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When my wife recently brought up AI in a masterclass for coaches, she did not expect silence. One executive coach eventually responded that he found AI to be an excellent thought partner when working with clients. Another coach suggested that it would be helpful to be familiar with the Chinese Room analogy, arguing that no matter how sophisticated a machine becomes, it cannot understand or coach the way humans do. And that was it. The conversation moved on.
The Chinese Room is a philosophical thought experiment devised by John Searle in 1980 to challenge the idea that a machine can truly “understand” or possess consciousness simply because it behaves as if it does. Today’s leading chatbots are almost certainly not conscious in the way that humans are, but they often behave as if they are. By citing the experiment in this context, the coach was dismissing the value of these chatbots, suggesting that they could not perform or even assist in useful executive coaching.
It was a small moment, but the story seemed poignant. Why did the discussion stall? What lay beneath the surface of that philosophical objection? Was it discomfort, skepticism or something more foundational?
A few days later, I spoke with a healthcare administrator and conference organizer. She noted that, while her large hospital chain had enterprise access to Gemini, many staff had yet to explore its capabilities. As I described how AI is already transforming healthcare workflows, from documentation to diagnostics, it became clear that much of this was still unfamiliar.
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These are just anecdotes, yes, but they point to a deeper pattern redrawing the landscape of professional value. As in previous technological shifts, the early movers are not just crossing a threshold, they are defining it. This may sound familiar. In many ways, AI is following the arc of past technological revolutions: A small set of early adopters, a larger wave of pragmatic followers, a hesitant remainder. Just as with electricity, the internet, or mobile computing, value tends to concentrate early, and pressure to conform builds.
But this migration is different in at least three important ways. First, AI does not just automate tasks. Instead, it begins to appropriate judgment, language and creative expression, blurring the line between what machines do and what humans are for. Second, adoption is outpacing understanding. People are using AI daily while still questioning whether they trust it, believe in it or even comprehend what it is doing. Thirdly, AI does not just change what we do; it reshapes how we see. Personalized responses and generative tools alter the very fabric of shared reality, fragmenting the cognitive commons that previous technologies largely left intact.
We are in the early stages of what I have described as a great cognitive migration, a slow but profound shift away from traditional domains of human expertise and toward new terrain where intelligence is increasingly ambient, machine-augmented and organizationally centralized. But not everyone is migrating at the same pace. Not everyone is eager to go. Some hesitate. Some resist.
This is not simply a matter of risk aversion or fear of change. For many professionals, especially those in fields like coaching, education, healthcare administration or communications, contribution is rooted in attentiveness, discretion and human connection. The value does not easily translate into metrics of speed or scale.
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