I've always believed that the most underestimated aspect of the AI ecosystem is not the model capability, but what to do when it goes out of control.

When AI is just an auxiliary tool,
mistakes can be covered by humans.
But when AI begins to make continuous decisions, call on each other, and execute automatically,
you will encounter a real problem:
you no longer have time to ask "why."

This is also why I pay attention to @inference_labs.
It doesn't try to prove that AI is "trustworthy,"
but directly admits one thing:
AI's judgments should not be trusted unconditionally.
Inference Labs chooses to stand after the judgment.
No explanation of the model's reasoning,
no beautification of the inference process,
only verifying one thing—
whether this behavior is within the permissible boundaries.

This position is very cold.
And it doesn't cater to narratives.

But the more autonomous the system becomes,
the more it needs this kind of "still controllable afterward" structure.
You can change models, frameworks, parameters,
but once the system scales up,
trust cannot rely on feelings,
it can only be maintained through continuous verification.
From this perspective, Inference Labs is more like laying a long-term foundational road:
not about making AI smarter,
but about ensuring that when it makes mistakes, the system can still stand firm.

This kind of thing doesn't show its importance early on,
but at a certain stage,
without it, the related AI development will stop moving forward.