Report: Physical AI Growing Faster Than Markets Are Ready to Deploy

The report jointly released by AWS Startups and Strand Partners has a title that directly states the problem it wants to explain—"Physical AI Growing Faster Than Markets Are Ready to Deploy." The report is based on a survey of 4,420 technology startups, of which 663 are physical AI builders, covering technologies such as robotics, autonomous systems, drones, computer vision and sensors.

The first set of figures the survey provides is on the supply side: 15% of startups across 20 markets are building physical AI. There is heat on the demand side as well—16% of startups are already using physical AI systems developed by others, 12% are piloting, and 29% are evaluating adoption. In other words, building, trialing and watching around this technology are already unfolding simultaneously.

But the report singles out "customer readiness" as the weakest link: only 26% of builders believe their local market is ready to deploy the technology, and 70% say slow customer adoption is constraining growth. This is the most memorable contrast in the entire piece—activity on the building side is clearly higher than the market's capacity to absorb it.

The way physical AI is developed determines how dependent it is on infrastructure. 72% of builders say cloud computing is critical to building, training or running their systems, and 62% say they could not get started without cloud. This is not "preferring cloud"—it is treating cloud as a precondition.

Simulation and synthetic data are also heavily relied upon: 62% of builders consider them critical. Compute is another hurdle, with 56% saying limited compute is slowing development progress.

On the deployment form for training and running, the report gives a mixed picture: 60% train models in the cloud, 54% run models on-device for real-time response, and 38% use both approaches. The "real-time" requirement of physical AI makes on-device running a necessary choice for a considerable share of builders, rather than an alternative.

The report breaks readiness into five dimensions, and the results show a clear high-to-low spread: talent and skills 68%, supply chain and components 60%, capital and investment 46%, regulation 40%, and market readiness 26%.

Talent ranks highest, indicating that builders are relatively confident about whether they can find the technical and engineering talent they need. Supply chain comes next, with 60% of builders saying they can procure the hardware they need, but the same report also notes that 48% say tariffs or trade barriers limit access to key components—being able to procure and procuring smoothly are not the same thing.

Capital and investment is only 46%, meaning fewer than half of builders say they can obtain the funding needed to build and scale. Regulation is 40%, with 58% saying regulatory fragmentation or uncertainty is slowing development. And market readiness ranks last at 26%, corroborating the 70% who say slow adoption is constraining growth.

On commercial deployment, the report's ranking is: manufacturing and automotive furthest ahead, with 51% of builders saying their products are already in use; logistics, warehousing and retail 47%; agriculture 40%; healthcare 37%.

These figures show that physical AI is not stuck at the prototype stage, but the degree of deployment varies significantly across industries. The gap between manufacturing and automotive and logistics, warehousing and retail is small, while agriculture and healthcare lag relatively behind, forming a gradient from 51% to 37%.

Among builders with paying customers, 31% say customers are mainly seeking faster or higher-quality work, 22% are filling labor shortages, and 17% are reducing labor costs. Overall, performance rather than replacing labor is the more common reason for adoption.

But this balance flips in different countries. Labor shortages are the primary adoption driver in Japan (34%), Germany (30%), and South Korea and Singapore (29% each); while performance leads in India (43%), Vietnam (40%) and Indonesia (37%). The report also mentions that in these performance-led markets, only 12% to 13% of customers cite labor shortages.

This difference suggests that the "selling point" of physical AI is not globally uniform: in markets with tight labor, it looks more like a tool to supplement hands; in other markets, it is first treated as a means to improve output quality and speed.

The report measures the geographic concentration of physical AI builders, not commercial deployment rates, and the two cannot be directly equated. By this measure, Germany leads with 24%, Japan and Israel 21%, South Korea and the United States 20%, France and Singapore 17%, and India 16%.

Particular attention should be paid to the coverage gap: China was not included among the 20 markets in this survey, so the country comparison cannot show its relative position in physical AI entrepreneurial base versus Germany, Japan, the United States and others.

But the report also notes that China is investing heavily in physical AI and is already the largest single foreign source of hardware components for surveyed builders, with 20% of builders listing it as their primary supplier. Viewed together with the 48% of builders who say tariffs or trade barriers limit access to key components, this constitutes a structural tension: one end of the supply chain is highly concentrated, while the other end faces access obstacles. The specific statistical basis and sample distribution of this figure are not explained in the source.

Builders are quite optimistic about the outlook: 76% expect physical AI to become mainstream in their industry within five years, of which 36% expect it within two years; three-quarters expect it to reshape their own business over the same period.

These are builders' subjective expectations, not realized facts, and need to be read in contrast with the constraints above. The report itself also notes that broader deployment requires greater customer awareness, technical skills, funding, clearer regulation, and infrastructure for training, simulation and deployment.

In other words, the 26% market readiness, 46% capital availability, 56% compute constraint, and 58% regulatory friction are precisely the thresholds that the 76% optimistic expectation needs to cross. The report was released by AWS Startups and Strand Partners, and its survey methodology and sample representativeness are not detailed in the source, and it lacks a first-party original report link and third-party independent verification, so the above figures should be regarded as conclusions of that report and its relay, rather than independently verified findings.