Blueprint: Engineering in the Age of AI

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We're engineers navigating the AI revolution alongside you. Through conversations with thought leaders, founders and innovators, we explore AI's impact on engineering - what's changing, what's possible and what's next. Join the conversation. New episodes twice a month. Brought to you by the team @Bench.

Episodes

  1. 6D AGO

    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 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 move Links from the show: https://develop3dlive.com/ Get in Touch: Stephen Holmes https://www.linkedin.com/in/stephenholmesd3d/ Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ Chapters ➡️

    38 min

  2. FEB 16

    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 modelsHow federated learning could unlock cross-company collaboration without compromising IPThe case for engineers deepening foundational knowledge rather than racing to automate everything Links from the show: https://decode.mit.edu/ Get in touch: Nomi Yu https://www.linkedin.com/in/nomiyua6175aadf85/ 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

    50 min

  3. FEB 2

    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 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 ever Links from the show: Abhinav’s Blog https://myphysicscafe.com/ Get in touch: Abhinav Tanksale https://www.linkedin.com/in/abhinav-tanksale-6259b5118/ 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

    22 min
  4. How AI is Changing the Human-Machine Interface in Engineering | Moritz Valentino Leone

    JAN 19

    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 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 point Links from the show: DeltaVision Hiring: https://deltavision.space/job-openings/ Get in touch: Moritz Valentino Leone https://www.linkedin.com/in/moritz-valentino-leone-b877b41a4/ 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.

    35 min
  5. AI in Engineering: Threat, Tool, or 10x Multiplier? | Ashraf Serour

    JAN 5

    AI in Engineering: Threat, Tool, or 10x Multiplier? | Ashraf Serour

    "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 Research https://news.mit.edu/2025/new-ai-agent-learns-use-cad-create-3d-objects-sketches-1119 VideoCAD https://ghadinehme.github.io/videocad.github.io/ Get in touch: Ashraf Serour https://www.linkedin.com/in/ashraf-sorour-3953919a Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ Chapters ➡ 00:00 Introduction 01:04 Identifying Repetitive Tasks for Automation 02:05 Barriers to AI Adoption in Engineering 03:22 How can Data Privacy with AI work in Engineering? 05:41 Exploring AI Tools in Engineering 07:15 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

    26 min
  • 5 Episodes

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We're engineers navigating the AI revolution alongside you. Through conversations with thought leaders, founders and innovators, we explore AI's impact on engineering - what's changing, what's possible and what's next. Join the conversation. New episodes twice a month. Brought to you by the team @Bench.

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