Threaded Miami: Daan Goossens of CoLab!

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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

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

5 de may de 202647 min

Threaded Miami: Daan Goossens of CoLab!

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