Hook
In May 2026, Chinese AI models processed 98 trillion tokens per month—85% more than their US counterparts. This isn't just a milestone in artificial intelligence; it's a seismic shift in the infrastructure that powers decentralized networks. For DAO Governance Architects like myself, the question is no longer whether AI will govern alongside humans, but which AI, under whose rules, and on whose infrastructure. The numbers from Apollo Global Management and The Kobeissi Letter are clear: the center of gravity in AI compute has moved east. But for blockchain ecosystems that pride themselves on neutrality and decentralization, this raises a chilling prospect—can we trust the models we build our smart contracts on?
Context
The data came from a report I reviewed yesterday as part of my ongoing work with the Conscious Code initiative. Chinese models now account for 20 of the top 50 most-used AI models globally, up from just five two years ago. American models dropped from 33 to 28 during the same period. Token processing volumes—a proxy for real-world adoption—grew 113% month-over-month in China versus 43% in the US. Anthropic publicly accused Alibaba of running a massive distillation campaign and is lobbying Washington for tighter chip export controls. Meanwhile, Alibaba banned its employees from using Claude Code, citing 'backdoor risks,' and forced an internal migration to Qoder. The Chinese government also removed over 14,000 AI products from the market for non-compliance.
For the blockchain industry, these events are not remote tech geopolitics. They strike at the very foundation of our belief in permissionless innovation. Many DAOs now rely on AI agents for everything from treasury management to proposal drafting. If the underlying models are subject to state control, censorship, or commercial capture, the promise of decentralized governance is hollow. I have spent the last decade auditing governance structures—from 2017 ICO whitepapers to 2024 ETF interfaces—and I can tell you: the most dangerous failure is not a code bug, but a hidden dependency.
Core: The AI-Infrastructure Stack for DAOs
Let's dissect what 98 trillion tokens per month means for decentralized systems. In my experience auditing 50+ whitepapers during the 2017 ICO boom, I learned that the true security of a protocol lies not in its smart contract code, but in the layer beneath it—the oracle feed, the consensus mechanism, the identity system. Today, that underlayer is increasingly AI.
First, technical route: Most blockchain applications that integrate AI—whether for risk scoring, automated market making, or governance simulations—rely on third-party API calls to large language models. If 85% of the world's model inference shifts to Chinese providers, then by default, the majority of blockchain's AI layer will route through infrastructure subject to Chinese data laws, the Cybersecurity Law, and potential state surveillance. We have already seen examples of centralized entities manipulating oracle prices. Imagine a model that refuses to generate a response because it conflicts with government policy. That is not a theoretical concern—during the 2020 DeFi Summer, I co-founded GoverningDAO to teach non-technical users how to audit Aave's risk parameters. We found that even simple aggregators could be gamed if the source data was monopolized.
Second, commercialization: The 98 trillion token advantage is partly driven by price wars. Chinese models are offered at a fraction of US pricing, sometimes free. For cash-strapped DAOs, this is tempting. But as I wrote in my 2022 'Resilience & Reality' newsletter during the bear market, trust is earned in bear markets. Cheap inference today can mean expensive lock-in tomorrow. When a DAO becomes dependent on a single model provider, the provider gains de facto veto power over the DAO's operations. I've seen this pattern before—in the 2024 ETF governance synthesis, we drafted the Institutional-Community Interface Protocol precisely to prevent such dependency. The same logic applies to AI providers: diversify or die.
Third, competition: The US still leads in model quality—GPT-5 and Claude 4 outperform Chinese equivalents on complex reasoning benchmarks. But the gap is narrowing. Token volume suggests that for most practical tasks, Chinese models are 'good enough.' In a decentralized context, 'good enough' often wins because of cost and speed. If DAOs start using Chinese models for the majority of their on-chain AI tasks, the entire governance ecosystem tilts east. I saw this dynamic play out in the 2020 DeFi yield farming craze: once a protocol gained liquidity, competitors couldn't catch up. Network effects are brutal.
Fourth, ethics and security: The distillation accusations and the backdoor claims are not just noise. If Alibaba indeed distilled Anthropic's model at scale, it means the Chinese models carry latent fingerprints of US innovation—but also potential vulnerabilities. In the Conscious Code project we launched in 2026, we argued that AI agents voting in DAOs must be auditable for bias, data provenance, and alignment. If the model's training data includes stolen intellectual property, the entire governance decision is tainted. Moreover, the Chinese government's removal of 14,000 products sends a signal: only state-approved AI will be accessible. For a global DAO, that is akin to having a single point of censorship.
Fifth, investment and tokenomics: The token processing volume explosion is a double-edged sword. While it signals massive adoption, it may also indicate unsustainable subsidies. Investors in AI-related crypto projects, such as Bittensor (TAO) or Render (RNDR), need to differentiate between genuine demand and subsidized usage. In my 2024 work with three major DAOs on the Institutional-Community Interface Protocol, we learned that network effects only matter if the underlying unit economics work. If Chinese models are burning cash to capture market share, the bubble will burst, and DAOs dependent on cheap tokens will be left stranded. The same risk applies to decentralized compute networks like Akash—if centralized Chinese inference costs are artificially low, it crowds out decentralized alternatives.
Sixth, infrastructure: Those 98 trillion tokens require massive GPU clusters. China is building them, partly with restricted chips like H20 and domestic alternatives like Ascend 910B. For blockchain projects that aim to decentralize compute (e.g., Filecoin, io.net), this competition for hardware is existential. If centralized Chinese data centers can offer 100x the capacity at lower cost, why would anyone rent GPU power from a decentralized network? The answer lies in trust and sovereignty, but only if users value those enough to pay a premium. My experience in the 2022 bear market taught me that during a crash, people prioritize survival over principles. The same could happen with AI infrastructure: when token prices drop, DAOs will flock to the cheapest inference, even if it means centralization.
Contrarian Angle
But here's the contrarian view—and it's one I struggled with during the Conscious Code summit in Zurich. Maybe Chinese AI dominance is actually good for decentralization. Hear me out. The US has its own form of centralization: Big Tech oligopoly. OpenAI, Google, Microsoft—they control the largest models. If Chinese models provide a counterbalance, it creates a multi-polar AI world, which aligns with the blockchain ethos of distributed power. Moreover, Chinese model providers like Alibaba and DeepSeek have released open-source models (Qwen, DeepSeek-V4) that can be self-hosted. A DAO could run its own instance, fully isolated from both US and Chinese jurisdiction. In other words, the competition between superpowers might accidentally accelerate the very infrastructure that enables autonomy.
But that's where pragmatism kicks in. Self-hosting a model like DeepSeek-V4 requires dozens of high-end GPUs and expert engineers. Most DAOs lack both. So in practice, they will use the cheapest API. And the cheapest API is almost certainly a Chinese one. The result: instead of decentralization, we get a new form of dependency—not on US tech giants, but on Chinese tech giants aligned with a single-party state. That is not the world we envisioned in the 2017 ICO whitepapers I audited. People first, protocol second. Always. And people need to be free from coercion, whether by state or corporation.
Takeaway
The 98 trillion token milestone is a warning call for the blockchain governance community. We must now build AI governance into our DAO architectures—not as a feature, but as a first-class primitive. In the next twelve months, I will be working with the Conscious Code team to publish an AI Agent Governance Standard for DAOs, combining the lessons from the 2024 ETF interface with the ethical frameworks we developed for human-AI symbiosis. The question is not whether AI will govern alongside us, but whether we will design the protocol to ensure it remains a servant, not a master. Code is law, but humans are the judges. And in a bear market, trust is the only mintable asset.
People first, protocol second. Always. Empathy is the ultimate security layer. Trust is earned in bear markets.
