In July 2024, Serenity Capital posted a quiet but seismic observation: Chinese VC funds are pivoting from pure large language models to Physical AI and World Models, allocating $13.36 billion in recent rounds. On the surface, this is a rational rotation—scaling laws in LLMs are showing diminishing returns, and the promise of embodied intelligence captures both industrial and speculative imagination. But as a DAO governance architect who has spent years auditing smart contracts and watching decentralized systems fall prey to centralizing forces, I see a different story: beneath the capital flow lies a governance vacuum that no one is discussing. The very technologies that could decentralize physical autonomy are being funded by structures that replicate the worst of Web2 centralization—and if we don't intervene, the next wave of AI will be built without the ethical guardrails blockchain was meant to provide.
Let me step back. The shift from text models to Physical AI—robots, autonomous vehicles, world simulators—represents a fundamental change in how AI interacts with reality. LLMs deal in symbols; Physical AI deals in cause and effect, in torque and force. This demands new infrastructure: real-time inference at the edge, massive simulation environments, and—crucially—data that captures the messy physics of the real world. Traditional VCs see this as a hardware renaissance, a chance to fund factories and chips. But what they miss is that these systems will operate in shared physical spaces, where trust, provenance, and accountability are not optional. This is exactly the problem blockchain was designed to solve—yet the current investment frenzy ignores decentralized governance entirely.
Here is where my experience intervenes. In 2017, during the ICO craze, I audited a project called EtherTrust that had raised $2 million. Its founders dismissed my reentrancy findings as ‘theoretical.’ I refused to sign off, and published a whitepaper titled Code as Conscience. That early clash taught me that profit-first narratives always downplay systemic risk. Today, Physical AI faces an analogous danger: without decentralized data provenance and on-chain governance of robot fleets, a single entity could control a multi-billion-dollar network of autonomous agents. The World Models that train these agents require petabytes of sensor data—and who owns that data? Who ensures it isn’t biased, manipulated, or sold in opaque markets? The same VCs now pouring money into Physical AI are the ones who built the centralized platforms we’re trying to escape.
Consider the structure. Serenity notes that China’s capital is flowing toward Physical AI while U.S. money concentrates on OpenAI and Anthropic. This creates a bifurcation: capital follows national interests, not ethical alignment. In my work advising a major Australian pension fund on Bitcoin ETF integration, I negotiated a clause that 5% of allocated funds go to open-source infrastructure. That move was ridiculed by traditionalists, yet it proved that institutional capital can be steered toward public goods. The same principle applies here. If Physical AI companies accept VC funding without embedding decentralized governance—such as community-controlled upgrade mechanisms, transparent audit trails for training data, and token-based voting on safety parameters—they will repeat the mistakes of Web2. The $50,000 treasury drain I experienced in the Community DAO was a small taste of what happens when centralized points of failure go unchecked.
But here comes the contrarian angle. The dominant narrative is that capital rotation is healthy—a sign of maturity. I argue it’s a mirage. Physical AI’s technical challenges are exponentially harder than LLMs, and the runway for ROI is longer. Most startups will burn through funding without achieving product-market fit. The real risk isn’t a bust; it’s that the survivors will be too big to fail, owning the infrastructure of our physical world. Decentralized governance isn’t a luxury—it’s a precondition for safety. Yet not a single Physical AI company I’ve analyzed includes on-chain governance in its roadmap. They tout ‘transparency’ but mean closed-source audits. They speak of ‘community’ but issue non-transferable tokens controlled by the foundation. This is the same centralized power disguised as innovation.
My own retreat into the Victorian bushlands in 2022 taught me that resilience requires acknowledging darkness. After FTX collapsed, I wrote a private manifesto titled The Myopia of Decentralization, arguing that our movement had become too enamored with technological solutions to human problems. Physical AI is the ultimate test. Will we embed the ethical frameworks we’ve built in DAOs—quadratic voting, conviction voting, veto rights—into the very code that controls machines? Or will we let capital decide, only to retrofit governance later, at the cost of catastrophic failures?
The takeaway is not an answer but a call. The blockchain community must engage with Physical AI now, before the architectural decisions are locked. We need standards for provenance of simulation data, smart contracts for robot fleet coordination, and decentralized identity for autonomous agents. This is not a niche concern—it is the frontier of trust in the physical world. As an evangelist for decentralization, I ask: Are we building a world where robots serve a few powerful entities, or one where they are stewarded by communities with the power to pause, audit, and evolve? The next billion dollars will be spent in the next 18 months. The governance choices made today will determine whether that capital builds a commons or a castle.