Chengdu, a city more famous for pandas and spicy hotpot than for cryptographic rigor, just released an ambitious 'AI+' Action Plan. Its targets are staggering: 2600 billion yuan in industry scale by 2027, with 70% of 'new-generation intelligent terminals and agents' permeating every sector. By 2030, that penetration hits 90%.
As someone who spent 21 years in blockchain — from auditing ICO whitepapers in 2017 to facilitating dialogue between BlackRock and DAOs in 2025 — I have learned one immutable lesson: trust no one. Verify everything. And this plan, for all its grandeur, offers precious little verification. It is a classic top-down command structure, reminiscent of the pre-DeFi era where we placed our faith in institutions, in opaque code, and in promises of efficiency without accountability.
Context: The Plan and Its Gaps
The plan, officially published by the Chengdu municipal government, sets out to 'empower thousands of industries' through AI. It proposes 100 innovative products, 100 demonstration scenarios, and 20 annual benchmark projects. The focus is on application, not foundational model research. There is no mention of specific algorithms, training frameworks (like Megatron or DeepSpeed), or model architectures (MoE, SSM). Instead, the policy leans heavily on existing compute infrastructure — the Tianfu Supercomputing Center (100 PFLOPS) and the planned Tianfu Intelligent Computing Center (1,000 PFLOPS by 2025).
The core narrative is 'smart terminals' — AI-enabled phones, IoT devices, industrial robots. But the definition of 'new-generation' is left deliberately vague. Is it edge AI? Agent frameworks? Embodied intelligence? The lack of technical specificity is not accidental; it allows the government to claim progress without being pinned down. Noise is cheap. Signal is rare.
From a blockchain perspective, the most glaring omission is any mention of data verification, algorithmic transparency, or decentralized governance. The plan promises to drive AI adoption across finance, healthcare, manufacturing, and culture. Yet there is zero discussion of ethical frameworks, algorithmic audits, or data privacy protections — despite China's own 2023 Generative AI regulations requiring content safety and model registration. This is a regulatory vacuum in the middle of a compliance storm.
Core Analysis: The Decentralization Crisis
Let me unpack why this centralized approach is structurally fragile, drawing on my own scars from the blockchain trenches.
1. The Oracle Problem Writ Large
In 2017, I audited Gnosis's prediction market mechanism. The fatal flaw was its reliance on a single oracle for price feeds. One compromised API could liquidate thousands of users. Chengdu's entire AI ecosystem depends on centralized data sources — government databases, corporate CRM systems, and monopolistic cloud providers. If a single data pipeline is corrupted or manipulated, the 'intelligence' driving 70% of terminals becomes garbage.
Blockchain's answer: decentralized oracle networks like Chainlink. But the irony is that even Chainlink relies on centralized node clusters for speed. During DeFi Summer 2020, I saw how that trade-off collapsed when a node went dark. Chengdu's plan has no fallback, no distributed validation. It is putting all its data eggs into one basket.
2. Verifiable Compute vs. Trusted Compute
The plan's compute infrastructure is concentrated in two centers. This is a classic single point of failure — physical, regulatory, and economic. Gold is heavy. Code is light. Centralized supercomputing is heavy and brittle. Decentralized compute networks (Akash, Golem, Render) offer resilience: if one node goes offline, thousands others take over. But Chengdu is building a fortress, not a mesh.
Moreover, the plan does not require on-chain proof of compute. How will the city verify that a factory's 'smart terminal' actually runs an AI model, rather than a simple if-else script? With no audit trail, the 70% penetration target becomes a rubbery number — easily inflated by counting a Bluetooth-enabled thermostat as 'AI'. I have seen this pattern before in crypto: vanity metrics without cryptographic proof.
3. Governance Without Checks and Balances
In 2021, I organized Soulbound Berlin — a gathering to explore NFTs as tools for community identity rather than speculation. I created non-transferable tokens for 40 artists and technologists, hoping to demonstrate that on-chain reputation could exist without financialization. Within hours, 90% of participants had sold their tokens for profit. The ideal was pure, but human greed — amplified by a permissionless system — corrupted it.
Chengdu's plan makes the same mistake: it assumes that central planners can align incentives without on-chain governance. The policy relies on government subsidies, cadre evaluations, and enterprise self-discipline. There is no mechanism for stakeholders ( citizens, small businesses, workers) to verify that AI is being deployed ethically. The risk of 'regulatory capture' is high: large incumbents will shape the benchmarks, the compute allocation, and the data access to favor their own products. Without decentralized governance, the 'public good' will be redefined as 'corporate subsidy'.
4. The Tokenization Blind Spot
The plan mentions 'innovative products' and 'scenarios' but never suggests tokenizing these assets. On-chain tokens could represent compute credits, data usage rights, or even reputation scores for AI agents. This would create a transparent, liquid market for AI resources, reducing dependence on opaque government allocations. But the plan is stuck in a Web2 mindset: central bank-issued currency, closed ledgers, and permissioned access.
I recall my 2025 work bridging BlackRock with grassroots DAOs. The institutional investors demanded auditable, permissionless settlement. Chengdu's plan offers none of that. It is building an AI ecosystem that will be hard to audit, hard to exit, and hard to challenge.
Contrarian Angle: The Case for Centralized Speed
Let me be fair. Centralization can be efficient. A top-down plan can mobilize resources faster than any DAO. China's industrial policies have historically succeeded in scaling solar panels, high-speed rail, and 5G networks. Chengdu's 2600 billion target, while aggressive, is not impossible. The city has a strong electronics manufacturing base (Foxconn, Intel), good universities (Sichuan University, UESTC), and lower labor costs than Beijing or Shenzhen.
The plan's focus on application rather than foundational research might actually be smart: let others build the GPT-killers; Chengdu will embed them into factories, clinics, and classrooms. Summer fades, but builders remain. In a bear market for AI hype, a city that secures real-world adoption could emerge stronger.
But here's the rub: even the most efficient centralized system is fragile. A single policy shift in Beijing, a single cyberattack on the Tianfu center, a single scandal involving biased AI in a hospital — any of these could crater the entire edifice. Decentralization is not just an ideology; it is an insurance policy. The question is whether Chengdu's planners see it that way.
Takeaway: A Fork in the Road
Chengdu stands at a fork. One path leads to a centralized, fast-but-brittle AI ecosystem that delivers impressive numbers on paper but crumbles under stress. The other path — slower, messier, but more resilient — integrates decentralized verification, on-chain governance, and token-based incentives.
Based on my experiences with the Gnosis audit, the DeFi Summer burnout, the Soulbound Berlin failure, and the BlackRock-DAO dialogues, I know which path history will judge better. Faith requires reason. Blind faith in central planning without cryptographic guarantees is not faith — it is folly.
Noise is cheap. Signal is rare. The signal from Chengdu's plan is that it cares about scale, not substance. Let us hope that before the 2027 deadline, someone in the municipal government reads a white paper or two. Otherwise, the only 'smart terminal' they will have built is a monument to hubris.