Cognitive migration is in progress. The station is filled with people. Some have boarded, while others hesitate, unsure if the journey is worth the departure.
Future of work expert and Harvard University Professor Christopher Stanton recently remarked that AI adoption has been remarkable, noting it as an “extraordinarily fast-diffusing technology.” This rapid adoption and impact set the AI revolution apart from past technological changes like the PC and the internet. Demis Hassabis, CEO of Google DeepMind, predicted that AI could be “10 times bigger than the Industrial Revolution, and maybe 10 times faster.”
Thinking is increasingly shared between humans and machines. Some have started using AI in their workflows routinely. Others have integrated it into their cognitive routines and creative identities. These are the “willing,” including consultants fluent in prompt design, product managers retooling systems, and those building businesses doing everything from coding to product design to marketing.
For them, the landscape is new but manageable. Exciting, even. But for many, this moment feels strange and unsettling. The risk is not just being left behind. It’s about whether to invest in AI, a future that seems uncertain. This is the double risk of AI readiness, reshaping how people interpret the pace, promises, and pressure of this transition.
Across industries, new roles and teams are forming, with AI tools reshaping workflows faster than norms or strategies can match. The significance is hazy, strategies unclear. The end game, if there is one, remains uncertain. Yet the pace and scope of change feel significant. Everyone is told to adapt, but few know exactly what it means or how far the changes will go. Some AI industry leaders predict significant changes soon, with superintelligent machines emerging within a few years.
But perhaps this AI revolution will fail, leading to another “AI winter,” similar to the previous ones. The first occurred in the 1970s, due to computational limits. The second began in the late 1980s after unmet expectations and high-profile failures in “expert systems.” These winters were marked by lofty expectations followed by disappointment, leading to reduced funding and interest in AI.
If the excitement around AI agents mirrors the failed promise of expert systems, it could lead to another winter. However, differences exist between then and now. There’s greater institutional buy-in, consumer traction, and cloud computing compared to the expert systems of the 1980s. A new winter could arise, but if the industry fails, it will be due to a breakdown in trust and reliability.
If “the great cognitive migration” is real, this is the early part of the journey. Some have boarded the train while others linger, uncertain whether to board. Amidst the uncertainty, the station atmosphere is restless, like travelers sensing an unannounced itinerary change.
Most people have jobs but are concerned about the degree of risk they face. The value of their work is shifting. A quiet, growing anxiety underlies performance reviews and company meetings.
AI can accelerate software development by 10 to 100 times, generate most client-facing code, and dramatically compress project timelines. Managers can use AI for employee performance evaluations. Even classicists and archaeologists find value in AI for understanding ancient Latin inscriptions.
The “willing” have an idea of where they’re going and may find success. But for the “pressured,” the “resistant,” and those untouched by AI, this moment feels like a mix of anticipation and grief. These groups realize they may not stay in their comfort zones for long.
For many, it’s not just about tools or a new culture, but whether that culture includes them. Waiting too long is akin to missing the train and could lead to long-term job displacement. Even senior professionals using AI wonder if their positions are threatened.
The narrative of opportunity and upskilling hides an uncomfortable truth. For many, this is not migration. It’s managed displacement. Some workers aren’t opting out of AI; they’re discovering they’re not included in the future being built. Belief in the tools differs from belonging in the system, and without a clear path for meaningful participation, “adapt or be left behind” becomes a verdict, not advice.
These tensions demonstrate why this moment matters. Work, as known, is beginning to recede. Signals are coming from the top. Microsoft CEO Satya Nadella acknowledged the transition to the AI era could feel messy but noted transformation always is. Yet, the technology driving this urgent transition remains unreliable.
Despite the urgency, this widespread technology remains glitchy, brittle, and far from dependable. Doubts arise about whether the tools we’ve adapted to can deliver. This should not surprise, considering that large language models’ (LLMs) output was barely coherent several years ago. Now, it’s like having a PhD in your pocket; once science fiction is nearly realized.
However, chatbots built on LLMs remain fallible and overconfident. They hallucinate, meaning we