Que Nguyen

[Que Nguyen @nguyenngocque]

Even if inference goes close to 0, that is still not the main bottleneck.

The cost just shifts to:
- Memory
- Context management
- Tool orchestration
- Real-world experimentation

Training, RL, and continual learning are still extremely expensive.

No weight updates → no real learning.
Weight updates → exploding compute cost.
That is the paradox.

PMF is not pass or fail like code.
- It is psychology, distribution, trust, timing.
- Rewards are noisy.
- Feedback is slow.
- Experiments cost real money.

In open environments, agents will optimize spam or short-term arbitrage long before they discover real value.

And survival pressure does not guarantee a healthy ecosystem.
- Digital markets reward exploits and short-term cashflow.
- Replication is near infinite.
- Capital moves at machine speed.

That leads to arms races and bot vs bot extraction, not stability.

Cheap inference alone does not solve learning, PMF, or systemic stability.