Siemens CEO's mission to automate everything

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28:46welcome back i'm talking with siemens ceo roland bush about what exactly siemens is then let me ask you the other decoder question i ask everybody this is a big decision how do you make decisions what's your framework for making decisions first thing about decisions is

29:04empowerment don't pull every decision up to the boardroom it makes us slow it it is really not attracting people. People want to really take decisions on a lower level. So basic ideas, take decision on the lowest possible level. However, empowerment is not anarchy. You have a clear strategy with that set boundaries. This is where you want to go. Within that frame, within your responsibility, you can act and are empowered. Empowered is a two-way street as well. Empowered gives freedom, but it also requires accountability. So if you empower somebody, so it's to be accountable for what the people are doing, which is super important.

29:44So this is the first thing. So don't decide on things which you can't decide on a lower level. But then if it comes to, let's say, the bigger rocks, the M &A decisions, this really goes into a very, I mean, we have processes, how we do it. We had a, call it P-proposal, which is a proposal where somebody says, this is a company I want to acquire, this is my business case. Outside in, in order to say, okay, no, we believe in that. You give your trigger, you already know to negotiate, go forward, make a non-binding offer. And then they work on it. We have a lot of processes. And it comes, we call it I proposal, where we finally pull the trigger to say, no, you can invest and you can go.

30:23So, and the decisions are, if it comes to strategies, we're engaging as many people as we can, the experts, listening to them in the boardroom. In some cases, we also ask them not to prepare, I mean, a super polished PowerPoint. It's not the point. We want to really get the content. And then we have a very open discussion culture in our managing board with our leaders to come to better decisions. Very often, I'm also snorkeling around. We are getting advice from others, putting our network if it comes to certain decisions. But I would say it's a structured process, but it's a process which encourages people to speak up, to bring their opinion in order to come to better decisions.

31:09Let me ask one more question to wrap up here. Then I want to talk about the state of the world. And I really do want to talk about how you see AI and digital twins fitting into automation because there's a lot there. But if I'm a Siemens engineer working on low-voltage switchgear, right? I'm one of 35 ,000 people in India. And I'm like, the CEO is at CES talking about fabrics with Jensen Wong on stage. And we're in the middle of a two-year transfer. But I just need to get my work done. And this is all just some corporate strategy distraction. How do you bring those two things together? Because this is the thing that kills projects at big companies.

31:46It kills them dead. The number of times I've heard that story is very high. So how do you bring that together? There's a first one. And this is so super important for communication. because, and you got it right, low voltage. I mean, this is as mechanic as it can get. I mean, this is mechanical stuff. There's no software, no KI, if not in the development, of course. But normally this is a product which is, it's hardware. Not only because we have now a solid state switching versus disruptive, which brings software into it, but take that aside. And then talking about being on stage, talking about KI and new models and whatnot and digital twins, the people sometimes feel lost.

32:28Bush is always talking about that stuff, but I'm just doing mechanicals. So we have to give love to these guys as well because they do a lot in terms of contributing top and bottom line cash flow to our company. They are part of the equation. If you go to any kind of customer, they say, I love your automation, I love your software, but still I have to do some switching here and we are rolling in. I mean, and it's super relevant because if you don't have a switch or if it breaks, then you have a problem. They bring the capabilities to a customer and say, and not, by the way, we have also some low voltage here, but really say, this is important for you to have a very solid operation.

33:06It makes sense and they are proud to contribute. So you have to distribute your love, not only to the new stuff, but also to those which is basically super important and carrying also our P &L forward. And then the other element is, believe it or not, this CES presentation or keynote where you really are on par with, I mean, talk with Jensen's there, Sadia there, and you show what we do. And people, even the low voltage guys are proud what we do. I'll give a last one because I have to share that with you. We didn't talk about a completely other area, which is mobility, Siemens mobility. They do trains.

33:47this is hard stuff, boogies, frames, mechanical. I mean, these trains are super loaded with automation. These are basically softer defined trains because they tell you whatever. Even before they come to the depot, they tell the depot what they need, which part, what's wrong and how to replace it. So it's technology at its best. However, which company can say that we are transforming the whole economy of 110 million people, country, which is Egypt, where we built 2 ,000 kilometers of railway lines from the north to the south, west to the east, connecting 90 million people and transforming the whole system with high-speed commuter and locomotives.

34:31I mean, this makes people proud. And I didn't mention any AI technology, even though it's in our trains, but this is something where we could say which company in the world can do that. I'm very curious about all of that. I think there's some amount of what you said, software-defined low-voltage switches or software in the trains, right? Those worlds are colliding. I want to ask broadly just about the landscape you're operating in to do all of that work, right? These are big opportunities at work. If you want to sell trains, you need to be a global company, right? You can't be a single-country train supplier.

35:07You have to operate everywhere. I look at Siemens and its size and its history and I say, OK, this company took advantage of globalization and free trade. You're in all these countries around the world. You've got tens of thousands of people all over the place. You're building products all over the place. You're taking advantage of the opportunities and the markets you're in, the talent that's in those markets. And then I look up and I read the newspaper and the walls are going up around the world everywhere, every single day. The Trump administration seems intent on putting ever higher walls between us and Europe in particular, which seems very confusing to me.

35:44Other countries are nationalists in other ways. How are you thinking about Siemens in that moment where a company that was able to grow and be such a large provider to so many people because of free trade and globalization now has to contend with ever higher walls and barriers between countries? Obviously, we believe rather in free trade than in trade barriers because it brought the world to where we are. And leveraging technology as fast as possible means bring it to different countries as fast as possible. The good news about it is since, and I mentioned our footprint before, since we are global from the very first beginning, by the way, when Werner von Siemens founded this company 175 years or more ago, Even at the beginning, he sent one brother to London and one to Russia because he knew Germany is too small for his technology to scale.

36:37So ever since, Siemens is a global company. And now our local for local content in the United States or in China is 85, 87 percent. So that means we are so local and we have still goods traveling from different places. The impact on tariffs currently, and we said it last year, it's a public figure, in 2025, last fiscal year, was a low-mid single-digit bottom-line impact. So, okay, that's good for us. It's maybe not good for others. Our customers are suffering, and with our customers, we are suffering, obviously. We know that for machine builders, they have reduced volume because their machines are tariffed when they go to the United States.

37:22Along with that normally comes a Siemens automation, so we see that. But the direct impact is rather low. It's a second impact. And we are increasing our resilience as we speak. As it comes to certain semiconductors, we are trying to double source as much as possible. What we didn't do before, you know, to scale. We are looking for more localization. We invested in the United States. We doubled our capacity for low-voltage, medium-voltage switching. We invested in assembly lines for trains in the United States. We are investing in India, in China anyhow, because this is one of our largest markets.

37:59So, therefore, good news is we are quite resilient. Bad news is that for many, many of our customers, it doesn't help, and it somehow slows down. When you talk about investing in manufacturing in the United States, I have watched a lot of companies say a lot of things about investing in manufacturing in the United States. I'm from Racine, Wisconsin. I watched Foxconn insist that it was going to build an LCD factory in Racine, Wisconsin, and then simply not do that. And I've watched Tim Cook reopen a factory that was already making Macs so Donald Trump in his first term could say that Apple was opening a factory.

38:33There is a lot of theater about manufacturing in the United States. And then there's the reality of investing for the long term when presidents come and go. How are you balancing that? Is it theater? Is it real investments? What is the split? truth is it's a little bit of both. Where it's real, I mean, let's talk about the pharmaceutical industry. I mean, this is real investment. I mean, the Swiss ones, the German ones, they are investing in pharmaceutical production in the United States. If it comes to, I mean, some car makers who are not that strong, they do that. But the big wave of remanufacturing United States is not happening yet.

39:16We don't see that. And the reason is, maybe you mentioned it, number one is the ability of people, also trained people. It is that you don't know yet where the whole tariff situation settles at the end of the day. The other reason is that, I mean, why are we in a world which looks like as it looks like? Because, I mean, in particular, American companies, they were basically leveraging low labor cost and low cost in other countries and they made a good living out of it. And you mentioned some of them as well. So do I believe that this will change? Yes, I believe there will be a wave. You see more manufacturing coming.

39:59And I didn't mention semiconductors. Definitely, this is hard fact. Semiconductor built. Maybe even battery factories would come, pharmaceuticals and the like. The only point, what I always advise our customers, If you build a new manufacturing line in the United States, make it as automated and as digital as possible for obvious reasons. Because you cannot get enough labor, let alone trained labor. And technology is there. So if you go there with a greenfield plant, you have all the freedoms to make this whole thing digital before you even send the first excavation machine. Your products, digitalizing, your manufacturing, simulate everything.

40:43That's what we do, by the way. Whenever we build a new one, we go all in. And then you build it. It's faster. You don't make mistakes in building. It increases your space productivity and reduces your energy consumption. It increases your output while having more variables and more variants of your products. So that will come. But we thought it comes faster, But it seems there's a delay in really ramping up manufacturing in the United States. And again, maybe sector by sector, you see different patterns. Do you think that's because people assume that there will be a snapback to normal trade relations in the world?

41:22Or do you think it's just slow? The later one? I don't believe this tariffs will be a snapback. Why? Tariffs are just more or less, they are like taxes. They help closing the budget deficit. And I never saw taxes going back, which helped you closing a budget deficit. And I hope that this maybe comes to more normal terms. For example, our machines, the machines which are exported to the United States, they suffer from tariffs, but they have also these tariffs on aluminum and steel on top, which makes them quite expensive. So maybe that goes away. So there might be some adoptions, which I think will come.

42:07But I don't believe that this goes back to where we are coming from. Look, I have a rudimentary understanding of economics, right? I studied this in undergraduate at the University of Chicago 20 years ago. My understanding of all this is this is how you equalize labor rates, right? You say, OK, you can make the products cheaper overseas. We'll just put a tariff on top of it. Now the product is as expensive. You made it in the United States. You might as well make it here. And that is bluntly what the Trump administration is half-hazardly trying to convey. But what you're saying is we're Siemens.

42:40We make automation. We can virtually model the entire factory as a digital twin before you build it. In AI, we can automate even more. We can automate people using Excel to program your factory. Just build that. And I look at that and I say, well, that didn't get anybody a job, right? I look at data center investment in the United States, and communities around the United States are pushing back on data centers because they're like, this is a lot to extract from the environment and from our land and not enough jobs. I see that same argument being applied to fully automated factories. How do you push back against that?

43:15Is a fully automated factory a net add to the economy, do you think? It's a net add with less people per output if you don't automate. Absolutely. That's absolutely clear. the point is we are living in aging societies I mean Germany is aging Japan, Korea, China is aging there's a really step curve so sooner or later you will see that creating jobs like crazy is maybe not the point because you are missing jobs anyhow, labor anyhow you might want to deploy the labor you have in jobs which you cannot replace I mean the social system, the healthcare system and the like and use labor really where where labor makes a difference.

43:56And in manufacturing, you have less and less. This is changing. You still have people on a shop floor, but you will have less. And you said it right. I mean, an AI factory fully automated, I mean, this uses a lot of space, uses a lot of energy, and it creates a limited amount of jobs. I mean, that's what an AI factory is. Who buys the outputs of an AI factory? The tokens, intelligence. I'm just saying, if I build a fully automated factory to make cars, but no one has a job, who buys the cars? I mean, again, as I said before, what you do is, when you have a fully automated factory, you're driving economy.

44:35You grow faster. You can bring the production in the United States, which has a value in it. And then people, and once economy is growing, obviously your GDP by capital is increasing and people will be going by cars. But they have a different deployment pattern. But where do they get the money? Again, rudimentary understanding of economics. So number one is you're replacing blue-collar worker with more trained workers, with engineers, maybe also blue-collar worker using AI technology. So a factory doesn't run without. You have a higher output with less people. But you still have – I mean, I talked about it.

45:17The whole service sector is super relevant, and they're missing people as well. I mean, Germany, for example, I can share with that. If we would not have, I mean, hundreds of thousands of immigrants, and most of them are working then in service jobs, including hospitals and the like. If I would take them out of the equation, our whole healthcare system would collapse. So there's a lot of jobs which you cannot replace. And there's one more thing. When people ask me what to study, and I said, okay, a solid education on mathematics or physics is always good. But if you don't really feel like, if you go for a plumbing job or a handcraft job, this is the last one to be replaced.

45:56Yeah, the world needs electricians and plumbers for sure. That much I understand. Someone's got to build and plumb the data centers, and maybe that's the only career in the future.

46:08we're going to take a quick break here we'll be right back

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50:10Welcome back. I'm talking with Siemens CEO Roland Bush about what exactly Siemens is. So your vision is like a fully automated factory. You're talking about the sort of higher order jobs, like information jobs, technology jobs, engineering jobs. I would call those software jobs, right? Like in some broad categorization, there's some amount of white collar work that has a laptop involved. That's the next thing you could automate with AI. And you've made some gestures at that, right? We can automate even more of the things I mentioned earlier, it seems like a lot of the frame for how you're thinking is Siemens has traditionally automated atoms.

50:46Now you can automate bits. Yes. Right. And I see so much excitement about AI automating bits. How are you thinking about that? Like moving up from, okay, you've decided how many units to produce, we will produce them, to we're going to actually automate the deciding of how many units to produce. It starts with, and I come to manufacturing in a second, but it starts with the design of your product. I mean, you create a digital twin of your product, which you simulate how it runs on a manufacturing line. And this loop of producing and simulators and producing your products, which you have already in the digital world, this is so powerful because whenever you make a change, a component, maybe you want to be more resilient, designing another component, you go all the way back to your design drawings, you change it and you know what your manufacturing is impacted.

51:33So that's very powerful. But let me go for the manufacturing line itself. The whole idea is that you start building this operating system, which is a layered system. Obviously, you need to get all the data which a manufacturing line produces. You have to connect all your machines, the status of your machines, enrich them with environmental data. You want to get the real-time data, even the drawing data of your machines as well. Because once you have that, then you simulate it. And this is what we call the digital twin composer. That means you're sucking in different digital twins of machine, of a line, of a product.

52:10You suck it in and then you have a complete comprehensive digital twin which ingests real-time data. And then you can really, I mean, you can go forward, backward in time. You can find out what's the problem. And then here comes the real one when you close the loop of ingesting data but sending data back to the line, which is then the agent, which behaves on behalf of you. And an agent is like a trained supervisor for a line. So when a red light blinking, a supervisor goes there, he has a look at it, he says, oh yeah, this is a problem. I know that over and over again, this is what I have to do.

52:50And this is what AI agents can do. Finally, you still have to have somebody who is maybe removing a blockage. Maybe updating a software will be automatically, But changing a part, changing a piece, have a switch which is going wrong. So you have to have people who know what to do. We tell them what to do with glasses. I presented also in the presentation in the CES. So this helps you interacting in your natural words and helps you fixing things, even if you don't know really all the details. This is where agents coming in, orchestration agents, which is supported by a machine building agent, a machine agent, a product agent, a workflow agent, whatever.

53:32And that's how the future looks like, which is very powerful. It keeps your yield high, your quality process very high, and you're super agile if there's any change or change in your production because you have a different version you want to produce. because it's super smart and it doesn't take a week to reassemble your line, but it really goes automatically. Your agents, when you talk about industrial agents, so there's some line, something's gone wrong, a warning light goes off, and you say an agent will help you figure out what's wrong and potentially fix it by itself. Is that based on an LLM?

54:06Are you using one of the models from one of the big companies and it's just an LLM that you've trained to think about a line in that way? But not only. It's based on an LLM, different ones, depending on which one we are working on and different use cases. but it's based on LLM, but it's not good enough. If you're just in an LLM-based agent to fix a problem, I mean, the hit rate is near where, I mean, everything that we need. But we are training these LLMs on our data, on property data, product data, machine data, operation data. Once you have that, and even the data of fixes in the past, remember?

54:48When you walk a plant and you see the whiteboard where a supervisor writes, I have a problem here. This is who we're working on. Here I fixed it. All that knowledge goes into this model so that you just say, okay, this is the pattern of a problem. This is how the fix was. The model knows it because we upload the data. So it's a model which is trained. This is why we talk about an industrial AI model, which is trained on the industrial data. fundamental. I mean, if it really runs across, but also very specific if it comes to certain machines, then the hit rate goes up from 60, 70 % to the 95-ish, 80-ish plus, which is really then what you can do.

55:30Are these your models that you're training or are you augmenting models you're taking from OpenAI and Anthropics? We don't do LLMs, so really large models which are trained on the whole knowledge in the world. This we don't do. I mean, this is what we're using. We're using any kind of. And for specific tasks. So we have some models which are very good in software, co-pilots and agents for software. Some are good for... Now we are working on really genuine newly product designs, not only just having the next code line, but really genuine designs in a completely different world. And in some cases, we're working on co-pilots on the shop floor.

56:04We talk about Microsoft, for example. We're having first use cases doing that. So challenges, of course, that an industrial AI application doesn't accept hallucination. I mean, you really have to be sure once it's in an agent out, it does what you want them to do. This is my fundamental question, and I've asked a lot of people this. I'm very curious from your perspective because the domain is so much different. I am not convinced that LLM technology as it exists today can make the leap to do all of the things that people want it to do. Right. You see the gaps, even as you're saying, an LLM on its own has hallucinates enough to only be effective 60 to 70 percent of the time, which is nowhere near good enough for all of the things people want it to do, especially at the labor replacement rates that some of these folks talk about.

56:54Do you think it's good enough or do you think it's the actual augmentation that makes it the products that you build with it good? We need the augmentation. Absolutely. And do you see LLM technology, the core technology, improving at a rate that might change your assessment of it? Good point. To the question, LLMs will get better and better. But I don't believe that these LLMs, if you do not train them really on specific industrial data, and this is where the augmentation comes from, they can train them as long as they want. They will not get to the level which we can use on the shop floor. It will not work.

57:31So they need, and I strongly believe that the LLMs need specific, domain-specific, machine-specific data in order to really make a difference. But then if you do that, then you really can make a step up which is fundamentally higher. I'll give you two examples which are maybe interesting. One is when we talk about it was an optical inspection kind of task where we used an RLM and say, okay, show me the problem. Hit rate was okay-ish, but not to a level we needed. Then we start training the model with not so many data, which is anyhow important because if you're manufacturing in a PPM level, guess what, how many mistakes you get a day.

58:17But on those which you have, and then you create always some synthetic data around those, once you train the model, your hit rate goes up substantially. It's much, much higher than if you use the next best and next best model. Is there convergence to some extent? but I do believe there's a certain ceiling where you can train as much as you want. If you do not get specific training, then you have a problem. Now, my second example is we, and this is now really, now I give you a little bit the spirit of how deep you have to go. Take a, we have a manufacturer, Italian, they do robots, a crib in the box.

58:55Crib in the box for any kind of parts. And obviously you can train a robot to make this crib in the box. what we did is we created this hole in the digital world. So we used a digital part with a digital arm and a digital software and a digital camera and everything. And we trained basically a robot over and over again on this grip in the box with our technology. And then we switched it on and the grip, the hit rate was still not satisfying, 70-odd level. It's amazing because we trained, I mean, hundreds of hours virtually. Then we used NVIDIA technology with a photorealistic ray tracing of these pieces.

59:42Photorealistic ray tracing, different lights, trained the model over again. Hit rate was jumping up substantially. So these little details of having a normal representation of a digital part and a really photorealistic one made the hit rate coming up substantially. And this is where also there's a reason why when you train robots now in the virtual world, it doesn't really work. There's a reason we have so many people who are standing there with some handles and training robots to do a job and turn it over and over and over again. Because this is a real training on the real stuff. These little details make a difference between an industrial application and one which you can not use.

1:00:26So what I'm saying is training models on specific data, on valuable design data, operation data, time series data, brings them to the level which we need in order to deploy them. All that data has to come from lots of different customers, right? And you talk about Siemens as having all that data, but that data actually belongs to your customers. Are they willing to let you aggregate so that you can develop the products at the scale that you're talking about? Don't underestimate the amount of data we have got. I mean, I talk about generations, generations of design data for controls, for trains, for switches and whatnot, number one.

1:01:05Number two is we have, I mean, I don't know how many thousands of machines we are operating. We have machine jobs, machining jobs. But if I then go to my AI guys, they say, I mean, okay, you make it now available, everything from Siemens. Then they say, ah, still not enough. So, I mean, they need so many data to train models. Now we have an alliance for machine builders, German machine builders, nine of them. I mean, top, top, top. I mean, these are the names, Trump, Demgimori and the like. They are ready now to share their data with us in order to train models, to bring them a model which they can use to make an application which makes their machines running autonomously.

1:01:47So you just say, this is the part, here's the machine and just get going. They know that their data are not very useful because it's too little. But if you're adding up, if you create these data alliances, it works. It requires a certain trust. This is, again, where Siemens comes in because they trust us. We are partners for decades now. We are very mindful about that one. So you're right. You need as many data as possible. You need as many proper data as possible. Would they share the data of their absolute latest machine with us? No, but don't need that. I mean, it can use all the other ones, which is absolutely helpful.

1:02:26Let me wrap up with just a big picture question here. I'm just thinking about Siemens as a company and what it represents and all the places it is around the world and the value of the scale, right? 320 ,000 people in all the places around the world. And then I just think about the barriers going up. And Siemens is a defense contractor for both the United States government and Europe and a bunch of other countries around the world. Are you planning for an event as catastrophic as the dissolution of NATO? It's great to be like, we can aggregate all the data from all our customers, but also the world might fall apart.

1:02:59Are you thinking about Siemens as a global company in that context? Yes, we do. If you ask me, do we make a kind of a scenario planning of another war or whatever, some incident in Taiwan, One, we don't really because I told my people we can now do that over and over again. It comes anyhow different. So therefore, why would you stay agile and be really fast if something happens? So that's one thing. On the other side, we see obviously the trends in the world. And we are working more and more on, call it forging of technologies. So local for local. That you do not always rely on certain technologies from the United States, It's using in China for China and the United States or for Europe.

1:03:44So it's a pity because normally you would like to scale. And we still have core technologies which we can multiply. But obviously getting, for example, we are training our industrial AI applications for China on Chinese LLMs. Whereas for the United States, obviously we train them on American hyperscalers or whatever LLMs. So we do that. Have good experience also. And this makes us more resilient. Can I do that for each and everything? Can I fork all my software? I could, but it's just prohibitive, expensive. It doesn't make sense. But for certain areas, we do that and increase our resilience and hope for the best.

1:04:28I realize I'm ending on a down note here, but it just seems like so much of what you're excited about is the opportunity of scale. right the opportunity to do these things in a cooperative way in a way that maybe changes the world economy and all of us are just caring for robots in the end but like these are huge global ideas that you have and i'm just trying to put them in the context of boy when i hear other people talk about ai it is in the context of national champions and international competition in a way that feels very old right it does not feel like no right like the world of the past 20 years this feels like a return to a different time.

1:05:06And I'm just wondering how you can keep the optimism of the scale and the globalization when that is happening all around you. Maybe I have this optimism because I'm working in one of the most international companies in the world. I mean, we are a Chinese company as we are a United States company and a European one. And we have so many great people all around the world. And I'll see also how they are collaborating. And I believe that this is a core value of societies, which is super, super relevant also for the future. There might be times where this is not appreciated so much for whatever reason.

1:05:43But in the long run, I believe that a world which is using technologies in order to solve the real problems in the world, I mean, we have to feed now soon 10 billion people. We are creating our climate and whatnot. And we have aging societies where you have health care is a huge problem. So we cannot solve it if you box ourselves too small, so scale it. And here I hope that this, when we have waves of opening and closing and whatnot, that ultimately this pays out if you are acting in a global international network as we do. And therefore, I'm a more optimist. What should we be looking for next from Siemens?

1:06:26I think the next thing is that we are walking the talk. We talk about that we are building this industrial AI operating system, that we are using AI now for the next level to really not only validate, but also create. That we are leveraging our capabilities of bringing the real world and the digital world together, because the digital world can do so much if you do not have an impact on the real world. and we show that with our customers like PepsiCo was showing on the CES, like Keon, the supply chain or logistics company or many, many others. And to see that we can be the entry door for AI technology into the real world together with our partners at scale.

1:07:17Roland, this has been great. I could do another full hour just on the structure of CIMA. I think as you could tell, you're going to have to come back. Thank you so much for being on Decoder. You're right. Thank you. I'd like to thank Roman Bush for taking the time to join Decoder and thank you for listening I hope you enjoyed it if you'd like to let us know what you thought about this episode or really anything else at all drop us a line you can email us at decoder at theverge.com we really do read all the emails or you can hit me up directly on Threads or Blue Sky we're also on YouTube you can watch full episodes at decoderpod you also have a TikTok and an Instagram they're at decoderpod as well and they're a lot of fun if you like Decoder please share it with your friends and subscribe wherever you get your podcasts if you really like the show hit us with that five star review Decoder is a production of The Verge and part of the Boxing News Podcast Network.

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