Peter H. Diamandis

For example, in the early days of Uber, great MTP, everybody should have a private driver.

But if you always ordered surge pricing, they knew that, and they would always charge you surge pricing, even though you and I would be standing next to each other.

I'm a cheapskate.

I never order surge pricing.

And I would get the cheap price, and you would not.

And so that kind of somewhat pushing the boundaries on the ethics side is now guided in this whole MTP architecture.

So that's the middle of it.

And then we have drive, which is the intelligence scaffolding and the engine around it, which I'll touch on.

And then shape, which is how does the organization work.

I don't need to get into them all in detail, but you'll get the general idea around it.

Okay.

So then we have the next step is to then look at the intelligence stack in detail.

And if you look at the diagram, you'll see this kind of architecture where we found six layers of what that core intelligence engine looks like.

And the best analogy we have for this is Boyd's OODA loop.

In the military, they have observe, orient, decide, act.

And it's a core flywheel at the middle, which is also the core of the solve everything framing.

When you have that inner loop going, then whatever you put into that loop starts having a positive feedback loop on everything else.

So we created the intelligence stack to act a bit like the OODA loop so that there's constant learning going on.

But around it is a very, very important wrapper, which is govern and assure.

which is the constraints and the oversight.