I continue to watch @ inference_1abs and the more I read it, the more I realize that it is not about AI being stronger, but rather about the underlying question of whether AI is trustworthy. Most AI reasoning nowadays is essentially black box, and you can only accept the results without being able to confirm whether they were calculated according to the rules. This cannot be sustained in finance, healthcare, and enterprise level systems in the long run. The idea of Inference Labs is clear: it does not require the model to be completely public, but the inference results must be verifiable. The Proof of Inference mechanism they proposed places AI inference on chain and generates zero knowledge proofs, allowing any relying party to verify the authenticity of the results without touching on model details. This step actually transforms' trust 'from a subjective promise to a mathematical fact. More importantly, this system has already reached scale. More than 300 million zkML proofs generated on the chain are not laboratory data, but results under real workloads. By collaborating with Cysic to introduce ASIC hardware, the cost and latency of inference proof have been pushed to a practical range, which means that high-frequency, real-time verification is no longer just talk. At the ecological level, @ inference_1abs combines with EigenLayer's AVS direction and has received funding support from Delphi. Its positioning is very clear, which is to provide auditable and accountable infrastructure for future AI agents. If AI really wants to participate in asset management and automated decision-making, this "evidence first, results later" structure is almost essential. Inference Labs is not a conceptual project, but rather a weakness in building trust for the era of autonomous computing, which deserves long-term attention. Inference @KaitoAI KaitoYap