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, and by extension questioning the broader wave of AI adoption across professional 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 adoption is already transforming healthcare workflows, from documentation to diagnostics, it became clear that much of this was still unfamiliar.
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 adoption 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, AI 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. Patterns of AI adoption reveal not just who uses the tools, but who feels that these tools reflect their professional identity and values.
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.
Yet AI tools often arrive wrapped in metaphors of orchestration and optimization, shaped by engineering logic and computational efficiency. In work defined by relational insight or contextual judgment, these metaphors can feel alien or even diminishing. If you do not see your value reflected in the tools, why would you rush to embrace AI adoption?
So, we should ask: What happens if this migration accelerates and sizable portions of the workforce are slow to move? Not because they cannot, but because they do not view the destination — the use of AI — as inviting. Or because this destination does not yet feel like home.
History offers a metaphor. In the biblical story of Exodus, not everyone was eager to leave Egypt. Some questioned the journey. Others longed for the predictability of what they knew, even as they admitted its costs. Migration is rarely just a matter of geography or progress. It is also about identity, trust and what is at stake in leaving something known for something unclear.
Cognitive migration is no different. If we treat it purely as a technical or economic challenge, we risk missing its human contours. Some will move quickly. Others will wait. Still others will ask if the new land honors what they hold most dear. Nevertheless, this migration has already begun. And while we might hope to design a path that honors diverse ways of knowing and working, the terrain is already being shaped by those who move fastest.
Pathways of cognitive migration
The journey is not the same for everyone.
Some people have already embraced AI adoption, drawn by its promise, energized by its potential or aligned with its accelerating relevance. Others are moving more hesitantly, adapting because the landscape demands it, not because they sought it. Still others are resisting, not necessarily out of ignorance but fear, uncertainty, or conviction, and are protecting values they do not yet see reflected in the tools. A fourth group remains outside the migration path, not because they overtly object to it, but because their work has not yet been touched by AI adoption. And finally, some are disconnected more fundamentally, already at the margins of the digital economy, lacking access, education or the opportunity to participate.
These are not just attitudes. They are positions on a shifting map. They reveal who migrates by choice or pressure, who resists on principle and who might never join.
The willing
Some people have not hesitated. Like early gold miners heading for California, they have embraced AI adoption out of curiosity, enthusiasm or a sense that it aligns naturally with their outlook. These are the willing migrants, those comfortable at or near the frontier: Consultants using language models to refine client proposals, developers accelerating their coding process, storytellers using AI-generated video. Some are exploring AI as a creative partner, others as a tactical advantage. For this group, the terrain feels not just navigable, but exciting.
Author: Gary Grossman, Edelman
Source: Venturebeat
Reviewed By: Editorial Team
