Blueprint: Engineering in the Age of AI

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 tasteand design thinking will matter more as building becomes free, and his vision for AI-driven personalised medical devices that could transform care for children withcongenital heart disease.In this episode, we cover:Why AI in engineering is where FEA was in the 1960s, and what that means for adoption timelinesHow engineers are shifting from builders to architects, and why understanding the problem matters more than unlimited computeThe case for bringing industry and academia together to define the real research questions, not just the interesting onesLinks from the show:Get in Touch:Mark Fugehttps://www.linkedin.com/in/markfuge/Conference Link & Topicshttps://event.asme.org/IDETC-CIEhttps://idetc.secure-platform.com/a/page/tracks_topicsMartin Bielickihttps://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

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 wideHow startups and incumbents are each positioning to win, and where the mergers and acquisitions wave is headingWhat's finally pushing engineering teams to question their legacy tools and moveLinks from the show:https://develop3dlive.com/Get in Touch:Stephen Holmeshttps://www.linkedin.com/in/stephenholmesd3d/Martin Bielickihttps://www.linkedin.com/in/martin-bielicki/Chapters ➡️

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 modelsHow federated learning could unlock cross-company collaboration without compromising IPThe case for engineers deepening foundational knowledge rather than racing to automate everythingLinks from the show:https://decode.mit.edu/Get in touch:Nomi Yuhttps://www.linkedin.com/in/nomiyua6175aadf85/Raihaan Usmanhttps://www.linkedin.com/in/raihaan-usman/Chapters ➡️00:00 Introduction to the Blueprint Podcast00:20 Nomi's Journey in AI and Engineering03:07 Understanding GenCAD and Parametric Design05:53 Data Quality and Collaboration in AI10:27 Challenges in Cross-Domain Learning12:41 Future of Engineering with AI16:29 Onshape & Their Dataset19:26 The Future of Engineering AI26:39 Verification and Trust in AI Systems34:52 The Future of Engineering Education43:48 Responsible AI Development and Ethical Considerations

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 platformsWhy starting with repetitive tasks like geometry cleanup and report writing is the smartest adoption strategyThe soft skills AI won't replace, and why they matter more than everLinks from the show:Abhinav’s Bloghttps://myphysicscafe.com/Get in touch:Abhinav Tanksalehttps://www.linkedin.com/in/abhinav-tanksale-6259b5118/Martin Bielickihttps://www.linkedin.com/in/martin-bielicki/Chapters ➡️00:00 Introduction to Abhinav Tanksale02:25 The Journey of Abhinav's Blog: My Physics Cafe 04:58 Will AI actually replace CAE Engineers?07:29 Siemens Digital Thread08:58 Adoption Patterns of AI in Engineering11:52 Advice for Managers on AI Integration13:29 The Limitations of AI in CAE14:36 The Future Role of CAE Engineers18:18 Could Standardisation be the Biggest Blocker for AI Adoption in Engineering?19:38 Envisioning the Future of CAE Workflows

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 chainThe four key clusters emerging in the AI engineering software landscapeWhat makes engineers actually adopt new tools, and why data consistency remains the biggest pain pointLinks from the show:DeltaVision Hiring:https://deltavision.space/job-openings/Get in touch:Moritz Valentino Leonehttps://www.linkedin.com/in/moritz-valentino-leone-b877b41a4/Martin Bielickihttps://www.linkedin.com/in/martin-bielicki/Chapters ➡️00:00 Introduction to AI in Engineering03:45 AI is best at breaking Silos07:27 Will AI be the "Final" solution in Engineering?11:35 The Future of AI in Engineering Design14:40 Moritz describes the motivation behind starting his blog.17:20 Emerging Clusters in Agentic Engineering: Simulation 20:30 The Text-to-CAD Cluster22:35 Adoption Challenges in Large Corporations27:24 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.

"If your job is just CAD modelling and you don't have deeper engineering knowledge, you better start learning - because in two to three years, that skill alone won't be enough."Ashraf, Design Engineering Manager at Toothsure, shares how his startup has embedded AI into their product development workflow from day one, and why he believes engineers who resist the shift are making a mistake.In this episode of the Blueprint Podcast, we cover:Why privacy concerns are a big blocker to AI adoption in engineeringThe difference between how startups and large companies are approaching AI toolsHow mapping your workflow end-to-end reveals where AI can actually helpMIT research on AI learning CAD modelling from YouTube videos (link below)Links from the show:MIT Researchhttps://news.mit.edu/2025/new-ai-agent-learns-use-cad-create-3d-objects-sketches-1119VideoCADhttps://ghadinehme.github.io/videocad.github.io/Get in touch:Ashraf Serourhttps://www.linkedin.com/in/ashraf-sorour-3953919aMartin Bielickihttps://www.linkedin.com/in/martin-bielicki/Chapters ➡00:00 Introduction01:04 Identifying Repetitive Tasks for Automation02:05 Barriers to AI Adoption in Engineering03:22 How can Data Privacy with AI work in Engineering? 05:41 Exploring AI Tools in Engineering07:15 Are engineers actually adopting AI?12:33 Should Engineers focus on Innovation?14:32 How an Engineering AI Assistant could save time!17:42 Advice for Engineering Managers to Implement AI 21:32 Conclusion: VideoCAD