Tech myths thrive in the gap between what we hope machines can do and what they actually do. They’re the half-remembered facts repeated at dinner parties, the confident claims made by someone who “works in tech” but won’t say where, the tidy stories we tell to make complicated systems feel manageable. Science, meanwhile, is slower, messier, and far less interested in being quotable.
These myths tend to surface in conversation at predictable moments: when someone’s phone battery dies and they blame “planned obsolescence,” when AI comes up at a cocktail hour and suddenly everyone knows it’s about to replace all human creativity, or when a new gadget launches and people ask whether it’s “listening to us.” Technology is social currency now, which means misinformation travels well—especially when it sounds smart.
One enduring myth is that technology progresses in straight, inevitable lines. We imagine innovation as a ladder: dial-up to fiber, flip phones to smartphones, humans to robots with feelings. In reality, technological progress zigzags, stalls, regresses, and occasionally collapses under its own hype. For every breakthrough, there are dozens of forgotten dead ends quietly collecting dust in research labs.
Another favorite is the belief that more data automatically equals more truth. Science is less romantic about this. Data without context is just noise, and algorithms trained on biased data tend to produce biased results with impressive speed. The myth persists because numbers feel neutral, even when they’re quietly reinforcing old assumptions in a new font.
Artificial intelligence suffers from particularly cinematic misunderstandings. We oscillate between thinking AI is either a sentient overlord plotting humanity’s downfall or a magical intern who can instantly solve everything if prompted. In practice, AI is neither conscious nor wise—it’s a pattern-matching system that is extremely good at certain tasks and astonishingly bad at others, often in the same sentence.
Then there’s the myth of technological objectivity: the idea that machines remove human error, emotion, or judgment. Science says otherwise. Humans design systems to choose what to measure and decide which outcomes matter. Technology doesn’t eliminate bias; it preserves it efficiently, sometimes with better graphics.
Privacy myths deserve their own category. Many people assume privacy disappeared the moment smartphones arrived, as if resistance is futile and consent irrelevant. Science—and law—paint a more nuanced picture: privacy erodes through specific choices, incentives, and regulations, not because technology has a personal vendetta against us. The fatalism is convenient, but not especially accurate.
We also love the myth that younger generations are “digital natives” who instinctively understand technology. Research suggests familiarity with interfaces is not the same as understanding how systems work. Knowing how to use an app does not mean knowing where the data goes, how it’s monetized, or who benefits when you tap “agree.”
Health-related tech myths circulate with particular confidence. Wearables are often treated as oracles rather than rough indicators. Science is clear: step counts, sleep scores, and heart rate variability are useful signals, not diagnoses. Your watch is not a doctor, nor will it be soon.
The myth of disruption—that technology automatically improves whatever it touches—also deserves skepticism. Innovation can streamline, but it can also destabilize labor, widen inequality, and introduce new problems faster than it solves old ones. Science tends to ask “under what conditions does this work?” while tech myths prefer “this changes everything.”
Part of why these myths persist is emotional, not intellectual. Technology promises control, certainty, and speed in a world that increasingly offers none of those things. Myths give us a sense of mastery without requiring us to read the fine print or accept ambiguity, which science stubbornly insists on.
The tension between tech myths and science isn’t a battle—it’s a negotiation. Myths simplify; science complicates. Both shape how we talk about the future, but only one is comfortable saying, “We’re not sure yet.”
Understanding the difference doesn’t require becoming an engineer or a skeptic-in-residence. It simply means pausing before repeating a confident claim and asking whether it’s evidence-based—or just a good story that happens to involve a screen.