Blueprint: Engineering in the Age of AI

Right Turns and Unprotected Lefts: Where AI Can (and Can't) Replace Engineers | Mark Fuge

In this episode, we sit down with Mark Fuge, Professor of Mechanical Engineering at ETH Zurich and Chair of Artificial Intelligence in Engineering Design, to explore the gap between what industry needs from AI and what academia is actually delivering, and why closing that gap starts with getting both sides in the same room. Mark shares his journey from writing Perl scripts at GE Aviation to pioneering ML for mechanical engineering back when the idea got you laughed out of job interviews. We discuss his concept of "use-inspired basic research," why the three fundamental challenges for AI in engineering are composition, abstraction, and uncertainty management, and how a helicopter manufacturer's request in 2018 led to an ETH course where students tackle real multi-physics design problems with today's AI tools. We also dig into his "right turns vs. unprotected lefts" analogy for understanding where AI can reliably take over and where human judgment remains essential, why taste and design thinking will matter more as building becomes free, and his vision for AI-driven personalised medical devices that could transform care for children with congenital heart disease. In this episode, we cover: * Why AI in engineering is where FEA was in the 1960s, and what that means for adoption timelines * How engineers are shifting from builders to architects, and why understanding the problem matters more than unlimited compute * The case for bringing industry and academia together to define the real research questions, not just the interesting ones Links from the show: Get in Touch: Mark Fuge https://www.linkedin.com/in/markfuge/ [https://www.linkedin.com/in/markfuge/] Conference Link & Topics https://event.asme.org/IDETC-CIE [https://event.asme.org/IDETC-CIE] https://idetc.secure-platform.com/a/page/tracks_topics [https://idetc.secure-platform.com/a/page/tracks_topics] Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ [https://www.linkedin.com/in/martin-bielicki/] Chapters ➡️ 00:00 Introduction to AI in Engineering 02:25 Mark Fuge's Journey into AI and Engineering Design 04:03 Bridging the Gap: Industry Needs vs. Academic Research 07:29 Understanding Industry Challenges in Engineering 09:32 Emerging Solutions and Startups in Engineering AI 11:50 Innovative Teaching: Preparing Students for the Future 16:21 The Evolving Role of Engineers in the AI Era 21:56 Perceptions of AI in Engineering 25:50 The Future of Human-AI Collaboration 30:23 Vision for Humanity: Engineering a Better Future

Startups, Incumbents, and the Race to Modernise Engineering | Steven Holmes

In this episode, we sit down with Steven Holmes, Editor-in-Chief at DEVELOP3D and producer of the DEVELOP3D LIVE conference, to explore what makes AI different from every other technology wave he's covered, and why this time the pressure on engineering teams is real. Steven shares his perspective from nearly two decades covering product design and engineering software, including why so few teams have moved beyond enterprise ChatGPT licences and what's keeping managers from giving engineers the tools they're asking for. We discuss whether startups or incumbents are better positioned to lead the AI shift, and why legacy software installs are becoming a competitive liability rather than a safety net. We also dig into what's driving some companies to rethink their entire technology stack, the parallels to earlier industry shifts like cloud CAD, and why the window to act is shorter than most engineering leaders think. In this episode, we cover: * Why the gap between AI awareness and actual adoption in engineering is still so wide * How startups and incumbents are each positioning to win, and where the mergers and acquisitions wave is heading * What's finally pushing engineering teams to question their legacy tools and move Links from the show: https://develop3dlive.com/ [https://develop3dlive.com/] Get in Touch: Stephen Holmes https://www.linkedin.com/in/stephenholmesd3d/ [https://www.linkedin.com/in/stephenholmesd3d/] Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ [https://www.linkedin.com/in/martin-bielicki/] Chapters ➡️

Why More Data Isn't Enough - AI, Parametric CAD, and the Ethics of What We Build | Nomi Yu

In this episode, we sit down with Nomi Yu, a researcher who recently graduated from MIT, where she was co-advised between the DeCo Lab and the Mechanosynthesis group, to explore how AI can enable better parametric CAD generation and why the ethical development of these technologies matters just as much as their technical capability. Nomi shares insights from her work on GenCAD 3D and the challenge of training AI models when usable CAD data is scarce. We discuss why simply having more data isn't enough, how synthetic datasets can address critical biases, and the potential of federated learning to let companies collaborate on training models without ever sharing proprietary IP. We also dig into the future of engineering workflows, including why the most successful companies will use AI as a starting point rather than a replacement, and the parallels between "vibe coding" in software and what could become "vibe engineering" in hardware design. In this episode, we cover: * Why data quality and bias correction matter more than data quantity for training CAD generation models * How federated learning could unlock cross-company collaboration without compromising IP * The case for engineers deepening foundational knowledge rather than racing to automate everything Links from the show: https://decode.mit.edu/ [https://decode.mit.edu/] Get in touch: Nomi Yu https://www.linkedin.com/in/nomiyua6175aadf85/ [https://www.linkedin.com/in/nomiyua6175aadf85/] Raihaan Usman https://www.linkedin.com/in/raihaan-usman/ [https://www.linkedin.com/in/raihaan-usman/] Chapters ➡️ 00:00 Introduction to the Blueprint Podcast 00:20 Nomi's Journey in AI and Engineering 03:07 Understanding GenCAD and Parametric Design 05:53 Data Quality and Collaboration in AI 10:27 Challenges in Cross-Domain Learning 12:41 Future of Engineering with AI 16:29 Onshape & Their Dataset 19:26 The Future of Engineering AI 26:39 Verification and Trust in AI Systems 34:52 The Future of Engineering Education 43:48 Responsible AI Development and Ethical Considerations

From Analyst to Decision-Maker: The Changing Role of the CAE Engineer | Abhinav Tanksale

In this episode, we sit down with Abhinav Tanksale, Technical Support Manager at Sentio Technologies and former Senior Crash & Safety Analyst at Magna, to explore the current state of AI adoption in CAE. Abhinav shares his perspective on where AI is genuinely delivering value today versus where the hype outpaces reality. We discuss Siemens' lead in AI integration, why standardisation at large OEMs can slow adoption, and the practical advice he'd give to managers looking to get started. In this episode, we cover: * The state of AI integration across major CAE software platforms * Why starting with repetitive tasks like geometry cleanup and report writing is the smartest adoption strategy * The soft skills AI won't replace, and why they matter more than ever Links from the show: Abhinav’s Blog https://myphysicscafe.com/ [https://myphysicscafe.com/] Get in touch: Abhinav Tanksale https://www.linkedin.com/in/abhinav-tanksale-6259b5118/ [https://www.linkedin.com/in/abhinav-tanksale-6259b5118/] Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ [https://www.linkedin.com/in/martin-bielicki/] Chapters ➡️ 00:00 Introduction to Abhinav Tanksale 02:25 The Journey of Abhinav's Blog: My Physics Cafe 04:58 Will AI actually replace CAE Engineers? 07:29 Siemens Digital Thread 08:58 Adoption Patterns of AI in Engineering 11:52 Advice for Managers on AI Integration 13:29 The Limitations of AI in CAE 14:36 The Future Role of CAE Engineers 18:18 Could Standardisation be the Biggest Blocker for AI Adoption in Engineering? 19:38 Envisioning the Future of CAE Workflows

How AI is Changing the Human-Machine Interface in Engineering | Moritz Valentino Leone

In this episode, we sit down with Moritz, Programme Manager at DeltaVision and former Director of Engineering at Hyperganic, to explore how AI is reshaping engineering workflows. Moritz shares his perspective on AI's role in breaking down knowledge silos between simulation, design, and manufacturing teams. We discuss the critical balance between AI-assisted speed and the transparency engineers need to confidently sign off on designs, particularly in high-stakes industries like aerospace. We also dive into Moritz's market research on AI engineering tools, examining the emerging clusters from generative design to physics simulation surrogates, and what it actually takes to get large engineering organisations to adopt new software. In this episode, we cover: * Why AI's greatest impact in engineering is democratising knowledge across the value chain * The four key clusters emerging in the AI engineering software landscape * What makes engineers actually adopt new tools, and why data consistency remains the biggest pain point Links from the show: DeltaVision Hiring: https://deltavision.space/job-openings/ [https://deltavision.space/job-openings/] Get in touch: Moritz Valentino Leone https://www.linkedin.com/in/moritz-valentino-leone-b877b41a4/ [https://www.linkedin.com/in/moritz-valentino-leone-b877b41a4/] Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ [https://www.linkedin.com/in/martin-bielicki/] Chapters ➡️ 00:00 Introduction to AI in Engineering 03:45 AI is best at breaking Silos 07:27 Will AI be the "Final" solution in Engineering? 11:35 The Future of AI in Engineering Design 14:40 Moritz describes the motivation behind starting his blog. 17:20 Emerging Clusters in Agentic Engineering: Simulation 20:30 The Text-to-CAD Cluster 22:35 Adoption Challenges in Large Corporations 27:24 Does having a focussed use case make it easier to adopt software? 29:58 Choose a Workflow to Automate? 31:52 Data consistency in Engineering teams.