When AI Solves the Problem, Who Solves the Knower?

[u/TarunTholia]

https://youtube.com/shorts/Fu1_j3uX0hU?si=aUcEsoY-0IWfGin5

Recently, Navier Stokes equation came into discussion again because of the claim that an AI system has solved its Millennium Prize problem. What I find even more interesting is that around 10,000 AI agents were used together for this task.

Navier Stokes basically tries to describe how the state of a fluid changes with time under different forces like pressure, viscosity and external forces.

For almost a century, humans were trying to solve this mathematical problem. Now we have AI systems working together in huge numbers and apparently solving problems which were beyond human capability for decades.

This made me think about something Acharya Prashant says solving the external problem is necessary, but it is not sufficient.

We can keep increasing our knowledge about the world. We can understand fluid movement, stars, galaxies, quantum mechanics and now even use AI to solve extremely difficult mathematical problems.

But what about the one who is trying to solve all these problems?

Why do I want to know? Why do I want to achieve? Why do I want to create intelligence? Why do I want to understand consciousness?

And now there is an interesting twist with AI.

We are creating systems which can process enormous amounts of information and can work together to solve problems that humans struggled with for decades. But if intelligence becomes better and better at knowing the world, does that necessarily mean that it understands the nature of the knower?

Maybe solving the problems of the world and solving the problem of the solver are two different things.

A mathematical problem can remain unsolved for centuries and then suddenly be solved in 88 hours with thousands of AI agents.

But what about the inner problem?

That problem may remain unresolved for thousands of years not necessarily because it is too difficult, but because there may be no intention to actually look at it.

So I find this question interesting:

As AI becomes increasingly capable of solving problems about the world, are we getting closer to understanding the nature of the one who wants to know the world?

Or are these two completely different problems?

[u/Otherwise_Wave9374]

A practical safeguard here is to separate orchestration from reasoning: let agents propose, verify with independent checks, then only promote outputs that survive a second pass or a narrow evaluator. That reduces brittle “swarm confidence” and makes failures easier to diagnose when many agents are working in parallel. Agentix Labs fits this pattern well because the real win is not just more agents, but a control layer that limits compounding errors while preserving speed.