Investment Trends in the Robotics Industry

When will we see a $1T+ robotic company? In my recent DeepTech Asia research, I've estimated valuation upsides for VC investors across different robotics segments based on 5 factors: 1. 𝗠𝗮𝗿𝗸𝗲𝘁 𝘀𝗶𝘇𝗲 - potential TAM 2. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - deployment speed 3. 𝗠𝗼𝗮𝘁 - long-term value vs commoditisation 4. 𝗦𝗶𝘇𝗲 𝗼𝗳 𝗶𝗻𝗰𝘂𝗺𝗯𝗲𝗻𝘁𝘀 being disrupted 5. 𝗦𝗶𝘇𝗲 𝗼𝗳 𝘀𝗶𝗺𝗶𝗹𝗮𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 in other industries Estimations span across 3 time horizons: 𝟱 𝘆𝗲𝗮𝗿𝘀 (𝗯𝘆 𝟮𝟬𝟯𝟬) — for VC/PE growth funds • 🚁 𝗔𝗲𝗿𝗶𝗮𝗹 & 𝗠𝗮𝗿𝗶𝗻𝗲 𝗿𝗼𝗯𝗼𝘁𝘀 ($𝟭𝟬𝟬𝗕+) disrupting Lockheed Martin ($135B), FedEx ($70B), Quanta Services, Inc. ($70B). • 📦 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗿𝗼𝗯𝗼𝘁𝘀 ($𝟱𝟬𝗕+) similar to Symbotic ($35B), Vanderlande, Dematic ($3-4B revenue). • 🧠 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗳𝗼𝗿 𝗿𝗼𝗯𝗼𝘁𝘀 ($𝟱𝟬𝗕+) hype around humanoids, dev tools a la Scale AI ($29B) for robotics. • 🦾 𝗥𝗼𝗯𝗼𝘁𝗶𝗰 𝗮𝗿𝗺𝘀 (~$𝟭𝟬𝗕) similar scale to KUKA ($4B), ABB Robotics ($5B), potentially FANUC ($40B+). 𝟭𝟬 𝘆𝗲𝗮𝗿𝘀 (𝗯𝘆 𝟮𝟬𝟯𝟱) — for early-stage VC funds • 🚗 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 ($𝟱𝟬𝟬𝗕+) surpassing Uber ($200B+), Caterpillar Inc. ($270B+), and eventually Tesla ($1T+) with better margins. • 🧹 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗿𝗼𝗯𝗼𝘁𝘀 ($𝟭𝟬𝟬𝗕+) nextgen operators disrupting McDonald's ($200B), DoorDash ($90B), Cargill ($100B) with higher margins. • 🏥 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗿𝗼𝗯𝗼𝘁𝘀 ($𝟭𝟬𝟬𝗕+) gradual disruption of Intuitive ($200B+) in the next 10-20 years. • ⚙️ 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 (<$𝟭𝟬𝟬𝗕) similar size to industrial automation players: Parker Hannifin ($110B+), KEYENCE CORPORATION ($80B+). 🧪 𝗟𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗿𝗼𝗯𝗼𝘁𝘀 (<$𝟭𝟬𝟬𝗕) Gradually disrupting Thermo Fisher Scientific ($220B+) 𝟮𝟱 𝘆𝗲𝗮𝗿𝘀 (𝗮𝗳𝘁𝗲𝗿 𝟮𝟬𝟯𝟱) — for strategic investors • 🤖 𝗛𝘂𝗺𝗮𝗻𝗼𝗶𝗱𝘀 ($𝟭𝗧+) similar to car OEMs (Toyota $300B+) and finally smartphone players (Apple $3T+). For detailed descriptions of each scenario, see the full DeepTech Asia report in comments below 👇 These are best-case scenarios that don't account for downside (execution, geopolitics, regulation). 💡 Conclusions for investors deploying in 2026: 𝗘𝗮𝗿𝗹𝘆-𝘀𝘁𝗮𝗴𝗲 (𝟭𝟬 𝘆𝗲𝗮𝗿𝘀): all these segments have high potential, even with 90% total dilution till exit. 𝗚𝗿𝗼𝘄𝘁𝗵-𝘀𝘁𝗮𝗴𝗲 (𝟱 𝘆𝗲𝗮𝗿𝘀): depends on entry valuation. Assuming a 50% total dilution till exit (2-3 rounds): • $𝟱𝟬𝟬𝗠 𝗲𝗻𝘁𝗿𝘆: almost any segment can still deliver 𝟭𝟬𝘅. • $𝟭𝗕 𝗲𝗻𝘁𝗿𝘆: 𝟭𝟬𝘅 remains plausible for 🚗 autonomous driving, 🚁 aerial & marine systems, 🏥 medical robots, 📦 warehouse automation, 🧹 service robots, and 🧠 robotics software. • $𝟱𝗕 𝗲𝗻𝘁𝗿𝘆: 𝟭𝟬𝘅 is realistically only possible in 🚗 autonomous driving or 🚁 aerial & marine systems. Do you agree with these estimations? #Robotics #VentureCapital #DeepTech #Unicorns #PhysicalAI #AutonomousSystems #China #VC

𝐉𝐮𝐬𝐭 𝐩𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝: 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐀𝐈 — 𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐢𝐧 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 🤖 After months of collecting info, news, breakthroughs, and funding announcements, I've put together a VC intelligence report on what I believe is one of the most consequential investment opportunities of the decade. 𝗧𝗵𝗲 𝗳𝗶𝘃𝗲 𝗳𝗼𝗿𝗰𝗲𝘀 𝗜'𝗺 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴: 1️⃣ Foundation models (VLA models now hold 41% of the industrial robotics market) 2️⃣ Simulation infrastructure compressing robot training from years to hours 3️⃣ Hardware cost deflation — 30x drop in a decade 4️⃣ The demand side is structural, not cyclical. Demographic labor shortages in the US, Europe, and China aren't reversible within any meaningful investment horizon 5️⃣ China-US geopolitical competition accelerating deployment on both sides of the Pacific 𝗙𝗲𝘄 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 (𝘄𝗲 𝗹𝗶𝗸𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀!) ➡️ $40.7B flowed into robotics VC in 2025 alone — a record — and Q1 2026 shattered all prior funding records with $300B invested globally ➡️ 79% of organizations are already engaging with physical AI, yet only 27% have moved beyond pilots. That gap is where the real investment opportunity lives!🔥 𝗕𝘂𝘁 𝗜 𝗮𝗹𝘀𝗼 𝘁𝗿𝗶𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁 🤔 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝗴𝗲𝗻𝘂𝗶𝗻𝗲𝗹𝘆 𝗵𝗮𝗿𝗱: 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗱𝗲𝘅𝘁𝗲𝗿𝗶𝘁𝘆, 𝗱𝗮𝘁𝗮 𝘀𝗰𝗮𝗿𝗰𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗽𝗶𝗹𝗼𝘁-𝘁𝗼-𝘀𝗰𝗮𝗹𝗲 𝗴𝗮𝗽 𝘁𝗵𝗮𝘁 𝟳𝟲% 𝗼𝗳 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗰𝗶𝘁𝗲 𝗮𝘀 𝘁𝗵𝗲𝗶𝗿 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲. The investment thesis isn't "robots are coming." It's about where value will disproportionately accrue — and right now, 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲, 𝗶𝘁'𝘀 𝘁𝗵𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲-𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗹𝗮𝘆𝗲𝗿, 𝘀𝗮𝗳𝗲𝘁𝘆 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝘁𝗼𝗼𝗹𝗶𝗻𝗴. The window for early positioning is narrowing. Full report linked below. If you're thinking about this space — as an operator, investor, or builder — I'd love to compare notes!📚 #PhysicalAI #Robotics #VentureCapital #HardTech #FoundationModels #AIInfrastructure #DeepTech

Bullhound Capital's new report on the robotics industry (based on research across Europe, the US, and China) highlights how AI advancements, declining hardware costs, and labor scarcity are accelerating adoption. The report emphasizes that deployment, integration, and operational learning are often more critical than technical capability alone—a conclusion that aligns perfectly with my professional experience at Boston Dynamics. Their key findings are: Labour scarcity is creating a structural demand floor for robotics The deployment moat is difficult to replicate – Real-world production environments generate operational data that cannot be fully recreated in simulation. Companies that deploy first accumulate proprietary learning loops that compound over time. Reliability matters more than capability – Unlike digital AI, where models compete primarily on capability, robotics systems compete on reliability, safety, uptime, and outcome accountability. The most durable economics may emerge from operational infrastructure – As hardware becomes increasingly commoditised, value shifts toward the layers that own workflow integration, deployment relationships, and recurring operational revenue. China, the United States, and Europe remain deeply interconnected – China leads in manufacturing scale and deployment density. The United States leads in AI research and software infrastructure. Europe retains strengths in industrial automation, embedded systems, and deployment expertise. The most investable companies may be those that operate across these ecosystem boundaries rather than within a single geography.

The global economy has an impending problem. While AI is compounding its ability at a historic rate, an aging population and declining fertility rates are already causing labor shortages. These trends, combined with declining costs of robotics hardware, underpin a compelling case for humanoid robots and physical AI. According to Morgan Stanley, the humanoid robot market is set to exceed $5 trillion by 2050. Even in 2025, the larger robotics space saw $21 billion of VC capital invested. And with a steady increase in patent activity mentioning “humanoid” over the past few years, these machines are already walking onto factory floors. For most of human history, productive output was a function of human muscle. Agriculture, manufacturing, logistics, and construction were all built around the physical limits of the human body. Because humans did the work, the built world standardized around human form: doorways, staircases, countertops, and tools are all designed for two arms, two legs, and hands that grip. Redesigning every factory, warehouse, and home around task-specific machines would be unfeasible. A humanoid robot that fits into existing infrastructure doesn’t need the world to change around it. Near-term use cases focus on structured, predictable settings, enabling a robot to learn quickly, make mistakes cheaply, and improve rapidly. My research team at Social Capital concluded that humanoid Robots will have the highest impact in these 7 areas: 1. Domestic Assistance: Supporting mobility needs, handling household chores, and providing medication reminders. 2. Manufacturing: Assisting assembly tasks, moving tools and parts, inspecting finished products. 3. Security & Monitoring: Patrolling facilities, investigating alerts, and assisting in emergencies. 4. Customer Service & Reception: Greeting and directing visitors, answering questions, and managing check-ins or bookings. 5. Facility Maintenance: Conducting routine inspections, performing minor repairs, cleaning, and sanitizing spaces. 6. Healthcare: Assisting nurses, delivering supplies or meals, monitoring patients. 7. Warehouse and Logistics: Picking and packing items, loading and unloading goods, and moving inventory in warehouses. By 2050, Morgan Stanley estimates that more than 1 billion humanoid robots could be working globally, with a market size of over $5 trillion. This is one of the biggest opportunities in the AI era.

Physical AI models will map and navigate the physical world. So… we mapped the physical AI model market to help you navigate the evolving space. Robotics is no longer bottlenecked by hardware. The real determinants of robotic performance and impact are now intelligence and the data and models that allow machines to operate autonomously in messy, unpredictable environments. In 2025, robotics companies raised a record $40.7B, and a growing share of that capital is flowing into physical AI, especially the models that let robots perceive, reason, predict outcomes, and act in the real world. We used our predictive intelligence to map the space and identify 70+ companies building this intelligence layer across the stack: • Data & simulation • Model architectures including VLMs, VLAs, and world models • Foundation models • Observability platforms What stands out about where this market is headed: 1️⃣ Proprietary training data is the choke point Physical AI models live or die by access to real-world robot data, which is scarce, expensive, and hard to replicate. Companies that control data through simulation, teleoperation, and deployed fleets gain a durable advantage and can dictate who gets access to capable models. 2️⃣ World models unlock true autonomy Vision and action are no longer enough. World models give robots the ability to predict, plan, and adapt over time, moving robotics from reactive systems to autonomous ones. That shift is why investment is accelerating and why this layer may decide the long-term winners. 3️⃣ Multi-robot coordination is still wide open Single-robot intelligence is advancing quickly, but coordinating fleets remains unsolved. The company that builds the orchestration layer for heterogeneous robots will define how physical AI scales across real-world environments. These companies are the foundation for intelligence that will power the physical economy. Explore the full market map and analysis below 👇

Physical AI is starting to look less like robotics - and more like the next cloud infrastructure race. This week, Mind Robotics raised another $400M to build AI-powered industrial robots, pushing its valuation past $3.4B. At the same time, a new wave of embodied AI companies - from Physical Intelligence to Skild AI - are attracting capital at infrastructure-scale valuations. The narrative is shifting fast: investors are no longer underwriting “robots.” They’re underwriting foundational control systems for the physical world. What most people are missing is that hardware is becoming the distribution layer, not the moat. The real asset is the data flywheel created by real-world interaction: motion, failure, correction, adaptation. Physical AI companies are converging on the same realization that defined cloud and autonomous driving - whoever owns the operational data layer compounds fastest. That’s why simulation, deployment infrastructure, and robotics middleware are suddenly strategic assets, not support tooling. The implication for founders is clear: vertical robotics companies may struggle unless they control proprietary environments or workflows. For investors, the bigger opportunity may sit one layer below - orchestration, simulation, embodied foundation models, and industrial data infrastructure. Physical AI won’t be won by the best robot demo. It’ll be won by the company that learns fastest in the real world. #PhysicalAI #Robotics #EmbodiedAI #VentureCapital #AIInfrastructure https://lnkd.in/gUY4-Zsx

The robotics boom hiding behind AI. $600B+ lined up for AI. About $110B sitting in robotics. Most people are reading it as an AI story. From the investor's perspective, I take it as a robotics signal. TechCrunch just published the 2026 unicorn list. 40 new companies, 18 are AI, only 3 are robotics. Bedrock Robotics. $1.8B. Apptronik. $5.3B. Gecko. $1.8B. Serious capital behind each of them. Barely noticed inside a list dominated by software. The industry takes this as confirmation that AI is everything. I take it as a signal. Because if you zoom out, the asymmetry is obvious. AI is crowded, robotics is not. AI app layer is easy to replicate, robotics is not. AI scales in software, robotics scales in the physical world. Everyone is racing to build intelligence, and almost no one is focused on execution. The global robotics market is $95B today. According to Precedence Research, it's headed to $376B by 2034, at a 46% CAGR. A category quietly rewiring entire industries: construction, manufacturing, healthcare, logistics, space. And what makes it particularly compelling is that unlike AI, robotics builds moats you genuinely can't copy-paste. Hardware, motion systems, real-world data loops, physical defensibility.   That's a different kind of durable. This goes beyond the investment thesis for me. I deeply believe robots will become one of the most transformative forces in human history. Not as a prediction. As something I feel every time I look at where this is heading.   When AI stops being just a thinking layer and starts moving through the physical world, everything changes. The combination of AI and robotics isn't just a market convergence. It feels like a new chapter for humanity. This is how I think about it: AI is infrastructure, robotics is execution. And markets rarely reward infrastructure alone. They reward whoever controls execution. I'm betting the most valuable companies of the next decade sit exactly at that intersection. Everyone is asking which AI model will win. But I keep coming back to a different question: who will own the layer that actually does the work?

FANUC says robot orders are surging. One reason: Physical AI. FANUC's March 31, 2026 earnings report showed strong robot demand in both the U.S. and China. The company cited continued automotive investment, but also noted that general industry orders are increasing as well. One comment from management stood out: "…orders have exceeded our initial expectations, and we believe a portion is due to the favorable assessment of Physical AI." For years, Physical AI was largely a research topic. Today, one of the world's largest robot manufacturers is stating that thousands of robot orders can be directly linked to customer interest in Physical AI solutions. That momentum helps explain FANUC's recent partnership with Google and Intrinsic. As Michael Cicco, President and CEO of FANUC America Corporation, stated: "By combining FANUC's industrial-grade robotics with Google's advanced AI, we're enabling customers to take on more complex, variable production while maintaining the reliability and performance that production environments demand." FANUC is also putting capital behind its outlook, investing $90 million to expand robot manufacturing capacity in Michigan. My take: We may be witnessing two powerful trends converging: 🔷 A manufacturing investment cycle driven by reshoring, supply-chain security, energy, and infrastructure investment. 🔷 An AI cycle that is making robots easier to deploy in environments that were previously difficult to automate. Here's the important caveat: many manufacturers still need to build or expand their factories before they can purchase automation equipment. The orders come later. That means the robot demand we're seeing now may only be the early signal of a much larger wave. Over $7 trillion in announced U.S. manufacturing and infrastructure investments is working its way through the pipeline. Rising industrial robot demand. Growing freight activity across rail, barges, and trucking. And now major robot suppliers openly citing Physical AI as a driver of orders. Am I too optimistic? The numbers seem to be making the case for me. #robotics #manufacturing #physicalai #automation

The single biggest trend in robotics right now is not any one technology. It is the convergence of three forces simultaneously: 1. Big Tech commitment. In one week (March 24, 2026), Amazon acquired a humanoid company, Google partnered its best AI with 20,000 robots, and Toyota deployed humanoids commercially. 2. Capital at scale. Over $4 billion flowed into Physical AI startups in March alone -- Mind Robotics ($500M), Rhoda AI ($450M), and more. 3. A new business model. Toyota's Agility Digit deal runs at $30/hour under robots-as-a-service. That changes the math for every manufacturer. The trend that is still underrepresented? Tactile sensing -- giving robots the ability to feel what they touch. As someone who has spent over 15 years developing electronic skin at NUS, I believe the companies that solve touch will define the next decade of automation. What do you think? Share your thoughts below:

Robotics, AI and Capital Allocation in Advanced Technologies Robotics and AI are moving from pilot deployment into core layers of advanced technological infrastructure, with clear implications for institutional and strategic capital allocation. Five developments are most relevant: 1. Humanoid robotics is moving into practical industrial deployment, particularly in logistics, inspection and operational support 2. Artificial intelligence is becoming the control layer for robotics systems, enabling real time autonomous decision making 3. Collaborative robots are expanding beyond controlled environments into heavier and more complex operational settings 4. Autonomous logistics systems are reshaping internal supply chains into connected operational networks 5. Human and machine interaction is becoming more intuitive, accelerating integration across existing infrastructure Market Analysis From our analysis, at this point in the cycle, deployment costs remain high and the market is fragmented, with companies still defining their niches. The most attractive institutional and strategic capital is being directed towards scalable, high value applications in advanced technologies where efficiency, precision and productivity gains are already demonstrable. The market remains in evolution rather than consolidation. For institutional and strategic capital allocators, we are identifying a curated pipeline of opportunities where these dynamics are most clearly visible. At Stirling Infrastructure Partners, we focus on high quality opportunities across energy infrastructure, advanced technologies and related systems, across both investment and M&A activity. We are actively engaging with major global corporations to acquire and coinvest in selected opportunities where scale and execution potential are clear. In this environment, execution determines value creation.