In early April, the New York State Assembly quietly advanced a bill that would require any data center over 100,000 square feet to disclose its energy consumption per compute cycle — and to share a percentage of gross revenue from AI workloads with the state’s renewable energy fund. The bill, still in committee, has already sent a tremor through the industry. Big Tech’s usual response — a flurry of lobbying and promises of net-zero pledges — is no longer enough. States are tired of being the silent hosts to energy-hungry behemoths that export profits while leaving behind strained grids and rising residential rates.
This is not a niche regulatory skirmish. It is the opening salvo in a war over the real cost of intelligence. The core argument is simple: if AI data centers are consuming public resources — water, land, and most critically, baseload electricity — then the public should share in the economic upside. The logic mirrors the profit-sharing model that some states have applied to oil and gas extraction. But the target this time is not fossil fuels; it is the digital kilowatt-hour that fuels the machine learning models dominating the market.
For those of us who have spent years in the crypto ecosystem, this feels like a familiar echo. In 2017, during my work on the Polymath whitepaper, I argued that tokenized equity could serve as a mechanism for distributing value from infrastructure back to communities. The same principle now applies to compute. The difference is that the state is stepping in where the market has failed to allocate externalities. The blockchain industry, which has long preached radical transparency and local value capture, now faces a mirror: can we offer a better solution than top-down regulation?
Let me be precise about the technical landscape. AI data centers consume between 10 and 50 megawatts per facility, with some exceeding 100 megawatts. A single training run for a large language model can emit as much carbon as five cars over their lifetimes. The insatiable demand for GPU clusters has driven Big Tech to sign power purchase agreements with nuclear plants, solar farms, and even restart old coal plants. States like Virginia, Arizona, and Oregon have seen their grid capacity test limits, leading to moratoriums on new data center construction. The profit-sharing bill is a direct response to the fact that these facilities pay little in local taxes, often benefit from massive subsidies, and create few permanent jobs after construction.
But here is where the crypto lens adds clarity: the same energy accountability that is being demanded of AI data centers has been a central debate in blockchain governance for years. During my tenure as a governance architect for MakerDAO, I analyzed over 500 proposals and found that the most contentious votes were always about energy consumption — specifically, the collateralization of assets that required significant compute power. The community’s solution was not a profit-sharing mandate, but a transparent, on-chain scoring system that weighted energy efficiency into the risk parameters. The result was a self-regulating mechanism that aligned incentives without state intervention.
Now, as states push for profit-sharing, they are essentially asking Big Tech to do what the crypto community has been experimenting with: internalize the cost of energy. The difference is that the state mechanism is blunt and jurisdictional. A profit-sharing law in New York does not affect a data center in Texas. The solution that blockchain can offer is a global, verifiable registry of energy consumption and value distribution. Imagine a smart contract that automatically diverts a percentage of compute revenue to a community-managed fund, with the terms enforced by code rather than by a state agency. This is not a theoretical exercise. I have seen versions of this in the CivicChain DAO, where we designed a governance structure that required every smart contract clause to reflect ethical data privacy principles. The same architectural approach can be applied to energy accounting.
Yet, I must be careful not to romanticize. The contrarian angle is that blockchain-based energy accountability is still largely unproven at scale. The few projects that have attempted on-chain energy credits — like the Renewable Energy Certificate tokens on Ethereum — have struggled with oracle reliability and double-counting. The state’s profit-sharing model, despite its flaws, has the advantage of legal enforceability. A DAO cannot sue a data center operator for non-compliance. A state can. So the real question is not which model is more philosophically pure, but which one can actually deliver accountability in the messy, real-world grid.
From my experience in the bear market of 2022, when I interviewed 50 long-term builders, I learned that resilience often comes from acknowledging the limits of technology. The blockchain industry should not dismiss state regulation as a threat; it should see it as a demand signal for infrastructure that can provide verified energy data. The industry’s obsession with tokenizing everything often overlooks the most basic function: proving that a kilowatt-hour came from a renewable source and was used for a specific computation. That is a data integrity problem, and it is one that blockchain is uniquely suited to solve.
Curating the soul in a world of derivative clones — this is the deeper challenge. The AI data center boom is producing derivative corporate structures that externalize costs onto communities. The state’s profit-sharing push is a clumsy but sincere attempt to force a more equitable distribution. The blockchain community, if it can deliver on its promise of transparency, has the opportunity to provide a more elegant, voluntary, and trustless alternative. But that requires moving beyond hype and building the oracles, the registries, and the governance frameworks that can handle the complexity of physical energy flows.
My own journey through the 2017 ICO era, the 2020 DeFi governance wars, and the 2021 NFT curation experiments has taught me that the most lasting innovations are those that align with the deep needs of the people they serve. The deep need here is not just for cheaper electricity, but for a system that respects the communities that host the infrastructure. Profit-sharing is a start, but it is a crude tool. A more precise tool would be a programmable, transparent, and community-governed energy market that automatically rewards localities for hosting compute resources.
As I write this, I am reminded of the 2025 CivicChain project, where we spent six months mediating between regulators and developers. The regulators wanted guarantees; the developers wanted freedom. The solution was a hybrid governance structure that allowed the DAO to adapt to local laws while maintaining a core commitment to user autonomy. The same hybrid approach could work for AI data centers: a smart contract that enforces profit-sharing to a local fund, but allows the community to vote on how those funds are used. The state provides the legal framework; the blockchain provides the execution layer.
The takeaway is this: the revolt against Big Tech’s energy appetite is not a bug to be patched, but a feature of a maturing digital economy. The state is waking up to the fact that digital infrastructure has physical consequences. The blockchain industry, with its native understanding of distributed resource allocation, can lead this conversation — but only if it stops treating regulation as an enemy and starts seeing it as a design constraint. The best architecture is the one that turns a constraint into a competitive advantage.
In the end, the question is not whether profit-sharing will happen — it will, in some form. The question is whether the system that implements it will be transparent, efficient, and fair, or whether it will be another opaque layer of bureaucracy. I believe the blockchain community has the tools to build the former. But we must act before the regulators cement the latter. The energy is already flowing. The only question is who gets to set the terms.
Curating the soul in a world of derivative clones.