Many projects are not wrong in their direction; rather, they face "phase-specific needs." Once the hype fades, the demand collapses, and no matter how much is written, it’s just a record of a cycle.

@inference_labs is different because it doesn’t target a specific application scenario but addresses a structural gap.

When a system begins to autonomously operate, the issue is no longer about whether the model is smart or not, but whether its behavior can be explained, reviewed, and held accountable. The training phase can be beautified, and the output can be packaged, but inference is where the action truly happens and where real risks are generated.

Without infrastructure that can verify the reasoning process, so-called AI agent collaboration can only stay at the demo level. When scaled up, the system will inevitably fail at the question, “Why should I trust this result?”

Inference Labs’ choice essentially acknowledges one thing:
Models will be constantly replaced, frameworks will be iterated repeatedly, but at the moment inference occurs, verifiable traces must be left.
This is not a matter of whether it’s easy to tell a good story, but an unavoidable position.
As long as autonomous systems continue to advance, this position will always exist. The rest is simply about who can make it more stable, lower cost, and more naturally integrated into the system.

From this perspective, its current form doesn’t matter. What matters is that it stands at the future.