What does the worst possible intern look like? You give them a task, they run off, and return hours later with something entirely wrong. You nudge them on why it’s wrong, send them off again, and they return with something wrong in a different way. After enough rounds of correction, they finally produce what you wanted — with dramatically more time spent correcting them than you’d have spent doing it yourself.

What about the best possible intern? You give them a task, and they return hours later with exactly what you wanted.

The detail the interns don’t generally share: both followed the same process, both failed a few times. The second one just didn’t show you the failures.

Most output we encounter in the world is like this. Behind every witty text you receive is a notes app full of discarded drafts. Behind every finished movie scene are dozens of takes with mumbled lines and poor timing. We’ve come to expect polished output precisely because the iteration that produced it stays hidden.

But AI tools don’t work this way, at least not yet.

When working with AI, it’s common for the tool to misunderstand your intent or fail at the task multiple times, with you nudging it to continue or try differently until it succeeds. This is a natural workflow when working with AI, but it’s deeply unnatural when working with people, because it’s the workflow of the worst possible intern.

The result is a gap between the opinions of new and experienced AI users. New users give the tool a task, watch it fail, and conclude that AI is incapable of real work. This would be reasonable behavior when evaluating a human expert (which incidentally is how these tools are often marketed) but it misses that AI tools are not yet capable of hiding their attempts from you. Experienced users ask the AI to keep trying, describing a feeling of “nudging” or “prodding” the AI past its failures until it completes the task. The end result is the same as the movie scene; you’re just forced to endure all the bad takes along the way.

This will partially resolve with time. AI products are already growing more deliberate about obscuring failures from users. Spend a few minutes asking ChatGPT to do complex research and read through its reasoning summaries, a large portion will consist of the model struggling with paywalls, random errors, its own moderation system, and so on. Some improvement, however, requires better contextual understanding on the part of the AI. Often the intern doesn’t fail because they can’t complete the task, but because they fail to grasp the request and the context required to interpret it. I’m optimistic about AI memory systems for exactly this reason.

But a subtler dynamic is also at play. If you expect the AI to make mistakes, you’ll want to observe what it does, and you’ll be disappointed by the failures that even a successful outcome required. If you trust the AI enough not to watch, you won’t witness those failures.¹ This creates a cold start problem: early, skeptical users see AI as far less reliable than users willing to let it work unsupervised. Trust begets more trust; distrust, more evidence for distrust.

For now, perhaps the best advice for early adopters is this: judge AI not by the first things it does, but by the last.