There are two developments in physical AI that, if they converge, could have massive implications for the construction industry.

First: Tool Intelligence

ZINOVA recently introduced “Tool Intelligence,” an approach designed to let robots operate common tools and learn from the physical interaction between tool and material.

The premise is simple: construction workers rarely perform work with their bare hands. They use drills, nail guns, saws, grinders, etc. A robot may not need a perfect human hand if it can reliably pick up a tool, position it correctly and operate it. Simple enough.

Just as valuable though, if not more so, is the data. ZINOVA aims for robots to learn from the feedback created during operations. A tradesperson can feel a drill bind, a screw seat or a saw move into a different material. Force, torque, vibration and resistance all provide information about what is happening at the point of contact. Giving robots access to those signals could help them learn not just how a tool should move, but what successful tool use actually feels like.

Near-Term Implications: Construction robots become useful without waiting a decade (or more) for a perfect general-purpose humanoid; while the bigger opportunity may be the data. Every tool interaction can capture the tool, material, position, force, vibration and result. At scale, that becomes a machine-readable record of how physical work gets done.

In a sense, robots are going to trade school, learning construction one tool and one operation at a time.

Second: Reasoning on the Road

May Mobility is attacking a different problem. A major challenge in self-driving has always been the enormous variety of roads and situations a vehicle may encounter. Performing well in a heavily mapped and tested environment is one thing. Arriving somewhere new and figuring it out is another.

May Mobility is betting that world models and reasoning can help close that gap. Instead of relying only on situations the vehicle has seen before, the system tries to understand the environment, consider how it might change, and choose an appropriate action.

The goal is to build a system capable of applying what it has learned to situations it has not explicitly seen before.

Near-Term Implications: if this approach works, autonomous vehicles (and perhaps other machines) could become easier to scale. The moonshot idea is that machines move from memorizing the physical world toward understanding it.

What the Future May Hold:

The game changer for the construction industry is the convergence of these two approaches to physical. Imagine a robot arriving at a jobsite it has never seen before, to build a house it has never built, on a slab it has never encountered. The lumber is imperfect, a compressor has been left in its path, one wall is slightly out of position, and the plans changed yesterday.

A sufficiently capable world model could allow the robot to understand the site as it exists, compare it with the intended design and reason through what needs to happen next. It might recognize that the bottom plate needs to be repositioned before framing continues, reject a badly warped stud, wait for a worker to clear its path or adjust after detecting that a nail did not seat properly. The important shift is that the robot would not simply be replaying a task it had been trained to perform. It would be interpreting a dynamic situation, just like a human would.

Combine machines that can reason through unfamiliar environments with machines that know how to use tools, and you get something construction has never had: a worker that can learn once, show up anywhere, and adapt in real-time. That is the inflection point where construction robots stop being prototypes and start ushering in physical abundance.

PG&E, Rewiring America, and Google just launched a first-of-its-kind virtual power plant (“VPP”) which aims to turn home appliances into a new form of energy supply for the grid. The newly launched SHARE program, short for Smart Home Assets for Reliability and Efficiency, is an effort to capture latent energy in household appliances. In other words, your water heater might just become a revenue-producing asset.

Here’s how:

Think of SHARE as pooling thousands of homes into one giant, software-controlled power plant. Instead of PG&E relying only on centralized generation when demand spikes, it can tap into energy already sitting inside homes: batteries can discharge, thermostats can ease off for a bit, and other flexible loads can shift to a different time.

To the homeowner, most of this should happen quietly in the background. You set the boundaries, software handles the timing, and in exchange your home can earn incentives for being available when the grid needs help. The key idea is that your house is no longer just consuming electricity; parts of it can now actively participate in the energy market.

Near-term Implications:

In the near term, this gives utilities another way to handle surging electricity demand without solving every bottleneck by building more power plants, transmission lines and substations. Instead, some additional grid capacity can come from equipment that already sits inside people’s homes. PG&E is explicitly testing whether targeted investment in household equipment can unlock capacity on the regional grid.

For homeowners, the bigger shift is conceptual: your appliances are starting to become economic assets. A battery can sell flexibility to the grid. A heat pump can decide when energy is cheapest to consume. An EV can eventually act as both transportation and energy storage. As more devices become connected and controllable, the house starts looking less like a collection of dumb appliances and more like a portfolio of assets managed by software.

What the Future May Hold:

Imagine a home ten years from now that behaves less like a collection of appliances and more like a small autonomous economy.

Your house knows the price of electricity in real-time. It knows how much energy is sitting in the home battery, the EV in the garage, the water heater, and whatever storage devices come next. It also knows your plans. Maybe you’re taking the family up to Napa for the day, which means the car needs to leave at 9:00AM with enough range for the drive, lunch, a few winery stops, and the ride home.

So, the house works backward from that.

Overnight, it waits to charge the EV until power is cheapest. By morning, the car is ready, the home battery has enough reserve for the day, and the water heater has already done most of its work. While you’re in wine country, rooftop solar keeps filling the home battery. By late afternoon, electricity prices spike, so the house sells some of that stored power back to the grid.

You come home at 7:30PM. The car still has 35% battery left. Your home agent knows you’re not going anywhere until morning, so it decides that some of that remaining vehicle energy is more valuable supporting the house for the next few hours than sitting idle in the garage.

You don’t manage any of this. Your home AI does.

Go a little further out and things get stranger. Your home might choose between competing energy markets, coordinate with neighboring houses, decide when to buy versus store versus sell, or summon an autonomous vehicle based partly on the energy it carries. A neighborhood could behave like a decentralized power plant made up of homes, cars, batteries, HVAC systems, and appliances.

At that point, the house is no longer simply connected to the grid.

It is a participant in it.

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As of midyear 2026, I think it’s safe to say we’re seeing physical AI have its “takeoff moment” as well. In only the past 60 days we have seen Zoox start limited commercial operation of its autonomous taxi, Doordash announce plans to start drone deliveries at scale, and Anthropic release its Model Hardware Standard (MHS). It now appears that, after roughly a 75-year hiatus, the U.S. will finally turn its attention (and capital) back to what secured it as a world power in the first place: industrialization.

I have spent my career in real estate finance, operating mostly within the housing and hospitality sectors (two segments set to converge on one another—more on that in a future article). Because things are changing so fast, I feel it’s critical to stay apprised of new tech, and its impending impact on real estate, housing, hospitality, and what a “HomeStack” might look like for all of us in 20+ years. I hope you enjoy.