I’ve been trying to capture how I’ve seen AI demystified: what’s really going on underneath it.

This clip is my best attempt so far.

The simple point I’m trying to land is that AI isn’t alien — it’s human meaning mechanics at scale.

Here’s the mental model:

Imagine everything "AI" knows is a huge pile of beads on a table. Beads that belong together end up near each other — like all the ‘building’ beads in one clump, all the ‘cooking’ beads in another.

When you ask a question, it’s like pointing at one bead. The AI then draws a little path from that bead to the nearby beads that fit best in "context" with your question. It follows the shortest, strongest path through the closest matching clump and picks those beads to make the answer.
If the path shot off to some far-away clump, you'd get a weird answer — like asking about concreting and it starts talking French pastries.

Another way to say it:

It’s like asking a local for food. The best answer comes from persons who have nearby experience, not global noise. AI works the same way: grab what’s closest to your question and build from that.

Even Proverbs 27:10 gives the concept:
 "better a neighbor near than a brother far off." Same rule for intelligence — human or machine, and AI actually is designed to lean into this truism.

* we carry meaning along lots of bipolar dimensions (love/hate, risk/safety, old/young…)
* we sit on continuous sliders, not on/off switches
* ideas form by moving through nearby “ridges” of meaning (one thought naturally leading to the next)
* memory isn’t a warehouse — what we retrieve shapes what we generate
* legacy is meaning transferred, not extracted

A concrete way to picture it: give a person (or a model) a question, and it lands somewhere in this space of meaning. From there it pulls in the most relevant nearby context, then generates the next coherent step.

If the context is clear and trusted, the jump from “what’s going on?” to “here’s what to do next” gets dramatically shorter.

If the context is messy, you get messy outputs. Same as people, just faster.

That’s where the business twist bites: advantage won’t come from “using AI” in the abstract. It will come from who can make their reality most legible to it — clean signals, shared definitions, good retrieval paths, real governance. The competitive divide widens exactly where you’d expect.

Anyway, that’s the thought experiment the video is trying to show:
The real leap isn’t that machines are becoming human. It’s that we’ve finally learned how to externalise something humans have always done internally:
* store meaning as geometry
* retrieve what matters in context
* generate coherent next steps
and
* (with governance) act in the world.

That’s the “Ideas Space” idea.

If it’s even half-right, the business question isn’t “should we adopt AI?”
It’s “how quickly can we make our world legible enough for agents to be trusted inside it?”

If you watch the clip, I’m curious what you think — does this model help, or does it miss something important?

And if you’re wondering about the tools or the prompting behind the video, ask. I’m happy to share what I used and how I approached it.

posted also onLinkedIn

Want the prompts? 

All yours: https://ap1.hubs.ly/y0qtw90