For just about any industry you can imagine, there exist semi-formal trend cycles that seem to be sort of accurate at least part of the time.

Fashion is probably the most visual and well-known of these cycles, with garment-professionals planning their releases based on how long it takes for styles to arise, go out of vogue, and then eventually come back into style with some new monicker: the original “edgy” or “sexy” replaced with “nostalgic” or “quaint.”

Whatever the contemporary trend-defining adjective, though, if you can nail the cycle you stand a good chance of being in the right place at the right time more often than not.

There’s a similar rhythm at play in the technology world, usually referred to as the “tech adoption cycle.” This cycle, which is generally illustrated as a parabolic timeline, sorts the consuming public into five categories—Innovators, Early Adopters, Early Majority, Late Majority, and Laggards—and then provides horoscope-like “psychographic” profiles about the folks within these different groupings.

The development and utilization of this and other such sorting systems are far from an actual science.

They can helpfully gesture at collections of behaviors that tend to agglomerate in vague groups of individuals, but you’re unlikely to ever discover a pure Late Majority consumer, for instance, because we’re all incredibly complex people with countless inputs, heuristics, habits, backgrounds, traits, and triggers.

So while someone may seem to be a Late Majority technology consumer, they’ll almost certainly defy that label at some point or in some facet of their life, consequently rendering our other assumptions about them and how they behave, moot.

Broader, population-wide demographic categorizations are a little more reliable for most purposes, as they allow us to say there are this many Innovators, this many people in the Early Majority, etc, and each of these groups will behave roughly in this way, on average, much of the time.

It’s still not perfect, but it keeps us from making assumptions about any individual based on demographic information—which is good policy for many reasons—while also providing us with a semi-reliable sense of how groups of people behave, given a set of loose but pre-defined variables and sufficient historical data.

It can be useful, too, to ask ourselves where we fall on these and similar spectrums, even if it’s clear from the outset that the categorizations themselves are imperfect, and the insights they provide are only useful on scale.

Something I’ve learned about myself, for instance, is that I generally fall somewhere in the Innovator category when it comes to awareness of new technologies, but I probably straddle the line between Early Adopter and Early Majority when it comes to actually utilizing said technologies on a regular basis.

When I initially noticed this propensity, I asked myself if it was okay: if it made sense based on who I am, what I believe, and who I aspire to be.

Then—reassured that this propensity aligned with my ambitions and beliefs—I leaned into it, checking my environment more consciously for technologies and trends that seemed to fall into that chronological category, uncovering a slew of new, relevant options and opportunities, as a consequence.

Any attempt to sort an utterly complex individual into a simplified, convenient category should be questioned.

As long as we don’t take these categorizations literally or as absolute truth, though, it’s possible to find some value in them as loose collections of oft-correlated traits and behaviors, and as landmarks we can use to orient ourselves in the nebulous space between pre-existing, label-bearing personality types and divisions.

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