Recently, I have been looking into @inference_labs' solutions, and the more I think about it, the more I realize they might have truly taken decentralized AI to a new level. In the past, running large model inference always felt like dumping everything into centralized cloud providers—expensive, low transparency, not to mention whether they can ensure no one tampers with your data.

But their approach is quite counterintuitive: breaking a single inference task into many tiny fragments, with nodes around the world collaborating on different parts—just one small computation each. After computation, instead of sending the data back directly, they generate a lightweight mathematical proof demonstrating whether your part was computed correctly and cleanly. Everything can be verified. Once the entire proof passes validation, the results are combined back, producing an inference output with a trust mark.

What I care more about is the underlying logical shift: computing power is no longer concentrated in a few giants, data can stay under user control, and the results are not maintained through trust but through math that directly determines the outcome. In the long run, as long as the incentive mechanisms are good, everyone could become a compute provider. The entire inference ecosystem would become more open, fluid, and participatory—a true marketplace for everyone.

The truly impressive part is turning verifiable computation from a concept in papers into a practical, usable engineering system. Perhaps this is the watershed moment where decentralized AI moves from concept to implementation, worth watching closely.

#Yap @KaitoAI #KaitoYap