AI doesn’t threaten intelligence; 
it exposes how little judgement most systems have.

I came across a post from an old colleague, questioning whether intelligence and knowledge still define who we are, now that AI can produce answers faster than we can formulate questions.

The idea itself isn’t new, but something about it stayed with me. Not because of the technology, but because of what it exposes when familiar markers of value stop being rare.

At that point, the discussion shifts. It stops being about machines and becomes a question of where human value actually sits.

This is not a story about AI replacing humans. Rather, it is about value moving away from knowledge and towards something less comfortable.

The Identity Crisis

For most of modern history, intelligence and knowledge functioned as a stable currency. Because of that, they became a convenient place to anchor identity, especially in professions built on expertise.

If you knew more than others, you mattered. If you could solve problems others could not, you were needed. Entire careers were therefore justified by being “the expert in the room”.

But, AI unsettles that arrangement.

Not through a sudden rupture, but through accumulation. Intelligence is no longer scarce, and knowledge is no longer accumulated slowly through years of exposure. Instead, it is produced, recombined, and delivered on demand, which increasingly turns what once differentiated individuals into something closer to a utility.

That shift is not merely technical.
It is personal.

I think that people are not afraid of machines becoming smarter. Rather, they are afraid of losing the thing their identity was attached to. The deeper fear is not replacement, but becoming ordinary.

Devaluation Before Replacement

The first visible effect of this shift is not replacement, but devaluation.

At the surface, nothing disappears. Code still matters, yet writing it is no longer a strong signal. Architecture diagrams still exist, although generating them has become trivial. Analysis is everywhere, which inevitably makes any single instance less impressive.

As this becomes normal, experience begins to blur. Juniors produce work that looks senior, while seniors feel their edge narrowing. Being “smart” becomes harder to see, and being “experienced” less decisive on its own.

I have seen design reviews where proposals looked flawless within minutes, coherent and confident. What took longer was realising that no one in the room was willing to own the long-term consequences of the decision.

This is usually where discomfort surfaces.

Some deny the shift and reduce AI to “just a tool”. Others lean too hard on it and start confusing speed with thinking. Many feel uneasy without quite knowing why, and drift towards complexity, jargon, and over-engineering. Not because those things help, but because they feel familiar.

What is really being lost here is status. Over time, effort stops translating cleanly into value, and visibility loses its power. Professions that built their legitimacy on expertise feel this first.

From Knowledge to Judgement

This is where the reframing becomes necessary.

I believe that human value was never rooted in knowledge alone, but in how that knowledge was used. As output becomes cheap, value moves upstream, away from execution and towards intent.

AI can generate options; however, it cannot own consequences. It can optimise locally, yet it cannot reason systemically. It can assist decisions, but it cannot be accountable for them.

Judgement, therefore, is not only intelligence.
It is intelligence that accepts responsibility.

In software architecture, this distinction is familiar. Getting a design on paper is rarely the hard part. The difficulty shows up later, in understanding what the system will tolerate, what the organisation will resist, and what the long-term cost will actually be. Quite often, the best decision is not to build at all.

That requires taste. Not taste as aesthetics, but as the ability to recognise which acceptable solution will age badly, which trade-off will hurt later, and where fragility hides.

I’m convinced that organisations will pay a premium for judgement precisely when the cost of being wrong becomes disproportionate.

This happens when decisions are expensive to reverse, when failure modes are asymmetric, and when a single mistake outweighs many correct choices. It also happens when technical decisions quietly carry operational, regulatory, or reputational consequences that surface long after delivery.

In those environments, execution is rarely the constraint. Direction is. Judgement matters because mistakes compound, and because someone eventually has to own them.

That said, there are also contexts where judgement should not dominate. Such as proofs of concept, short-lived tools, and low-risk experimentation. They benefit more from speed than deliberation. Recognising this boundary matters.

From Sailors to Navigators

For years, technical professionals were trained to be excellent sailors.

They learned frameworks, patterns, tools, and best practices, optimised routes, increased speed, and learned to manage storms. Execution became the measure of competence.

AI excels at sailing. It is fast, tireless, and precise.

But sailors do not choose destinations.

Navigators choose direction, define purpose, and judge whether the destination justifies the cost. AI accelerates sailing; navigation remains human.

However, judgement does not eliminate risk. It compresses uncertainty into conscious, owned choices.

Also, It does not scale easily, which means fewer people will operate at that level. Operating there is harder.

Final Thought

Because competing with machines on the “what” is unsustainable, the only durable advantage lies in mastering the “why”. In understanding intent, consequences, and direction rather than output.

Seen from that angle, work does not lose meaning; this shift concentrates it, by trading the comfort of execution for responsibility.

That’s the recalibration many of us were overdue to face, and one that will ultimately define who remains relevant in an AI-saturated world.