The people making the biggest AI decisions understand it the least

The people making the biggest AI decisions understand it the least. I watch it happen every week.

The concentric circles of AI understanding

Picture knowledge of where AI is actually heading as a set of concentric circles. At the centre sit the people running the frontier labs and the researchers shipping the models. They see the curve up close. Move one ring out and understanding drops sharply. By the time you reach the outer rings, the traditional-industry executives holding the budgets and the large consultancies advising them, real understanding is thin.

Here is the uncomfortable part. The money and the strategic decisions sit in those outer rings. The clarity sits at the centre. The gap is widest exactly where the stakes are highest.

Where I stand

I see this from a particular vantage point. I build with these tools daily. I co-founded AdBrain AI and shipped an agentic system at one of Europe's largest insurance brokerages that now resolves 67% of customer service cases without human intervention. That work keeps me close enough to the frontier to feel where it is moving. Not inside any single lab, not captured by one company's narrative, but near enough to build with the latest models rather than read summaries about them.

From there, the distance between people who have watched a demo and people who have actually built something is wider than anyone in a boardroom admits.

The chain of advice

Think about the chain of advice that reaches most leadership teams. A traditional-industry executive trusts a consultancy. The consultancy briefs a partner. The partner reads an analyst report. The analyst watched a keynote. By the time a recommendation lands on the boardroom table, it can be two or three steps removed from anyone who has shipped a single thing with these tools. Each layer smooths the edges, removes the uncertainty, and quietly loses the truth that the people closest to the work would never have rounded off.

Most strategy I encounter is built this way. A quiet game of Chinese whispers, played across enough hand-offs that the original signal barely survives. It is one reason so much AI transformation turns into theatre.

What genuine insight actually requires

So what does real insight look like? In my experience it needs four things together: real domain expertise, hands-on use of the frontier tools, a forecaster's discipline about probability and evidence, and the willingness to change your mind quickly when the facts move.

Miss any one and the picture distorts. Domain expertise without hands-on use gives you confident opinions about tools you have never touched. Hands-on use without discipline gives you hype. Discipline without the willingness to update gives you yesterday's forecast defended to the death.

Even the models cannot replace the human

There is a detail here that surprised me. In recent forecasting benchmarks, the strongest models now edge out expert human forecasters. You might think that settles it, that we should simply ask the model. It does not. The sharpest forecasts come from a capable human working with a powerful model. Neither alone is best. The human supplies the intent, the taste, the judgement about what actually matters. The model supplies the breadth and the speed.

The same is true of understanding AI itself. The people who see clearest observe the frontier up close, yet refuse to take any single lab's word as gospel.

What to do about it

I am not saying every executive needs to write code. I am saying the further a decision sits from anyone who has genuinely built with these tools, the more carefully you should question it. Ask where the advice came from. Ask who in the chain has actually shipped something. Ask how many hands it passed through before it reached you.

When you think about your own AI strategy, can you name a single person in that chain who has actually built with these tools, rather than watched someone else demo them? Trace it back. What you find is usually the real story.

If you want AI strategy from someone who builds with these tools rather than summarising them, get in touch.

Frequently asked questions

Why do the people making the biggest AI decisions often understand AI the least?

Because understanding concentrates at the frontier, with the labs and the people building with the models, while the budgets and strategic decisions sit in the outer rings, with traditional-industry executives and the consultancies advising them. The gap between clarity and authority is widest exactly where the stakes are highest.

What separates genuine AI insight from noise?

Four things held together: real domain expertise, hands-on use of frontier tools, a forecaster's discipline about probability and evidence, and the willingness to change your mind quickly when the facts move. Miss any one and the picture distorts into confident ignorance, hype, or yesterday's forecast defended to the death.

Can AI models now forecast better than humans?

In recent benchmarks the strongest models edge out expert human forecasters, but that does not settle it. The sharpest forecasts come from a capable human working with a powerful model. The human supplies the intent and the judgement about what matters, the model supplies the breadth and the speed. Neither alone is best.

Why is consulting-led AI strategy so often wrong?

Because it travels through a chain of hand-offs, from executive to consultancy to partner to analyst to keynote, and each layer smooths the edges and quietly loses the truth that the people closest to the work would never have rounded off. By the time the recommendation lands, it can be two or three steps removed from anyone who has shipped anything.

How should leaders sanity-check the AI advice they are given?

Ask where it came from, who in the chain has actually built something with these tools, and how many hands it passed through before it reached you. The further a recommendation sits from a builder, the more scrutiny it deserves.