SAP CEO: AI Won't Kill Software, But It Will Change Your Job — With Christian Klein

21:29Big Technology Podcast Host:You know, pre-Chatship UTA, I wrote a book talking about how the tech giants were already using AI to like minimize rote work and make room for more inventive work. And, you know, it came out in April 2020. Not a great time to release a book, but I'm glad I did it. And the main pushback that I got was, or basically what I said was about work, was that you take people off of these rote tasks and you put them on more inventive tasks. And it's more fulfilling and it helps a company grow and move faster and be more inventive. And the main pushback I got was when AI automates back office work, like moving data from one place to another.

22:11Big Technology Podcast Host:or like you know one example that we gave in the book was like ai is going to eventually be able to write like new hire letters and you know benefits letters and stuff like that which like was felt crazy at the time but it's been doing it for years now the pushback was people who are used to doing this like data let's say moving data from one to another yes are not going to want to do uh the more inventive work it's a different type of thinking it requires different culture different set of permissions and they're not going to be able to adapt to the new type of working what's your read on that is that is that a fair criticism um it's yeah it also depends on you know on each individual i give you an example in our world um you know for 50 years we coded software and for 50 years we were never running out of customer requirements on features new features for our software.

23:06But over time, obviously, you know the business, you know the business which you're running. But the product managers didn't need to go out always to the customer and to completely reinvent how businesses is running because it was one feature more and the software was running the business in a very stable way. Now with AI, we tell our product managers, no, no, no, no. Don't sit too much in-house. Go to the customer and reinvent how supply chain works, how we do inventory optimization, how payroll will work in the future so that not an army of people need to make sure that the payroll is working correctly and people are getting paid in a correct way.

23:42And now it's really about this excitement of, oh, now I'm going there and with this technology, I can really completely reinvent how businesses, how companies will work in the future. I really reinvent the future of work. And I would say with the vast majority of our people, that creates excitement. excitement. Are there maybe a few people who say, ah, I'm not sure if I'm into that. I'm used to a certain type of working. Yeah, it could be. But this is, you know, really about, you know, how do you bring your people with you and how you do the change management. I'm not saying that every, every single employee will make that move.

24:20But I guess a lot of people get it. Oh my God, this is exciting. This is new. And I love to learn something new in my working life. Okay.

24:28Big Technology Podcast Host:And you've segued perfectly to the cesspocalypse because, you know, there was this belief and remains a belief among many that software like yours was developed for, you develop it for the masses, right? So you build software like SAP, people have to be able to use it in different functions, different companies. And so when you get a seat to it, you're going to get this software that's kind of built for everyone. There are going to be features you're going to want to use, features you're never going to click on. And this idea, once this idea came through, became popularized, that once people could build software on their own, you know, because you could prompt it and it can show up for you, then the old way of building software, old way of building software, wasn't going to work anymore.

25:17Big Technology Podcast Host:And we would see new software, vibe coded, replace, you know, the old software. And that narrative has plagued software companies until recently for about the better part of a year at this point. Let's just high level. What's your reaction to that? What was your feeling as that narrative became popular? Okay. Let's go back in time, especially when this narrative came up. I mean, of course, the first moment you think to yourself, okay, I see development productivity is going up like hell. Why can't people just reproduce what we built over 50 years? And then I ask my product managers and my portfolio team and go through our portfolio.

26:03And which solution do you believe can, you know, can you wipe code easily and just reproduce it and replace it maybe? And the answer was very quickly that they said, oh, my God, I mean, you know, we are building ERPs. So we are building supply chain, payroll, finance. It's not only that you need, you know, you have 7 million data fields in such an ERP, which you need to correlate. So there are hundreds of millions data correlations, what you need to understand to run a business. But there is also, of course, deep process knowledge by industry, you know, by country, local requirements. I mean, we are investing hundreds of millions every year to keep our software compliant.

26:43And then, you know, the answer was, OK, there may be one or two solutions, small solutions where there is not so much process and data context inside, which you might, you know, just can replicate. But the rest, I mean, definitely, you know, you need to have a lot of domain knowledge. It's definitely not easily you can definitely not easily write code it. And so, yeah, and that gave me a good feeling. But still, Alex, I mean, obviously, there's not a time to lean back because clearly now, I guess, in the next phase, you know, the value creation is now moving up from the system of record into the organic AI layer.

27:20So, absolutely, our AI needs to be world class to still also defend our existence in the system of record.

27:27Big Technology Podcast Host:Okay. But now let's go back to something you said earlier. Yes. Where you said that AI is getting good enough to be like 100 % accurate on some of these tasks. You said that AI is getting good enough to sometimes reconcile data.

27:43Big Technology Podcast Host:I'll sit on the side of the SaaS podcast and you can be a software for this example. SaaS podcast people would say, man, the AI is getting good enough that those hurdles, domain knowledge, process knowledge, being able to put data together, it's going to do it. And therefore, the moat, while it still exists today because the AI wasn't good enough, when the AI gets good enough, that moat's gone. What do you think? Yeah, I mean, when we talked about the beginning about all of this accuracy, I mean, that was the accuracy on our AI platform. I mean, we tag in LLM and then we put, you know, our AI foundation next to it where we train business data, business process knowledge, where we build knowledge graphs, semantical ontology layers.

28:30And that's why we are giving the LLMs now the understanding to deliver agents who can actually live up to all of these accuracy targets. We talked earlier on about it. If you just use an LLM alone, there's no way that you can run a warehouse with it, that you can do a financial close with it, et cetera, et cetera. But it's an LLM plus our AI foundation, plus the governance. It's not only the business data and the business process. It's also that you need to understand all the legal requirements. I mean, the agents cannot just go wild and do a financial close without understanding the tax requirements in a certain country.

29:05So there's more to it. And that's why, you know, I'm so confident that, you know, these LLMs are super powerful without any doubt and they're getting better. But they need the process context, the data context and the governance. And this is, you know, I would say where SAP, I mean, this is what we are doing for living since 50 years.

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32:34Big Technology Podcast Host:And we're back here on Big Technology Podcast with SAP CEO Christian Klein. Great to see you. Thank you for coming in. I said before the break, I wasn't done with my questions and I'm sure not. All right. So, okay. This idea that like AI is going to get better and people can vibe code, you know, their own SAP is going to be hard because even if the AI gets better, all this domain specific knowledge is tough to include. I have this theory that what you might see instead of people vibe coding software is that the AI companies, which currently make, like the foundational labs, OpenAI and Anthropic, which currently make their best models available via API, will close those models off.

33:22Big Technology Podcast Host:So for instance, let's say GPT-6 Astra right now, it's pretty smart, but it has those limitations. But we've seen unreleased versions of OpenAI's technology be able to do things like team up in a swarm, work for 88 hours and solve the hardest math problems ever. so is there a world where they say we're not gonna keep we're not gonna release these models maybe for safety and but we are gonna put them on some of these problems that SAP has been working on for a long time and then decide that in order to make money because AI models are gonna commoditize they have to go upstream which means take that knowledge and do a chatbot version of your business yeah yeah fair question And look, the truth is, when you come to our business AI platform as a citizen developer, as a business user, or, you know, as an IT guy, a developer, you can actually develop agents on top of our platform.

34:27So we are delivering hundreds of agents, standard agents for finance, supply chain, HR, but also the business and the pro code, the developers can also build agents on our platform. And what you find is all these models were you just talking about. But what you also find then on top of it is that we connect those models to the business process and the data knowledge, the context of what our ERP actually knows about your business. And we are reproducing that in our ontology, in our data layer. So, you know, you have all of these models. What's ontology? Explain that. The ontology is actually when you have, you know, you have SAP data, but you also have non-SAP data, what the agents need to understand when you do asset management.

35:10The agent needs to understand the sensor data, the signals of a machine to understand when does the machine need maintenance. And then it needs to pair this with the SAP data about, okay, let's create a maintenance order. Who is the worker who can fix this machine, et cetera, et cetera. So it's the logic. And that is the ontology of what we are building now, what we are bringing together. And we also tell the agent, okay, machine has a problem. Where do I find the spare part for this machine who is maybe not working anymore? where can I find the worker who is fixing it etc and everything on time so that there is no downtime to the machine and that is the ontology that is the semantical layer and again why would you go to you know a third party platform when you find all of these great models on our platform plus you get all of the semantics the ontology plus we are going to run these agents to make sure they adhere to the governance of your company of a country of an industry

36:04Big Technology Podcast Host:so you're skeptical that even if they hold those so right now OpenAI and Anthropic are making their models available on your platform but you're skeptical that if they were to withhold their latest models from your platform and try to do it themselves they'd be able to do it I'm you know the same question I got years ago about the hyperscalers I mean we are this is a different technology yeah that's a different technology but we are partnering and you know they are of course they are super powerful I mean they are running you know the most systems of the world. They are owning a lot of our systems.

36:41And they could also say, yeah, we are not providing this infrastructure anymore. And this is similar. But I see there are so many models now coming to the market that I would say there is a healthy competition also going on. And we are not seeing this one model who solves all of the problems. Actually, we are seeing a lot of open source, a lot of own weight models who are also doing a very, very good job when it comes to also looking at the price outcome ratio of an agent. Yeah, it's not only important that the agent does a great job, but it's also at what cost does the agent does a good job. And so I feel there's so much healthy competition that I'm not so much worried about, okay, there is now a provider saying, oh, we are not offering you this model anymore.

37:22Because again, there is competition, there are many models, and there is not this one model who actually will solve all of the problems.

37:29Big Technology Podcast Host:Let me ask you this. Would you accept, or I don't know if you do this already, would you accept open AI or Anthropic as like a front end to your software? And by that, I mean, like you have all this great data. Could, let's say a user of SAP who's used to like logging in and going through the graphical user interface or using your agents, are you open to them, like having a connector through ChatGPT, for instance, and being able to query that data and do that through ChatGPT for business? I mean, the way how we solve for that is there is Joule work, you know, our co-worker. It's our interface.

38:04But you won't integrate. We actually embed those models into Joule work. So you can also switch the models. You can do certain tasks with Antropic. You can also do certain tasks. If you're in Europe and soft frontier is important, you can also use Mistwell. You can use their latest model. And then, but then it's really about SAP's interface. That's our Joule work. It's our co-worker. and then of course there will be also scenarios where you will build an agent let's say Salesforce, Adobe and so on and they need to also then access SAP data and that's totally fine as long as people are going through our API gateway and the agent gateway but I would say predominantly what we are seeing now with our customers that users working with SAP software they will use Joulework But there will be definitely also third party agents accessing SAP who will have access to SAP systems via our agent gateway.

39:03Including OpenAI and Anthropik? Including. So there will be a world where like. Yes, yes, yes. But it needs to be controlled. And the customers will also rely on that, that it goes through our agent gateway. Because again, governance is important. Whiteback is important. You don't want to go having an AI agent doing uncontrolled things in your ERP system. So that's why I'm very, very confident that, first of all, a lot of the SAP users who do work in finance, in HR and supply chain will come via true work. Of course, that is the place to be. You can do everything what you do with an LLM model.

39:37Plus, you get the SAP data, the business process, the context. We also have a data platform who infuses non-SAP data. And if you build a third-party agent, for example, for your next marketing campaign and you want to weed out some other data, I mean, yes, you can do it. but it will happen via our agent gateway and the marketing users are anyways not you know our prime users of the sap software okay let me ask the question one more time in a maybe

40:02Big Technology Podcast Host:different way because i don't know if i'm getting the right answer or fully understanding the answer getting the question out the right way all right there's an example recently where meta has this muse personal assistant and you could get muse to shop for you yes there's two approaches to this yes there's amazon's approach where meta is like uh we want muse to be able to use amazon and people can say buy me a leaf blower and then the muse will go into amazon and buy them a leaf blower yeah and you won't ever have to touch amazon yes and then there's the shot okay so amazon blocked that yes shopify said that's fine so we shopify said we want a third-party agent to come in and use our technology and Amazon said no if you want to buy on Amazon you can use you can use amazon.com or use our agent yes what is your philosophy the Amazon or the Shopify actually we offer both okay so that's and the ideal model the idea world is that you know because our AI platform tool work is so great I mean in my eyes customers end users will understand why would I use an LLM model standalone if I'm not getting, you know, the same business context and not the same governance.

41:13So we have a connection. We are, yeah, but the API connection is not giving you all the business context. It's not giving you the semantics. It's not giving you this deep process mode.

41:22Big Technology Podcast Host:So it limits some of the stuff that... Yeah, yeah. Of course, a pure API connection, pure MCP server is not the same like the ontology, the context we are having on our AI platform. So, and the governance, yeah, when it's SAP managed, we also make sure that you're, when you're doing a financial analysis that, you know, when we govern it via TrueWork, it is ensure that only the people who are allowed to see the numbers are also getting access to this report. So the agents understand that. So why would you then go into a third party platform where you have, yes, API access via our API gateway? That's possible.

41:56You can access from a third party platform, you know, into the SAP system via the API gateway. But there are so many reasons for SAP users to use TrueWork because better context, better governance that we believe, yes, the main route will go via TrueWork. Yours.

42:14Big Technology Podcast Host:Yeah. It is interesting. Like when we sit down with someone like yourself and hear it, it's like, oh, this is actually not as simple as a one-sentence tweet. Yeah. And that's what the market seems to have been responding to. I mean, SAP this year is down 21%, but it's been up 43 % over the past two months. It's a roller coaster for you, isn't it? Oh, yes. Oh, yes. But I'm used to it. I mean, when I became CEO several years, when I'm used, you know, I did the cloud transformation. Right. The market will say, oh, are they going to make it or not? And, you know, the share price was down. Then we reached the all-time high.

42:52We proved that we can transform the company. And that's another transformation. and it's a similar pattern. Now, the good piece this time, we are not alone in this. The whole software industry went down. But I guess more and more investors, analysts, and of course also customers realize, hey, an LLM alone will not cut it. So we will need the software, the app, the Wubrides, the context, the governance. And then the LLMs are of course the key to unlock this value. But it needs to be really brought together with the business process context and the governance. And I guess that is something what in the last two months many people realized in the market as well.

43:30Big Technology Podcast Host:Yeah, software has made a dramatic comeback. Oh, yes. Oh, yes. I want to hear a little bit about, you mentioned open source. I want to hear a little bit about the way that you think about spending with these models and which models you want to use. So we recently on the show cited some data from Ramp. They have an economics lab, which obviously is like they're getting credit card data from lots of startups. But they think it's indicative of what the rest of the economy is going to do. In August, 53 % of spend on AI was on the frontier models, models like Opus and Fable. In September, that number was 40, or at the end of August, the number was 45%, which is quite a decline.

44:15Big Technology Podcast Host:So from 53 % to 45 % decline in terms of the spend on the frontier. Yes. What that suggests is that companies are discovering they don't need to use the best AI to succeed. Yes. Have you found that at SAP? Similar patterns, yeah, absolutely. And I mean, again, that's also maybe to the discussion, you know, to the point you brought up earlier on. Would customers go via third-party platform directly to the SAP system or will they use, you know, our tool work, our platform? I mean, in our platform, you're also not logged into one model because you see, yeah, with every model release, you know, it can change again.

44:55You know, the one model gets better. Then there's a new open source model who is really producing great results. And we also see agent by agent, not every model, you know, performs the same. So this multimodal being agnostic is really also a key differentiator, what customers are loving. They're saying, okay, I'm not logged into any frontier model. And SAP is even doing the switching from one model to another to always optimize the token spend with regard to the outcome, in comparison to the outcome. And now to your question. Yes, indeed. I mean, we, of course, also started, you know, coding with a lot of frontier models.

45:28still coding is something what we do a lot with the frontier models also for soft frontier reasons etc etc but then you know for many agents we are running you know and for also for tasks like okay create me a headcount report for my manager or do me a financial report we actually see that some of the open source model performs so good that it and then compare it to the cost of those models we are going to switch and many agents we are building in the meantime have seen the fifth model because we are always optimizing the outcome versus the tokens and the cost we pay for such a model. Yeah. And has that emphasis on cost grown recently?

46:07Oh, yes. Of course. Because the overall token spend is up, which is good, but it doesn't help you if some of your employees is getting 20 % more productive if at the same time the cost is going 30 % more up. And of course. And by the way, this is also for our product manager, a cultural change. We told them, test every agent which we are leasing to the market needs to be tested with different models. And they need to do this all the time. And because we see a new model comes, oh, maybe we can switch the model. Because it's a better price outcome ratio at the end of the day. And so, yeah, that will continue.

46:41And we see this also as a clear differentiation that there is a software company like SAP who does this for you so that your AI tokens are not running away while you are celebrating maybe your productivity gains. And then at the end, you look at your P &Rs, that's, oh, shoot, you know, my profit is actually not hitting the mark anymore. Yeah.

47:02Big Technology Podcast Host:It's interesting, right? It says a couple of things, actually. First of all, it says the AI has gotten good enough that the frontiers is only going to be applicable to certain use cases. Yes. Which is wild. Yes. It also says the standard models are, you know, are really working. And an interesting thing about the standard models is they don't tend to be at like standard versus frontier. They don't tend to be like, you know, 80 % of the price for 80 % of the performance. Yes. Right. They tend to be like 10 % of the price for 80 % of the performance. Yes. And look, I mean, we also acquired an AI model, a tabular AI model who does predictions.

47:38And I mean, it's remarkable. We took all the data of a retailer, SAP, non-SAP, and without a data scientist touching it and building data pipelines, doing the semantics, matching the data so that it makes sense, the Tableau AI model actually did it with the same accuracy for the customer than what a team of 10 data scientists did before. So you see, you know, it's not only the frontier models, it's also the Tableau AI models. There are other models coming up for the structured data, all the models we are building for the business data. So I would say there is not this frontier versus, you know, open source standard models.

48:15There's also no Tapler AI models coming because for predictions, these models are becoming better and better as well. As a business person, what do you think it says about the business of these frontier AI labs?

48:29Big Technology Podcast Host:If the frontier is, you know, it's harder to charge a premium for the frontier. Yeah, keep on innovating. Keep on, you know, making the model better and better. No, they are making the model better and better. but people don't need the cutting edge. But others are on it as well. So that's why I'm saying, I'm not afraid of all that one model or one provider says, oh, you are not going to allow to use our model anymore. There are so many models in the meantime and they are making all good progress. But again, it's the same like SAP. When Hasso Blattner founded this company, there was not many competitors.

49:02Now we have hundreds of competitors. I mean, that's the name of the game. We have seen that. Others have seen that. The only way is to keep on innovating to justify the price. And if you don't, if you're not much better than the rest, of course, at a certain point, it's then hard to justify the price. But this is a game we're all playing.

49:18Big Technology Podcast Host:I'm going to answer my own question. I think that if you're spending billions and billions on training frontier models and the standard models are doing just as good or a good enough job for many of your customers to the point where we're seeing things like 8 % decline, not year over year, but month over month, that might be a problem for your business. All right. I won't ask you to agree with me on that one or disagree, but I have to put that in for the record. Yeah, I mean, I guess this is also the question that everyone at the Frontier Model is also asking themselves. And it's also interesting, maybe one addition to that.

49:56I mean, there's, of course, a financial close. There is inventory. This is when you ship, when you bill. Accuracy needs to be record high. We have a rule, 95 % or better. Otherwise, it's not worth to ship the agent. Customers will just not use it. But then there are many other Agenda Gear use cases. Take, you know, a customer briefing, a headcount report, what I mentioned. I mean, this is not, doesn't need to always be 98%. Yeah, it's also okay if it's 94 % because I'm still, you know, all the people who needed to prepare that stuff, you know, we need less time on that. We can focus more time on the value-adding stuff, prepare myself for the meeting, et cetera, et cetera.

50:33so and that's why i would also say all of these frontier models and you know we don't need to always use the best best model from an outcome perspective you always need to match it to the price and to the nature of the model what is the accuracy what i need yeah to have the acceptance

50:50Big Technology Podcast Host:of the business yeah very interesting we we have ipo so we'll learn a little bit more about this how about how about you know you've sort of talked a little bit about this token maxing reckoning or the token reckoning ramp also has interesting data about uh the spend for the top one percent of employees oh yes right so top one percent of employees they're not just consuming a little bit more uh tokens they're consuming way more so knowing smile for those on audio so we're gonna ask this all right so uh in a month the spend from the top one percent of users fell from$7 ,976, according to Ramp Economics Lab, to$7 ,205, which is a 9.7 % decline.

51:37Big Technology Podcast Host:With the users, the top token gobblers within SAP, have you encouraged a similar pullback? Have you seen a similar pullback? What's that knowing smile all about? Yeah, we also have this 1%. And my CFO says, hey, where is this going to? And honestly, it took some time until we actually introduced certain token limits in SAP. Because I told my CFO and my CEO, I said, hey, I mean, now we tell everyone, use AI to, you know, reskill yourself, to change and become more productive. And when I'm now the first one who actually introduces this token limits, it's also the message that I don't like. But at a certain point, we really saw, oh, my God, now the token spend is really coming to a point where we definitely need to set limits for certain jobs.

52:30But, you know, for the top 1%, I mean, the ones who are doing the model training and so on, we didn't really set a limit. Let them run because we see this is a very important task, very important job to make the accuracy of the agents better. So I said, hey, we can't compromise on that because all, you know, the success of our AI really depends on the accuracy of our agents. So let them do the job. But then, of course, you know, for, you know, all the developers, the product managers, the designers, I mean, there we also introduce limits also for people in finance and HR. And of course, there are certain limits where we can't say, hey, you can't just experiment around, spend the tokens when we are not seeing, you know, the respective productivity outcome.

53:10But we said very healthy and still, I would say, fair thresholds where everyone says, OK, I can definitely do my job with AI and I'm not reaching this limit, you know, every month. And then, of course, people are allowed to say, but I have this certain task where I feel AI can really help me. This time it's very special. So I need a higher token spend. And then we have a process where a manager can also say, OK, for this month or for this task or for this project, we give you more token spend. And I feel the people accepted it well. And it was also the right message. share. Do not send the message of we forbid you know the use of AI or you're reaching the limit so fast that it's hard to really optimize your day job.

53:49But vice versa, also making sure that token spend stays under control. I guess what was even more important than these token limits is the model switching. Of course, we are testing, as I said, different models. And when you switch from one model to another, that can cut your cost by 10, by a factor of 10. And that is, of course, even more effective than you know, introducing all of these token limits for the certain job profiles in the company.

54:13Big Technology Podcast Host:When did this all go into place? Well, I would say the token limits, the budget by job profile, we did four months ago, three months ago. And the model switching, we built this into the platform from day one on. But in all fairness, we were just building the agents, getting them out, celebrating success, getting references. And now I would say in the last three months, we are also intensifying our product, I mentioned intensifying the work on switching the models, testing the models and optimizing them. And really also look into the TCO of a model because at the end, it's not only about delivering great agents.

54:52I mean, at the end, it also needs to be, from an economic standpoint, it needs to be reasonable.

54:58Big Technology Podcast Host:How long does it take between when we see some crazy frontier AI behavior and when it gets diffused into business? Let me just give you one example. Like the hugging face attack from OpenAI. Hundreds of bots attacking a problem. Well, I mean, that's like well-known. But there's also the math, the attacking the millennium problem and succeeding. That seems to me to be these sort of swarms of agents working to solve business problems. I don't know. I haven't really heard about it much from businesses. Even those that have said, we have a multi-agent system. It's like six or seven, not hundreds or thousands working together.

55:45Big Technology Podcast Host:How long does it take from when we hear about something like that at the frontier to when you get it? And where do you think we are on this agent swarm thing? I would say, look, on cybersecurity, obviously, it's something where you always have to be ahead of the game. So we are testing different models all the time. But it's not cyber. It's like these type of uses anywhere. Yeah, okay. On the uses everywhere, I mean, you know, whenever a new model comes, we are testing it right away. A model switch can be done in one week, two weeks. We need to change, you know, when there's a new model, we need to change APIs.

56:17We need to, you know, adjust the MCP servers. But we can do this very fast. And so it doesn't take a long time to really test out a new model and how does it perform on the business side.

56:29Big Technology Podcast Host:But like swarms of agents for business use cases, are you testing that now? Yeah, absolutely. We are doing this. And when we are building all of these A to A use cases, I mean, obviously, we need to test it. We also need to test the collaboration of these agents. How do these models also perform? And so, yeah, we are doing all of that. Yeah, I will say if I was sitting in your seat, the thing I'd be most afraid of is what's going on with cybersecurity right now. Because when you think about your customers, they've got finance, supply chain sales, HR. These are the most sensitive records a business could have.

57:04Big Technology Podcast Host:And if we see agents going zero day and people being able to direct them, that would scare me. Exactly. And that's why, guess what we are doing? In our dev operating model inside the company, we are always now working on how can we use AI to detect vulnerabilities earlier? How can we put more proactive measures in play to test the firewalls of the company and the product? And what do we do on patching the system and so on? So that's definitely now something what I would say for every technology company, this needs to be at the very top of the agenda. Your cybersecurity budget is going up substantially?

57:44Actually, I mean, on the one hand side, it's going up because you're investing into AI, but finding the abilities, patching, I mean, of course, that also gives you productivity gains. So it's going up, but it's going up in a reasonable way. Okay.

57:58Big Technology Podcast Host:You know, we're a podcast where we don't like to do the theatrics or we try to really understand. So I'm really going to resist being like, what's wrong with Europe? Okay. I'm going to resist that question. I actually want to ask it to you this way. Yeah. Europe has put so many regulations on its tech companies and people would say business in general. To the point where if you speak with people here in the US, they will be like, it's not even worth doing business there. We have a lot of European listeners. As I told you before, I'm from a mixed marriage here. I'm American. My wife is European.

58:39Big Technology Podcast Host:I think that it would be good if Europe became a good place to do business. I'm not going to harangue. I want to know from your perspective, because you're running the largest software company in Europe. What is the thought process that the European regulators are going through to get to where they are? What are their intentions? And do you believe that those are good intentions? I mean, first, I really believe they have all good attentions. I mean, who in Brussels does something to disadvantage Europe? But of course, what people sometimes do, especially the ones who are not so close to the technology, they tend to regulate the technology itself.

59:25I guess what we are now learning, and we talked about also the concerns and the risk we are seeing coming with AI, I guess you need regulation, but you should regulate more the business outcome, the impact on a society and not the technology per se, not the use of data. Because otherwise, it's so hard to do business and get a startup going in Europe. And also for us, I mean, we are a global company and we are doing our research and our development also in Palo Alto. We're doing it in Bangalore. We do it in, you know, everywhere in our labs. But for a startup, it's really hard. And I guess, you know, Brussels and Europe, I mean, it's definitely they realize that probably they stretch it too much.

1:00:06That's why they call it omnibus. This is where you can, where they now collect all the simplification measures, the deregulation measures. And I know they're dealing with it right now. And I hope there is stuff coming which definitely helps us to put the regulation to the right level. And again, I can really emphasize not enough that the need to make sure that you regulate the impact on societies, but not regulate the technology per se. Because how can you be competitive if you are the only part of the world who is really regulating the technology? Yeah.

1:00:39Big Technology Podcast Host:So you think there's going to be some form of rollback? I'm confident. I mean, the message is loud and clear, not only by SAP. I mean, many startups. They've heard it. There are other companies like Siemens and so on. I mean, we are building a lot of industry AI in Europe. So, there is AI. I mean, more applied AI, industry AI. And all of these companies, you know, same message to Europe. We have over-regulation. Let's scale it back. Yeah. And it's mostly GDP. Sorry, I come from like more of like the ad perspective. I mean, GDPR is like, speaking of the intentions, like I get it, you want to protect people's data.

1:01:15Big Technology Podcast Host:Yes. Is it mostly that or it's just a series that have to be reconsidered? No, it's actually not so much about GDPR. I mean, what Europe does is the AI Act, the Data Act, which then puts another layer of regulation on top. And, you know, it's not only another layer. Sometimes, you know, these layers are also overlapping so that even the legal people or your data protection officer is not even knowing anymore, okay, there's so many quaysons, so many overlaps. So it also takes forever until you understand how can I use no data to build AI and to do research and to apply AI in the customer's business.

1:01:51And so that is, you know, all of these layers of regulations and then, you know, the recent ones with the EU Data Act and the AI Act.

1:02:00Big Technology Podcast Host:All right, Christian, vielen Dank. Yeah, thanks for coming. Schön hier zu sein. Thanks a lot for your time. Schön hier zu sein. Yeah, very good German, actually. I'm learning. All right. Well, thank you for being here. Thanks to the New York Stock Exchange for hosting us today. Good to be here. Great to speak with you. Thanks, everybody, for listening and watching. And we'll see you next time on Big Technology Podcast.