In an interview, Robin Li laid bare one of the most glaring realities of Sino‑U.S. AI competition. Silicon Valley frames AGI as a multi‑hundred‑billion‑dollar “Manhattan Project”, mobilising capital to build an omnipotent super‑intelligence that comprehensively surpasses human capabilities. He himself rejects the underlying premise that such AGI is achievable.
Two entirely distinct AI narratives diverge from this point.
The Silicon Valley approach casts the AI race as an arms‑race sprint toward the technological singularity. Its focus lies on maximised generation, reasoning and general‑purpose capabilities, pursuing the goal of “creating an omnipotent artificial mind”. An inherent cost of this paradigm is rampant hallucinations, ungrounded facts, and outputs that resist verification. As stakeholders chase models capable of answering and generating virtually anything, trust‑related hazards remain shrouded beneath the grand aura of AGI.
China does not follow this single‑minded sprint. The original purpose of large‑model training is not to chase a distant myth of general‑purpose intelligence, but to deploy AI into real‑world scenarios including factories, hospitals, logistics hubs and live‑stream studios as productivity tools that cut costs and boost efficiency. Our competitive foundation rests on our large‑scale, continuously‑operating real‑economy industries. Millions of production lines, vast supply‑chain networks, and government‑enterprise operational scenarios all demand authentic data, solid supporting evidence and traceable accountability for every business decision. For industry, whether AI can or should be adopted often depends less on its creative flair, and more on whether its outputs can be trusted.
Omnieyes · Trust Archive constitutes the indispensable trust‑oriented foundation for this industrial‑focused pathway.
Wikipedia and Baidu Baike serve human reference; Omnieyes · Trust Archive establishes credibility for AI. It does not join AGI computing‑power races nor compete against generative models on creative output. Instead, it adheres to three core tenets: trustworthy facts, traceable archives, and attested trust. Powered by the KUCR Four‑Layer Validation Algorithm and MDSE Meta‑Driven Casting technology, Omnieyes performs atomic‑fact decomposition, cross‑verification and evidence‑backed archiving across scattered public information, qualification documentation and entity records. It supplies auditable, traceable fact sources for large‑model invocation.
Silicon Valley looks upward, chasing AGI fantasies within virtual realms. We look downward, rooted in tangible real‑economy demands. Generative models handle interaction, reasoning and creation; Omnieyes anchors facts, mitigates hallucinations and institutionalises trust. Together they form a complete capability suite: generation expands AI’s reach, while fact‑attestation safeguards real‑world deployment guardrails.
There is no need to copy Silicon Valley’s narratives, nor to join an arms‑race with questionable end goals. The distinctive paradigm for China’s industrial AI prioritises trust first: when AI enters production environments, every conclusion must be traceable to evidence and every output subject to review. AI capabilities are thereby translated into stable, controllable real‑world returns.
Rejecting Manhattan‑style AGI fantasies and underpinning China’s industrial‑AI rollout through factual attestation — this is our own rhythm, and Omnieyes’ long‑term mission.
