Reinventing Reality: How Physical AI Is Redesigning the Built World

For decades, we have been quietly redesigning the digital world to be machine-legible. Web pages carry schema markup and semantic HTML so search engines can parse them. APIs expose structured data so software can talk to software. Even our laptops and phones are designed around how machines process information, not just around how humans use them.

Physical AI flips that same problem into three dimensions. Every street, store, warehouse, field and hospital was originally built around humans: our eyes, our hands, our attention spans, our walking speed. As autonomous systems take over parts of that work, those environments stop being purely human spaces. They become shared spaces, tuned for both people and machines.

That is a much bigger redesign than most of the AI conversation currently admits, and it is already underway. Warehouses look nothing like they did a decade ago. Aisles are narrower, floors are QR-coded, and lighting and ergonomics no longer cater to humans in aisles where people have stopped walking. The same logic is reaching hospitals, farms, construction sites and eventually our streets. Standardised lighting. Robot-readable signage. New architectural conventions for spaces that machines and humans both occupy. It is the physical equivalent of adding schema markup to a page.

Streets: the city rebuilt around autonomous vehicles

The biggest shift is not the self-driving car itself. It is the city that gets rebuilt around it. This is precisely why we backed Distance Technologies: deviceless mixed reality and heads-up displays for automotive and aerospace, turning the windshield and the cockpit into a new perception layer that speaks both to drivers and to machines.

Stores: retail reshaped by physical AI

Retail was the first place most consumers met physical AI, through self-checkout and shelf analytics. The next decade looks more radical. When shelves are restocked by robots, inventory is read by cameras, and checkout is a walk-out event, the store no longer needs to look like a store. Aisles optimised for trolleys and human browsing can shrink. Dark stores and micro-fulfilment units take on the volume. The front-of-house turns into a showroom, a fitting lab, an experience. The back-of-house turns into a densely packed, highly automated fulfilment engine. The very ratio of public to private square footage flips.

Warehouses and factories: designed for robots, not forklifts

This one is already in motion. Amazon, Ocado, BMW, Figure, Agility and hundreds of others are deploying AMRs, humanoids and automated picking at scale. Ceiling heights, floor markings, lighting levels, aisle widths and safety zoning all change when the dominant actor on the floor is a mobile robot rather than a forklift driver. Factories are on the same path. The cell, the line and the jig were human-scale. The next generation of physical AI blurs the boundaries between manipulation, logistics and inspection, and the architecture of production will follow.

We see this directly in our portfolio. Makiina is building lower-cost robotic arms for industrial use, expanding the addressable market for manipulation. Spogen AI layers intelligence onto heavy machinery so the same physical asset can operate more safely and more autonomously in a shared human and machine environment. Distance's perception stack is as relevant on a factory floor as it is on a motorway.

Fields, hospitals and beyond: physical AI everywhere we work

In hospitals, surgical robots, delivery carts and ambient clinical intelligence will reshape floor plans, with even the nurse station reorganising around machine-generated signal rather than paper charts.

Construction sites, ports, mines, energy facilities and logistics hubs all follow the same arc. Each one is a large physical estate originally designed for human labour. Each one is becoming a robotics estate.

None of this happens overnight. But the direction is unmistakable. Once an environment is shared between humans and machines, the optimum design is no longer purely human.

Two ends of the physical AI spectrum

There is a useful question hiding inside all of this: do we want a world that has been re-engineered for robots, or robots dexterous enough to handle our world as it is? Almost every physical AI bet sits somewhere on that spectrum, and where a startup lands says a lot about its thesis.

At one end you have re-architecting the environment. New warehouse layouts, new theatres, new fields. Deep, durable, slow, and only really pays off where the operator is buying fresh greenfield capacity. At the other end you have machines that show up and just work in the messy world we already live in. Easier go-to-market, faster wedge into existing customers.

Levtek is a clean example of the second approach in our portfolio. The robot is deliberately designed to slot into existing warehouses and the way humans currently work, with autonomy added as a layer rather than as a precondition. That makes the sales motion a lot easier than competing end-to-end systems, which tend to need a brand new facility to be built around them. The wedge today is a smarter robot that fits the environment. The longer arc is increasing autonomy on top of that wedge.

Both ends of the spectrum will produce winners. The most interesting companies are usually the ones that know exactly which end they are choosing, and why.

Where the investable physical AI opportunity sits

At FOV we see three layers of opportunity in this shift, and we are actively backing founders across all of them.

• The machines themselves: robots, autonomous vehicles, drones, surgical systems, agri robots. The physical half of physical AI.

• The perception stack: cameras, LiDAR, radar, tactile sensors, specialised silicon, and the foundation models that fuse them. This is exactly where our spatial computing thesis meets our robotics thesis.

• The environments themselves, which we think are underrated. Digital twins, robot-ready infrastructure, V2X, warehouse operating systems, the software and hardware that turn a legacy space into a machine-friendly one.

You cannot deploy a million robots into a world that was never designed for them, and the companies building that missing middle layer will capture an enormous share of the value.

We recently mapped the early-stage European robotics landscape across those first two layers, the machines and the perception stack, into twelve application verticals and six enabling stack categories. It is the ground truth sitting behind this thesis, and a useful starting point for anyone looking at where European physical AI is actually being built. Explore the European robotics landscape map.

Reality, in other words, is up for reinvention

We started with the digital world. The web, the API, the laptop and the phone were all quietly redesigned over the last few decades to make machines first-class users. The same redesign is happening to the physical world. Streets, stores, factories, fields and hospitals are being rebuilt to accommodate intelligent machines, and a new generation of robots is being designed to fit the world we already have.

That is a generational redesign of the built environment, driven by a generational shift in who and what operates inside it. It is one of the most consequential places to be investing right now.

If you are building in this space, at any of the three layers above, come talk to us.