Software investors have been taught a simple mental model on where value accrues:
companies building deep infrastructure at the bottom of the stack that is hard to replicate, e.g. NVIDIA, AWS, Cloudflare, Snowflake
companies building applications with incredible distribution at the top of the stack, e.g. Facebook, Instagram, OpenAI, Figma
Everything in the middle gets compressed.
I think that model breaks down in robotics - it’s just way too big and too broad for single winners! Instead, we’ll see winning robotics companies in mining, agriculture, logistics, deep sea, defense, manufacturing, healthcare, and dozens of other verticals. These companies will win because they operate in different environments with different safety requirements, regulations, buyers, economics, and failure modes. A winning underwater robot is likely to be irrelevant in a hospital setting. A robot designed to pick and pack fragile inventory stock will be irrelevant when maintaining a solar farm.
With this in mind, I think robotics will produce many large companies, rather than one dominant infrastructure or application layer.
I expect those companies to broadly fall into two categories:
vertically integrated companies that own a specific workflow end-to-end, and
companies that benefit from the modularization of the robotics stack.
1/ Vertically Integrated Companies
Think of these companies as taking Apple’s path in robotics: increasingly owning the hardware, software, models, deployment, and customer relationship around one valuable physical job. What’s most important for these companies is owning the operating loop.
Take, for example, a robotic mining company - they may start with third-party vehicles, NVIDIA compute, open models, and off-the-shelf sensors. But, so long as they own the operating loop, every hour teaches that company something a generic robotics company cannot know. They learn about how dust affects perception, how different routes change tire wear, which slopes create control problems, and which component failures produce downtime.
That knowledge improves the system. It improves the policies, maintenance schedules, sensor configurations, fleet-management software, and customer workflow. Over time, it also tells the company which hardware should be customized, built, or acquired. The company earns the right to build custom hardware because it owns the job.
I think that same pattern will appear everywhere physical work is valuable and technical. For example: an agricultural robotics company will learn which weeds emerge at each crop stage, how weather and light affect vision, which treatments work, and where its equipment loses performance. A subsea maintenance company learns about marine conditions, asset degradation, battery performance, and the specific equipment customers need inspected or repaired. A logistics company learns how a facility actually operates: where inventory sits, when workers intervene, what causes exceptions, and which tasks create the greatest bottlenecks.
This deployment data makes the system more useful and more economical over time.
This gives those companies a deep competitive advantage, enabling them to sell outcomes, i.e. tonnes moved or packages packed, and not just machines or hours. They may start with third-party components, but the operating loop gives them a growing advantage over generic robot OEMs.
2/ Modular Platform Companies
The winners in this category will be the companies that make it dramatically easier for other people to build, deploy, and operate robots.
Rather than owning a single workflow, such as mining or agriculture, they will provide the shared infrastructure that many robotics companies rely on: fleet management, remote operations, simulation, data infrastructure, safety tooling, hardware interfaces, and marketplaces for task-specific models and components.
Their opportunity comes from the robotics stack becoming more modular. Hardware will get cheaper. Open models will become good enough for more tasks. Components will become more interoperable. And new platforms will let teams deploy robot fleets without rebuilding every layer of the stack from scratch.
A company should increasingly be able to combine a low-cost robot body from one vendor, a specialized gripper from another, an open model, and a shared fleet-management layer, then deploy the system into a real workflow. That is a much more accessible world than one where every robotics company has to build its own hardware, models, tooling, and operating software.
This is what open systems do well. They lower the cost of experimentation, bring in more builders, and speed up the entire market.
The “Android for robotics” opportunity is real.
But open source alone is not a business model. Open models and interoperable hardware will drive adoption, but the best platform companies will own a control point that compounds, e.g. the default fleet-management layer for heterogeneous robotics or managed data and simulation infrastructure. The key question for builders and investors alike is whether every new deployed fleet makes the platform better.
A company managing thousands of robots across different environments will learn which components work together, where deployments fail, and how to reduce the time and cost of getting a robot into production. Those insights improve the platform, helping attract more builders and fleets, helping further improve the platform.
This is a different type of operating loop from the vertically integrated company. The vertical company learns how to perform one specific job better than anyone else. The modular platform company learns how to help many different companies deploy robots more effectively.
Both can create enormous businesses.
At Canonical, we are particularly excited by vertically integrated companies with proprietary access to difficult physical workflows that improve with every deployment. We also want to meet teams building the modular platforms that help the broader robotics ecosystem build and deploy faster.