When a new tool spreads quickly, reactions tend to cluster at the extremes.
Some people reject it outright.
Others dive in headfirst.
Most oscillate between curiosity and unease, hoping someone else will decide what makes sense.
AI has triggered all of these responses — sometimes in the same person, on the same day.
One common mistake is to treat AI as something that can simply be ignored. As if refusing to engage were a neutral stance. In practice, this usually means continuing to live and work in environments increasingly shaped by tools one hasn’t examined — or chosen.
Another is the opposite: going all in. Adopting AI everywhere, as fast as possible, without stopping to ask what is being gained, what is being lost, and who benefits most from the acceleration. Speed can feel like clarity. It rarely is.
There is also the temptation to wait for guidance from above — from governments, institutions, or experts. History suggests that by the time official answers arrive, habits are already formed and defaults are hard to undo.
Some organisations delegate the problem entirely: to consultants, to vendors, to “the market”. This often replaces reflection with frameworks, and responsibility with compliance.
Then there are the inventors and builders — the companies developing AI systems, and those who customise existing models and present them through branded interfaces. Their role is not sinister, but it is not neutral either. They operate under incentives: profit, growth, influence, visibility. These pressures shape not only what tools can do, but how they are framed, promoted, and normalised.
Confusing these incentives with universal interest is another easy mistake.
Perhaps the most subtle trap is believing that there is a single correct attitude toward AI: enthusiastic or cautious, optimistic or critical, for or against. Reality is messier. Most meaningful positions lie somewhere in between — curious but careful, open but not passive.
Rejecting everything closes doors.
Accepting everything closes questions.
Neither helps much.
The challenge is not to decide once and for all what AI is, but to remain attentive to what it is doing — to our habits, our expectations, our sense of agency.
This series doesn’t aim to provide answers ready-made. It aims to make certain questions harder to avoid.
If “wait and see” is not enough, and “everything everywhere” is too much — what does a more deliberate middle path look like?