The state of robotics in 2026, through the eyes of someone who moves physical goods for a living
I’ve spent the last decade importing, warehousing, and shipping rugs. Turkish supply chains. A warehouse in Easton, PA. Pallets moving across Amazon, Wayfair, and our own DTC site. My world is physical — heavy, dusty, measured in square feet and container loads.
So when Travis Kalanick dropped a 1,700-word manifesto on March 13 at atoms.co/vision announcing his robotics company Atoms — the culmination of eight years in stealth, thousands of employees, and over a billion dollars raised — I didn’t read it like a VC hunting for deal flow. I read it like someone who moves physical goods for a living and felt something click.
What follows is my attempt to think out loud about what’s actually happening in robotics right now — the real numbers, the physics constraints that matter, the global geography of who’s building what, and why I believe the biggest opportunities will belong not to the people inventing robots, but to the people who understand the messy reality of deploying them. I’m not a robotics expert. I’m a rug guy who pays attention. But I think the distance between those two things is collapsing, and that’s exactly the point.
Where I’m coming from — and why it matters
My dad ran a computer networking business in the ’90s. He connected law offices, dry cleaners, and small businesses with networks — selling hardware, running cable, building gaming PCs on the side. I watched that transformation happen in real time. When I was in elementary school, less than 10% of kids had a computer at home. By the time I graduated high school, the iPod had just come out and was still a novelty. I saw firsthand what happens when technology meets small business operations: everything changes, slowly at first, then all at once.
That pattern has repeated itself across every industry I’ve watched since — e-commerce, mobile, cloud computing, AI. Each time, the winners weren’t always the people who invented the technology. They were often the people who understood a specific domain deeply enough to know where the technology should go. Jeff Bezos didn’t invent the internet. He understood books and logistics.
Today I’m the founder and CEO of Well Woven. We design and sell area rugs — thousands of SKUs, sourced primarily from Turkey, warehoused in Pennsylvania, sold across every major marketplace. I’ve also built side projects like FurniPulse, a furniture industry news aggregator, because I can’t stop tinkering. I’ve spent years optimizing operations, managing supplier relationships across continents, navigating warehouse workflows, and obsessing over the gap between how things should work and how they actually work.
I say all this not as a résumé dump, but as context. I operate in atoms, not bits. And that orientation is precisely why the robotics moment feels personal.
What Travis Kalanick actually said — and the idea I can’t shake
The most interesting idea in the Atoms manifesto isn’t the robots. It’s a framework Kalanick calls “valuable unknown truths” — the idea that competitive advantage comes from knowing things others don’t know, which lets you do things others can’t do. These unknown truths compound: organizations that get good at discovering them develop advantages that widen over time.
His framing is deliberately provocative. CPUs manipulate bits; manufacturing manipulates atoms. Storage stores bits; real estate stores atoms. Networks move bits; transport moves atoms. The digital world has been optimized relentlessly for three decades. The physical world remains, in his words, “largely untouched territory.”
Kalanick’s stance on humanoid robots is the most contrarian part: he’s explicitly anti-humanoid. He wrote about watching a humanoid half-marathon and thinking they’d all be better off with wheels. His pancake factory analogy crystallizes it — if you need 1,000 pancakes per hour, a humanoid flipping them individually is absurd. You build a specialized machine with a heated iron cooking 100 at once. He calls them “gainfully employed robots” — machines designed for specific jobs, not designed to look like us.
Atoms’ product is a “wheelbase for robots” — a standardized chassis with power, compute, and sensors that can be outfitted for specific industrial tasks. They’re acquiring Pronto, Anthony Levandowski’s autonomous mining vehicle startup, and targeting food production, mining, and transport. Over a billion dollars raised. Thousands of employees. Eight years of quiet building.
But here’s the idea I can’t shake: the unknown truth in robotics isn’t whether the technology works. It’s knowing how to deploy it in a specific environment, with specific constraints, specific products, specific labor realities. That gap between “the technology exists” and “we know how to make it work here” is enormous. And it’s where the real value will be created.
The physics are humbling — and that’s actually what makes this interesting
One reason I find robotics fascinating rather than intimidating is that the constraints are brutally honest. Software can paper over a lot of inefficiency. Hardware can’t. Physics doesn’t care about your pitch deck.
Here are the numbers that stopped me cold.
Power is the binding constraint for mobile robots. Current humanoid robots achieve only 2–4 hours of operation per charge. The battery can only be about one-eighth of the robot’s total weight — any heavier and the machine can’t balance or move efficiently. Compare that to an electric vehicle, where the battery is roughly one-third of the car’s total mass. That single ratio — 12% versus 33% — reshapes everything about how you design and deploy a mobile robot. It means shorter operating windows, more charging infrastructure, and fundamentally different operational workflows than the “drop it in and forget it” fantasy.
Battery energy density is improving at about 5–8% per year. Current lithium-ion technology delivers 200–350 Wh/kg. Projections put solid-state batteries at 600–800 Wh/kg by 2030, but those won’t reach robotics production scale until 2028 at the earliest. This isn’t a software problem that an overnight breakthrough will solve. This is a materials science problem that improves on geological timescales compared to Moore’s Law.
Perception degrades in exactly the conditions where you need it most. LiDAR — the laser-based spatial sensing system most robots use to navigate — suffers a 56% reduction in point cloud density in heavy rain. More than half your spatial data, gone because it’s raining. At 40+ mm/h rainfall, LiDAR systems can’t reliably detect standard traffic signs regardless of their reflective material. Standard depth cameras fail on transparent and reflective objects — glass, metal, water. Factories provide controlled lighting and known objects, achieving >99.9% accuracy. Real-world environments? Variable lighting across 8 orders of magnitude, unknown objects, clutter, moving people. The gap between demo conditions and deployment conditions is where most robotics companies die.
Manipulation reveals Moravec’s Paradox in full force. The human hand has 27 degrees of freedom, 30+ muscles, and tactile sensors at 1mm intervals in fingertips. Individual robotic tactile sensors can now exceed human resolution at the component level — vision-based tactile fingertips resolve down to 30–100 micrometers. But at the system level — where perception, actuation, control, and durability must work together seamlessly — robots remain far from human versatility. Grasp success rates hit 94–97% for known objects in structured settings. In cluttered environments with novel objects? 83–87%. For transparent objects? 84%. Production environments need >99%. That 10-point gap between demo and deployment is the gap between a YouTube video and a working business.
But here’s the statistic that really rewired my thinking. Industrial robot arms advertise mean time between failure (MTBF) of 40,000 to 100,000 hours. Sounds incredible. But the actual robot cell MTBF — the entire working system — is only about 87 minutes. Why? Because 80% of failures come from peripheral equipment: grippers, conveyors, sensors, fixtures. The arm is fine. Everything around it breaks.
That’s not a robotics problem. That’s an operations problem. And operations is something people like me actually understand.
The $75–130 billion opportunity nobody talks about
While the tech press obsesses over humanoid robots — Figure AI at $39.5 billion valuation, Tesla’s Optimus, the Beijing humanoid half-marathon — the real money in robotics is flowing through a market that sounds deeply unsexy: systems integration.
The global robotics systems integration market is worth $75–130 billion and growing at about 10% annually. It’s the business of taking off-the-shelf robot hardware (arms, mobile bases, sensors, grippers) and assembling, programming, deploying, and maintaining them for specific applications. And it’s extraordinarily fragmented — the top three integrators control just 3% of the market.
The reason this matters is that 80% of robots are currently sold to automotive companies and large enterprises. The other 90%+ of manufacturers — the 250,000+ small and medium factories in the United States alone — have barely been touched. Only about 12% of small manufacturers currently use any form of robotics. That’s 220,000+ factories with zero robots. Two million US manufacturing jobs are projected to go unfilled. The labor shortage is acute, the need is real, and the deployment infrastructure doesn’t exist at scale.
This is where the Robots-as-a-Service (RaaS) model enters. Think of it as the SaaS revolution applied to physical automation. Instead of a $160,000+ capital expenditure for a palletizing system, you pay monthly. The provider handles deployment, maintenance, software updates, and performance guarantees. If the robot doesn’t perform, you don’t pay.
Formic, a Chicago-based startup, is the clearest example. They build zero robots — they source cobots from manufacturers like Yaskawa, design custom cells, deploy, maintain, and guarantee performance. Seventy-five percent of their customers had never used a robot before signing up. They’ve logged over 400,000 production hours across their fleet. The RaaS model eliminates the 18–24 month enterprise sales cycle for CapEx purchases and opens the vast majority of manufacturers who are too small or risk-averse for traditional automation.
The global RaaS market hit roughly $27 billion in 2025 and is growing at 17–18% annually. Fleet deployments grew 31% in 2024. Within transportation logistics specifically, RaaS grew 42%.
The companies that won weren’t built by roboticists
Here’s the pattern I keep returning to: the biggest value creation in commercial robotics over the last decade came from operators, not inventors.
Locus Robotics was born when Amazon’s 2012 Kiva acquisition stranded Quiet Logistics, a 3PL. Bruce Welty, an operations guy, started an internal robotics division. Rick Faulk joined as CEO in 2016 with zero robotics background — he was a career tech executive from Cisco and Intronis. His exact quote: “We look like a robot company, but we’re actually a software company.” Locus improved DHL’s pick speed from 78 to 150 units per hour — a 92% improvement. They now operate 13,000+ robots across 350+ sites in 18 countries, at nearly a $2 billion valuation.
6 River Systems was founded by Jerome Dubois and Rylan Hamilton, former Kiva implementation executives — not inventors. Their “Chuck” robot was deliberately designed to look like existing pick carts, reducing training friction. It delivered 80% of goods-to-person productivity at 20% of the cost. Shopify acquired them for $450 million in 2019, four years after founding.
Formic, founded in 2020 by Saman Farid — a former VC at Baidu Ventures with 15 years in robotics/AI investing — builds zero robots. They source, integrate, deploy, and manage. Their 98% contract renewal rate proves the model. Farid’s defining insight: “In the future, companies will either be building new robots, or operating them and generating value from them, but not both.”
In every case, the competitive advantage wasn’t a novel actuator or a breakthrough algorithm. It was understanding the workflow, the user, the economics, and the deployment reality better than anyone else. The technology was assembled from available components. The value was in the integration and the operations.
The global map is shifting — and it matters more than you think
You can’t understand robotics without understanding geography, and the geography is shifting fast.
China installed 295,000 new industrial robots in 2024 — more than every other country combined. They now have over 2 million robots in operational use. Robot density has exploded from 97 per 10,000 workers in 2017 to 470 in 2024. Guangdong province alone produces 40%+ of China’s industrial robots and 80% of service robots. Unitree, based in Hangzhou, sells a functional humanoid robot — the G1 — for $16,000. Their newest model, the R1, starts under $6,000. China controls an estimated 85–90% of global humanoid unit volume and dominates the component supply chain: motors, reduction gears, sensors, batteries. Inovance Technology shipped over 5 million robotic joint servo motors in 2025, achieving 70%+ domestic replacement of Japanese suppliers.
Japan remains the historical anchor: FANUC holds ~17% global industrial robot market share with 260+ service locations in 100+ countries. Nabtesco controls approximately 60% of the global market for precision reduction gears — the critical component in every robot joint. Yaskawa has shipped 540,000+ robots.
South Korea has the highest robot density on Earth: 1,012 per 10,000 workers. Hyundai owns Boston Dynamics and is building a 30,000-unit/year robotics facility. Samsung and Doosan Robotics are both making major moves in collaborative and humanoid platforms.
Germany is anchored by KUKA (owned by China’s Midea Group), Franka Robotics, Festo, and the dense Mittelstand network of precision manufacturers. The European collaborative robot market is growing faster than any other region.
In the United States, five robotics clusters are emerging:
- Pittsburgh — Carnegie Mellon’s Robotics Institute, 250+ advanced technology companies, 7,300+ robotics jobs. Aurora, Argo AI’s successor companies, and a deep pipeline of CMU spinouts. The repurposed steel infrastructure means lab space at $15/sq ft.
- Boston — MIT, MassRobotics innovation hub, Amazon Robotics (formerly Kiva), Locus Robotics, the legacy of Boston Dynamics and iRobot.
- Bay Area — AI-native robotics startups, Figure AI, Physical Intelligence ($5.6B valuation building foundation models for robots), unmatched VC access.
- Austin — Apptronik ($350M+ raise), Tesla’s Gigafactory and Optimus development, fast-growing deep tech scene.
- Detroit/Ann Arbor — The legacy auto cluster is pivoting to robotics, with autonomous vehicle spinoffs and manufacturing automation startups.
These hubs recently formed the USARC Alliance (United States Alliance of Robotics Clusters) to coordinate the national ecosystem.
For someone in my position — running a company with Turkish supply chain relationships, Pennsylvania warehousing, and customers across multiple channels — this geography matters in a very practical way. The robots that might one day transform my operations will be assembled from components made in Shenzhen, running AI models developed in San Francisco, mounted on platforms designed in Japan, deployed using business models pioneered in Chicago. The supply chain for automation has its own supply chain, and it’s every bit as global and complex as the ones I navigate for rugs.
What the AI moment means for robots (and why it’s harder than you think)
The most important technology shift happening in robotics right now is the emergence of Vision-Language-Action (VLA) models — AI systems that see the world, understand natural language commands, and output physical actions. Physical Intelligence’s π0 model, running at 3–5 billion parameters, demonstrated a robot bussing tables and making espresso for 13 continuous hours. Figure AI’s Helix model powers robots at BMW’s Spartanburg plant doing 10-hour daily shifts.
These models are following the same democratization arc as image generation. HuggingFace’s LeRobot library and open-source models like OpenVLA and Octo are making this the “Stable Diffusion moment” for robotics. The barrier to entry for AI-powered robot control has dropped from approximately $10 million to $10,000.
But — and this is the critical caveat — the data gap between language AI and robotics AI may be unbridgeable for years. Ken Goldberg at UC Berkeley estimates the gap between robot foundation model training data and mature LLM data could be up to 120,000x. You can scrape the entire internet for text. You can’t scrape the physical world for robot training data.
Simulation helps: NVIDIA’s Isaac Lab achieves 1.6 million frames per second across 8 GPUs, enabling robots to train walking and navigation in simulation and deploy to real hardware with zero real-world experience. Boston Dynamics’ Spot learned stair climbing entirely in simulation. But zero-shot sim-to-real transfer fails for dexterous manipulation — the physics of contact, deformation, and friction are too complex to simulate accurately. Real-world fine-tuning remains necessary for anything involving hands.
Kalanick himself acknowledged this tension. The tech stack for physical AI, he wrote, “is not for the faint of heart.” But he also offered a crucial insight: no single company has to master every layer. The more cross-stack competence you develop, the better positioned you are — but you don’t need to do it all.
Where I think this goes
I don’t have a grand theory or a masterplan to announce. What I have is a growing conviction shaped by a decade of operating in the physical world: the next ten years in manufacturing, warehousing, and logistics are going to look radically different from the last ten. Not because the technology is brand new, but because the deployment layer is finally maturing.
The patterns are clear. RaaS lowers the barrier. Foundation models make robots more adaptable. The operators who understand actual work — the cluttered environments, the weird SKU edge cases, the 87-minute MTBF reality — are going to matter as much as the engineers who build the arms. Maybe more.
I keep coming back to my dad’s business in the ’90s. He wasn’t inventing networking technology. He was a guy with a van, cable, and deep knowledge of how a dry cleaner’s back office actually worked. He understood the deployment reality. The technology existed; the unknown truth was knowing exactly how to make it useful for the specific customer standing in front of you.
Robotics in 2026 feels like networking in 1995. The hardware is real. The software is accelerating. The infrastructure is fragmentary. And the people who will build the most lasting companies might not be the ones building the robots at all — they might be the ones who understand where the robots need to go, and why.
I’m just getting started thinking about this. More to come.
— Adem
This is the first in a series of posts exploring the robotics landscape from an operator’s perspective. Subscribe to follow along, or connect with me on LinkedIn.
I’m the founder and CEO of Well Woven, and I write about technology, physical goods, and the future of operations at ademogunc.com.
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