Has AI Use Reached an Inflection Point, or Not?

As always, we might well disagree about the latest statistics on AI usage.

The proportion of U.S. employees who report using artificial intelligence daily rose from 10 percent to 12 percent in the fourth quarter of 2025, a Gallup survey finds. 

Frequent use, defined as using AI at work at least a few times a week, has also inched up three percentage points to 26 percent.

source: Gallup 

The percentage of those who use AI at work at least a few times a year was flat in the fourth quarter of 2025.  

And nearly half of U.S. workers (49 percent) report that they “never” use AI in their role.

As always, that data will be interpreted in several possible and contradictory ways:

• Not every job role requires AI

• Some use cases and verticals use AI heavily

• Adoption has reached an inflection point

• Adoption is quite fast

• Adoption is slowing

source: Gallup 

Some of us might argue that AI is at an adoption rate inflection point, the historical precedent being that adoption shifts to a higher gear once about 10 percent of consumers use any particular technology. 

Also, Amara's Law suggests the impact is likely to be less than we expect in the short term (as in, “now” or “today”), while long-term impact will be greater than we anticipate.

Amara’s Law suggests that we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

Source

“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is a quote whose provenance is unknown, though some attribute it to Standord computer scientist Roy Amara. Some people call it the “Gate’s Law.”

Some products or technologies (and AI might be among them) can take decades to reach mass adoption, especially if we start tracking adoption from the time a new technology is discovered, rather than “starting the clock” when “commercialization” begins. 

The “next big thing” will have first been talked about roughly 30 years ago, says technologist Greg Satell. IBM coined the term machine learning in 1959, for example, and machine learning is only now in widespread use. 

Alexander Fleming discovered penicillin in 1928, it didn’t arrive on the market until 1945, nearly 20 years later.

Electricity did not have a measurable impact on the economy until the early 1920s, 40 years after Edison’s plant, it can be argued.

It wasn’t until the late 1990’s, or about 30 years after 1968, that computers had a measurable effect on the US economy, many would also note.

The point is that it is way too early to discern the actual productivity gains AI will eventually deliver. We will expect more, and be disappointed, over the short term. But we will underestimate impact over the longer term. 

And there is good reason to believe that the uptake in adoption has only just been reached.