Intent, Meaning and Internalization as True Bottleneck for AI?

Written by me, slightly tweaked with AI – This is a topic that keeps coming back to me. We are getting exceptionally good at automating cognitive processes—reading summaries, getting information served, optimizing workflows. It’s efficient, fast, and sometimes almost scary in terms of how it may change society, jobs and what being human is all about.

But this automation focuses on the task. What about the thinking?

At the same time, the process of internalizing knowledge—of building true understanding, intrinsic motivation, and tacit knowledge—is often what builds the unique expertise and competence that guides our most important choices.

An AI can give you the summary, but it cannot give you the wisdom you gained from struggling with the original text.

This, I believe, could be the true bottleneck.

We can’t automate internalization. I simply can’t see automating this part of the process unless we take the human completely out of the loop (which, of course, might be an option).

But then what?

Who will set the direction? Who will take responsibility? An AI can process information, but it cannot internalize consequence, intent, or meaning. It can’t be held accountable.

And how much innovation and challenging of the status quo will actually happen?

This leads me to the “normal distribution” problem. AI models are trained on the sum of existing human data; by definition, they are masters of the average. If all our teams and leaders begin to rely on the same models for “insight” or “strategy,” will we all be pulled into a ‘Valley of the Average’? Or would we create “teenage-revolt-AI” to challenge and break the status quo randomly, a bit like mutations in DNA sometimes leads to evolutionary leaps?

Will we be endlessly confined by this statistical curve, chained to the mean while risking the very means to break free? Will we see innovation or new paradigms happen in the middle of this curve, or do we need to protect our outliers?

Or—is it so that we are actually there already? Is our own thinking already so confined to a normal distribution that AI is simply a mirror, reflecting our existing flock mentality back at us?

The challenge isn’t just to implement AI. It’s to decide how we use it. How do we ensure our teams use these tools to amplify internalization, not replace it? Or will replacement simply be a too good of a business case to ignore?

Are we willing to pay the “efficiency tax”—letting things take more time—to allow our people to build that deep, irreplaceable, human knowledge? Or – Is it too costly, becoming a competitive disadvantage…