Decoder with Nilay Patel

Siemens CEO's mission to automate everything

February 9, 2026

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  • Siemens is undergoing its fastest and most fundamental reinvention, driven by technology, focusing its vast industrial technology portfolio across Digital Industries, Smart Infrastructure, and Mobility. 
  • Siemens is actively restructuring its divisional organization into horizontal 'fabrics' (data, technology, sales) to break down silos, which is necessary because AI and data do not respect traditional organizational boundaries. 
  • The future of industrial automation relies on training domain-specific AI models on proprietary industrial data, as generic LLMs alone lack the necessary precision and reliability for shop-floor applications. 
  • Siemens is actively planning for geopolitical fragmentation by adopting a "local for local" strategy, including training industrial AI applications on region-specific LLMs (e.g., Chinese LLMs for China, American LLMs for the US), despite the prohibitive cost. 
  • The CEO maintains optimism about globalization and scale, believing that international collaboration is essential for solving massive global challenges like feeding 10 billion people, climate change, and aging societies. 
  • The next major focus for Siemens is demonstrating the practical application of their industrial AI operating system to bridge the digital and real worlds at scale, evidenced by customer collaborations like the one with PepsiCo. 

Segments

Defining Siemens Today
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(00:05:17)
  • Key Takeaway: Siemens is undergoing its fastest transformation yet, focusing on technology that enables the transformation of everyday life across industries.
  • Summary: Roland Busch describes Siemens as a company that constantly reinvents itself, now focusing on technology that transforms the everyday. He details the company’s pervasive influence in manufacturing, buildings, and energy systems.
Siemens Corporate Structure
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(00:07:48)
  • Key Takeaway: Siemens is organized into four main businesses (Digital Industries, Smart Infrastructure, Mobility, Healthineers) operating within a complex, three-dimensional matrix involving regions and verticals.
  • Summary: Busch explains how the company organizes its vast operations, confirming that the business lines, rather than regions, are the predominant organizational driver, though a matrix structure exists.
One Tech Company Transformation
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(01:17:08)
  • Key Takeaway: Siemens is creating horizontal ‘fabrics’ (like data and sales fabrics) to break down divisional silos, driven by the realization that AI does not respect organizational boundaries.
  • Summary: Busch details the ‘One Tech Company’ program aimed at unboxing the organization. This involves creating horizontal layers to scale technology and data sharing across formerly siloed businesses, managed through a structured change process.
Automation and Job Concerns
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(00:03:29)
  • Key Takeaway: Busch argues that automation is necessary due to aging societies, allowing labor to be deployed in non-replaceable service jobs while manufacturing output grows.
  • Summary: Patel presses Busch on the dystopian implications of pure automation leading to job loss. Busch defends the vision by citing global demographic trends and the need to focus human labor on service sectors.
Globalization and Trade Barriers
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(00:03:45)
  • Key Takeaway: Siemens is resilient to rising trade barriers due to its deep localization (85-87% local content in key markets), but tariffs slow down customers.
  • Summary: Busch discusses how the rise of nationalism and trade walls challenges the global model Siemens benefited from. He notes that tariffs have a low direct impact on Siemens but hurt their customers, leading to increased investment in local-for-local capacity.
Digital Twins and Industrial AI
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(00:50:13)
  • Key Takeaway: The next frontier of automation involves using AI agents trained on proprietary, high-fidelity industrial data (like photorealistic digital twins) to autonomously manage and fix production lines.
  • Summary: Busch explains how Siemens is moving from automating atoms to automating bits. This involves creating comprehensive digital twins that ingest real-time data, allowing AI agents to supervise and correct manufacturing processes with high accuracy.
Data Sharing and Alliances
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(01:00:17)
  • Key Takeaway: Training effective industrial AI models requires massive amounts of specific, proprietary data, leading Siemens to form data alliances with machine builders.
  • Summary: Busch confirms that Siemens’ internal data is insufficient for training top-tier AI models. They are collaborating with German machine builders, who share data on older equipment to jointly develop autonomous application models.
Geopolitical Risk Planning
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(01:02:17)
  • Key Takeaway: Siemens prioritizes agility and local-for-local production over detailed scenario planning for catastrophic geopolitical events like NATO dissolution.
  • Summary: Patel asks if Siemens plans for extreme scenarios like NATO collapse. Busch responds that they focus on maintaining agility and resilience through localization rather than trying to predict unpredictable global disruptions.
Data Needs and Sharing
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(01:01:47)
  • Key Takeaway: Access to as much proper data as possible is crucial, even if the absolute latest machine data isn’t shared.
  • Summary: The discussion touches on the necessity of acquiring extensive and high-quality data, noting that while the newest machine data might be withheld, data from older machines is still highly beneficial.
Geopolitical Risks and Resilience
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(01:02:07)
  • Key Takeaway: Siemens must plan for catastrophic geopolitical events, such as the dissolution of NATO, despite the focus on global scale.
  • Summary: Nilay Patel questions how Siemens, as a major defense contractor operating globally, plans for extreme geopolitical instability. The CEO responds by emphasizing agility and scenario planning, noting that unexpected events always differ from predictions.
Localizing Technology Stacks
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(01:03:03)
  • Key Takeaway: Siemens is adopting a ’local for local’ strategy, forging technologies regionally (e.g., using Chinese LLMs in China) to increase resilience.
  • Summary: The CEO explains the trend toward forging technologies locally to avoid reliance on specific nations. This includes training industrial AI applications using regional LLMs (e.g., Chinese LLMs for China, American hyperscalers for the US), although fully forking all software is prohibitively expensive.
Optimism Amidst Global Competition
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(01:04:05)
  • Key Takeaway: Optimism is maintained through Siemens’ deeply international structure and the belief that global collaboration is essential for solving major world problems.
  • Summary: The host expresses concern that the current environment feels like a return to old national competition rather than the globalization Siemens thrives on. The CEO counters that his optimism stems from working in a company that is simultaneously American, European, and Chinese, emphasizing that global collaboration is necessary to address issues like feeding 10 billion people and climate change.
Next Steps for Siemens
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(01:06:02)
  • Key Takeaway: The immediate focus is on ‘walking the talk’ by building the industrial AI operating system and demonstrating the impact of connecting the digital and real worlds at scale.
  • Summary: The CEO outlines the next steps: delivering on the promise of the industrial AI operating system, using AI for creation (not just validation), and proving the capability to bring digital advancements into the real world with customers like PepsiCo.