“An average email or line of code is fine. Average art isn’t. To make something alive with AI, we have to resist its pull toward average by working beside it, shaping what it gives, and listening for what’s missing. Sometimes what’s needed is a good, old-fashioned mistake or two.”
(Frank Chimero; Beyond the Machine – Creative Agency in the AI Landscape).

This quote captures something central in the conversation many people are navigating right now. Not only about AI as a technology, but about pace, quality, and professional judgment in a time where digital processes are accelerating faster than most people and organizations have time to reflect on.

Today, AI can generate text, code, design drafts, and concepts in seconds. It is impressive. And useful. And it dramatically increases speed. But precisely because of that, a new and more pressing question emerges: What is the human contribution when the average becomes extremely easy to produce?

AI Pulls Toward the Probable, Not the Alive

Generative models are statistical systems. They excel at predicting what looks like something we have already seen before. That makes them powerful for standard tasks and repetition. It also means they naturally gravitate toward the average.

That is not a problem in itself. An average email is often perfectly fine. So is a lot of code. But in disciplines where quality, aesthetics, and differentiation matter, it quickly becomes a challenge.

What is interesting rarely emerges where something is most likely. It appears in the omissions, in the editing, in what does not immediately fit the pattern.

Working Beside the Machine

One of the most useful ways to understand the relationship between humans and AI is to ask where we position ourselves in relation to the technology.

Working under the machine means accepting its output as the answer. Letting speed and efficiency define quality. It is fast. But it is also risky.

Working beside the machine is something else entirely. It means using AI as an instrument rather than a replacement. Something that can play along, but still requires a performer.

From that position, professional expertise becomes more important, not less. This applies to taste and judgment, but also to the ability to think critically, to deselect, and to sense when something feels alive and when it is merely average, and perhaps a little lifeless.

Constraints as Raw Material

In creative and strategic processes, value rarely lies in perfect output.

It lies in the strange outputs. The imperfect phrasing. The things that sit right on the edge of being ridiculous, too much, too odd, too skewed. That is where something new emerges. This applies to AI as well. Not because AI is creative in itself, but because its limitations can act as resistance.

When ambiguity is used deliberately, and the result is later shaped, edited, and prioritized through human competence, space for quality emerges. It requires time. And attention. But that is precisely where the difference is made.

As Speed Increases, So Does the Demand for Judgment

In the digital industry, development is moving at an absurd pace. New tools, new possibilities, new workflows. There is no doubt that this creates significant momentum. And momentum, in isolation, is positive and important. But it must not come at the expense of substance, which risks being slowly eroded.

The more that can be produced automatically, the more important the human filter becomes. Not as a brake, but as a quality mechanism.

Resisting the average is not an obstacle. It is the work.

A Conscious Choice

Standing beside AI is not a nostalgic defense of old ways of working. It is an active choice about how technology should be used.

Not to remove speed, but to ensure direction and help preserve quality within automation.

So we must remember this: In a time when so much can be generated in seconds, taking responsibility for what remains standing matters more than ever.