CAD, BIM, and the AI Leap: Qonic & Raven!

The Hidden Infrastructure Behind Engineering Software: Tech Soft 3D, HOOPS AI and the Future of 3D

Tech Soft 3D has spent nearly 30 years quietly powering much of the engineering software industry. Its SDKs sit beneath hundreds of CAD, CAM, CAE, additive manufacturing, construction and PLM applications. Now the company is betting that the next competitive frontier will be built around something even more fundamental: access to rich, contextualized 3D engineering data. In this special edition of AI Across the Product Lifecycle, I speak with Jonathan Girroir of Tech Soft 3D about the changing architecture of engineering software and the company’s latest moves. We discuss why engineering workflows are shifting from files toward APIs, why cloud adoption is producing hybrid rather than purely cloud-native architectures, and why multi-CAD support now requires far more than importing neutral geometry. Jonathan explains the thinking behind HOOPS AI, Tech Soft 3D’s framework for encoding native 3D geometry for machine-learning applications. We examine practical use cases including geometry search, feature recognition, part reuse, costing and the connection of CAD data with procurement and manufacturing information. We also challenge some of the industry’s louder claims. Is text-to-CAD genuinely close to transforming engineering, or is the industry underestimating the difficulty of training models on accurate, structured design data? Could OpenUSD become a common contextual layer across engineering and manufacturing? And will engineering experience its own “OpenAI moment” before 2030? The conversation also covers Tech Soft 3D’s emerging Data Hub, QIF, DGN, cellular-volume visualization, lightweight web viewers, build-versus-buy decisions, startup support and the longer-term impact of quantum computing on simulation. A grounded discussion about where engineering AI is delivering value today, where the hype is outrunning reality, and why the unglamorous glue between systems could remain one of the industry’s biggest opportunities. Timeline 00:00 Introduction 00:37 What Tech Soft 3D does today 01:21 HOOPS, SpinFire and 30 years of engineering software 02:41 Cloud, hybrid architectures and data sovereignty 05:30 Connected workflows and engineering ecosystems 06:33 Why rich multi-CAD data matters 07:51 What engineering-software customers now demand 09:29 Rendering, OpenUSD and new interoperability models 10:53 Tech Soft 3D’s major 2026 announcements 11:09 Introducing HOOPS AI 12:24 Text-to-CAD: breakthrough or premature hype? 13:31 Geometry search, part reuse and contextualized 3D data 15:01 SpinFire mobile, cellular volumes and the Data Hub 16:32 The customer problems behind the roadmap 18:31 Why HOOPS AI generated the strongest response 20:15 Supporting engineering products measured in decades 22:43 What customers actually want from AI 23:26 Where AI creates real engineering value 24:04 Where engineering AI is overhyped 24:46 Why topology, PMI and design intent matter 25:49 Extending visualization across the enterprise 27:59 Web-based 3D collaboration and distributed teams 28:40 Build versus buy in engineering software 31:02 Openness, interoperability and intellectual property 33:36 Advice for engineering-software startups 35:17 How Tech Soft 3D supports early-stage companies 35:49 Will engineering have an OpenAI moment? 38:45 Quantum computing and the future of simulation 40:15 The underestimated opportunity between systems 40:37 What is next for HOOPS AI and the Data Hub 41:16 How to begin evaluating Tech Soft 3D 41:42 Closing thoughts

The Claude Code Moment for Factories? Cognyx + Oplit

What happens when the “Claude Code moment” reaches hardware engineering, supply chain, and the factory floor? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with two French industrial AI founders building directly into that shift: Matthias Berahya-Lazarus, CEO & co-founder of Cognyx Thibaut Wilhelm, CEO & founder of Oplit Cognyx is building an AI engineering platform for hardware — what Matthias frames as “Claude Code for industrial products.” Oplit is building an AI supply chain platform for industrial companies — using agentic supply chains to optimize factory performance. This conversation goes well beyond generic copilots. We talk about why AI may expose that many companies never had a real digital thread, why supply chain is such a strong playground for agents, why engineering AI needs executable knowledge rather than another chatbot, and why the future factory will be far more software-heavy, automated, and AI-native than most people expect. Key themes: • AI shifting the bottleneck from execution to product thinking • Supply chain as code • The move from planners doing repetitive scheduling to managers defining objectives • Why advanced factories may stop planning in Excel by 2030 • Why engineering needs composable, executable knowledge • Why digital maturity determines whether industrial AI works or fails • Why reindustrialization will not look like old factories coming back • Why the most interesting industrial AI companies are solving deterministic, messy, high-value operational problems If you care about PLM, digital thread, engineering software, manufacturing AI, MES, supply chain planning, industrial data, or the future of European reindustrialization, this is a conversation worth hearing. #IndustrialAI #AgenticAI #SupplyChainAI #EngineeringAI #DigitalThread #PLM #ManufacturingAI #FactoryOfTheFuture #Reindustrialization #HardwareEngineering #Cognyx #Oplit #ThreadMoat

AI for Engineering Is Leaving the Demo Phase

Text-to-CAD. Autonomous simulation. Agentic workflows. AI copilots for PLM. Engineering teams “10x faster.” Most of it still sounds like science fiction. But what happens when you put two founders building real AI-native engineering software in the same conversation? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Pradyut, co-founder of Bild, and Martin Bielicki, co-founder and CEO of Bench, about what AI is actually changing in engineering software, CAD data, simulation, product development, and manufacturing workflows. Bild is building CAD data management that connects engineering to manufacturing. Bench is building an AI orchestration layer across CAD, simulation, PLM, and beyond. The discussion cuts through the hype: AI is already changing how startups code. QA and validation are becoming the bottleneck. Prompting matters less than context. Frontier model cost is becoming a real burn-rate issue. And engineering AI will not move as fast as software AI because CAD, simulation, manufacturing, sourcing, and PLM are different technical worlds. The real unlock is not “AI replacing engineers.” Timeline 00:00 – Introduction: Bild, Bench, and AI across engineering 00:29 – Pradyut introduces Bild 00:54 – Martin introduces Bench 01:16 – The OpenAI moment 01:55 – Bench was created because of the LLM breakthrough 02:25 – Bild’s early exposure to DALL·E 03:33 – How AI changed startup coding 04:01 – Cursor, Claude Code, Slack, Graphite, and same-day delivery 05:45 – Multi-agent development 06:37 – Why “looking good” is becoming commoditized 07:28 – Why old software stacks limit AI innovation 08:21 – Prompting vs context 09:53 – From prompts to loops 10:40 – Frontier LLM costs 11:20 – Token costs as the new AWS-style shift 12:15 – AI spend caps and productivity measurement 13:45 – Cheaper models and model routing 14:39 – Right model, right task 15:39 – AI and engineering org structure 16:19 – QA, validation, and human-in-the-loop checks 17:58 – How AI may reorganize hardware teams 18:50 – Multi-agent coding conflicts 21:16 – Where AI lives inside the product stack 21:42 – Bench: AI for context, planning, and judgment 22:43 – Bild: opt-in AI for CAD data and IP boundaries 24:19 – When will engineering have its OpenAI moment? 25:04 – Why engineering AI evolves use case by use case 26:35 – Faster adoption in consumer products? 27:04 – From text-to-CAD to DFM and manufacturability 29:09 – The coming CDFAM AI demo wave 30:05 – Advice for young engineers 30:43 – Don’t compete with agents. Build differentiated skills. 33:05 – Creativity roles and AI in physical sciences 35:06 – Why top engineers become more valuable 35:57 – Digital transformation reality check 37:21 – Prints, redlines, and physical sign-offs are still alive 39:31 – Can startups move faster than legacy vendors? 40:10 – Big OEMs asking for AI engineering visions 41:15 – Buying AI vs buying value 42:50 – Why transformation programs route back to incumbents 43:37 – Build vs Windchill 45:30 – Startup visibility vs legacy vendors 47:56 – Capability checkboxes vs real user experience 49:13 – AI agents for CAD and CAE workflows 49:33 – End-to-end orchestration and organizational readiness 51:49 – Keeping skilled engineers in the loop 52:26 – Trust but verify for hardware AI 53:39 – Where to meet Bild and Bench 55:31 – Closing remarks Featuring Pradyut of Bild and Martin Bielicki of Bench. Hosted by Michael Finocchiaro. #AI #EngineeringSoftware #CAD #PLM #Simulation #Manufacturing #DigitalThread #IndustrialAI #HardwareEngineering #Startups

Who really owns the Bill of Materials?

BOM Wars, Part 2: Why Engineering, Manufacturing, ERP, MES, Service, and AI Still Can’t Agree The BOM debate is back. And somehow, it got even more dangerous. In this Future of PLM panel, Michael Finocchiaro brings together Christine Longwell, Gus Quade, Brion Carroll, Pat Hillberg, David Schultz, and Oleg Shilovitsky for a high-energy debate on one of the most persistent fractures in product lifecycle management: Who really owns the Bill of Materials? Engineering says the EBOM defines the product. Manufacturing says the MBOM defines what can actually be built. ERP says the operational BOM is what matters. MES wants execution context. Service wants the as-maintained truth. And AI? AI is useless unless all of this data is normalized, contextualized, and connected. This episode goes deep into EBOM vs MBOM, recipes vs discrete manufacturing, fashion vs aerospace, service BOMs, circular economy, Conway’s Law, ISA-95, product memory, data governance, and why every “single source of truth” eventually collides with organizational reality. The conclusion? The BOM is not just a list of parts. It is a battleground between systems, silos, budgets, ownership, and the future of industrial AI. Timeline 00:00 – Introduction: BOM Wars, Part 2 00:54 – Christine Longwell and Gus Quade join the panel 02:20 – Autodesk’s MaintainX acquisition and service implications 03:14 – Jörg Fischer’s provocation: “The BOM doesn’t exist” 04:30 – ERP BOM vs MES vs MBOM: where does manufacturing truth live? 05:31 – Engineering defines the product, manufacturing defines the action 08:04 – Why BOM logic changes by industry 09:50 – Fashion, fabric, tech packs, suppliers, and PLM 12:29 – Should EBOM, MBOM, as-built, as-shipped, and as-serviced live in one system? 14:55 – Product memory and algorithmic BOM transformation 16:00 – Conway’s Law: why BOM structures mirror organizations 19:21 – Can product memory connect engineering and manufacturing logic? 21:02 – Is the MBOM a separate object or just a different view? 22:33 – Digital thread, service BOMs, and lifecycle responsibility 24:44 – TWA 800, aircraft traceability, and why as-built data matters 27:30 – Why silos exist because organizations exist in silos 28:29 – AI orchestration across PLM, MES, ERP, and service 30:36 – Service BOM, software BOM, spare parts, and terminology chaos 32:31 – Why AI needs normalized data before it can add value 33:58 – Conway’s Law and the limits of database-driven transformation 37:55 – EBOM vs MBOM through the CAD and manufacturing lens 39:31 – When does a product become “real”? 41:47 – CIOs, data governance, and who should own cross-silo truth 44:38 – Autodesk’s data model approach and the unified product record 45:06 – PTC Orbit, Jetstream, and the race toward digital thread platforms 48:36 – Master data models, ontologies, and common exchange standards 50:32 – Closing question: what BOM belief will be wrong in five years? 51:27 – Oleg: data misalignment and unit-of-measure disasters 54:44 – Gus: CAD-optimized vs ERP-optimized structures will stop being binary 57:37 – David: the danger of returning to point-to-point integrations 59:14 – Brion: role-based UX on top of shared product ontology 61:16 – Pat: digital thread will eventually collapse EBOM/MBOM boundaries 62:50 – Christine: BOM is product definition, not just a parts list 63:57 – Do we need BOM Wars Part 3? Featuring: Christine Longwell Gus Quade Brion Carroll Pat Hillberg David Schultz Oleg Shilovitsky Hosted by Michael Finocchiaro #PLM #BOM #EBOM #MBOM #DigitalThread #Manufacturing #ERP #MES #EngineeringSoftware #AI #ProductLifecycleManagement #FutureOfPLM

Aras enters the Leader's Quadrant!

🚨 PLM just had a major market signal. For the first time since 2008, Gartner has published a Magic Quadrant for PLM — and Aras is now positioned alongside the traditional PLM giants. In this breaking-news episode of AI Across the Product Lifecycle, I sit down with Josh Epstein, CMO of Aras, to unpack what this means for the PLM market, why digital thread has become central to enterprise software strategy, and why “governed engineering AI” may be the real battleground for the next generation of product development platforms. We discuss why PLM has become too important for analysts to ignore, how Aras positions itself differently from Siemens, Dassault Systèmes, and PTC, and why AI in engineering cannot just be another copilot bolted onto messy enterprise data. The key question: Can AI transform engineering without a governed, explainable digital thread underneath it? Josh also goes deep on Aras Innovator Edge AI, Thread RAG, product memory, context graphs, lifecycle-aware AI agents, and what the engineer’s workday could look like when PLM starts decomposing into governed micro-experiences and agent-driven workflows. If you care about PLM, digital thread, engineering AI, enterprise software, or the future of product development, this one matters. ⏱ Timeline 00:00 — Breaking news: Gartner brings back the PLM Magic Quadrant 00:36 — Why did Gartner wait so long after 2008? 02:06 — Has the PLM market fundamentally changed? 04:28 — Why PLM is more complex than ERP or CRM 06:05 — Aras vs. the “Big Three” PLM incumbents 06:37 — Why CAD-agnostic PLM may now be an advantage 07:21 — Governed engineering AI vs. generic AI hype 09:37 — Trust, governance, observability, and explainability 11:34 — Why AI needs the digital thread to be actionable 12:45 — PLM data complexity: versions, effectivity, access, context 15:05 — How to market AI in 2026 without overpromising 15:53 — Aras Innovator Edge AI, Thread RAG, and workflow agents 17:08 — Product memory, context graphs, and decision traces 19:08 — Does Gartner validation change the sales conversation? 21:17 — Is PLM still the right category name? 23:50 — Cognitive digital thread vs. product memory 26:00 — What does an engineer’s day look like in three years? 27:00 — Adaptive PLM, micro-experiences, and agent-driven work 29:30 — Why PLM AI cannot just be dumped into a data lake 31:30 — The physical-world constraint: “close enough” is not enough 32:00 — Has PLM had its OpenAI moment yet? 🎯 Subscribe for more conversations on AI, PLM, CAD, manufacturing software, digital thread, and the next generation of engineering platforms. 💬 Comment THREAD if you want more deep dives on PLM, governed AI, and the engineering software startups reshaping this market. #PLM #DigitalThread #EngineeringAI #Aras #Gartner #MagicQuadrant #ProductLifecycleManagement #AI #EnterpriseAI #Manufacturing #CAD #PDM #ProductDevelopment #IndustrialAI #AgenticAI #AIEngineering #DemystifyingPLM #ThreadMoat