The OPTIM Update

Bogdan Cristei
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  • Tecnología

Deep conversations with the founders, investors, and operators building real-world AI - robotics, automation, industrial systems & AI infrastructure. Past the headlines, into how these technologies are really built, deployed, and scaled. Hosted by Bogdan Cristei, venture partner and former systems engineer.

Episodios

  1. 13 jul

    Why Pixel-Based Robot AI Is an Expensive Detour | Chao Cao, Sancho

    Advanced manufacturing has automated the individual process steps. CNC, 3D printing, photolithography - however complex the step, we can build a machine for it. The handoffs between those machines still run on people, and in cell therapy that means $300,000-a-year scientists spending their days loading and unloading equipment while contamination risk and throughput bottlenecks drive up the cost of every dose. Chao Cao (Co-Founder & CEO, Sancho - CMU robotics PhD, autonomy lead for CMU's DARPA Subterranean Challenge entries, ex-Boston Dynamics AI Institute) makes the case that the dominant approach to robot intelligence, pixel-based VLA models trained on massive datasets, is an expensive detour. Sancho bets on 3D geometric world models and test-time reasoning instead, and runs the entire stack on a single onboard compute module. We cover the lasagna analogy for factory workflows, the math on the most expensive labor doing the lowest-value work, what five nines of reliability does to the data question, Waymo vs. Tesla as a data-quality argument, three years of sending robots into tunnels and caves, the October-to-March sprint from incorporation to NVIDIA's GTC keynote, the oversubscribed seed round co-led by Fusion Fund and Catapult, and why regulated cleanrooms are an easier place to start than a grocery store. Chapters: 00:00 Intro 01:10 Roadmap and why the company is called Sancho 02:15 The lasagna problem: the gap between machines 04:51 $300K scientists loading machines by hand 06:04 Why fixed automation isn't the answer 07:02 Bet #1: 3D geometry over pixels 09:46 Bet #2: test-time reasoning over data scaling 11:25 Running the whole stack on onboard compute 13:11 Three years in the dark: DARPA SubT lessons 16:15 October to GTC in five months 18:35 The oversubscribed seed round 19:01 Two founders, two autonomy worlds 21:08 Why start in the most regulated environments 23:48 Hot takes: Waymo vs. Tesla and wasted data 25:58 What Chao wishes he knew before starting 27:30 Hiring: perception and loco-manipulation 29:50 Where to find Sancho   Learn more about Sancho: https://www.sancho.com The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of Optim VC. Subscribe to the newsletter: https://www.optim.vc Follow Bogdan on LinkedIn: https://www.linkedin.com/in/bogdancristei/

  2. 16 jun

    Building the Missing Data Layer for Physical AI | James Kujareevanich, Vision Lab

    James Kujareevanich is the co-founder and CEO of Vision Lab - building the missing data layer for physical AI. They go into factories, capture first-person video of real operators performing real tasks, and turn that into structured training data for frontier labs and robotics companies. The origin story starts with an MIT PhD student who strapped a camera to his head to automate lab documentation before egocentric data was even a thing. They built a human training tool first, got pulled into the robotics data business by demand from AI labs, and just closed a $6M round to scale. We cover:00:00 - Intro00:46 - From McKinsey Bangkok to an MIT PhD with a camera on his head02:36 - The Christmas pivot: from training humans to training robots03:41 - Why robotics doesn't have its "internet" yet04:50 - What the data product actually looks like05:27 - Egocentric, tactile, teleoperation - every lab wants something different07:12 - A factory capture from start to finish07:53 - "My dad runs a factory" - the first test case08:31 - Getting factory owners to trust you09:40 - Industrial influencers and the factory network10:12 - Scaling across India, Thailand, Vietnam, Indonesia11:30 - What frontier labs learned from the pilots12:57 - The gap between what labs want and what you can deliver13:16 - Closing a $6M round and doubling the team in two weeks14:00 - New verticals beyond manufacturing14:42 - The synthetic data question15:14 - Chaos theory and why sim data compounds errors17:02 - Where defensibility lives in this business17:20 - 80% of raw footage is unusable19:29 - What the market gets wrong about robotics timelines21:28 - Hot take: convergence to 2-3 big players22:32 - The 10-year vision: becoming the Siemens of robotics23:18 - Advice for robotics founders Learn more about Vision Lab: https://thevisionlab.ai The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Subscribe: https://www.optim.vc

  3. 25 may

    Building the Foundry for Physical AI | Mike Xia, Anvil Robotics

    Mike Xia is the co-founder and CEO of Anvil Robotics - building the foundry for physical AI. They make the hardware, software, and data tools that let robotics teams go from zero to model training in days vs months. They've shipped over 100 robots, manufacture in Taiwan, and just raised a $6.5M seed round. Mike gets into the economics of building and shipping a $5,000 arm, why most teams are fighting their own hardware before they can even start on AI, and what's structurally broken in the supply chain that not enough people talk about. We cover:00:00 - Intro00:45 - What physical AI teams actually go through before training a model03:16 - Why the existing robot stack was built for a different era04:10 - What it's actually like setting up an SO-100 at home05:21 - The leap from toy arms to real payloads08:01 - What you get on day one with an Anvil dev kit09:12 - What kilohertz-rate sensor fusion actually unlocks11:19 - The false tradeoff between payload and force compliance14:35 - Why vision alone isn't enough: the dentist analogy16:15 - The economics of a $5,000 arm20:01 - Scaling from 150 robots to 200 a month21:30 - Why all customers came inbound22:10 - Retention and repeat orders24:47 - If open source isn't the moat, what is?28:15 - Why the supply chain is a relationship, not a transaction28:36 - How to do customization without becoming a services company31:30 - How many of 1,500 new robotics startups survive 24 months?34:52 - The most technically wrong thing teams are doing in 202638:57 - What happens when your whole fleet breaks and you don't know why40:40 - What will look obvious in five years42:29 - Where to learn more about Anvil Anvil Robotics: https://anvil.bot The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Subscribe: https://www.optim.vc

  4. 13 may

    Useful Now: The Case for Application-Specific Robots | Arjun Subramaniam of Factory Intelligence

    Arjun Subramaniam is the founder and CEO of Factory Intelligence - a physical AI company training tactile foundation models for industrial manipulation. He's toured 70+ factories, deployed robots on real shop floors, and is making the contrarian bet that application-specific systems beat humanoids and general-purpose foundation models right now. His first workcell has eight robots building electrical outlets for $3/hour. We cover:00:00 - Intro00:44 - What 70 factory visits taught him about deployment vs. demos02:47 - No SLA in a research paper - why factories are a different game04:23 - Why he put a packaging machinery veteran in the COO seat06:34 - The "Useful Now" thesis and where the robotics narrative is wrong08:53 - The Tesla vs. Waymo parallel for robotics10:01 - You can't buy your way into a large enough manipulation dataset10:27 - Why vision alone isn't enough for industrial tasks12:54 - The pen-in-a-bin problem: why vision-only models are too slow14:37 - Why robotics is not like LLMs - there is no single scaling law16:32 - The application-specific full-stack quadrant: why no one else is here17:12 - Best version of the model-first argument - and how he pushes back19:50 - What happens to humanoids if "Useful Now" works21:56 - Inside an electrical prefab shop - what actually happens in there23:53 - Prefab-Cell-E1: eight robots, $3/hour, 9x productivity24:44 - What "tailing an outlet" means - the actual task, step by step28:01 - Wire-bending model generalizing to colors it was never trained on29:16 - The integration trap: why custom fixtures wreck margins31:29 - When do you know deployment economics actually work32:08 - The data flywheel: why 50% success rate is the threshold33:29 - Touch is filling the gap where vision saturated35:14 - Combining neural nets with classical control - and why both matter37:44 - The world action model: image, proprioception, tactile, action, all in39:39 - You can't buy your way to multimodal data from the internet40:42 - If this works: data centers on the moon Factory Intelligence: https://factoryintelligence.com The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Subscribe: https://www.optim.vc

  5. 16 abr

    Why the World's Most Advanced Factories Are Still Flying Blind | Jared O'Leary, SirenOpt

    Jared O'Leary, Co-Founder and CEO of SirenOpt, breaks down why even the world's most advanced factories are still flying blind - and what a physics breakthrough out of UC Berkeley is doing about it. We cover what advanced manufacturing actually is and why it's different from anything most people picture, the sensing gap that's been gating progress for decades, and what cold atmospheric plasma reveals about a material that no other instrument can. Then we get into the commercial reality - who's already deployed, how the business model works, and why the data moat gets harder to close with every passing month. Jared co-invented the core technology at UC Berkeley alongside his co-founder and CTO Ali Mesbah, a tenured professor who left to build SirenOpt. Their customers include Tier-1 manufacturers across turbines, batteries, and semiconductors in North America, Europe, and Asia. Learn more about SirenOpt: https://www.sirenopt.com Volta Foundation presentation mentioned by Jared:https://youtu.be/a4aW8uxGMQk?si=MGQJI7hNhzzf5pfH The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators. Subscribe: https://www.optim.vc

  6. 17 mar

    Why Heavy Industrial Work Demands a Different Kind of Robot | Gary Chen & Conley Oster, Raise Robotics

    Gary Chen and Conley Oster are co-founders of Raise Robotics - they build mobile manipulators for construction sites, steel fabrication facilities, and shipyards. Environments where the work is genuinely dangerous, the labor shortage is acute, and the dominant solution being pitched to investors right now - humanoid robots - may be fundamentally the wrong answer. In this episode we cover: What it actually feels like to spend a day on a heavy industrial job siteWhy humanoid form factor is the wrong frame for most of these applicationsThe self-driving car parallel: we didn't solve autonomous driving by putting a robot in a taxiWhy "simple" tasks like drawing a line or drilling a hole are deceptively complexThe tribal knowledge problem - losing the human LLMs built over decades on job sitesWhat a job site looks like in 2030The shipbuilder who got 2 applicants for "welder" and thousands for "robot welding technician"Raise Robotics: https://raiserobotics.ai/ The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators. Subscribe: https://www.optim.vc

  7. 6 mar

    How AI Models for Robotics Are Really Built, Deployed, and Sold | Dr. Asad Tirmizi, Trener Robotics

    Dr. Asad Tirmizi, Co-Founder and CEO of Trener Robotics, breaks down the full lifecycle of AI in industrial robotics. We cover how models are actually built - the training pipelines, the data, and what separates people who've done it from people who've read about it. Then we get into deployment reality - what happens when AI hits a real factory floor. And we close with the part nobody wants to discuss - how do you actually sell this stuff through integrators and OEMs. Asad previously worked at Vicarious (acquired by Google) and ByteDance's robotics program. Trener recently raised a $32M Series A co-led by Engine Ventures and IAG Capital Partners to scale their Acteris platform - a robot-agnostic AI skills platform that works across ABB, Universal Robots, FANUC and others. Learn more about Trener Robotics: https://trener.ai The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators. Subscribe: https://www.optim.vc

  • 7 episodios

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Deep conversations with the founders, investors, and operators building real-world AI - robotics, automation, industrial systems & AI infrastructure. Past the headlines, into how these technologies are really built, deployed, and scaled. Hosted by Bogdan Cristei, venture partner and former systems engineer.

Información

  • Creador
    Bogdan Cristei
  • Años de actividad
    2026
  • Episodios
    7
  • Clasificación
    Apto
  • Copyright
    © 2026 OPTIM Ventures
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