Nice Meal. Must Be the Stove.
AI is weakening the old signals organisations used to identify talent, judgment, and capability.
AI is making polished output cheap and weakening traditional proxies for competence.
Leaders now need better ways to identify judgment, originality, and decision quality.
The organisations that adapt fastest will test humans for reasoning, not production fluency.
After I published my recent piece on AI and “the Clankers”, a previous work mate commented: “It is a fine article, but does bear the hallmarks of being written substantially with AI.”
It was a fair observation.
AI absolutely helped smooth parts of the piece. But the comment stayed with me for a different reason.
Because it exposed something much larger now happening inside organisations.
AI is weakening many of the traditional proxies we used to identify talent.
For years, companies rewarded people who could produce polished outputs. Strong presentations. Sharp written communication. Clean synthesis. Structured strategy language. Entire careers were built around the ability to look competent in information-rich environments.
The problem is that AI is becoming very good at all of those things.
Which means leaders now face a harder question:
How do you identify actual capability once polished output becomes cheap?
This is already happening. Executives are reading immaculate briefing papers and wondering whether the person behind them can actually think. Managers are reviewing beautifully structured strategy decks and quietly questioning how much original judgment sits underneath the formatting and fluency.
The old signals are weakening.
That matters because most organisations were designed around observable output. Promotions, influence, and credibility often flowed toward the people who sounded the smartest in the room.
AI changes the economics of that competence.
A mediocre thinker with AI can now produce work that superficially resembles the output of someone much stronger. Not equivalent. But close enough to create management noise.
The premium shifts away from production and toward discernment.
Can this person make good decisions under uncertainty?
Can they identify what matters before the data is obvious?
Can they ask better questions than everyone else?
Can they explain why the machine is wrong?
Can they see second-order consequences?
Can they earn trust?
These were always valuable traits. AI simply makes them easier to distinguish from performative competence.
Ironically, this may improve some organisations over time.
For years, many companies accidentally rewarded presentation fluency over insight, activity over judgment, polish over originality, and information control over decision quality. AI compresses the value of those things.
That does not mean expertise disappears.
It means expertise becomes harder to fake.
A useful analogy came back to me after that comment.
If someone eats an extraordinary meal, they might say:
“You must have a great stove.”
But experienced chefs know the stove is not the meal.
The stove matters. Great tools absolutely matter. But tools amplify capability unevenly. A great chef with an average stove still outperforms most amateurs with a commercial kitchen.
AI works the same way.
A weak strategist with ChatGPT is still a weak strategist. They simply become faster at producing plausible-looking work. Meanwhile, exceptional operators become dramatically more effective because the machine removes low-value friction around execution.
That distinction is going to matter enormously for leadership teams over the next few years.
The answer is not to ban AI. That will fail, and in most roles it would be a strange objective anyway.
But leaders do need moments where the tool is removed.
My kids still do some assignments and exams by hand. Not because handwriting is the future, but because sometimes you need to see what is actually in the student’s head.
Workplaces need the equivalent.
Not permanently. Not theatrically. But deliberately.
Ask someone to explain their recommendation without the deck. Give them a live problem and watch how they reason. Ask what they changed after using AI. Ask which part of the machine’s answer they rejected and why.
The test is no longer whether someone can produce polished output.
The test is whether they understand it well enough to defend, adapt, challenge, and improve it.
That creates a practical leadership shift.
Use AI for production.
Test humans for judgment.
The board paper can be AI-assisted. The discussion should not be. The strategy deck can be polished with tools. The executive presenting it should still be able to handle ten minutes of unscripted challenge.
This is how organisations cross the chasm.
They stop pretending AI use is the issue.
The real issue is whether the human has become more capable, or merely more fluent.