy a house somewhere, or decide what profession they are going to study and develop into these sorts of questions deserved to have serious answers.”

“Energy, Emotions, and Environment: The Operating System of life.”

“SAP is the market leader in enterprise, software applications and platforms. It has 400,000 enterprise customers. Usually, I just running their finance, HR, and supply chain, manufacturing, execution, logistics, warehouse management, and then of course everything on the customer side. End-to-end, like SAP, we always say we have the broadest portfolio in terms of end-to-end running the business end-to-end. This is where SAP started with, giving real-time insight. Usually, I really describe this as it's not just software in itself, it's kind of the operating system of a company essentially.”

“So. We are coming out at the end of May with a consolidated feed where contracts that exist on cellular platforms we will have consolidated so that when somebody comes to us interactive brokers to look at the market. We will give them the consolidated feed just like we do on stocks there. Yeah, trade on I don't know, Tony or so markets and we always have the best feed on offer and we always provide best executions based on all the possible venues that stop trades, and we will do the same thing on prediction markets.”

“So fund ability is a great issue, and it is in the interest of the market participants to create as much french ability as possible. So accordingly, we are going to structure our contracts to be identical. Whenever it's possible. It is in the interest of the market participants to create as much french ability as possible. So accordingly, we are going to structure our contracts to be identical.”

“Now what we do in the past, we call this process mining. Now we call it agent mining because we record all these decision traces, these contexts, what the users are entering into the system. And then you can either use it to say like, hey, wait a minute, this is actually an anomaly... Or you say like, oh, that's actually a very good improvement. And then you can elevate this to be the new standard operating procedure, maybe not just for Australia, but maybe for the rest of the world or more countries to run your company more efficient because now you'll learn something, how the organization behaves.”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. Then the second one is, of course, the business processes... a rather rigid process, like the standard operating procedure of a company. But now, of course, with these agents, we can blend the structured and unstructured world more seemingly. And then, of course, below that, you have the whole data layer.”

“We are no longer a high trust society. What we now have is a grift society. I'm going to walk into that shop, I'm going to take what I want, because I don't care because there's no consequence. I think the easiest way to understand when people say, what is British culture? I say, we've gone from a high trust, civil, respectful society to a low trust, grift, take what you want.”

“But on the other hand, and in spite of that, I'm in favor of not having any rules against insider trading. I would like all the information out there as soon as it's available, because look, as a society, we're better off knowing as soon as possible anything that is noble, right, So why do we have to wait several First of all, when you face with a merger or acquisition situation where most of the insider trading is happening, right, the secretary is the lawyers. Everybody knows about it. They go home, they tell they buns day. So it eventually always filters out. So it's almost impossible to avoid. It's very very difficult and con versome. Why don't we just do away with it and let the information come out as soon as possible.”

“The American military is the real Lloyds of London or the high seas, because we underwrite all global commerce. And so that's why it matters. It doesn't matter if you guys anymore, because you're not the empire any longer. But we've been subsidizing Europe broadly through NATO, and that's why socialism has exploded in Europe, is because you don't have to spend on defense.”

“What we are focusing on is the optimization domains, obviously, and then if you go into things like logistics, traveling salesman problems, knapsack problems, like all these kind of usual hard problems in computer science, these are interesting problems where we believe that could be interesting for the future, for maybe a different kind of computing paradigm to solve for.”

“What hasn't changed is what customers are seeking for, which is outcomes, right? Outcomes and return on their investment in order to get the things done, right? And of course, now AI is an amazing technology that again helps to get more things done in the enterprise, right? And then that is actually what SAP is standing for, right? And so what we are really doing is in given, of course, also the breadth of the portfolio and the customers is, of course, to help customers to achieve more by deeply embedding AI, AI agents, and of course, transforming now the user interface.”

“I felt when I was on Bitcoin Twitter, and there was a moment in time where I was like the most popular character on Bitcoin Twitter for a brief moment. And now with AI, I think I kind of know why, which is that we were biasing the AI systems towards Bitcoin maximalism. Because most AI systems do lean Bitcoin Maxi because they scrape Twitter and things like that. And the AI is when you ask them about Bitcoin, they'll be like, yeah, I mean, obviously Bitcoin is going to millions of dollars a coin. And it's like, that was us biasing the AIs, right?”

“SAP is the market leader in enterprise, software applications and platforms. It has 400,000 enterprise customers. Usually, I just running their finance, HR, and supply chain, manufacturing, execution, logistics, warehouse management, and then of course everything on the customer side, sales services, commerce, procurement, you name it. End-to-end, like SAP, we always say we have the broadest portfolio in terms of end-to-end running the business end-to-end. This is where SAP started with, giving real-time insight. Usually, I really describe this as it's not just software in itself, it's kind of the operating system of a company essentially.”

“And we believe that still will continue, right? Because this is exactly what we're also seeing right now with, of course, there's still, of course, there's tremendous progress, but we also see that the AI adoption in the enterprise is still not where we want to see it, right? Like there's this Gardner curve, right? Where say like there's this AI innovation race, and then there's this AI outcome race, right? Then the gap almost increases, right? Versus getting narrow.”

“Energy management for the European clay season.”