The core mechanism of the Transformer is called Attention. As AI grows more capable, what feels scarce now isn’t machine intelligence—it’s human attention.

In the past, many things stayed undone simply because execution was expensive. Building a system, running an analysis, validating an idea—all of it used to take time and people. “We can’t do that many things” was our natural constraint. That limit is loosening: ideas can quickly become prototypes, solutions can be tested in hours, and autonomous AI assistants can work nonstop. “Doing” has become cheap. What’s costly now is attention. For personal growth, that changes the game—the competition is no longer about who can build something, but about who knows where to focus their effort.

AI Goes Astray More Often Than It Fails

Anyone who builds AI systems has seen this pattern: they rarely fail completely. Each step looks reasonable, yet the system drifts further from the real goal. When a person makes a mistake, it’s caught within minutes; an automated system can make dozens of moves in that same time. Once the direction is wrong, it doesn’t stop—it efficiently amplifies the error.

So the human role hasn’t vanished; it’s evolved. You no longer need to perform every step yourself, but you must define what success means, set boundaries, check direction at key points, and call a stop when things go off course. You can delegate execution, but never direction.

More Power, Same Bandwidth

AI stretches what we can do, but not what we can truly pay attention to. You might now push ten things forward instead of three—but the day still has only twenty‑four hours. You still have to judge whether results are right, whether the direction is worth it, and which task matters most.

Recently my washing machine shook violently during the spin cycle. I spent days discussing fixes with AI. It offered endless ideas—thicken the base plate, switch to a steel frame, raise the machine, add retractable casters—each analyzed in detail. What actually solved it were a few simple actions: seeing that the base was hollow, taking photos, measuring dimensions. Days of discussion shrank to one number I had to measure myself and a much simpler fix. AI spreads options easily; folding them back into reality takes human attention.

You’ve probably felt this too: a project half‑done, a study half‑finished, an automated pipeline running, an idea partly validated. None are failures, yet none are complete. They hang like loose threads waiting for you to tie them off—and they don’t disappear on their own. You feel busy, but what’s really growing is the number of loose ends.

I call this “attention debt.” AI makes starting things too easy, and every start quietly reserves a slice of your future focus. It’s related to “cognitive debt,” but what worries me isn’t lost understanding—it’s the attention already spent in advance.

Opportunities Multiply, Depth Still Requires Focus

With AI, it seems possible to dabble in everything—coding, design, research, side projects—and make something decent in each. It feels like you can grasp it all.

But when “making something decent” becomes universal, shallow success loses value. What truly separates people is what AI can’t replace: deep understanding, judgment, clarity in defining problems, and the willingness to own outcomes. These grow only through long, concentrated effort; they can’t be spread across ten directions.

That’s why I’m wary of “doing a bit of everything.” Diffused returns are mostly illusions—you’re holding many threads but building little substance. In the past, limited ability filtered choices for you; now you must filter them yourself, and the sooner the better.

Cheaper exploration is still a good thing—trial and error costs less, finding your path is easier. The key is knowing when to bring things to a close loop: define what counts as success, what means it’s time to quit, and when to look back. Otherwise “keep exploring” becomes an excuse to avoid choosing.

Managing Attention in Layers

Attention management isn’t just an efficiency trick—it runs from daily habits to long‑term strategy.

Daily — manage energy. Pick one thing each day that truly deserves your focus. Let AI handle the rest, but keep your priorities clear.
Planning — manage trade‑offs. Regularly clear the loose threads. Ask: if this hadn’t started today, would I still start it? If not, let it go. Treat “stopping” as a real action—add “evaluate, continue, or stop” alongside “start, execute, finish.”
Strategy — manage direction. Every so often, ask yourself: if you could bet on only one thing for the next few years, what would it be? AI can help you do more, but only you can decide where to place the bet.

Execution — manage correction. Let AI pause at checkpoints and ask for review—but checking itself consumes attention. Too many stops will drown you. Place them before irreversible actions, at forks in direction, or when costs spike.

Closing

The Transformer’s Attention mechanism answers one question: amid vast information, what should come next? Humans face the same question—except AI has multiplied what we can process, execute, and explore, while our attention hasn’t scaled with it.

It’s easy to think, “Since AI can do everything, I should do everything too.” Real growth may come from the opposite choice: let AI extend your reach, but keep your focus for the few things that truly matter.

As “doing” grows cheaper, one question remains worth asking again and again: where does your attention truly belong?