Siemens CEO's mission to automate everything

Summary

Decoder Podcast Summary: "Siemens CEO's Mission to Automate Everything"

Host: Nilay Patel (Editor-in-Chief, The Verge)
Guest: Roland Busch (President and CEO, Siemens)
Date: February 9, 2026

---

Episode Overview

In this episode, Nilay Patel sits down with Roland Busch, CEO of Siemens, to explore the inner workings and grand ambitions of one of the world’s most consequential – yet opaque – industrial technology companies. The conversation delves deeply into Siemens’ evolving structure, its relentless drive to automate the physical and digital worlds, and the challenges of globalization and rising geopolitical tensions. Patel presses Busch on the utopian and dystopian potentials of full-spectrum automation, the company’s navigation of global trade barriers, and the future of industrial AI.

---

Key Discussion Points & Insights

1. What is Siemens?

[05:11]–[07:18]

• Busch on Siemens’ Core Mission:

  • Siemens is not a consumer-facing company but is ubiquitous in the infrastructure underpinning modern life—cars, buildings, power grids, and healthcare.
  • The company continually reinvents itself; what’s constant is transformation through technology.
  • Quote:

    “Every third manufacturing line in that world is run by Siemens controls... We are controlling, I mean, something like a little bit less than 50% of electrons are touched by Siemens technologies.” (Busch, 06:16)

• Organizing Principle:

  • Siemens enables other companies’ operations via hardware and software platforms, reflected in everything from car manufacturing to CT scanners.

2. Siemens’ Corporate Structure and Transformation

[07:57]–[20:12]

• Matrix Organization:

  • Businesses are organized around verticals like Digital Industries (software and automation), Smart Infrastructure, Mobility, and Healthineers.
  • Regional divisions exist (e.g., US, China, India, Germany), but business units have primary profit/loss responsibility.
  • Siemens is moving from deeply divisional “boxed” structures toward more horizontal “fabrics” (data, technology, sales).

• One Tech Company Program:

  • A major ongoing transformation aims to standardize systems and data across the company—breaking silos to enable faster, more collaborative innovation.
  • Quote:

    “AI in particular doesn’t respect silos. AI doesn’t respect data silos. They don’t respect any kind of boundaries. The world is squeezing out the small. You see that this is adding more and more. And the more data you have, the more capabilities you have, the stronger you are.” (Busch, 18:37)

• Culture & Change:

  • Movement toward horizontal integration is challenging, especially in a centuries-old firm. Core to progress: clear vision (“North Star”), staff engagement, transparent communication, and targeted external hiring for transformation.

3. Empowerment and Decision-Making

[29:35]–[31:46]

• Framework:
  • Decisions should be made at the lowest reasonable level for agility, but within strategic boundaries and with accountability.
  • Large decisions (such as M&A) follow structured board-level processes, incorporating input from various stakeholders.

4. Bridging Vision and Day-to-Day Work

[31:46]–[35:20]

• Challenge:
  • How do you connect grand corporate strategies (“fabric,” AI, digital twins) to front-line employees working on “traditional” products?
• Answer:
  • Value every contribution—legacy hardware is critical for operations and customer satisfaction. Showcase pride in enabling massive projects (e.g., Egypt’s 2,000 km railway).
  • Quote:

    “You have to distribute your love not only to the new stuff, but also to those which is basically super important in carrying also our P and L forward.” (Busch, 33:31)

5. Siemens and the Return of Protectionism

[35:20]–[39:22]

• Globalization vs. Barriers:

  • Siemens has always been “local for local,” with 85–87% content localized in key markets. The direct impact of trade barriers is manageable for Siemens, but harming some customers.
  • Resilience strategies: increase local production, diversify sourcing, and maintain footholds in all major regions.

• On US Manufacturing:

  • Real manufacturing investment is uneven; most new plants succeed when highly automated and digitalized due to labor shortages.
  • Quote:

    “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.” (Busch, 40:34)

6. Automation, Jobs, and the Economy

[43:54]–[46:33]

• Automation’s Net Effect:
  • Automation increases output with fewer people due to aging societies and shrinking workforces—labor will shift to sectors like healthcare and human services.
  • However, fully automated factories (like data centers) mean fewer jobs per dollar invested, raising concerns about who benefits.
  • Quote:

    “An AI factory, fully automated, I mean, this creates, uses a lot of space, uses a lot of energy and it creates limited amount of jobs. I mean that's what an AI factory is.” (Busch, 44:51)

7. From Atoms to Bits: The New Frontier of Automation

[50:23]–[56:37]

• Engineering the Whole Factory:

  • Siemens now automates not just physical processes (“atoms”) but also design, simulation, and operations (“bits”), using digital twins and AI agents.
  • Vision: AI agents analyze and optimize in real time, suggesting or implementing fixes, based on live production data.

• On AI Models:

  • General-purpose LLMs (e.g., from OpenAI) aren’t accurate enough for industrial reliability; Siemens builds on top with domain-specific training.
  • Quote:

    “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... you can train them as well as you want. They will not get to the level which we can use on the shop, shop floor.” (Busch, 57:21)

• Data Aggregation and Trust:

  • Industrial partners are beginning to pool data with Siemens to enable more robust and useful AI models, relying on Siemens’ trustworthiness, but guard their most sensitive data.

8. Navigating Geopolitics and the Future of a Global Company

[62:39]–[65:25]

• Scenario Planning for a Fragmented World:

  • Siemens increases resilience through “local for local” approaches, developing regional versions of software and training industrial AI on local models (e.g., Chinese LLMs in China).
  • However, fully fragmenting software globally is cost-prohibitive; Siemens must balance scale with localization.

• Optimism in International Collaboration:

  • Despite rising walls, Busch maintains optimism that collaboration and scale ultimately benefit the world—especially in facing challenges like climate change and aging demographics.
  • Quote:

    “There might be times where this is not appreciated so much for whatever reason, but in the long run, I believe that a world which is using technologies in order to solve the real problems in the world... we cannot solve it if you box ourselves too small.” (Busch, 65:33)

9. The Road Ahead for Siemens

[66:40]–end

• Siemens aims to deliver on its promised industrial AI operating system—bridging digital and physical worlds for customers in everything from supply chains to beverage manufacturing.

---

Notable Quotes & Timestamps

• “We reinvented ourselves over and over again... This is the fastest and the most fundamental [transformation] we ever had because of technology.”
  (Busch, 05:17)

• “AI in particular doesn’t respect silos. The world is squeezing out the small. The more data you have, the stronger you are.”
  (Busch, 18:37)

• “Empowerment is not anarchy. You have a clear strategy with boundaries. Within that frame... you are empowered. Empowered is a two way street... it also requires accountability.”
  (Busch, 29:45)

• “An AI factory... creates, uses a lot of space, uses a lot of energy and it creates limited amount of jobs. I mean that's what an AI factory is.”
  (Busch, 44:51)

• “I don't believe that these LLMs, if you do not train them really on specific industrial data... will get to the level which we can use on the shop floor.”
  (Busch, 57:21)

• “In the long run, I believe that a world which is using technologies in order to solve the real problems... we cannot solve it if you box ourselves too small. So scale it.”
  (Busch, 65:28)

---

Important Timestamps for Segments

| Topic | Approx. Time | |----------------------------------------------------|---------------| | Siemens' identity & mission | 05:11–07:18 | | How Siemens is structured and why | 07:57–20:12 | | New “One Tech Company” initiative | 16:07–20:12 | | Leadership, culture, transformation | 20:12–24:29 | | Decision-making & empowerment | 29:35–31:46 | | The link between vision and day-to-day work | 31:46–35:20 | | Globalization, localization, and US manufacturing | 35:20–41:58 | | Automation’s impact on jobs | 43:54–46:33 | | Automating “bits”: AI, digital twins, and agents | 50:23–56:37 | | LLMs and industrial AI | 54:08–57:21 | | Data, trust, and AI alliances | 60:53–62:39 | | Planning for geopolitical fragmentation | 62:39–65:25 | | Siemens’ vision for the future | 66:40–67:30 |

---

Tone and Style

• The conversation is candid, technical, occasionally philosophical, and infused with witty asides from Nilay Patel.
• Busch answers deeply and earnestly, acknowledging organizational challenges and uncertainties while maintaining optimism for technology-driven transformation.

---

Memorable Moments

• When Nilay presses on the dissonance between a fully automated world’s utopian efficiency and the potential for mass job loss, Busch responds with a realistic but hopeful answer rooted in demographic trends.
• The pride with which Busch describes Siemens’ role in building Egypt’s railway, connecting 90 million people, is palpable (“...which company in the world can do that?” [34:37]).
• Both host and guest frequently return to the necessity—and difficulty—of connecting high-level strategy and vision to the practical realities of a massive organization.
• The complexity of Siemens’ organization is openly acknowledged, with Patel joking:

  “If you tell me how the company is structured, I can tell you 80% of your problems. But in the case of Siemens, it seems like I'm still trying to figure it all out...” (Patel, 15:15)

---

In Summary

This episode provides a uniquely transparent look into Siemens, a pillar of global industry, as it seeks to become the “central nervous system” of automation in the 21st century. Roland Busch shares not only Siemens’ ambitious technological vision but also the hard realities of managing legacy, culture, global politics, and economic uncertainty. Listeners will come away with a clearer understanding of industrial transformation at planetary scale—and the hard questions automation poses for workers, economies, and society.

Transcript

[Host/Announcer]

Support for Decoder comes from Adobe. Life is unpredictable, and that means you need your projects to adapt with whatever gets thrown at you. That means mastering the ability to pivot and collaborate with others to reach your goals. Adobe gets that, which is why they made a tool that's just as flexible as you are. PDF spaces In Acrobat Studio, your PDF files are no longer static. Instead, they're living documents that flex with you and your project's needs. Learn [e-mail] do that with Acrobat. Support for this show comes from Doppl. Maybe that ping you just got is an urgent message from your CEO. Or maybe it's a deepfake trying to target your business. Doppel is the AI native social engineering defense platform that's fighting back against impersonation and manipulation as attackers use AI to make their tactics more sophisticated. Doppel uses it to fight back from automatically dismantling cross channel attacks to building team resilience and more Doppel outpacing what's next in social engineering? Learn [e-mail] that's D O P P E L dot com.

[Roland Busch]

The world moves fast.

[Neelai Patel]

Your workday even faster.

[Host/Announcer]

Pitching products, drafting reports, analyzing data. Microsoft 365 Copilot is your AI assistant.

[Neelai Patel]

For work built into Word, Excel, PowerPoint, and other Microsoft 365 apps you use.

[Host/Announcer]

Helping you quickly write, analyze, create and.

[Neelai Patel]

Summarize so you can cut through clutter and clear a path to your best work. Learn [e-mail] M365Copilot hello and welcome to Decoder. I'm Neelai Patel, editor in Chief of the Verge and Decoder is my show about big ideas and other problems. Today I'm talking with Roland Busch, the CEO of Siemens. Now Siemens is one of those absolutely giant, extremely important and yet fairly opaque companies that we love to dig into here on Decoder. At a very basic, very reductive level, Siemens makes the hardware and software that lets other companies run and automate their stuff. You've seen the Siemens logo everywhere, whether it's under the hood of your car, stamped on the control system of a fancy building, or scattered across factory floors. But it but since it's not really a consumer facing company, it's hard to know what ties all those ideas together and what some 320,000 Siemens employees across the world are actually working on. How all of those people are organized and how they all work together is wildly complicated. Roland and I spent some real time just talking through the Siemens corporate structure, which for the true decoder heads out there was incredibly fascinating. Roland and I also spent a lot of time talking about automation broadly. And what happens is AI brings automation out of the physical world of factories and into the digital world of the front office, the world of accounting and procurement, the things that help decide what the factory should be doing. Roland's vision is for Siemens to automate the entire factory process upstream and downstream of actually making things. And you'll hear him describe that outcome as fairly utopian, smooth, seamless, optimal operation, very German. But I wanted to press him on how dystopian that sounds to a lot of us because in Roland's vision of pure automation, it seems like there's a lot of people who just don't have jobs anymore. And the ones who do have jobs don't really have a lot of autonomy or fulfillment from them because they're basically just doing what AI tells them to do. So we talked about that pretty directly. And if that's not a lot of complicated, heavy decoder ideas already, well, Siemens is also a government and defense contractor on both sides of the Atlantic and a company whose growth historically has been tied directly to free trade and globalization in the post war era. There's a lot going on right now that challenges how that world works, especially as tensions keep rising between the US and Europe. So I asked him directly, has Siemens gamed out what it will do if NATO collapses? Because that's not as far fetched an idea as it used to be. As you can tell, there's a lot in this one and Roland was game for it all. I think you'll leave with a lot to think about, certainly more to think about whenever you see all those Siemens logos all over the place. Okay, Siemens CEO Roland Bush, here we go. Roland Busch, you are the president and CEO of Siemens. Welcome to Decoder.

[Roland Busch]

Thank you. Nelly, nice to meet you.

[Neelai Patel]

It's nice to see you. It's nice to meet you as well. There's a lot to talk about. Siemens is a huge company. It has a long history. You've been in a lot of businesses, you've been out a lot of businesses. You have worked there since the 90s. The world is very complicated right now and Siemens is a very big, very complicated multinational operating in that world. I'm curious how you are thinking of all that. Let me just start at the start. Siemens isn't a consumer company. I think a lot of decoder listeners like have seen the logo, but maybe don't understand the company. How would you describe Siemens today? What is the company?

[Roland Busch]

And it's indeed not that Easy. We came a long way. It's more than 170 years since the company was founded. And we made, I mean, so many changes in our portfolio, in our company. Actually, when people talk about it, I say there's one constant in our history which is that we reinvented ourselves over and over again. And absolutely we are now in the midst of another reinventional transformation with one difference. This is the fastest and the most fundamental one we ever had because of technology. And then the people ask, what is Siemens about? Because you have now Siemens healthineas, you have Siemens Energy, you have Siemens. And actually it's not that easy to describe because Siemens healthiness has the task in the name, it's about healthcare. Siemens Energy has the task in the name, it's about energy. But Siemens is not that clear. So here is how I explain it. We transform with our technology. The everyday for everyone, okay, doesn't get you closer. But now the point is you have to look behind the curtain and then you see what Siemens technology does. When you see a car passing by, eventually all of them are touched by Siemens technology. Is it either cars, these are designed by our technology or they are manufactured. Every third manufacturing line in that world is run by Siemens controls. If you walk through New York, you cannot walk a block without passing by a building which is automated by Siemens technology. I think we are, we are controlling, I mean, something like a little bit less than 50% of electrons are touched by Siemens technologies. And our transmission and our distribution systems, low voltage systems. And if you talk about health scanners, then, I mean, if you get a scan somewhere in the world, the likelihood that It's a Siemens CTOM R scan is a little bit shy of 50%. So, and this is what we do, we produce, we have an A, we have technology which enables others to transform there every day. And that is what Siemens about.

[Neelai Patel]

I listen to that and I experience Siemens everywhere. I'm the person who pays attention to how is this building automated. I talk to a lot of car CEOs, I hear about Siemens as a supplier to the car industry quite a bit. It sounds like what you are describing basically is you operate things for people, right? Or you build technologies or products that operate other things for people. There's a lot of things in the world to operate. How do you organize the company? How do you think about where there's opportunity and where there's growth, investment, and then how do you think about your resources there? Because it seems like we operate things for everyone. That's a pretty wide remit that, that you could focus down in any number of ways.

[Roland Busch]

No, absolutely. And here comes the point, and this is a absolutely valid question because now we are active in so many different industries. I mean it's industries, manufacturing, process industries, but we also are in buildings and grids, in, in mobility, so people and signaling systems. So the first basis of Siemens is, and this is where our value sits, it's in our technology platform. Is it our design software? We have one of the largest software companies in the world. If it comes to industrial software, we are the largest. And with our software you can build the most comprehensive physics based digital twin of whatever product you do. And we are now expanding into molecules. So another one is automation technology I just talked about. Is it either for discrete process manufacturing, we also go for software defined automation which is kind of a disruption and anything. We are the largest automation company. We are automating grids, we are automating buildings, we are automating signaling systems, we are automating trains, we are automating manufacturing. So the underlying technology is what the value is. Now we are bringing this technology to different verticals. So markets is it the industrial markets, food and beverage, chemicals, automotive, machine builders, utilities, mobility operators and the like. And then at this point the domain know how comes to into play. So having technology is one thing, but having the domain know how to deploy it to talk customers, language is another one. And on top comes quite obvious. The whole thing is now supercharged by AI technologies which we are rolling in as we speak. We have a long history regarding AI. Actually one of the first supercomputers doing machine learning algorithms was synapse one. I mean this is in 90s Siemens had the most powerful one. This was the grand grandfather of the GPUs today. And we have since then we are working with artificial intelligence technologies. But this is now obviously a new level which we want to bring it to. So the organization obviously we are organizing according to business, they are reflecting the markets we are acting in. But you have to look at from the back the underlying technology, including the data which is super relevant.

[Neelai Patel]

I'm very curious to talk to you about AI and automation. I think that's very important. I think digital twins you've been in for a minute. I'm curious about that. It seems like the future of automation is very rich, right? We're moving from Siemens automating a lot of atoms, automating the physical world to automating bits. And that's a long conversation. I want to come to. I just want to stay focused on the company for one more turn here and, and kind of ask the decoder questions because I feel like the structure of Siemens says a lot about the company itself. I was reading your last letter to shareholders. You were talking about how you've divested portfolio companies where you weren't the best owner. You're, you're exiting some businesses, you're obviously investing in others. How is Siemens organized today? How is the company structured?

[Roland Busch]

We are running according to businesses. One is Digital Industries which is all about the software, the automation piece. One is smart infrastructure. Here you find our building technology, medium voltage, low voltage, but also the grid, grid automation, grid control, grid control software. The third element is Siemens Mobility where we have our trains, high speed locomotives, commuter metro, light rail, but also rail infrastructure including turnkey projects which is part of that. And the last one since we are still consolidating to hunt the Siemens Healthineers which we hold still some 70% which is Siemens Healthineers own listed company, DAX listed company we are about to let go. So we announced a spin off of 30% from the 65 fish kind of. But it's a separate company so that's how we organized. Of course we have our corporate organizations like Strat it, we run obviously horizontally. We have our M and A department talked about our portfolio. So that's what a corporation normally has got. And maybe one more special thing is we still have research. So we still invest out of our 8% in terms of revenue or 6.5 billion. A portion of that goes into research advanced. So we do, we work on quantum computer. We don't build ones but we run on the software, the middleware, how to use it, the applications. And we have also machine learning, CAI experts who are doing research there, AI experts. So that's roughly how the company runs. And then talking about regions, we have in total including Halcynes Now 320,000 people. You have 45,000 sitting in the United States, 30,000 in China, 35,000 in India, roughly 85,000 in Germany. Still it's a German company. There's a lot of manufacturing here.

[Neelai Patel]

Um, so that's a lot of people. Let me ask you that split between Regions. I think a theme on decoder recently that you are just a part of this trend is a lot of these companies are a lot bigger than people think. You, you described divisions, you described Regions. Those are both potentially first order organizations for companies, right? I talked to lots of CEOs and regions is just the first order organization. Other companies are divisions. You have both. How, how, how do those Interactions.

[Roland Busch]

This is a constant discussion because we have very strong regional leads, we have strong businesses. So this is a matrix and every company has this matrix. And the first question is which one is the predominant line? Is it the business or the regions? In our cases, a clear answer, it's the businesses. So the business have the full P and L. Regions are, let's say the second derivative behind it. Still very strong region in some businesses. Let's take for example our low voltage business switching technology. Low voltage. This business is fully run by regions. So you have independent, you have China, you have United States, you have Europe, and this is their P and L. So roll it up by regions. If you talk about automation, the next level is still, I mean it's motion control for machine builders, it's factory automation, it's process automation, and then come the regions. So therefore you still have a different kind of setup depending on the business, how we serve technologies. And then the third dimension, just to make it a little bit more complic, is the verticals, because factory automation, take factory automation, which is maybe the strongest automation business we have got. They run into any kind of factory automation you can imagine. I mean the discrete and hybrids, I mean food and beverage, automotive, machine builders is by machine control is another one. But then you have a lot of battery manufacturing and the like, semiconductors. So therefore this is the third dimension to surf verticals because each of them has a different language, different applications. So we are having a very, let's say three dimensional matrix. But there's a clear lead and this is driven by the business lines.

[Neelai Patel]

I ask everybody under coder how the businesses are structured and the joke I always make is if you tell me how the company is structured, I can tell you 80% of your problems. But in the case of Siemens, it seems like I'm still trying to figure it all out to even get to where the problems are. When you think about that organization and you describe things like a common platform or shared innovation across these zones, or the investments you need to make in AI, a lot of your competitors, you know, they're new, they're essentially functionally organized. Yes, there's one person driving the business in the case of some of these startups. Siemens is very old. It is, it's organized divisionally and then obviously there's layers of organization between it. How do you think about investing in the core technologies, the core platforms, in that structure? Because it seems like all of your divisions should be doing it, perhaps in redundant or repetitive ways.

[Roland Busch]

You're right. When I explain my organization you can identify the problems or the opportunities, put it that way. And here comes the point. I mean I talked about. We are investing six and a half billion R and D and obviously this goes into different businesses and some of them have a higher share. This is 8% on average. Some of them are sitting on definitely more than 10, 13, 15. Some others are more on three, on four. So capital allocation, is it R and D, is it Capex? Also spendings that's done business by business. Each of them has a business case and we allocate capital in some cases. We obviously want to allocate more capital in higher growth areas. And I talk organic capital allocation. If it comes to M and A, this is something that happens on the board. I mean, we come up with proposals and we see where we want to spend more money and where we focus on in our M and A and where not. But then here comes the point. And this is the part of our one tech company program. Actually I started last year together with my fellow board members, maybe one of the most fundamental transformations of our organizations because you're completely right, we are very much boxed below these businesses. I talked like digital industries. We have software and then we have automation. Automation are three boxes. Factory automation, process automation, motion control. Below that you have here in segments. So I just didn't give you that complexity as well. So we are very much boxed. And what we want to do is we are taking layers out so we bring that into bigger boxes. So actually we are targeting for six units. But then we also say we want to create fabrics, which is a kind of operating system where we have horizontal ones. So we have a data fabric, we have a technology fabric, we have a sales fabric. So the idea of NDS fabric is it's a thin layer, but it's a very strong one where we are really scaling horizontally as much as we can. For sales fabric, for example, we want to use the same tools, the same nomenclature for customers. And believe it or not, if I ask today how much revenue we do with BMW, people have to run out and get numbers together. This stops now because we have one identifier for BMW and with a push or bottom, I know what to do. So they sell the same sales methodology. Also the customer journeys, they should all alike. Technology fabric is that we don't do things over and over. When we talk about a digital platform where we sell, we build it only once and sell our portfolio. So this is a change. We are unboxing our organization in two little boxes. And the reason is, number one is technology And AI in particular doesn't respect silos. AI doesn't respect data silos. They don't respect any kind of boundaries. The world is squeezing out the small. You see that this is adding more and more. And the more data you have, the more capabilities you have, the stronger you are. I mean it's a fact. Look at the big companies. So therefore we have to play the strengths of Siemens and this requires a different way of running this company. This is behind our one tech company program which is really pulling in horizontal as much as we can, yet respecting different go to markets and different kind of technologies or applications of technologies depending on the verticals we are serving, not losing our strengths which we have built over so many years while scaling horizontally. Does it make sense?

[Neelai Patel]

It does. I've never been so excited to talk about structure with anyone as I have been with you, because that seems very hard. It seems like what you're describing to me is a multi hundred year old company that has traditionally been very divisional, trying to get to some functional structures so that you can move faster. And that traditionally has come with culture costs. It has come with disruption inside the company. It's come with inertia. How are you dealing with that at a company the size of Siemens? There's 320,000 people. They can't all be happy with you.

[Roland Busch]

Yeah. Give a little bit of insights how we, how we did, how we did it in the past and how we do it right now. It's our one tech company program. In the past we had many restructurings and many changes. The point was a managing board. These are typically something, but we have now seven people and we used to have 15 or more whatever in the past. These guys were going together with their strategies in a room, defining a new structure, laying out a New York chart. And then dear colleagues, this is where we want to go and we reshuffle. You can imagine how that goes down this time. We created a North Star. Where we want to be. The North Star is basically what I sketched to you. These fabrics, the businesses but allow them to focus on what they need to do, focus on their customers, on their applications, but yet we want to get horizontals into it. And then we said this is a North Star and here are, we call it tracks the tracks to the North Star. These are the points where we really want to touch. For example, our CRM system for automation which was completely scattered. We want to do that. And this is a blueprint for the whole company can go on and on. And then we engaged people to say let's go. And you work with us now on this tracks and how to change it. That means we give the people an opportunity to contribute, to bring their ideas, but we have a clear idea where we want to in order to move first. Secondly is obviously you need to communicate a lot. You have to explain what you do, because you're describing something where people don't know where it goes. They're not experienced to work that way. But you have to talk it over and over again, explain why we do it, what's the benefit and what changes for the people. And for some, they said, I don't experience any change. Yes, because we don't touch everything. We touch only things where you really can improve. And some we just let go because why would you fix something what's not broken? And the next one is we definitely also train our people, because transformation is not just shifting boxes, it's a different set of values. Collaboration is a much, much more important element in it. So we are putting a lot of emphasis in helping them also. And change is not only a structure, it's also the processes behind the way, how you lead. And the last thing is obviously you also want to inject capabilities from the outside also on a higher level, where you have people who know what good looks like, if it comes to a super professional sales organization, if it comes to AI technologies and developing models. And this helps a lot. And we do that all over the company on the lower level, higher level, but if you talk now, really highest level, if you come with people who really have the gravitas, who bring the experience, where nobody would doubt that if they say this is how modern software looks like they have authority by themselves without giving them the stars and stripes. So they just put people together and say, this is how we go. And people listen and follow. So this is the package. And you need all of them, all of them in order to make this transformation. And it seems to work. I had a huge respect. I mean, I'm a lifetime at Siemens. I saw in my 30 years of Siemens so many changes and programs. And before pulling the trigger of making this big change, which is the deepest one for the last at least 20 years, you think about twice, because if this vessel runs in the wrong direction, you have a problem and we have to deliver at the same time. But I pulled the trigger because I knew that we have to change the environment. Technology is changing so fast, so we have to be on the forefront. But I'm quite happy because it runs now for one year and good progress. There's another one ahead of us. So by the end of the fiscal year, so by end of this, by October, October, November, we are basically through with all the big moves and the changes and we are already grooving in the first batch of changes we made. Sales seems to work quite well. We are grooving in and then within two years, I would say we finally are ready to, to, to scale.

[Neelai Patel]

We're going to take a quick break here. We'll be right back.

[Host/Announcer]

Support for the show comes from Twilio Even in today's digital landscape, there are builders, you know, the creators and entrepreneurs who envision products and platforms that have never been done before. And those folks need an open space to tinker, play and ultimately build. Think of it as a digital workbench. That's where Twilio comes in. Whether you write code or shape strategy, if you build, you belong. Twilio's customer engagement platform is the ultimate toolbox for developers, designers, business leaders, and everyone in between. If you're scaling a startup or transforming an enterprise, you need a customer engagement platform that can keep up. And with Twilio, you can combine data, AI and real time communications in one open, flexible space to build, test and scale with confidence. You can create without limits, without workarounds and without compromise. Just the freedom to build your way. Bottom line, Twilio is the ultimate builder's toolbox. So what will you build today? Learn [e-mail] that's T W I L IO.com be a builder with Twilio. Support for decoder comes from Adobe. For every big idea, your Documents folder tells a story. Let's say you've just finished pulling together a brief, so you hit export on final version PDF, but then you open the file and you immediately notice a typo. Several versions later, you're exporting final v4 actual final draft PDF. Adobe Acrobat Studio can save you the digital clutter with PDF spaces. It takes your documents and turns them into a living project that you can engage with, get insights from, and collaborate with others on. You can gather all your files into one workspace and have a whole conversation with your AI assistant about it and ask questions to get deep insights about your project. You can even invite people to your PDF space and let them add files, comments, notes, and more. You could doodle in the margins or even turn your project into your own personal podcast episode. Acrobat Studio lets you generate an audio overview of your project in just one click. Learn more at Adobe.com do that with Acrobat support for this show comes from LinkedIn. For small businesses, every hire matters, but the time and resources required to hire right are Limited. Luckily, LinkedIn Hiring Pro is built for that reality. It's your hiring partner designed to help you hire with confidence by servicing only the right candidates without turning hiring into another full time job. Hosting a job isn't always the hard part. It's finding, connecting with and screening the right candidates. Hiring Pro streamlines the entire process from drafting your job to shortlisting candidates and conducting AI powered interviews for initial screenings. Conversational interface lets you describe what you need in plain language. No recruiter jargon needed. Nearly 60% of hires find a candidate to interview within a week. With Hiring Pro, you spend less time searching and more time connecting with the right talent. Hire right the first time, post your first job and get $100 off towards your job [e-mail] partner. That's LinkedIn.com partner. Terms and conditions apply. Support for today's show comes from upwork. One of the biggest growth hacks is realizing you don't need to do it all by yourself. Upwork makes it easy to bring in the right freelancer when you need them so you can stay focused on what you do best. Upwork is a one stop platform to find, hire and pay expert freelancers across web and software development, data and analytics, marketing, business operations and more. So you can fill skill gaps, launch projects faster and scale support up or down without committing to full time headcount. You can browse files, review past work and get help scoping the role so you can hire with confidence and get started quickly. And with Business plus you can get access to the top 1% of talent on Upwork and with AI powered shortlisting, you'll get matched to the right freelancer in under six hours. No endless searching required. You can visit upwork.com right now to post your job for free and connect with top talent ready to help your business grow. That's up w o r k.com Upwork.com.

[Neelai Patel]

Welcome back. I'm talking with Siemens CEO Roland Bush about what exactly Siemens is. Let me ask you the other Dakota question I ask everybody. This is a big decision. How do you make decisions? What's your framework for making decisions?

[Roland Busch]

First thing about decisions is empowerment. Don't pull every decision up to the boardroom. It makes us slow. It is really not attracting people. People want to really take decisions on a lower level. So basic idea is take decision on the lowest possible level. However, empowerment is not anarchy. You have a clear strategy with that set boundaries this is where I 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 empower, somebody says to be accountable for what the people are doing, which is super important. So this is the first thing. So don't decide on things which you can decide on a lower level. But then if it comes to, let's say the bigger rocks, the M and 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, now we believe in that. You give you a trigger, you are ready now 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. And the decisions are, if it comes to strategies, we are 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 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, pulling our network if it comes to certain decisions. But I would say it's a structured process, but it's a process which is encourage people to speak up, to bring their opinion in order to come to better decisions.

[Neelai Patel]

Let me ask one, 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 that is, 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 CEOs at CES talking about fabrics with Jensen Huang on stage. And we're in the middle of like 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, kills them dead. The number of times I've heard that story is very high. So how do you bring that together?

[Roland Busch]

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 Car E, if not in the development, of course, but normally this is a product which is hardware. Not only because we have now solid state switching, which is disruptive, which brings software into it. But take that aside and then talking about being on stage, talking about car E and new models and whatnot and digital twins, the people sometimes feel lost Bush 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 they 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. It 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 in carrying also our P and 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 Chenzens 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. This is part stuff, bogies, frames, mechanical. I mean these trains are super loaded with automation. These are basically software 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. I 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?

[Neelai Patel]

I'm very curious about all of that. I think there's some amount of, you said software to find low voltage switches or software on 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. You have to operate everywhere. I look at Siemens and its size and its history and I say, okay, that the, this company took advantage of globalization and free trade, right? You're, 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, 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. Other countries are nationalists in other ways. How are you thinking about Siemens in that moment, 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.

[Roland Busch]

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. So ever since Siemens is a global company and now our local for local content in United States or in China is 85, 87%. 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 and in 2025, last fiscal year was a mid, low, mid single digits 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. Along with that normally comes 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 those chaos. We are looking for more localization to invest in the United States. We doubled our capacity for low voltage, medium voltage switching. We invested in assembly lines for trains United States, we are investing in India, in China anyhow, because this is one of our largest markets. So 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.

[Neelai Patel]

On when you think talk about investing in manufacturing United States, I have, I have watched a lot of companies say a lot of things about investing in manufacturing 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 watched Tim Cook reopen a factory that was already making max. So Donald Trump in his first term could say that Apple is opening a factory. There is a lot of theater about manufacturing the United States. And then there is the reality of investing for the long term. When presidents come and go. How are you balancing that? Is it theater or is it real investments?

[Roland Busch]

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 carmakers who are not that strong, they do that. But the big wave of remanufacturing United States is not happening yet. We don't see that. And the reason is maybe you mention it, number one, is the ability of people, also trained people. It is that you don't know yet where the whole travel tariff situation settles at the end of the day. The other reason is that 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. And I didn't mention semiconductors definitively this is hard fact. Semiconductor builds, maybe even battery factories would come pharmaceuticals and the like. The only point what, what I always advise our customers, if you, 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 even send the first excavation machine. Your products digitalizing your manufacturing simulate everything. That'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.

[Neelai Patel]

Do you think that's because people assume that there will be a snapback to normal trade relations in the world, or.

[Roland Busch]

Do you think it's just slow the later one? I don't believe these tariffs will just snap back. 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, 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. But I don't believe that this goes back to where we are coming from.

[Neelai Patel]

Look, I have a rudimentary understanding of economics. I studied this an undergraduate at the University of Chicago 20 years ago. My understanding of all this is this is how you equalize labor rates, right? You, you say, okay, 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 in United States, you might as well make it here. And that is bluntly what the Trump administration is haphazardly trying to convey. But what you're saying is we're Siemens, we 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. Like 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? Are is a fully automated factory a net add to the economy?

[Roland Busch]

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're missing jobs anyhow, or 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 labor makes a difference. And 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 creates, uses a lot of space, uses a lot of energy and it creates limited amount of jobs. I mean that's what an AI factory is. Yeah.

[Neelai Patel]

Who buys the outputs of an AI factory?

[Roland Busch]

The tokens intelligence. Right.

[Neelai Patel]

I'm just saying like if I build a fully automated factory to make cars, but no one has a job, who buys the cars?

[Roland Busch]

I mean again, as I said before, I mean what you do is when you have a fully automated manufacturing, you're driving economy, you grow faster, you can bring the production to the United States, which has a value in it, and, and then people. And once economy is growing, obviously your GDP per capita is increasing and people are going buying cars, but they have a different deployment, where do they get the money?

[Neelai Patel]

Again, rudimentary understanding of economics.

[Roland Busch]

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 higher output with less people, but you still have, I mean I talked about it. The 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 hundred 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 shop, this is the last one to be replaced.

[Neelai Patel]

Yeah, 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. We're going to take a quick break here. We'll be right back.

[Host/Announcer]

Support for the show comes from Shopify starting a new business. It could be a lonely endeavor, especially in the beginning. And when you're starting out, it's more important than ever to make sure you have the right tools at hand. Shopify is the commerce platform that millions of businesses around the world rely on to sell their products online. If you're asking yourself what if people haven't heard about my brand? Shopify helps you find your customers with easy to run email and social media campaigns. And if you get stuck, Shopify is always around to share advice with their award winning 24. 7 customer support. Best yet, Shopify is your commerce expert. With world class expertise and everything from managing inventory to international shipping to processing returns and beyond, it's time to turn those what ifs into with Shopify today you can sign up for your $1 per month trial and start selling today. @shopify.com decoder go to shopify.com decoder that's shopify.com decoder right now get up to 20% off select online storage solutions put heavy duty HDX to to good use protecting what's important to you. The solid impact resistant design prevents cracking and the clear base and sides make items easy to find even when the totes are stacked. Find select online shelving and tote storage up to 20% off at the Home Depot. To organize every room in your home from your garage to your attic, visit homedepot.com how doers get more done when.

[Neelai Patel]

It comes to the new Melania move, here are some important numbers to remember.

[Host/Announcer]

Forty million.

[Neelai Patel]

That's how much Amazon paid Melania Trump's.

[Host/Announcer]

Production studio for the rights to the film. It's the highest price ever paid for a documentary. 35 million. That's about how much Amazon spent marketing the film. 28 million. How much went to the first lady and 7 million.

[Neelai Patel]

That's how much the Melania movie made.

[Host/Announcer]

On opening weekend, which is honestly pretty Good.

[Neelai Patel]

And certainly more than many box office insiders projected.

[Host/Announcer]

So how did this movie get made? Who's it for?

[Neelai Patel]

And if this is finally Melania Trump's.

[Host/Announcer]

Side of the story, what does she have to say? That's coming up on Today Explained from Vox. Listen weekday afternoons, wherever you get your podcast.

[Neelai Patel]

Podcasts.

[Roland Busch]

This week on net worth and chill. I'm talking about what happens after you've mastered the basics. How to build wealth that actually lasts for generations. With the top 1% holding nearly a third of the nation's wealth and 98% of them being men, breaking into generational wealth isn't just about getting rich. It's about changing who gets to stay rich. Plus, I'm explaining the great wealth transfer, $124 trillion about to change hands over the next 25 years, and what it means for you. I'm answering your questions about calculating your net worth. You should rent or buy to build wealth. And how to pass your retirement accounts to your kids without losing them to probate court. Whether you're just getting started or already maxing out your 401k, this episode will show you how to think bigger than just making money today. Listen wherever you get your podcasts or watch on YouTube.com YourRichBFF.

[Neelai Patel]

Welcome 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. Those are, 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 or how you're thinking is Siemens has traditionally automated atoms. Now he 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'll produce them to. We're going to actually automate the deciding of how many units to produce.

[Roland Busch]

It starts with, and I come to manufacturing in a second. But it starts with the design via 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 simulate 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. So 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 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, the drawing data of your machines as well. Because once you have that, then and you simulate it. And this is what we call the digital twin composer. That means you're sucking in different digital twins of machines, machine of a line, of a product, you 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 comes 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 there's 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. And 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 worlds and helps you fixing things, even if you don't know really all the details. This is where agents coming in or orchestration agents, which is supported by a machine building agent, a machine agent, a product agent, workflow agent, whatever. And 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, it's super smart and it doesn't take a week to reassemble your line. But it really goes automatically.

[Neelai Patel]

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? Are you, are 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.

[Roland Busch]

But not only it's based on an LLM, different ones depending on which one we are working on, on different use cases. But it's based on LLM. But it's not good enough if you're just in an LLM or an LLM based agent to fix a problem. I mean the hit rate is near, I mean everything what we need. But we are training these LLMs on, 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 when you walk a plant and you see the whiteboard where 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 drained 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 a 60, 60, 70% to the 95 ish 8 ish plus which is really then what you can do.

[Neelai Patel]

Are these your models that you're training or are you augmenting models you're taking from OpenAI and Anthropic.

[Roland Busch]

We don't do LLMs. So really large models which are trained on the whole knowledge in the world this would don't do. I mean this is what we were 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. A completely different world. And in some cases we're working on co pilots on the shop floor. We talk about Microsoft for example, we having the first use cases doing that. So challenges of course that an industrial AI application doesn't accept hallucination. I mean you really, you really have to be sure once you send an agent out it does what you want them to do.

[Neelai Patel]

This is my My fundamental question, and I've asked a lot of people this, I'm very curious for your perspective because the the domain is so much different. I am not convinced that LLM technology as it exists today day 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 as it hallucinates enough to only be effective 60 to 70% 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. Do you think it's good enough or do you think it's the actual augmentation that makes it, the products that you.

[Roland Busch]

Build with it good enough? We need the augmentation. Absolutely.

[Neelai Patel]

And do you see LLM technology, the core technology, improving at a rate that might change your assessment of it?

[Roland Busch]

Good point. To the question. LLMs will getting 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, you can train them as well as long as you want. They will not get to the level which we can use on the shop, shop floor. It will not work. So 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 fundamental higher. I 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 LLM and say okay, show me the process problem. Hit rate was okayish, 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. But on those which you have and then you create obviously some synthetic data around those, once you train the model, your hit rate goes up substantially. It's much, much higher than if you lose 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 well 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. Tega, we have a manufacturer Italian, they do robots crib in the box, the box for any Kind of parts. And obviously you can train a robot to make this grip 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, an additional camera, I mean, 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 now because we trained, I mean, hundreds of hours virtually. Then we used Nvidia technology with a photorealistic ray tracing of these pieces, photorealistic 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 why I have so many people who are standing there with some handles and training robots to do our job and turn 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. So 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.

[Neelai Patel]

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?

[Roland Busch]

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. Number two is we have, I mean, I don't know how many thousands of machines we are operating. We have machine, we have machine jobs, machining jobs, 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, nah, still not enough. So, I mean, they are, 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, Tim, Gimori 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. So 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. 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.

[Neelai Patel]

Let me wrap up with just a big picture question here. I'm just thinking about Siemens as a company and what it represents in all the places 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, 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 like the dissolution of NATO? Like, it's great to be like, we can aggregate all the data from all our customers, but also the world might fall apart. Like, do you. Are you thinking about Siemens as a global company in that context?

[Roland Busch]

Yes, we do. If you ask me, do we make a kind of a scenario, planning of another war or whatever, of some incident in Taiwan? 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 using in China, for China in United States, United States or for Europe. So 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 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.

[Neelai Patel]

I 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 seeing 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 the world of the past 20 years. This feels like a return to a different time. And I'm just wondering how you can keep the optimism of the scale and the globalization when that is happening all around you.

[Roland Busch]

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 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, but 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 eating our climate and whatnot. And we have aging societies where healthcare 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 that therefore I'm a more optimist.

[Neelai Patel]

What should we be looking for next from Siemens?

[Roland Busch]

I 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, 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 Kion, 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.

[Neelai Patel]

Roland, this has been great. I could do another full hour just on the structure of cme. I think as you could tell, you're going to have to come back. Thank you so much for being on Decoder.

[Roland Busch]

Thank you.

[Neelai Patel]

I'd like to thank Roland Busch for taking the time to join Decoder and thank you for listening. I hope you enjoyed it. If you like, let us know what you thought about this episode or really anything else at all, drop us a line. You can email us atdecoder the verge.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 Decoder Pod. You also have a TikTok and an Instagram. They're at Decoder Pod 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 Media Podcast Network. The show is produced by Kate Cox and Nick Statt. It's edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder Music is by Breakmaster Cylinder. We'll see you next time.