Imagine a man with a Swiss Army knife for a body. His arms and legs can extend in any direction, bend into any shape, and move at extraordinary speeds. His spine can elongate into a helicopter, his hands can turn into an almost unlimited number of tools, and his feet can turn into ice skates, roller blades, and more.

This is some transhumanists’ dream, a future where we can completely trick out our bodies and transcend the limitations of human biology. It’s also a description of what the title character from the 1983 cartoon Inspector Gadget can do.

Rose Eveleth is an Ideas contributor at WIRED and the creator and host of Flash Forward, a podcast about possible (and not so possible) futures.

For those who aren’t familiar with the cartoon, the premise is simple: Inspector Gadget is, as his name implies, an inspector, or detective. He’s also a walking gadget, who can turn his body into nearly anything. And yet, with all that power, Gadget can’t solve a single mystery. Every episode Gadget is called upon by his boss Chief Quimby to help solve a crime, nearly all of which are perpetrated by the villain Dr. Claw. For some reason or another, Gadget is always accompanied by his 10-year-old niece, Penny, and her dog Brain. And despite being equipped with every tool he could possibly need, it’s the brilliant Penny, a completely boring noncyborg, who saves the day every time.

Sure, the cartoon (and subsequent film adaptations) are over the top and ridiculous. But our hapless detective can teach us something about the ways we think about bodies, bionics, data, and the future of human-machine interfaces. Gadget’s antics poke real holes in the fantasies that some transhumanists and “body hackers” have about how the body works, and what we might be able to ask it to do.

Early in the first episode of Inspector Gadget (“Monster Lake”) there’s a scene that establishes the entire premise of the show. While our titular Gadget tries to find the instruction manual for the car he’s driving, to deal with the “overheating engine” (in fact, the car is on fire because an evil robot spewed flames at it), he takes his hands off the wheel. Penny, as will become a recurring theme in the show, saves the day by actually paying attention to her surroundings, and noticing that the car is about to fly off a cliff. She grabs the wheel and averts disaster, completely unbeknownst to her bionic uncle. Gadget has seemingly unlimited physical resources at his disposal, but cannot use them to save his life (literally).

It is in scenes like this that I think of two things: Three Mile Island and butter production in Bangladesh. Let me explain. The former is the biggest nuclear meltdown in American history. The latter is a spurious economic predictor proposed in 1998 to poke fun at forecasting markets. But they’re tied together by the same thing that dooms Gadget: an excess of information. Three Mile Island (like Chernobyl and other nuclear accidents) happened for a variety of reasons—lax regulations, slashed budgets, overworked employees, scientific rivalries—but during the most critical moments of the disaster, it was marked by information overload. The control panel at the nuclear plant was designed to display all kinds of data, but there was no way the operators could keep track of the whole system at once. In a sea of signals, you can miss the most important ones.

Or, you can see one that means nothing at all, as in the case of butter production in Bangladesh, a signal that economist David Leinweber described in 1998. According to his calculations, three things could “explain” the performance of the S&P 500 with 99 percent accuracy: American cheese production, the Bangladeshi sheep population, and butter production in Bangladesh. Leinweber was intentionally poking fun at the methods he employed, arguing that with enough data but insufficient context you can correlate almost anything. At first, Leinweber wasn’t even going to publish the work, he simply thought it was a funny trick. But then, “reporters picked up on it, and it has found its way into the curriculum at the Stanford Business School and elsewhere,” he writes in the paper he did eventually publish. “Mark Twain spoke of ‘lies, damn lies and statistics.’ In this paper, we offer all three,” he writes.