Keith Rabois

But what this is showing is because of Moore's law and because of general computation, it's just not necessary.

You just have to let go, give up control.

And that's very hard for some people.

I would just say that that's like intuitive knowledge versus like the expressly labeled human knowledge.

The question for me is, if everybody was so reliant on human labeling initially, if you're an investor now, when you see these grok-4 results, how do you make an investment decision that's not purely levered to just computation?

So if you look at these results, does it mean that there's 300 to a thousand basis points of lag between just letting the computers vibe itself to the answer versus interjecting ourselves?

If interjecting ourselves slows us down by 300 to a thousand basis points per successive iteration, then over two or three iterations, you've totally lost.

So what does it mean for everybody that's not grok

when they wake up today and they have to decide, how do I change my strategy or double down?

To your point, when these models are fully divorced from having to learn on the known world and instead can just learn synthetically, then everything gets flipped upside down to what is the best hypothesis you have or what is the best question?

You could just give it some problem and it would just figure it out.

One of the seven most difficult problems in math with the most important problems in math is proving a general solution to this thing called Mavie-Stokes, which is basically like viscous fluid dynamics and conservation of mass.

We use it every day in the design of everything.

You know what?

It hasn't been proved.

Isn't that the craziest thing where you're just like, how is this even possible?

We use it to design airplanes, to design everything.

It hasn't been proved.

And so you could just point a computer at this thing and you would unlock all these incredible mysteries of the universe.

And we would probably find completely different propulsion systems.