There is a world of difference between asking a robot, “Help me do this,” and asking, “What should I do?” AI can be remarkably good at the first question but lead you astray with the second. New users often learn that distinction the expensive way.

I’ve taught thousands of people how to use agentic AI, and I’ve seen a similar pattern. People get into trouble after they have some success with AI and try to push it further. Quite often, the thing they want on the other side just isn’t there.

At first, you’re skeptical of AI’s ability to provide any real help. With a little time and training, you figure out how to give it context, build skills, and assign the kind of donkey work it can reliably do unsupervised.

The moment that sinks in is liberating. Suddenly, you’ve got more time for creative work while the AI handles the grunt work.

As you get more reps in and the AI becomes reliable on this kind of work, your confidence in it grows. Then, somewhere along the way, you start giving it more credit than it’s due.

That’s where the drift begins.

The robot really isn’t equipped for the why. Put simply, the robot can’t exercise judgment and taste like you can. I’ve seen a lot of new models come out, and they keep getting smarter. Yet they still fail miserably in matters of taste and judgment.

I think the reason is context. The robot hasn’t lived your life. It doesn’t truly know your preferences. No matter how smart the model gets, its assertions of judgment are based on incomplete and inadequate context. It may have read a staggering amount of material, but it hasn’t been you.

I understand why people fall into this trap. The AI is fast, and it’s very confident in its answers. It feels authoritative. And yet, when you get beyond donkey work, these robots can be full of bullshit.

It doesn’t help that vendors of these frontier models keep promising the technology will change your life. Artificial intelligence is a transformational technology. It just isn’t nearly as transformational as the guy trying to pump up his company’s stock price claims.

People still give it more authority than they should. They ask it to do harder things than it can handle. After all that early success with donkey work, they start trying to make the AI smart enough to make those calls for them.

The fact is, it can’t make that judgment for you.

Trying to outsource judgment and taste to AI is an expensive way to get bad results. It’s like asking your toaster to pick the best bread for your turkey sandwich.

Also, even if it could exercise judgment and taste, do you really want it to? Isn’t that your job? Shouldn’t you get to decide what bread goes on your sandwich?

I see this often with people going through my coursework. They reach a point of frustration and give up on the utility of artificial intelligence because they pushed it beyond its capabilities.

My advice is always the same. Scale back. Get back to the useful zone.

Give the robot work where you know the outcome and can tell whether it’s any good. Limit its scope to jobs where you can provide enough context.

AI is great for grunt work that a computer can do. Stay in that space, and you’ll spend remarkably little time managing the AI. Draw clear lines in your head about how far you can take it and where it needs to step aside so humans can take over.

The reason I like the term “donkey work” so much is that it captures what AI does best. Granted, that doesn’t justify a trillion-dollar stock valuation, but it can make a real difference in your life.

You’re the human. You make the map. Hand the robot the shovel.

When you go beyond that, friends, there be dragons.