The robots are among us. They’re carrying passengers through the streets of San Francisco, delivering parcels over the air, and working their way across farms, factories, mines, and industrial sites.
The collision we’re now witnessing between artificial intelligence and the physical world is often described as “Physical AI” – machines capable of perceiving their surroundings, making decisions, and taking action among us in the real world. But every new machine that clocks in has a second job we rarely see. It creates data. Lots of it.
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In the US, the Federal Aviation Administration expects the commercial drone fleet to grow from approximately 966,000 aircraft at the end of 2024 to around 1.18 million by 2029. And in factories around the world, there was an estimated 4.66 million industrial robots working in 2024, while another half a million were installed during the year – more than twice the number installed a decade earlier.
Today’s autonomous machines are vastly more capable, but moving all that accumulated experience off them can still be surprisingly manual. In 2026, the fastest way to move masses of data off a machine is still a person carrying a hard drive. And as autonomous machines spread further into our cities and industries, keeping that information moving could become one of the defining infrastructure challenges of the robotics age.
Data runs both ways
Imagine what an autonomous vehicle experiences. Cameras watch the roads from multiple angles, sensors and AI measure and make sense of the surrounding environment, while on-board systems track position and performance of the vehicle itself. Decisions are made and recorded in real-time as pedestrians cross roads, traffic lights change, and thousands of other vehicles and connected technologies move and exchange data around it.
Delivery drones, agricultural machinery, warehouse robots, and autonomous industrial equipment may operate in different environments, but the logic tracks. Every journey or task leaves behind a digital trail of sensor readings, video, telemetry, diagnostics, and operational logs.
And the information doesn’t travel in only one direction. These machines may also need fresh maps, software updates, configurations, AI models, and instructions from the computing infrastructure behind them. What they experience in the physical world flows back toward data centers, while the intelligence needed for their next task travels outward.
Multiply that exchange across millions of machines and an interesting new layer of internet traffic begins to emerge that we’re only just beginning to think about.
The direction of travel was visible as early as 2019, when a landmark university paper on 6G identified mobile robots, drones, and autonomous vehicles would drive demand for ubiquitous wireless intelligence – its vision for the 6G era included networks capable of supporting a 10,000-fold increase in traffic. The US commercial drone fleet alone is now heading toward seven figures, while industrial robots already number in the millions globally.
Perhaps most notable to casual observers, autonomous vehicles are becoming far more prevalent on the streets of our capitals and major cities. In June of this year, machines surpassed 57% of web traffic globally – the first time that bots have outnumbered humans – and they use networks in a very different way. Cloudflare predicts that non-human traffic could eventually surpass human traffic by a factor 1,000 within the next five years.
And a lesson learned by one machine needn’t remain with that machine. There’s an opportunity here that only connectivity can unlock. Google DeepMind’s Gemini Robotics 1.5 showed skills transferring “zero-shot” between physically different robots – a task taught using a two-armed Franka robot was executed by a humanoid, without any data collected on the target machine.
Sit with that implication. A person who masters a technique carries that knowledge around in their head, and when they retire, much of that experience leaves with them. A robot can potentially hand what it has learned to every other robot on the network, which means experience stops being “local” and starts becoming a shared asset across the network, compounding intelligence for the company’s moat.
The physical footprint of robots is spreading outward, but the data traffic they create remains concentrated at central locations. AI may increasingly have wheels, wings, and arms, but their intelligence and interactivity with the world remain still wholly dependent on their relationship with the nearest data center hub, and that’s a problem we now have to solve.
What’s the hold up?
When a robot arrives back at the depot and “clocks out” for a while, it presents an ideal opportunity to recharge, undergo inspection, run self-diagnostics, download updates, and get the data it needs for its next shift. This is when potentially enormous volumes of data, depending on the fleet size, need to be moved both to and from the device.
Sites that house, charge, or service machinery can therefore find themselves functioning as miniature data hubs, dealing with bursts of traffic as machines arrive and depart. And as fleets grow, so does the strain on the network connecting all these hubs. When uploading and downloading take longer than charging or maintenance, the network becomes the operational bottleneck to scale.
A new layer of connectivity
A wired network simply can’t evolve at the same speed as the mobile economy. A fleet operator might need to open a new depot in another city, place drone nests closer to customers, or connect autonomous equipment at temporary industrial sites. Extending fiber to each location would involve trenching, permitting which are often cost and time intensive.
Wireless connectivity offers a faster and more flexible approach. Links can be installed without digging or waiting for permits, allowing operators to connect new sites quickly. Links can also be redeployed or expanded as fleets grow as their physical footprint expands dynamically.
Wireless optical communication (WOC) could provide that high-capacity bridge. Using narrow beams of light, it carries fiber-like capacity through the air between two points with a clear line of sight, up to 20 Gbps across distances of up to 20 kilometers. It can be installed on existing radio masts or buildings in a matter of hours.
For autonomous fleets, that creates a way to rapidly offload telemetry and operational data, upload new maps and models, and return high-value assets to work instead of leaving them waiting at the depot.
WOC isn’t magic though. Optical links still need line of sight, and heavy fog and rain can degrade their connectivity. That’s why serious deployments pair them with an RF path and engineer them for availability rather than simply assuming it. It won’t replace fiber, but it will extend fiber’s reach to the places fiber can’t go, and it’s getting smaller, faster, and more affordable as the technology moves from mechanical to solid-state beam steering with silicon photonics. Light is the answer.
The arrival of electric vehicles taught us to look beyond the machine and consider the infrastructure behind it. Autonomous machines will require us to do the same again, but this time what needs to be replenished isn’t just the machine’s battery – it’s the machine's experience, and from it, the shared intelligence of the entire fleet.
Robots are becoming extraordinarily good at navigating the physical world. We shouldn’t leave the final mile of their learning journey to someone carrying a hard drive across a parking lot.
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