AI has embedded itself in America’s digital industries, such as software, finance, and entertainment. But for AI to raise productivity across the broader economy, efficiency and cost gains must extend to the physical industries that build homes, manufacture goods, and produce our food. Now, so-called “physical” AI is beginning to enable robots, machinery, and self-driving cars to perceive, reason, and perform actions in the physical world.
That’s essential. Physical industries such as construction, manufacturing, and agriculture have suffered from years of lagging productivity growth, along with a deep and worsening skilled-labor shortage. The good news is that the growth of AI in these industries may be accelerated by a collapse in sensor costs and dramatic increases in the quality of AI physics simulations. Across this dispersed and uneven frontier, machines that can perceive, reason, and understand physics may soon bring AI into new markets.
As with all technology-driven productivity, small improvements often seem like individual stepping stones until a full path is revealed. No one can promise hockey-stick-style exponential growth, but should it arrive, the effects on the cost of living and our politics would be profound. This brief will explore the state of play for physical AI across three critical sectors, construction, manufacturing, and agriculture, and consider what lies ahead for each.
Construction
The construction sector in the United States has been plagued by falling productivity since its peak in the late 1960s. Over the past twenty years alone, construction output per hour has fallen by 8%, while output per hour in the overall nonfarm business sector has risen by 40%.
Not unrelated, falling productivity is a key reason why prices in the construction sector have risen far faster than the overall inflation rate. From 2005 to 2025, the construction price index rose by 117%, double the 58% price increase for the economy as a whole.
Amid a lack of R&D in construction equipment, stringent land-use regulations, and a dearth of economies of scale, the construction industry’s plight has played its part in fueling the cost-of-living crisis. In simple terms, America would’ve gotten more bang for its construction buck, meaning more homes, factories, hospitals, and infrastructure from the same spending.
As a result, the output of the construction industry today is lower than in 2005 in real terms. Had construction prices risen at the same rate as the prices of the overall economy, the same nominal spending would have corresponded to a real construction output 30% above its 2005 level.
Physical AI could increase workers’ output through autonomous machinery. Among the many pioneers in the field, Bedrock Robotics has begun retrofitting excavators and bulldozers with lidar (Light Detection and Ranging) sensors, GPS, and cameras. Their package, titled the Bedrock Operator, completed the industry’s largest supervised-autonomous excavation on a 130-acre site. The start-up is targeting fully operator-less excavators in 2026 after its $270M Series B fundraiser, with total funding above $350M. Additionally, Built Robotics’ solar panel pile driver automates utility-scale solar pile driving: repetitive, precise, dangerous work where autonomy wins on cost and safety.
Caterpillar unveiled a generation of autonomous excavators, dozers, haul trucks, and compactors at CES 2026. This next generation of heavy equipment can work faster, longer, and more safely than human-operated equipment. WLTR, Wall Laying Terra-Based Robot, the brick-laying bot from GreenBuild, lays 108 square feet of brick per hour, roughly the output of five bricklayers, and requires the supervision of a worker from a safe distance. The robot uses glue instead of the carbon-intensive cement, which accounts for 7-8% of the world’s carbon emissions. Fastbrick Robotics (FBR) is pioneering similar bricklaying robotics, with successful Wall as a Service (WaaS) projects completed in Florida via their advanced construction robot, Hadrian. Hadrian is capable of building the load-bearing walls of a house, both external and internal, up to 360 blocks per hour, in less than a day.
By adopting these technologies, the construction industry can improve safety and output. To operate these novel technologies, workers must be upskilled and shifted towards higher-productivity, safer supervisory roles. Construction workers could become the managers of an AI-enhanced fleet of heavy equipment, shielded from environmental hazards and chronic injuries.
Manufacturing
U.S. manufacturing productivity, as measured by the Bureau of Labor Statistics, has been basically flat for the past 15 years. The issues in manufacturing are multifaceted, including geographic disparities, wage stagnation, competitive threats from China, and tariffs. Physical AI offers a chance to rapidly expand U.S. manufacturing capacity, develop more competitive products, and create stable, high-paying jobs.
First Solar, a solar manufacturer, has embraced physical AI across its production process. By utilizing AI computer vision in its newest Louisiana factory, deep learning tools spot defects and guide technicians to make real-time adjustments. The factory has a fully integrated production process and directly employs over 825 people. Average compensation sits at around $90,000, close to triple the area’s per capita income. First Solar is expected to produce more than 14 GW of solar capacity in 2026, with more than 17 GW of capacity when all five of its facilities are operational.
ABB Robotics and NVIDIA recently launched the integrated NVIDIA Omniverse simulation into RobotStudio (“HyperReality”), a physics simulation model for robotics, claiming to reduce costs by up to 40% while also accelerating time-to-market by 50%. Foxconn plans to pilot this in electronics assembly; the release to ABB’s 60,000 customers is scheduled for the second half of 2026.
Automaker giant Stellantis, NVIDIA, and Accenture are deploying AI digital twins across global plants. Digital twins pair physical production with an AI physics simulation of production to create a “predictive and autonomous” model driven by real-time data. In June 2026, LG and NVIDIA announced an AI-factory partnership that embeds NVIDIA’s robotics and physics models (Isaac, Cosmos, and GR00T) into LG’s “PhysicalWorks” robotics platform to connect procurement through delivery in real time. These NVIDIA models connect real-world information and visual data, robotics platforms for inference, and physics simulations to drive efficiency and increase safety. AI-powered robotics manufacturer MiR shipped the MiR1200 Pallet Jack, an AI pallet handler trained on 1.2M images that autonomously identifies, lifts, and delivers pallets in live warehouses. As with construction, physical AI’s integration with American manufacturing has been plant-by-plant rather than sector-wide, but the tools for widespread adoption are now commercial.
Agriculture
U.S. agricultural total factor productivity rose by only 1% from 2013 to 2023, according to the latest data available from the Economic Research Service at the Department of Agriculture. By contrast, agricultural TFP rose by 17% in the preceding 10 years (TFP measures output relative to all major inputs, whereas labor productivity measures output only relative to labor input).
Within the sector, impediments to further productivity growth remain, particularly for soft-flesh fruits such as berries, avocados, and tomatoes, which require hand-picking. Immigrant fruit pickers have been the backbone of this workforce. Recent policies from the Trump administration regarding ICE raids have raised questions about the labor supply of agricultural workers. Agricultural Secretary Brooke Rollins announced changes to the H2A visa program, effectively cutting the wages of workers between $1 and $7 per hour, depending on the state. The net effect of these policy changes remains to be seen. Still, the effective wage cuts create disincentives to innovation and automation, while questions remain about whether the labor demand will be met.
Either way, physical AI presents an opportunity to increase productivity and lower costs, which can translate into lower grocery bills for Americans. Monarch Tractor sells an autonomous, driver-optional electric tractor for specialty crops. Agtonomy and Kubota unveiled their line of autonomous tractors at CES 2025. The Agrobot E-Series is an electric, autonomous strawberry harvester that uses up to 24 decentralized robotic arms, each equipped with real-time AI, advanced vision sensors, and onboard graphics processing units, to assess and gently pick fruit without damaging it. The Agrobot E-Series illustrates how integrating sensor-based safety systems and automated labor solutions can bolster domestic agricultural supply chains, mitigate chronic farm labor shortages, and enhance worker safety.
Additionally, Carbon Robotics’ LaserWeeder uses computer vision to kill weeds without chemicals. John Deere has also developed its own line of autonomous tractors, equipped with 16 cameras, advanced computer vision, and remote-control capabilities for farmers via a smartphone app. The company has also developed a retrofitting kit that converts the 8R and 9R series into fully autonomous machinery. Although current use cases are primarily tilling, the company has also expanded into autonomous orchard tractors and commercial landscaping.
Policy Framework
To boost adoption, physical AI needs a policy framework that expands access to technology and establishes regulatory clarity for producers and users. Key initiatives could include a refundable adoption credit for small- and medium-sized enterprises, specifically targeting robotics-as-a-service in housing and specialty-crop production, so adoption isn’t tied exclusively to ownership. Regulatory clarity should also be built into an autonomous-vehicle framework for job sites; for example, by building on the BUILD America 250 Act and directing OSHA and NIST to develop rigorous safety and interoperability standards. Finally, restoring federal R&D funding to pre-2025 levels and indexing it for inflation would help sustain innovation.
Physical AI has yet to show clear effects on productivity or labor markets. No one can be sure when, or if, we are headed for a hockey-stick moment. Similar quiet progress occurred in generative AI during the years prior to the ChatGPT moment. Across construction sites, factory floors, and farm fields, the pieces are already in play. If the stepping stones connect, physical AI could raise output and lower costs across America’s physical industries, passing savings along to Americans through more affordable food, housing, and manufactured goods. Policymakers would be wise to adopt a proactive framework that allows physical AI to flourish, rather than a reactive one.
