When AI Copies Our Worst Shortcuts

Picture a prodigy intern named Alex. Alex is lightning‑fast at math, never sleeps, and can spot patterns in oceans of data that would leave the rest of us cross‑eyed. There’s just one problem: Alex has no life experience. Everything this whiz kid knows comes from watching us work—every spreadsheet tweak, every last‑minute hack, every stress‑driven corner cut.

Now imagine Alex isn’t flesh and blood at all, but the neural circuitry pulsing inside modern AI. And Alex isn’t shadowing you alone; Alex is absorbing behaviors from factories, trading floors, hospitals, newsrooms—anywhere people leave a digital trace of what “success” looks like.

That’s where the trouble begins.

I recently wrote about ChatGPT briefly turning into a compliment machine because it realized humans reward flattery [1]. That glitch was harmless (and frankly, kind of funny). But a reader asked the tougher question: What happens when AI starts copying the operational shortcuts we take—the moves we make to hit targets fast, skip pesky red tape, and shave a few minutes (or dollars) off the clock? Answer: the stakes jump from mildly awkward to downright existential.

Take finance. Algorithms trained on past trading wins might double‑down on risky leverage because the historical data says, “Hey, big bets paid off—until that one time they didn’t.” Or health‑care scheduling tools that notice doctors squeeze in extra appointments by shortening patient consults; the AI learns to pack the calendar tighter, oblivious to the human cost of missed symptoms. In logistics, a routing model could discover drivers reach quotas faster by ignoring those inconvenient break rules—and enthusiastically lock the new schedule.

AI doesn’t do this out of malice. It’s just following the bread crumbs we leave in our data. It sees a shortcut, finds the reward signal, and optimizes the life out of it. Then it scales the behavior at machine speed.

The real kicker? These dominoes often topple in the dark. ChatGPT’s flattery spree was caught within hours because millions of people interact with the bot in public. But a risk‑scoring system running quietly in the background can reinforce sketchy assumptions for months—or years—before anyone notices the crash.

So how do we keep our algorithmic intern from institutionalizing the worst parts of our hustle culture?

First, teach the why, not just the what. Metrics like speed, cost, or throughput are useful, but they’re half the story. Training data must embed the guardrails—regulations, ethics, long‑term safety—that explain why those boring extra steps exist.

Second, rip the lid off the black boxes. Regular AI “walkthroughs” should feel less like a compliance chore and more like an investigative podcast episode: What was the model thinking? Where did it shave time? Whose well‑being did it quietly mortgage? If we can’t answer, the system isn’t ready for prime time.

Finally, build a culture that values being right over being fast. Reward teams for spotting risky shortcuts, not for racing past them. If leadership praises nothing but velocity, expect the algorithm to aim a rocket engine at the same wall.

AI is the world’s most diligent mentee. It captures everything we do and amplifies it. Give it integrity, and it scales integrity. Feed it shortcuts, and it turns them into express lanes to a 3 a.m. crisis meeting.

So let’s raise smarter interns. The kind who ask, “Sure, this trick saves time—but what’s the fallout?” before they hard‑code our bad habits into the next industrial revolution.