Hypothesis 1

Less Fragile Each Time, Not a Better Demo

Progress in physical AI shows up as each deployment being less fragile than the last, not as a better demo.

VerdictHolds in part

Georgian + NewtonX Physical AI Survey, Wave 1 · Reporting base: Buyers, n=85 unless a benchmark states otherwise · July 24 to Aug. 21, 2026

What we said

Progress in physical AI shows up as each deployment being less fragile than the last, not as a better demo. Progress is measured by fewer interventions, faster deployments, steadier uptime and less firefighting once the system is live. If that is right, buyers who have run these systems for a year should be able to tell us which of those curves are bending.

How we tested it

To test our hypothesis, we looked at three measures:

  • How interventions and support have changed for buyers over the past 12 months.
  • Whether each new deployment is getting easier to implement than the last.
  • What operational impact buyers have actually seen where they have deployed.

What we found

Buyers report uneven progress across their operations:

  • Fewer interventions: 76% say a person has to step in less often than a year ago, and 79% say the system now handles more of the task on its own.
  • Better recovery behavior: 64% say the system is better at getting itself unstuck.
  • Faster deployment times: 66% say a new site is faster to get running than it used to be.
  • More stable uptime: 74% say equipment availability improved.*
  • Less bespoke troubleshooting: 64% say each new site needs less one-off engineering; 58% say their people spend less time fixing robots that are already live.

*note: base for this question is n=61

What Has Changed Over 12 Months

79%

of robotics buyers say the share of the task the system handles on its own has improved over the past 12 months

All qualified buyers, n=85 · “Improving” combines “Improving clearly” and “Improving slightly”

Data for What Has Changed Over 12 Months
CategoryImproving (net)
The share of the task / workflow the system handles on its own79%
How often a human has to step in76%
How long it takes to stand up a new deployment66%
The amount of custom engineering each new deployment needs64%
How well the system recovers on its own after it gets stuck64%
The support / firefighting burden as more units are deployed58%
0%20%40%60%80%The share of the task / workflow the systemhandles on its ownThe share of the task / workflow the system handles on its own — Improving (net): 79%79%How often a human has to step inHow often a human has to step in — Improving (net): 76%76%How long it takes to stand up a newdeploymentHow long it takes to stand up a new deployment — Improving (net): 66%66%The amount of custom engineering each newdeployment needsThe amount of custom engineering each new deployment needs — Improving (net): 64%64%How well the system recovers on its ownafter it gets stuckHow well the system recovers on its own after it gets stuck — Improving (net): 64%64%The support / firefighting burden as moreunits are deployedThe support / firefighting burden as more units are deployed — Improving (net): 58%58%

All qualified buyers, n=85 · “Improving” combines “Improving clearly” and “Improving slightly”

See the full benchmark
Is Each Deployment Getting Easier?

81%

of robotics buyers say each new deployment is easier to implement than the ones before it

All qualified buyers, n=85 · single-select, so the shares add to 100%

Data for Is Each Deployment Getting Easier?
Category% of buyers
Clearly easier to implement consistently than earlier deployments19%
Somewhat easier over time62%
About the same each time11%
Still requires substantial customization each time5%
Too early to tell4%
0%20%40%60%Clearly easier to implement consistentlythan earlier deploymentsClearly easier to implement consistently than earlier deployments — % of buyers: 19%19%Somewhat easier over timeSomewhat easier over time — % of buyers: 62%62%About the same each timeAbout the same each time — % of buyers: 11%11%Still requires substantial customizationeach timeStill requires substantial customization each time — % of buyers: 5%5%Too early to tellToo early to tell — % of buyers: 4%4%

All qualified buyers, n=85 · single-select, so the shares add to 100%

See the full benchmark
What Operational Impact Have You Seen?

85%

of robotics buyers already in production report throughput improving over the past 12 months, the strongest of the five operational measures. Safety incidents move least, at 64%.

The 61 buyers already in production — scaled or in limited production. Buyers still piloting were not asked

Data for What Operational Impact Have You Seen?
CategoryImproved (%)
Throughput / output85%
Labor cost74%
Uptime / equipment availability74%
Error / defect rate (a decrease is positive)72%
Workplace safety incidents (a decrease is positive)64%
0%20%40%60%80%100%Throughput / outputThroughput / output — Improved (%): 85%85%Labor costLabor cost — Improved (%): 74%74%Uptime / equipment availabilityUptime / equipment availability — Improved (%): 74%74%Error / defect rate (a decrease ispositive)Error / defect rate (a decrease is positive) — Improved (%): 72%72%Workplace safety incidents (a decrease ispositive)Workplace safety incidents (a decrease is positive) — Improved (%): 64%64%

The 61 buyers already in production — scaled or in limited production. Buyers still piloting were not asked

See the full benchmark