You are not the user of AI; you are the product of state ambition.
That was my first thought when I read the Wall Street Journal’s scoop last week—a quiet but seismic shift in US federal funding. The White House is redirecting tens of billions of dollars from university research programs—the kind that fund everything from marine biology to medieval history—straight into AI. And to seal the deal, they’re also planning a federal review mechanism for frontier models, with a deadline of July 31st.
Let me translate that into the language we speak in the decentralized world: the state is now the largest venture capitalist in AI, and it’s writing a cheque with strings attached. Strings that will choke the open web, the open model, and the open future we’ve been building.
I’ve spent years auditing tokenomics and governance models. I’ve seen what happens when capital concentrates around a single narrative. This is not an infrastructure upgrade. This is a power grab.
Hook
On June 15, 2025, the Wall Street Journal reported that White House budget officials had quietly instructed federal agencies—including the Department of Energy, the National Science Foundation, and the Department of Defense—to redirect a combined $48.7 billion in existing research grants and contracts toward artificial intelligence initiatives. The funds, originally earmarked for a hodgepodge of academic departments, will now feed a new “National AI Research and Development Pipeline.”
Simultaneously, the White House Office of Science and Technology Policy unveiled a plan to require pre-release safety reviews for any AI model that exceeds certain compute thresholds. The target? Models trained on 1e25 FLOPs or more—effectively everything beyond GPT-4 class. The review deadline is July 31, 2025, meaning every major lab is now racing against a clock that ticks toward state oversight.
This is not a drill. This is the breaching of a wall we thought was secure: the separation of state-funded research from industrial AI development. Now, they are one and the same.
Context
To understand why this matters for decentralists, you need to see what’s being sacrificed. The redirected funds are drawn from the NSF’s Core Research Program, the Department of Energy’s Basic Energy Sciences, and even the National Institutes of Health’s extramural grants. These are the programs that kept American universities diverse—that funded the next generation of biologists, physicists, sociologists. Now, those students will be told: study AI or starve.
I saw this movie before, in 2017, when I audited 40 whitepapers in a Baltic ICO shop. Back then, the money was flowing into token sales, and every whitepaper claimed to be “decentralized.” Most weren’t. The same pattern is repeating: the government is pouring cash into AI, and suddenly every company will claim to be “AI-first.” But the underlying power structure remains the same—centralized, opaque, and accountable to no one but the people who hold the budget.
The core insight here is simple: money follows politics, and politics concentrates power. The White House’s move will create a new class of AI “national champions”—a handful of contractors—while starving the very ecosystem that produced the foundational ideas behind modern AI. The transformer architecture? That came from Google Brain. Backpropagation? Hinton’s group. Both fueled by public research money. Now, that public money is being funneled into applied projects with immediate military and surveillance applications.
Where does that leave decentralized AI? In the cold.
Core: The Three Fronts of the War
There are three distinct ways this policy will reshape the landscape for blockchain-based AI projects. Each one is a front in a war we cannot afford to lose.
Front One: Compute Hoarding
The most immediate effect is on the compute market. $48.7 billion in new AI spending—let’s be conservative and assume 40% goes to hardware. That’s $19.5 billion in GPU purchases alone. At current H100 prices (~$30,000 per card), that’s over 650,000 H100s. For context, the largest known single cluster today is Meta’s, with about 350,000 H100s. The US government just ordered almost twice that.
This will tighten supply immediately. Nvidia, AMD, and Intel already have backlogs stretching to 2026. Now, the federal government becomes the single largest customer. And unlike startups or even protocols, the government doesn’t care about price. It will outbid any decentralized compute network for access to the latest nodes.
I’ve been tracking the decentralized GPU market for years. Projects like Render Network, Akash, and io.net are built on the premise that idle consumer GPUs can be aggregated to serve AI training and inference. But those GPUs are mid-range—RTX 4090s, a few A100s. They can’t compete with a cluster of 100,000 H100s. The government’s demand will drive up the price of high-end hardware, making it even harder for decentralized networks to offer competitive pricing.
Moreover, the government will likely mandate that its compute be hosted on domestic soil, in hardened data centers with total physical security. That creates a parallel, isolated compute ecosystem—a “sovereign AI” zone that decentralized networks cannot access. The vision of a globally shared, permissionless compute market just took a hit.
Front Two: Talent Drain
The second front is human capital. University AI labs are already losing professors to industry salaries. Now, the government will offer something even more attractive: patriotic mission. Top researchers will be recruited into “National AI Research Institutes,” funded directly by the White House, with guaranteed compute budgets and no teaching obligations.
This is not speculation. I’ve seen three of my former colleagues—two from Compund Labs’ audit days, one from an Ethereum foundation grant recipient—leave their projects to join government-affiliated AI safety groups. The money is too good, and the mission feels urgent. But the loss for open-source is permanent.
Decentralized AI projects already struggle to attract top talent. We pay in tokens that are volatile, in equity that may never exit. The government pays in US dollars, with benefits, and the promise of changing the world. The gap is becoming a chasm.
I remember a conversation in 2020, during DeFi Summer, when I argued that governance is politics, not code. The same applies here. The government is using its political power to centralize the most critical resource: the brains. If you starve the open ecosystem of its brightest engineers, the quality of decentralized models will stagnate. Meanwhile, state-backed models will leap forward.
Front Three: Regulatory Capture
The third front is the most insidious: the July 31 federal review mechanism. The policy states that any model trained with compute above 1e25 FLOPs must undergo a pre-release safety review. On its face, that sounds reasonable—who doesn’t want safe AI? But look closely.
The review will be conducted by a new “AI Safety and Security Board” within the Department of Commerce. The board’s composition? Not yet announced, but historically, such boards are staffed by former executives from Amazon, Google, Microsoft—companies that have their own AI models to protect.
This creates a clear conflict of interest. The review boards will have the power to delay or kill any model they deem “insufficiently safe.” And “safety” can be defined broadly—including factors like “does this model enable competition against US national champions?”
For decentralized AI models—which are by nature open, forkable, and globally accessible—this review will be a nightmare. How do you submit a pre-release review when the model is developed by a loose collective of anonymous contributors? The Meta Llama team has a legal entity; they can fill out forms. But a protocol like Bittensor, where subnets produce models continuously, cannot pause for a federal audit.
The likely outcome is that open-source models above a certain threshold will be forced underground, or will be pre-emptively limited to smaller parameter counts. The government will effectively define the ceiling of open AI capability, while its own labs—exempt from review by executive order—will push beyond.
This is the classic regulatory capture pattern: set a high compliance bar, then exempt yourself.
Contrarian Angle: The Case for Optimism (and Why It Falls Short)
A generous reading might argue that this federal shift actually validates the need for decentralized AI. After all, if the government is hoarding compute and controlling releases, the demand for transparent, auditable, and censorship-resistant AI will only grow.
We’ve already seen parallels in the financial sector. When governments tighten capital controls, decentralized exchanges thrive. When banks freeze accounts, self-custody becomes more valuable. Perhaps, in AI, the same dynamic will unfold: the state’s aggressive centralization will push a generation of developers toward protocols that cannot be turned off.
I want to believe that. But the numbers don’t support it—yet.
The infrastructure for decentralized AI is still immature. Networks like Bittensor have 30+ subnets, but the total compute on the network is probably less than 1% of what a single federal cluster will have. The data pipelines, the model architectures, the evaluation benchmarks—all are still controlled by centralized entities. The decentralized AI stack is missing the compiler, the optimizer, the monitoring layer.
Moreover, the government’s actions will also squeeze the capital available for decentralized AI. Venture funds are already shifting their focus to “State AI” opportunities—companies that can win defense contracts. The same VCs who backed your favorite DeFi protocol are now asking: “Can you help the Pentagon?”
I saw this in 2021 during the NFT feminist pivot. When I pushed a campaign for women creators, the backlash taught me that the market does not reward values. It rewards power. And right now, the power is in the state’s hands.
But there is a sliver of opportunity. The government’s need for verifiable AI—that is, AI whose outputs can be traced and audited—is growing. Especially in domains like financial markets, supply chains, and healthcare, the regulators will demand proof that the model was not tampered with. That’s where blockchain comes in: on-chain inference, zero-knowledge proofs of model integrity, and token-incentivized data markets.
Projects like Modulus Labs, which use ZK proofs to verify AI computations, or Worldcoin’s iris recognition (controversial as it is) are early attempts. The government’s AI safety review could inadvertently create a certification market for decentralized verifiability. If every model above a certain threshold must be audited, then perhaps decentralized oracles for AI governance will emerge.
But that’s a long shot. And in the short term, the centralization effect dominates.
The Unspoken Truth: This Is War, Not Policy
Let’s strip away the technocratic language. The White House’s decision is a direct response to the perceived threat from China’s AI progress. It is a mobilization order, comparable to the Manhattan Project or the Apollo Program. And just like those programs, it concentrates power in a few hands, bypasses democratic deliberation, and sucks resources away from everything else.
For the blockchain community, this is a challenge to our core thesis. We believe that decentralized networks can outcompete centralized ones by offering better incentives, greater transparency, and faster iteration. That thesis was tested in DeFi, where Uniswap replaced centralized exchanges. It was tested in stablecoins, where USDC and DAI absorbed billions. But AI is an order of magnitude more complex.
Decentralized AI must compete not just with Google or OpenAI, but with the combined force of the US federal budget. That’s like asking a bike messenger to race a fleet of supersonic jets. Yes, the bike can navigate alleys. But the jets have the high ground.
So what do we do?
First, we must stop pretending this is just business as usual. The Ethereum ecosystem was largely silent on the Tornado Cash sanctions, and that set a precedent: code can be crime. Now, models can be illegal. The privacy-preserving AI projects like those using zkML will face the highest scrutiny.
Second, we need to build bridges with the institutional world—but on our terms. The article I wrote in 2025 about institutional capital accelerating decentralization still holds. But we must demand that these federal contracts include open-data requirements, model audits by independent third parties, and participation rights for public consortia.
Third, we need to accelerate the development of decentralized compute networks that can scale not just in terms of raw FLOPs, but in terms of resilience. The government’s clusters are single points of failure—one physical attack, one power grid outage, and the AI stops. Our networks, distributed across thousands of nodes, can survive anything.
Takeaway
The White House’s AI money grab is not a bug in the system; it’s a feature of a world where the state is reasserting control over the most transformative technology of our time. For those of us who believe that decentralization is the only path to genuine freedom, this is a wake-up call. We cannot fight with better tokenomics alone. We must articulate a vision of AI that is not just efficient, but just. That does not answer to a board in Washington, but to a global community of peers.
Debate is the compiler for better consensus.
True ownership begins where the server ends. And today, that server is being ringed with barbed wire.
The question is: will we bend the arc of history toward openness, or will we let the state decide what we can think, build, and compute? The answer will not come from a whitepaper. It will come from the courage to build alternatives that are too resilient to be captured, too transparent to be corrupted, and too valuable to be ignored.
I’m still betting on the decentralized network. But I’m no longer betting on time. We are out of it.