AI Across The Product Lifecycle Podcast

Engineering’s Spatial AI Moment - Campfire & Gravity Sketch

What happens when AI, virtual reality, and spatial computing move beyond demos and start reshaping real engineering work? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Jay Wright, Co-Founder and CEO of Campfire, and Oluwaseyi “Shay” Sosanya, Co-Founder and CEO of Gravity Sketch, about the future of immersive engineering workflows. This is not a “metaverse” conversation. It is about what spatial tools can actually do for product development, design reviews, manufacturing validation, training, collaboration, and digital transformation. Jay explains why AI is becoming a first-class user inside Campfire, acting almost like another participant in a 3D workspace. Shay breaks down why Gravity Sketch keeps humans at the center of the design process while using AI to remove friction, speed iteration, and help teams communicate better. The conversation covers the hard parts too: why LLMs still struggle with geometry, why industrial companies remain cautious about cloud and AI adoption, why employees are already using AI tools outside official policy, and why the next breakthrough in engineering may not be AI replacing CAD, but AI controlling and accelerating the tools engineers already use. For anyone working in CAD, PLM, industrial AI, digital thread, manufacturing, design, or engineering software, this is a sharp look at where spatial computing is actually useful and where the hype still needs to become workflow value. Featuring: Jay Wright, Co-Founder & CEO, Campfire Oluwaseyi “Shay” Sosanya, Co-Founder & CEO, Gravity Sketch Host: Michael Finocchiaro, AI Across the Product Lifecycle Transcript source:   TIMELINE 00:00 Welcome and guest introductions 03:05 Jay Wright on being bullish about AI after ChatGPT 04:33 Shay Sosanya on cautious optimism and the speed of AI progress 07:06 Why 3D geometry is harder for AI than language 08:42 AI capabilities are moving faster than expected 10:07 How Gravity Sketch adopted AI in software development 12:27 Campfire’s AI-assisted development workflow 13:32 AI agents in meetings, code, and product workflows 16:11 Using AI with existing 3D assets, BOMs, documents, and legacy data 18:26 Campfire’s spatial workflows for engineering, training, and sales 20:02 Where AI sits in the software stack 20:28 Campfire’s spatial agent as a first-class user 21:46 Gravity Sketch’s human-first approach to AI in spatial design 23:36 Foundation models, 3D generation, and geometry engines 25:29 AI cost, IP protection, customer data, and bring-your-own-LLM models 28:00 Has engineering had its ChatGPT moment yet? 29:05 Why physical product development will see staged AI adoption 31:17 The engineering-to-manufacturing gap 32:13 Simulating manufacturing workflows before production 34:12 AI connectors, Blender, Fusion 360, and tool control 35:18 Advice for young engineers worried about AI 39:41 Making real products, not just AI-generated concepts 40:00 Digital maturity in industrial companies 41:21 Why many manufacturers remain at low digital maturity 42:31 Headsets, cloud, InfoSec, and adoption barriers 43:39 Employees are already using AI and immersive tools informally 46:57 Can agile startups move industrial customers faster than incumbents? 48:17 Campfire on solving workflows rather than selling AI novelty 50:29 Gravity Sketch on value, workflow depth, and avoiding AI hype 53:09 Where to see Campfire and Gravity Sketch next 56:12 Closing thoughts

Physics has a ChatGPT Moment - Vinci 4D Special Edition!

Riverside Event Title Physics Has a ChatGPT Moment: AI, Simulation, and the Future of Engineering What happens when AI stops guessing and starts solving physics? In this episode of AI Across The Product Lifecycle, I’m joined by Hardik Kabaria, co-founder and CFO of Vinci, and Andy Fine of the Fine Physics Consortium, for a sharp discussion on one of the biggest shifts in engineering software: AI-native physics simulation. Vinci is building a physics intelligence layer: a foundation model for physics designed to answer real engineering questions around heat transfer, thermo-mechanical deformation, high-fidelity simulation, and manufacturing-resolution analysis. Hardik says Vinci is already deployed with tier-one hardware companies and can run simulations from hundreds of millions to over a trillion degrees of freedom.   This is not vague AI hype. We dig into what makes AI simulation credible, why deterministic physics matters, how engineers can validate results, and why thermal problems are becoming mission-critical across semiconductors, electronics, batteries, EVs, data centers, robotics, and advanced manufacturing. If your product generates heat, deforms under load, consumes power, or depends on simulation to avoid expensive failures, this conversation matters. Timeline 00:00 — Introduction: Vinci, Fine Physics Consortium, and the “OpenAI moment” for simulation 01:11 — What is physics intelligence? 02:18 — Why physics is universal and governed by differential equations 03:08 — Physics-based AI vs. surrogate models 04:01 — What makes a physics foundation model credible? 06:51 — Why business value beats white papers 08:33 — Where Vinci fits in the engineering workflow 10:16 — Heat transfer, fluid dynamics, and choosing the right wedge use case 11:14 — Vinci’s focus: semiconductor and electronics thermal problems 13:23 — Thermo-mechanical deformation and why materials warp 14:49 — Multi-physics simulation as a long-standing engineering holy grail 16:06 — Yield, reliability, and manufacturing risk in electronics 17:04 — ROI: faster design loops and thousands of analyses per day 19:23 — Uncertainty, validation, and trust in AI simulation 20:08 — Training on 45TB of physics simulation data 21:47 — Residual norms and transparency at inference time 24:42 — 300 million to 1.2 trillion degrees of freedom 25:51 — GPU requirements and why Vinci is built for modern hardware 27:09 — Quantum computing, GPUs, and future scalability 30:22 — Wedge use cases: chips, boards, servers, batteries, defense, robotics, steel plants 31:45 — Who buys AI-native simulation software? 33:50 — Why thermal engineers are Vinci’s first target users 35:06 — Power, cooling, throttling, and data center energy constraints 36:25 — What throttling means in chips, EVs, and thermal runaway scenarios 39:58 — Deployment, IP protection, Docker containers, cloud, and on-prem 41:27 — How to convince skeptical engineers 43:00 — Path to adoption: start with the customer’s real benchmark 44:16 — What engineering leaders should do next 45:47 — The physics brick in the AI factory of the future 46:03 — Final debate: can there ever be one general foundation model for all physics? Join us for a practical, skeptical, deeply technical conversation about what AI can actually do for simulation, hardware design, and the next generation of engineering software. #AI #Simulation #EngineeringSoftware #PhysicsAI #DigitalThread #Semiconductors #ThermalEngineering #CAE #ProductDevelopment #AIAcrossTheProductLifecycle #TheFutureOfPLM #BetterCallFino

FoPLM: Introducing Product Memory! w/Special Guests!

Riverside Event Title Product Memory: The Missing Layer Between PLM, Digital Thread, and AI Agents Riverside Event Description Everyone talks about the single source of truth. Then the real product decision happens in a meeting, spreadsheet, email, Teams chat, supplier exchange, or inside someone’s head. In this episode of The Future of PLM, I’m joined by Oleg Shilovitsky of OpenBOM, Rob McAveney CTO of Aras, Brion Carroll of Digital Solution Group, David Segal of TCS, and Jonathan Scott of Razorleaf for a sharp discussion on one of the most important emerging ideas in PLM and enterprise AI: Product Memory. The core question: If digital thread connects the data, what captures the reasoning? PLM manages parts, BOMs, changes, documents, requirements, and workflows. But it often misses the “why” behind decisions: assumptions, rejected options, supplier constraints, manufacturing context, cost tradeoffs, effectivity logic, and informal reasoning. This discussion explores whether Product Memory becomes the next layer above PLM, ERP, MES, QMS, ALM, supplier systems, documents, and collaboration tools: a contextual, semantic, AI-ready memory of how product decisions are made across the enterprise. We cover: Can Product Memory avoid becoming another inconsistent data layer? What should be captured, and what should be filtered out? Why does eBOM-to-mBOM still break so many digital threads? How do semantics and ontology determine whether AI can trust product context? Can AI agents safely recommend or execute PLM changes? How do we capture human decision-making without scaring the humans? Timeline 00:16 — Introduction: single source of truth, broken digital threads, and Product Memory 03:02 — Oleg defines Product Memory beyond single source of truth and digital thread 06:28 — Rob on dependency graphs and hidden context in unstructured documents 08:36 — Brion on Product Memory as an “orb” fed by siloed enterprise systems 11:39 — Jonathan on semantics: why “part” means different things across functions 13:46 — David on Product Memory from an enterprise architecture perspective 18:21 — Avoiding inconsistent data across PLM, ERP, PIM, e-commerce, and supply chain 22:09 — Why engineering-to-manufacturing translation is so hard 25:00 — Why engineering release is not the finish line 30:05 — Missing memory: decisions in people’s heads, spreadsheets, and informal actions 33:57 — Why skipping change steps can slow the enterprise down 35:57 — AI agents, requirements ingestion, and asking “why” like a three-year-old 39:48 — Why AI agents must document their own reasoning 42:49 — Product Memory flywheel: capture, review, flow, and distribution 45:35 — Industrial AI, physical AI, agentic AI, and real-time product memory 48:21 — Semantic consistency, meta layers, and vetting data before Product Memory 52:15 — Dependency graphs, imperfect data, and improving ontology over time 55:12 — Human maturity: is the organization ready? 56:56 — Where companies should start looking for missing Product Memory 1:03:58 — Rob’s call to action: start capturing decision traces now 1:05:03 — Closing: eBOM, mBOM, ISA-95, and semantic translation This is not a theoretical PLM buzzword session. It is a practical debate about architecture, governance, trust, and human maturity before AI agents can operate safely inside the product lifecycle. #PLM #ProductMemory #DigitalThread #AI #AgenticAI #EngineeringSoftware #EnterpriseArchitecture #BOM #MBOM #EBOM #Manufacturing #TheFutureOfPLM #BetterCallFino

Bananaz and Jiga - Design Faster. Source Smarter. Ship Sooner!

What happens when AI hits both sides of the engineering equation: design and sourcing? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro sits down with Adar Hey, CEO and co-founder of Jiga, and Or Israel, CEO and co-founder of Bananaz, for a grounded discussion on where AI is actually creating value in engineering right now. Bananaz is building an AI layer on top of CAD to automate manual engineering work, while Jiga is rethinking custom part sourcing with software, supplier intelligence, and AI-enabled operations.   This is not a hype piece. The conversation gets into the real tradeoffs: where LLMs help, where deterministic workflows still matter, how engineering startups are using AI internally to ship faster, how customers think about ROI, and why security, traceability, and IP protection still make or break adoption. It also explores a bigger question: when will engineering have its true “OpenAI moment”? Adar argues adoption in physical industries takes time even when the technology is ready, while Or says the shift is already underway and could become unmistakable in 2026 to early 2027.   One of the strongest parts of the episode is the discussion around digital maturity. Both founders place many target customers around a 2 to 3 out of 5: digital enough to understand the value, but far from autonomous or agentic. From there, the discussion turns practical: how do you introduce change without breaking habits, and how do you prove business impact across engineering, manufacturing, and supply chain?     If you care about CAD copilots, sourcing automation, engineering productivity, AI in industrial software, startup execution, and the future of digital engineering, this episode is worth your time.   Timeline 00:14 — Intro: Adar Hey of Jiga and Or Israel of Bananaz 00:40 — What Bananaz does: AI layer on top of CAD 01:24 — What Jiga does: sourcing custom parts more efficiently 02:20 — Were they bullish or skeptical on AI in 2022? 06:01 — How AI changed the way they build software 10:50 — Token costs, burn rate, and ROI of AI tools 14:22 — Where AI sits in the product stack 18:00 — Off-the-shelf LLMs vs open-source models 20:15 — Bring-your-own-model vs vendor-managed AI 22:22 — Security, SOC 2, and protecting customer IP 26:01 — Are they more bullish now than in 2022? 27:28 — Who owns IP when designs are partially AI-generated? 31:52 — Advice for younger engineers worried about AI replacing jobs 35:49 — When will engineering get its “OpenAI moment”? 40:09 — Digital maturity of current customers 42:29 — Do tools like Jiga and Bananaz move the maturity needle? 47:30 — Closing thoughts and where to meet the founders next         Hashtags #AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #Jiga #Bananaz #AIAcrossTheProductLifecycle #BetterCallFino

Threaded Miami: Daan Goossens of CoLab!

What happens when engineering teams suddenly have the equivalent of 10,000 new AI coworkers? In this Day 2 Threaded Miami session, Daan Goossens of CoLab lays out a sharp version of the problem: AI can massively increase productivity, but if companies do not have the right collaboration, context, and decision-making structure in place, they will not scale output. They will scale chaos. That framing runs through the entire talk and makes this one of the clearest strategic discussions from the event.  Daan explains that CoLab’s vision for the future is not about replacing engineers. It is about helping humans do more with more, pairing human judgment with AI agents and the right engineering context. His point is that the engineer remains accountable, especially in safety-critical industries, but AI can dramatically expand what teams are capable of if it is embedded responsibly.  He reduces the path to scaled engineering productivity down to three ingredients: human collaboration and decision-making, strong AI agents, and relevant context and data. Miss one of those and the whole thing breaks. That is why CoLab is building around engineering collaboration first, especially design review, where teams already need to bring together multiple stakeholders, surface issues, and make decisions quickly without drowning in screenshots, PowerPoints, email chains, and Teams messages.  Daan also gives a concrete look at where CoLab is going next. He shows how their design engagement system and AI reviewer, Otto, are evolving into a broader engineering operating system strategy. One especially strong example is the SimScale partnership proof of concept, where a user can request a static load analysis on a crane truss, let the AI build and review the simulation plan, run the simulation, and then bring the results back into CoLab for collaborative review alongside the rest of the design context. The broader message is clear: engineers should not have to keep jumping between disconnected tools just to understand the impact of a design decision.  This is less a product demo than a thesis on where engineering software is headed. CoLab is betting that the future belongs to platforms that can combine collaboration, AI, and engineering context in one place, while partnering with the best core tools rather than trying to rebuild everything themselves.  If you care about AI in engineering, design review, simulation workflows, or the emerging idea of an “engineering OS,” this episode is worth your time. #ThreadedMiami #CoLab #EngineeringAI #DesignReview #Simulation #DigitalThread #IndustrialAI #ProductDevelopment #EngineeringOS #ManufacturingTech #AI