Tom Thomas is a writer with a career spanning forty years in publishing, technical writing, public relations, and popular fiction writing.
“My business now is to weave circumstance, happenstance, intention, and mischance into stories.”

The Machine Condition:

A Waste of Intelligence – October 4, 2026

Putting aside—at least for the moment—the potential danger of rogue intelligences or gangs of their minion bots breaking into government, industrial, and commercial computers, crashing systems, and bringing down civilization, or writing and releasing the genetic code of a plague that is 100% lethal to human beings, or empowering a cyborg rebellion … what good things might artificial intelligence accomplish? And, for this exercise, I’m also putting aside all the ways in which a smart computer might replace human activities like writing, painting, and composing that existing human beings have or will become too lazy or untalented to perform for themselves.

And then, what is left? Oh, lots! All the things that computers with their monomaniac focus, lightning-fast assessment power, and unlimited attention span might accomplish.

Some of these things are already being done. For example, Google’s subsidiary DeepMind is already threading the amino acid strings translated from RNA sequences into protein folding configurations that would take human beings thousands of hours to unravel (AlphaFold). The same organization is applying artificial intelligence to assessing the effects of mutations on the human genome (AlphaGenome Atlas), predicting global weather patterns (WeatherNext), and discovering and analyzing new crystalline material compounds (GNoME). More please.

But what else could the fixed attention of an advanced computer system accomplish?

The Federal Aviation Administration is already using artificial intelligence to support human air traffic controllers in monitoring actual airplane flight paths. The system is not ready to take over complete control, but when it does, a machine intelligence’s unflagging attention and lightning reflexes will be miles ahead of human eyes on radar screens and human voices over radio. But why limit this capability to one massive governmental system?

Already, various services like Knightscope offer mobile security robots to patrol, observe, and report at industrial sites. Combine this with machine intelligence to monitor, analyze, and summarize the reports, incorporating facial recognition to identify friends and foes and visual and audio pattern recognition to identify potentially suspicious actions, and you have a 24/7 system that never blinks, never nods, and never sleeps. The next step will be moving from wheel-based machines to flying drones—again guided, routed, and called to trouble spots by an artificial intelligence—and you expand the perimeter and move across difficult terrain like stairs and obstacles like fences.

Really, anything that moves, in any amount in any system, will benefit from the sorting and pattern matching that these machines can do. Think of materials and components moving through a company’s supply chain, inventoried at the factory, brought together on the assembly line, and moved to the loading dock, all tracked by a brain that never sleeps and never misreads a number or confuses a shipping code.¹ Think of packages moving through a delivery system—or why do you think Amazon is so far ahead on artificial intelligence? Think of baggage moving through an airline’s handlers, from check-in to plane to baggage claim, and never having to apologize for lost luggage. Eventually, these systems will be counting cars on the freeway—unless the Flock cameras are already doing that—and snowflakes in a blizzard.

We shouldn’t worry too much about replacing humans in everyday tasks and collapsing civilization. Humans will always be better, more imaginative, more creative writers, painters, composers … and thinkers. And humans are able to futz up and collapse civilization all by themselves. What we need machine intelligence to do is perform those functions where human beings have to look really hard to find a pattern—and can still miss it nine times out of ten; where their imaginative tendencies and the boredom factor make them tune out of tedious tasks; and where capabilities and conveniences we haven’t even thought of yet will make living for the average human being faster, better, and smoother.

And all of that is right around the corner.

1. The “hallucinations” of the large language models (LLMs) used to generate text and computer code derive from the fact that these models work by trying to predict the next word or token for a given assignment by drawing on patterns held in a massive database composed of existing texts. A computer system that observes and tracks physical items in real time, rather than trying to create a whole new pattern from a single prompt, would experience no such confusion.

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