Beijing announced on July 21 a dedicated AI+ action plan. The headline: special support for embodied intelligence enterprises. The promise: compute subsidies and curated datasets. The missing element: a transparent ledger. No immutable record of who receives what, how much, and under what conditions. This is not a critique of intent. It is an observation of structural risk.
The AI industry is transitioning from model experimentation to application deployment. Governments are stepping in to supply the scarce resources: compute and data. Beijing's plan is one example. It mirrors the early days of DeFi liquidity mining. Centralized entities allocate resources to attract participants. The participants build on that subsidy. When the subsidy stops, the participants leave. The parallel is exact. In 2020, I analyzed Uniswap's AMM gas efficiency. The same efficiency metrics apply here. Subsidized compute inflates the appearance of progress. Real adoption requires sustainable unit economics.
The policy focuses on four verticals: industrial AI, medical AI, cultural tourism, and food safety. For embodied intelligence, it promises special support. The support includes compute power and datasets. This is a direct intervention in the supply chain of AI development. The ledger remembers what the narrative forgets: subsidies create dependency.
Let me quantify the risk. Based on my audit of 50+ ICO whitepapers in 2017, I learned to separate structural integrity from marketing. The same framework applies here. The Beijing policy lacks a verification mechanism. No smart contract enforces the allocation of compute credits. No oracle reports on dataset usage. The process remains opaque. In the crypto world, we call this a central point of failure. Without a public audit trail, the most efficient companies may not receive the resources. The narrative of 'supporting innovation' may mask a legacy of rent-seeking. We do not build in the dark; we audit the light.
The dataset support creates an even deeper problem. The policy mentions connecting hospitals and research institutions for medical AI. This creates a data silo. The ownership of the data remains unclear. In my 2021 analysis of Bored Ape Yacht Club's rarity distribution, I exposed how artificial scarcity inflates value. Here, the scarcity of high-quality medical data is real. But the policy does not propose data unions or on-chain consent mechanisms. The patient has no visibility into how their data is used. The blockchain can solve this. A permissioned ledger can record data provenance, consent, and usage. Codifying the intangible: how art becomes asset. This is not a theoretical exercise. It is an immediate implementation gap. The technology exists. The policy ignores it.
The core insight is this: the Beijing AI+ plan is a centralized subsidy program dressed in the language of innovation. It creates a temporary market but does not build a self-sustaining ecosystem. The token economy teaches us that no amount of emission can replace organic demand. In the long run, the projects that survive will be those that generate real value, not those that receive the most compute credits.
Let me apply the quantified cultural decoding method I developed during the NFT boom. The hype around embodied intelligence follows the same rarity distribution pattern. The policy selects a few 'core enterprises' and showers them with resources. The rest scramble for leftovers. This creates a winner-takes-all dynamic that suppresses competition. The mathematical model shows that concentrated subsidy produces diminishing returns beyond a certain threshold. The policy does not acknowledge this.
Now the contrarian angle — the angle that narrative hunters care about. The conventional wisdom says government support accelerates AI development. The contrarian view is that it may stifle it. When resources are allocated by committee, the most important innovation often comes from unexpected corners. The early internet was built without central planning. Crypto itself emerged from a Cypherpunk mailing list, not a government grant. The real bottleneck for embodied intelligence is not compute or data. It is the absence of a decentralized market for these resources. A blockchain-based compute marketplace, where providers compete on price and trust, could be more efficient than any subsidy program. The policy fails to recognize this alternative.
Furthermore, the legal status of these supported enterprises is fragile. Most AI startups in China operate as limited liability companies. They have no token governance, no on-chain identity. When the policy changes — as it inevitably will — they have no recourse. The ledger remembers what the narrative forgets. In 2022, I activated an emergency risk protocol after the Terra collapse. The same principle applies here: diversify reliance. A company whose entire business model depends on a single government subsidy is not a company. It is a cost center.
In my 2026 work designing a framework for verifying AI-generated content on-chain using zero-knowledge proofs, I saw the future of trust. The same cryptographic primitives can audit compute subsidies. Imagine a smart contract that releases compute credits only when verifiable milestones are met — tracked on-chain. Imagine a dataset registry where every record carries a hash and a consent token. This is not science fiction. It is engineering that already exists. The policy could have embraced it. It chose opacity instead.
The takeaway is forward-looking. The next narrative to watch is not the AI+ action plan itself. It is the emergence of decentralized compute and data markets that operate independently of state support. Projects that tokenize compute, like Akash or Render Network, are building the infrastructure for a permissionless AI economy. The question for investors is not whether Beijing will succeed in its plan. It is whether the eventual collapse of centralized subsidy will create a vacuum that decentralized networks can fill. The ledger does not lie. We do not build in the dark. We audit the light.
