The Sharpening Loop

Last week, a snowstorm rolled into town and blanketed the city in snow that the wind shaped into large drifts. I had gone out for dinner and found myself driving home in the middle of it—windshield partially iced over, traction far from a given. One of the reasons I love my car is the way it keeps me connected to the road. The steering tells me the road is slick before I have to act on it. You can feel the point where traction is about to be lost and have enough time to make a correction. You can tell which tires are slipping and which still grip. When I make a mistake—turn in too aggressively, carry too much speed—I feel the consequences immediately. The cost of error isn’t hidden; it’s present enough to make me drive better. The car doesn’t try to do the work for me. It tells me the truth and trusts me to act on it.

Trust as a Design Choice

Every tool sits somewhere on a spectrum from respects the user to replaces the user’s judgment.

Compare my car to one that intercepts the steering input, second-guesses throttle application, and silently applies corrections before the driver even registers a problem. That car is easier to drive in a snowstorm. It’s also teaching its driver absolutely nothing. And the moment the system encounters a scenario its engineers didn’t anticipate, the driver who never felt the road has no instincts to fall back on.

The distinction isn’t analog vs. digital—it’s whether the tool assumes competence or incompetence. One machine says here’s what’s happening—I trust you to handle it. The other says don’t worry about what’s happening—I’ll handle it for you. Both get you home tonight. Only one makes you a better driver.

The Sharpening Loop

There’s a familiar saying: the craftsman sharpens the tool. But the more interesting half is that the right tool sharpens the craftsman.

A typewriter makes you a better editor because you can’t cheaply revise—you learn to think in complete sentences before your fingers hit the keys. A car that telegraphs the road through its steering makes you a better driver because you have to read what it's telling you, not just point toward a destination.

Good tools demand something from you, and in meeting that demand, you grow. That’s the sharpening loop: you invest effort into the tool, the tool asks effort of you, and over time both the work and the worker get better. Tools that remove the demand also remove the growth. They might make the output easier, but they make the person holding them gradually less capable.

The AI Question

AI is the most consequential version of this choice most of us will face. And the pressure to choose wrong is enormous.

Consider two ways a team might use AI to handle customer problems. In the first, an AI system reads incoming requests, diagnoses the issue, drafts a response, and sends it—maybe with a human glancing at a queue of auto-resolved tickets. The team’s throughput doubles overnight. In the second, the AI surfaces the relevant history, flags patterns the agent might miss, and drafts a response the agent can edit, rewrite, or discard. Throughput improves less dramatically, but the agents stay in the loop.

Six months in, the first team can’t explain why their resolution rates are declining. The AI is handling cases it was never trained on, and nobody catches it because nobody reads the tickets anymore. The people who understood the product deeply haven’t touched the work in months. They don’t understand it anymore. The system is confident and frequently wrong, and there’s no one left who can tell the difference.

The second team is slower on paper. But their agents are sharper than they were six months ago. They’ve internalized the patterns the AI surfaces. They catch edge cases the model misses because they’re still in contact with the work. When the AI is wrong, they know—because they never stopped paying attention.

The vendor pitch is always the same: your people can’t handle the complexity, so let us automate it away. It sounds like efficiency. It’s actually a bet that you’ll never need the judgment you’re discarding. And that bet almost always loses. The sharpening loop doesn’t care whether the tool is mechanical or digital—remove the human from the friction, and the human gets dull.

Choosing Your Instruments

Every tool you adopt is a bet on what kind of practitioner you want to become. The frictionless option is always available, and it always feels like progress in the moment. But friction is often where the learning lives.

The question I keep coming back to: Does this make me sharper, or does it let me be dull?