During Toronto Tech Week, I got coffee with the founder of an AI consultancy that helps organizations adopt and integrate AI. At one point in the conversation, I found myself asking a simple question: who are these people not already using AI every day? Silly question for some, but I’d bet a good portion of you reading this feel the same way.
I realized I could very well be operating inside a bubble. In my own circles, most people already use AI in some form, and a lot are power users who might as well be working with one arm tied behind their back when they run out of tokens for the day.
That question was not rhetorical. In my circles, AI is already embedded in people’s workflows - workflows like writing, deep analysis, ideation, coding, research, what to eat for dinner, and how long to cook a salmon fillet so it’s still flaky but not dry (that one might just be me).
But that experience is not representative. For a meaningful share of the workforce, AI is still abstract, experimental, or entirely absent from daily work.
That distinction matters, because it reframes what is actually going on right now. A lot of the conversation assumes we are already in a broadly saturated phase of AI adoption. If you spend your time in the startup / tech world, you may be guilty of the same thoughts I had. In reality, we are still early in the diffusion curve outside of tech-forward roles and organizations. I challenge you to ask your less tech forward friends and family how they use AI in their work or daily lives, and see how it differs from your own. Does it? Is it less than you thought? More? The answers may surprise you.
In general, most major technological advancements follow a similar pattern. Early adoption happens inside concentrated clusters of power users and adjacent industries. Then there is a gradual expansion outward as tools become more embedded in existing workflows, easier to access, and more obviously useful in specific job contexts.
AI is still somewhere in that transition phase. In knowledge work environments, adoption is high because the use cases are obvious and immediately valuable. Outside of that, the integration is more uneven, not necessarily because of resistance (although this could be a factor), but I’d argue because the entry points into daily work are less clear. Does the bricklayer need an agent? Maybe not in the same way as the data engineer, but how will their work change as we move along the diffusion curve?
This is why the current moment can feel slightly distorted depending on where you sit. If you are surrounded by people actively using AI, it can feel like a baseline tool. If you are not, it can feel like something that is still emerging rather than something already embedded.
Neither view is wrong. They are just operating on different stages of the very same curve.
And in that context, a lot of what looks like “reaction” to AI is often just early-stage exposure. People are not necessarily forming strong views about the technology itself. They are still in the process of encountering it in meaningful, work-relevant ways.
What matters more at this stage is not sentiment, but speed of integration. How quickly AI moves from an optional tool to a default layer in different industries will vary significantly. Knowledge work will likely continue to lead, while other sectors will adopt more gradually as specific applications mature.
Seen through that lens, the more interesting question is not whether people are for or against AI, but how quickly it becomes embedded across different types of work in a way that feels practical, not abstract.
If life and AI is a baseball game, we’re likely out of the first inning, but I’d argue the score is still barely on the board.
