Agents and Simulation

We also get into learnable simulation, physical AI, robot safety, and the practical business value of industrial AI. The big question isn’t just which models to use—it’s how to make the right decisions under uncertainty.

How do you scale AI in a heavily regulated company with more than 100,000 employees? Jan Tönnissen has spent over 18 years at Bayer, most recently leading an IT delivery platform behind MyGenesis. The internal GenAI and agentic platform grew from a few hundred users to 25,000 and more than 60,000 assistants. He also built Bayer’s AI community of 19,000 colleagues. In this episode, Jan explains why guardrails don’t have to slow people down and can instead pave the way. He also describes how the EU AI Act became a catalyst rather than a hindrance. He walks through Bayer’s Agent Hub, a “telephone book” for every agent in the company, and the AI gateway that gives full transparency over LLM and token consumption. We also cover: Real use cases: research literature screening cut from weeks to days, marketing content, and “Friday,” Bayer’s new agentic assistant for every employee The token debate: from maximizing to minimizing use, self-hosted open-weight models, small language models, and counting token costs as CapEx AI-assisted engineering: where it delivers 10x and where it delivers 3x Who owns agents in production, and why ownership has to sit with the business The next wave: physical AI and agents moving closer to the shop floor Three takeaways for midsize machine builders: build a coalition of the willing, choose your first battle carefully, and define your build-vs-buy decision tree up front And one closing reminder: if you can’t measure it, you can’t steer it.

We’re seeing an explosion of AI in engineering: models turning drawings into 3D designs, agents helping create plant layouts, and new tools challenging the way engineers work. So where will the value come from next—engineering software, AI platforms, or the context data behind them? I also explore whether agents could leap across the traditional automation pyramid and connect directly with industrial systems. Then I talk with Bosch’s Thorsten Brach about connecting AI agents to industrial robots using MCP, MHS, and SRCI. We get practical about robot integration, deterministic PLC functions, and why wheeled robots may arrive in factories before legged humanoids. The big question: how do we make these systems easier to use without losing the expertise needed to keep them safe and reliable?

Did OpenAI really solve the Navier–Stokes problem? In this episode, we ask mathematician Gonzalo Caolabora. and AI professor Günter Klambauer what the proof means, why it has been difficult to verify, and whether large language models are discovering mathematics or combining ideas from existing research. We also discuss why AI can excel at problems with verifiable answers, where today’s systems still struggle, and how AI could reshape mathematical research and careers. The big question: as proofs become more automated, what will mathematicians focus on next?

We talk with Arburg’s Stefan Lamp and Werner Faulhaber about how AI is changing injection molding—from predicting process stability and quality to helping operators troubleshoot machines. They explain why time-series foundation models matter, where domain expertise makes the difference, and how simulation can shorten the setup of a new process. We also get into the practical questions: Can agents safely recommend machine settings? What holds back cloud connectivity and predictive maintenance? And how should manufacturers measure AI’s real-world value? A grounded look at what’s working now—and what still needs solving.

In this episode, I sit down with David Rodriguez to explore how large language models are reshaping the way we interact with industrial data. We dive into the Pi System’s pivotal role across foundational industries and discuss practical strategies for integrating AI—without compromising sensitive operational information. David shares his experience on creating actionable knowledge graphs, running local LLMs, and the evolving relationship between human operators, agents, and data visualization. If you’re curious about the real-world challenges and future promise of AI in manufacturing, this conversation delivers clear insights and actionable takeaways. Join me as we unpack what it takes to move from data to decision—safely, efficiently, and intelligently.

In this episode, we dive deep into the world of agentic AI and its transformative impact on industrial organizations - thanks to Jana Kirchheim from Microsoft. We share fresh insights from our conversations with industry leaders on how hyperteams—blending humans and AI agents—are changing the rules of leadership, governance, and knowledge management. You'll hear practical examples from companies like Bayer and discover how even mid-sized firms can harness agents for real business value. We discuss the challenges of orchestrating thousands of agents, building effective governance, and capturing critical tacit knowledge before it disappears. Join us as we explore what it takes to be brave, adapt, and thrive in the era of superintelligent industrial AI.

In this episode, I sit down with Nathan Gruber, co-founder and CEO of Manex AI, to explore how autonomy is reshaping the way leading organizations operate. We dig deep into the challenges of legacy manufacturing models, the rise of agent-based AI systems, and why speed and feedback loops are the new competitive edge. Nathan shares his firsthand experience at BMW, the genesis of Manex AI, and how their solutions are closing the gap between customer feedback and engineering action—faster than ever before. We also discuss what true autonomy means for the future of work, the evolving role of humans alongside AI agents, and how European industry can adapt to this seismic shift. If you’re curious about the intersection of software, hardware, and organizational change—or just want a glimpse of the autonomous enterprise in action—this conversation is not to be missed.

In this episode, we dive deep into the evolving landscape of industrial AI—from the fierce competition on model leaderboards to the real-world impact of open versus closed AI systems. We question whether it’s necessary to follow every new model or if specialization is the future, using examples from industrial time series and the latest in material science. Alongside our guest Alexander Hammer from Dunia, we explore how AI and autonomous labs are accelerating the discovery of next-generation materials, bridging the gap between simulation and reality. As we reflect on what it means to 'think' in AI—beyond just language or images—we challenge listeners to consider the missing modalities shaping tomorrow’s technology. Join us for insightful stories, candid debates, and a glimpse into the frontier where AI meets the physical world.

In this episode, I sit down with Bastiaan Oud, CEO of Simcon, to unpack the seismic changes AI is bringing to computer-aided engineering. We dive into how agentic and physics-based AI are revolutionizing everything from injection molding simulation to the very roles of engineers and software developers. Bastiaan shares real-world insights on shifting from traditional coding to orchestrating intelligent agents and how this is transforming both product development and organizational structures. If you’ve ever wondered what happens when agents — not just people — drive innovation, this conversation delivers a firsthand look. Join me as we explore the future of engineering, the evolving workplace, and why embracing these changes could be your biggest competitive edge.