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

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

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

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

🚨 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

What happens when some of the most respected voices in PLM gather in a Spanish vineyard to discuss AI, digital transformation, trust, community, and the future of engineering?

In this special Share PLM Summit 2026 edition of The Future of PLM Podcast, host Michael Finocchiaro is joined by Jos Voskuil, Oleg Shilovitsky, Rob Ferrone, Patrick Hillberg, Nina Dar, and Maria Morris for a candid, unscripted discussion about the ideas that emerged from one of the industry’s most unique events.  

The conversation explores why the human side of PLM remains the hardest part of transformation, whether AI will fundamentally reshape consulting and knowledge work, how organizations build trust during digital change, and why community may be becoming more important than technology itself.

From AI adoption and organizational change to conference design and the future of professional expertise, this episode offers practical insights and thought-provoking perspectives from some of the industry’s most experienced practitioners.

• The evolution of Share PLM Summit and its human-centered approach
• AI’s impact on engineering, consulting, and PLM careers
• Why trust may be the real ROI of conferences
• Lessons from successful and unsuccessful PLM transformations
• Human adoption versus technical implementation
• Digital transformation beyond software deployment
• The future of work in an AI-driven world
• Community, collaboration, and knowledge sharing

00:00 Welcome & introductions
01:20 Why Share PLM Summit feels different
03:30 Breaking away from traditional PLM conferences
05:45 Why attendees travel across continents to attend
07:35 PLM as a people-centered discipline
09:50 AI, digital overload, and human connection
12:40 Measuring conference ROI beyond leads and sales
15:10 Most impactful presentations from the summit
20:05 Data, AI, and the Gentelligence perspective
22:10 Helena Haapio’s keynote and the future of work
24:50 Will AI replace consulting and expertise?
30:05 AI, critical thinking, and engineering risk
31:10 Sponsors, trust, and community building
36:00 Workshops, learning, and audience engagement
42:20 Sustainability and digital product passports
48:20 The Share Nest initiative
51:55 The future of conferences and professional development
58:20 Trust as the new business currency
01:01:00 Community, networking, and collaboration
01:03:40 The value of disagreement and debate
01:05:00 One word that defines Share PLM Summit 2026
01:07:00 Closing thoughts

#PLM #AI #DigitalTransformation #Engineering #Manufacturing #Industry40 #DigitalThread #DigitalTwin #ProductLifecycleManagement #IndustrialAI #FutureOfPLM #SharePLM #EngineeringLeadership #SystemsEngineering #Innovation #TechnologyLeadership

AI in manufacturing does not fail because the demo is bad.

It fails when the answer cannot be trusted.

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Fay Goldstein, Co-Founder and CEO of Bardin AI, and Scott Lionello, Co-Founder and CPO of Omnae Technologies, about where industrial AI is really going: beyond chatbots, beyond copilots, and into the operational workflows that actually run manufacturing businesses.

Bardin AI is building an application engineer for industrial automation sales and support teams, helping them answer complex technical questions without escalating everything to senior engineers. Omnae is building supply chain collaboration software that allows AI agents to operate safely across real suppliers, buyers, orders, invoices, and messy enterprise data.  

The conversation goes straight into the hard parts of industrial AI:

trust, auditability, determinism, human-in-the-loop workflows, knowledge graphs, API costs, token burn, procurement risk, sales engineering bottlenecks, and why “just add a chatbot” is not enough when mistakes touch contracts, general ledgers, supply commitments, or customer trust.

Fay and Scott also discuss how AI is changing startup operations and software development, why young professionals need to show AI fluency rather than fear AI replacement, and why mid-market manufacturers may adopt practical AI faster than large enterprises waiting for top-down transformation programs.

The big takeaway: the next wave of industrial AI will not be about flashy demos. It will be about operational relief.

Fewer escalations.
Faster quoting.
Cleaner supplier collaboration.
Better support workflows.
Safer automation.
More trust in the decisions AI helps make.

This is a grounded, founder-level conversation about how AI is moving into the less glamorous but highly valuable parts of the product lifecycle: sales, support, procurement, supply chain, and the industrial back office.

Topics covered: industrial AI, agentic AI, supply chain AI, procurement, pre-sales engineering, industrial automation, knowledge graphs, AI trust, human-in-the-loop workflows, manufacturing software, digital transformation, enterprise AI, startup innovation, and the future of AI across the product lifecycle.

What happens when AI moves beyond chatbots and starts reshaping the actual tools engineers, architects, and designers use every day?

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Chloë Guidi of Qonic and Moritz Rietschel of Raven about the AI-native future of CAD, BIM, and AEC workflows.

Qonic is building a modern, cloud-based BIM platform from scratch, including its own solid modeling kernel, with a mission to make BIM lighter, faster, more accessible, and more data-rich. Raven is building AI-first workflows for complex design environments like Rhino, Grasshopper, Revit, Tekla, and Archicad, helping users navigate fragmented toolchains with less friction.

The conversation cuts through the hype and focuses on what is actually changing:

AI-assisted software development.
AI-native design workflows.
Smarter BIM quality checks.
More accessible CAD and AEC tools.
The economics of LLM-powered software.
The difference between “software built with AI” and “software that only makes sense because AI exists.”

Chloë and Moritz also discuss whether engineering and BIM are heading toward their own “OpenAI moment,” why open standards and data quality matter, and what young engineers should do as AI changes the skills required to stay relevant.

This is a practical, founder-level look at how AI is moving into the real workflows of design, modeling, validation, and engineering decision-making.

Topics covered: AI in CAD, AI in BIM, AEC software, digital twins, Rhino, Grasshopper, Revit, engineering workflows, AI coding, MCP, open standards, startup innovation, and the future of AI-native engineering tools.

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:  

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

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