Robert Schouwenburg

We just wrapped up a huge week at Figure - here’s what we announced: 1/ Announced >$1B in Series C. This gives us the strongest balance sheet in humanoid robotics - critical to scaling Helix (our AI) and BotQ (robot manufacturing) 2/ Launched a partnership with Brookfield, who manages $1T in assets globally. Together we’re planning to scale AI infrastructure for Helix, already collecting real-world data for Helix pretraining, and working commercially to deploy robots 3/ Announced Project Go-Big - our push to build the world’s largest humanoid pretraining dataset. We are scaling out human video collection and our F.02 humanoid can now learn directly from human video. This project is accelerated by our partnership with Brookfield, who owns over 100,000 residential units As a founder, timing is everything. Too early or too late is fatal Right now, we’re in the sweet spot for humanoids: advanced hardware is maturing that can walk and do human-like tasks, production manufacturing is spinning up, and most importantly - neural networks are delivering. I believe we’re at the starting line of scaling synthetic humans to billions of units

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A new unicorn in robotics! 🦄 Gecko Robotics from Pittsburgh has reached a $1.25 billion valuation after raising $125 million in new funding. They build robots and AI software to inspect and improve important infrastructure like power plants, factories, and military systems. That's a great example of a modern robotics company that doesn’t JUST build robots. 📊 A positive sign for the entire robotics market. Congrats Jake Loosararian and team! Enjoyed this? ♻️ Repost to help 1 robot find a new workplace.

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Reshoring manufacturing to the U.S. hinges entirely on advances in automation and robotics—and those advances, in turn, rely on next-generation tools like integrated development environments, foundation models, smart robotics skills, and flexible automation platforms. This isn’t merely an incremental shift; it’s the next industrial revolution. I estimate it will unlock up to $30 trillion in GDP over the next decade, as countries compete fiercely for leadership. Eclipse is our time!

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Today we’re making one of the biggest announcements in Zipline’s history. We signed a $150M contract with the U.S. Department of State that will come with $400M of co-commitments from our African partner governments. This will expand life saving autonomous delivery to 130M people in Africa. Countries bet on Zipline because it saves lives and saves money. A recent study found that we were the most cost-effective way of vaccinating zero-dose children ever studied (.66 cents per child). This is a big shift for US policy. We’re now deploying AI and robotics infrastructure into the developing world to save lives and help countries leapfrog into the future and turbocharge their economies. This will help secure US tech and manufacturing leadership for next decade.

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1.7× faster inverse kinematics in pure Python, GPU-accelerated! Fully open-source and built for scale [📍Bookmark for later] PyRoki, a modular toolkit for robot kinematic optimization, supporting - inverse kinematics - trajectory optimization, and - motion retargeting. Built for flexibility, but also fast: ✅ Runs on CPU, GPU, and TPU ✅ Outperforms cuRobo in speed, success rate, and accuracy ✅ Fully in Python, easy to integrate and extend Whether you’re working with industrial arms, simulation environments, or humanoids, PyRoki is designed to fit your stack and scale with your hardware. Thank you, Chung Min Kim, for sharing!! Website: pyroki-toolkit.github.io Code: https://lnkd.in/diYsBX4b Paper: arxiv.org/abs/2505.03728 This one’s for researchers, builders, and anyone tired of debugging their IK stack.

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K-Scale Labs crosses $1M in sales within 72 hours of launch Early-stage robotics company K Scale Labs has reported $1 million in orders within the first three days of its commercial launch on July 1st. The sales figure represents 111 units of the company's K-Bot humanoid robotics platform. While the sector has been dominated by well-funded players like Tesla and Figure AI, K Scale's early traction demonstrates potential for alternative approaches in this market segment. We'll be watching to see whether this initial sales velocity can be sustained as the company moves from orders to production and delivery. For daily robotics, humanoids, and tech insights Follow K-Scale Labs Follow Bots n' Beans

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David Moodie, founder of Foundry Lab, joins Joel Johnson and me on Tool Or Die to discuss how he’s reinventing metal casting with a digitized, tooling-free process that could reshape U.S. manufacturing. We dig into why casting is one of the four most critical industries identified by the White House for reshoring, the shortage of skilled workers, and why his New Zealand-born startup is moving to the U.S. to support defense and industrial manufacturing.

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One of the hardest parts of building Varuna was sourcing its 120+ components. Trust me, it isn’t easy. Most of these parts come from foreign vendors, who already have long waiting lists. That means a single component can take 6–8 months, or even a year, before it finally reaches our PierSight headquarters. Now imagine, after all that wait, if one component arrives defective. Ordering a replacement means going through the same months-long cycle again. Easy Solution? Order extra components in advance. But here’s the problem - every extra component locks up precious CapEx, which means other critical areas lose out on funds. So you’re stuck in a dilemma: order more and block capital, or order less and risk delays. Welcome to the supply chain reality of space tech - where the real challenge is finding the right middle ground. At PierSight, we handle this through rigorous internal discussions and careful planning, even before we start building a satellite. Having a team with ISRO experience has been a huge advantage - helping us streamline timelines and prepare for every scenario without over-allocating capital. And once you build your first payload or satellite, the picture becomes even clearer. You gain a much deeper understanding of what to order, how much to order, and how to map the entire supply chain. That’s when supply chain management stops being a challenge and starts becoming your competitive edge.

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Agentic extraction from technical docs has huge applications in manufacturing/construction/energy sectors 🔥 Built a quick demo to show how this actually works in practice 🧵 These industries have heavy technical manuals with diagrams, tables, and schematics that engineers have to manually process and it’s super tedious. LlamaExtract helps you build an agent that can extract structured data directly from the LM317 voltage regulator datasheet - it ensures accurate, consistent JSON output through multimodal understanding + validation loops. Demo notebook: https://lnkd.in/g_Hgt7jx  Try it in LlamaCloud: https://lnkd.in/g9Wpqn7w

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🚀 NEW SERVICE ANNOUNCEMENT 🚀 Excited to announce 4.0 Hero's new Node-RED Co-Development Service powered by FlowFuse! After years of helping manufacturers connect their industrial systems, we're formalizing our approach to co-developing scalable IIoT solutions using Node-RED and FlowFuse. Why this matters for industrial organizations: ✅ Rapidly connect legacy equipment to modern systems ✅ Create custom dashboards and data pipelines without deep coding ✅ Manage Node-RED at enterprise scale with FlowFuse ✅ Own your solution and continue enhancing it after our engagement We're not just building solutions FOR you - we're building them WITH you, ensuring knowledge transfer and self-sufficiency. Who's already using Node-RED + FlowFuse for industrial applications? I'd love to connect! #IIoT #NodeRED #FlowFuse #DigitalTransformation #IndustrialAutomation #EdgeComputing #Industry40 #PLC

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The $42B AI robotics revolution isn't coming. It's in your backyard. I've watched robots evolve from workshop novelties to industry game-changers... For decades, robots could only perform repetitive, pre-programmed tasks. Now they have AI adapting in real-time, a complete game-changer for business. Training that once required weeks now happens in under 24 hours. Let me break down where this is happening: Food production now uses: • Soil sensors optimizing watering • AI kitchens preparing meals • Automated harvesting • Autonomous logistics Restaurants like Kura Sushi have robots handling repetitive tasks, freeing staff for customer service. Beyond restaurants, Ocado deploys picking robots with centralized AI, slashing order times and costs. The delivery robot market will grow from $500M to $4.98B by 2033. Why? They solve shipping's costliest challenge: the last mile, something I understand deeply from years in the industry. Healthcare applications show the most impressive results: Robotic surgical systems deliver: • Recovery cut from 6 weeks to 2-3 weeks • 80% fewer blood transfusions • 50% shorter hospital stays • 65% fewer complications AI systems review mammograms 30x faster with 99% accuracy. Throughout hospitals, robots sanitize rooms, sort medications, and transport samples. Previously, robots required expensive technical teams. As someone passionate about robotics since childhood, I've watched this barrier prevent adoption... until now. I built RobotLAB to own the last mile of robotics and AI. We have team members in your city who arrive on the same day because whenever you need us, we'll be there, resolving problems in hours, not days. This revolution isn't theoretical. It's here, practical, and with the right partner, it's manageable for your business today.

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You’ve raised $1.5M to build your IoT product and you’re facing the decision that will determine whether your company scales…or spends the next six months cleaning up a $300K mistake. Most of that budget doesn’t disappear in one dramatic failure, it dies in silence - from assumptions, delays, and blind trust no one warned you about. We’ve seen it over and over again. Prototype works. Investors are excited. You start asking factories for quotes… And boom - $300K gone before the first run even lands. MISTAKE #1: You quote too early. If your prototype hasn’t been stress-tested by real operators, on a real line, with test jigs, takt time, and QC built in— You’re not quoting. You’re gambling. The best founders don’t send CAD files and hope. They “simulate” the chaos before it costs money. MISTAKE #2: You expect the factory to flag risk. “If something’s wrong, the factory will tell me.” No. Factories aren’t built to protect your business. They’re built to execute instructions. If your CM (contract manufacturer) isn’t giving feedback, surfacing edge cases, or pushing back on complexity - That’s a red flag. And no…it’s not a “cultural thing.” It’s a signal that your build is being treated like a task, not a system. You’re not stepping into a gold mine. You’re stepping into a machine that runs exactly what you hand it—flawed or not. At low volume, these problems are invisible. But when you scale? That tolerance stack-up turns into yield loss. That “should be fine” turns into 12% field failure. That vague test plan? Becomes 3,000 miscalibrated units. And when that happens - you’re the one in front of the board explaining what broke. MISTAKE #3: You pick the factory that feels easy. It’s local. It’s “trusted.” It’s fast. But can it build 10K units at your complexity level without bottlenecking your launch? Speed isn’t who says yes the quickest. It’s who gets it right the first time. The cleanest $10M IoT launches we’ve seen all had one thing in common: Their pre-production wasn’t rushed. It was designed. So use this window to do what most teams don’t: - Lock in second-source suppliers - Stress test the line before POs go out - Find a CM that doesn’t just build—but thinks Most founders delay these decisions until they’re forced to react. The ones who win treat pre-production like a pressure test. They make the invisible visible - before they burn through capital, timeline, or trust. If you’re in the pre-production phase and want the exact checklist we use to de-risk every first build: Comment “PLAYBOOK” and send me a connection request. I’ll send you the same system we use with funded teams before their first PO goes out. Use it to flag blind spots, avoid 6-figure mistakes, and launch clean while everyone else is still re-spinning boards. 👊🏻

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Scaling tech in niche industries like supply chain is hard. Because good engineers have options. They don’t have to fight through bad documentation, tribal knowledge, and fragmented architecture. They can just leave. And we felt that. There aren’t many experienced software engineers in our space, so we thought: let’s grow our own. Hiring smart people. Teaching them the domain is the easy part. This video unpacks the hardest part—teaching context.

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