The Anthropic vs. Pentagon standoff can be seen as both a contractual dispute and another skirmish in the never-ending culture war that has consumed public discourse in the United States. It is neither, and it is both.
Another way to think about “Claude-gate”, I guess, is as two separate questions that are being collapsed into one. These questions are:
- Is the Department of Defense (recently rebranded as the Department of War) acting legitimately here?
- Do Dario Amodei and the folks at Anthropic have the legitimate authority to decide how a civilization-scale technology gets used?
To answer these questions, we need to explore what AI is, how it is evolving, and why this particular dispute may be the first of many difficult questions we, as a society, will have to deal with in the coming days.
The answer to the first question is straightforward. The DoD did not act legitimately.
The Pentagon signed a contract with Anthropic in July 2025, under the Trump administration, with agreed usage terms. Those already included the two restrictions – on using models for domestic surveillance and in autonomous weapons. Anthropic was already a willing partner, and its models were deployed for both offensive and defensive purposes.
Then, in January 2026, the DoD demanded renegotiation. They wanted to use the models for “all lawful purposes” – effectively removing the carve-outs. When Anthropic refused, the DoD threatened to call Anthropic a “supply chain risk to national security.” This last designation, until last Friday, had been reserved for foreign adversaries. Huawei and ZTE, both Chinese companies, saw their US businesses destroyed after being labeled as supply chain risks. The DoD was given six months to transition away from Anthropic, and other vendors were asked to comply “immediately.” They also threatened an invocation of the “Defense Production Act” – effectively nationalizing Anthropic.
To quote Amodei:
“those two threats are inherently contradictory: one labels us a security risk; the other labels Claude as essential to national security.”
The second question is a lot harder to answer, and I think, more interesting.
The Truman Principle
Ben Thompson, who writes Stratechery, and Gregory Allen of CSIS (the Center for Strategic and International Studies) explored this in a recent interview. They talked about the parallels between this moment in AI and the Manhattan Project. Thompson’s take was that if a private company had stumbled onto nuclear fission in 1944, the State would have nationalized it without debate.
The question is whether AI warrants the same treatment.
Thompson makes two arguments:
- The role of politicians: Politicians are best positioned to make decisions about transformative or disruptive technology because they represent the will of the people. Their job is to integrate across domains. Experts, in contrast, see through a single lens. What makes sense in one dimension might be actively harmful in the light of others. Someone has to weigh these dimensions against each other. Experts can’t, because their expertise is precisely what prevents them from doing so.
- Democratic legitimacy: Truman decided to use nuclear weapons on Hiroshima and Nagasaki. That decision wasn’t morally uncontested then, and isn’t now. But, as Commander-in-Chief, it was his to make. He had input from scientists (such as Oppenheimer), military planners, and moral advisors. He also had to face an electorate that was tiring of war. He made a decision that would eventually be judged by the ballot box.
Applied to the current dispute: Amodei is not elected. He is a scientist whose company has built the most capable AI today. Anthropic’s worldview and its safety philosophy, however sincere, is that of a small group of extremely privileged (and AI-pilled) people in San Francisco. The argument that they get to set the terms of how a sovereign government uses this technology is hard to sustain on democratic grounds. And as bombastic as Pete Hegseth is, he is ultimately the representative of an elected government.
Thompson’s arguments are persuasive. But they rely on an equivalence between AI and nuclear technology that I don’t think holds.
Where the Analogy Breaks
The Manhattan Project comparison is a useful starting point. Amodei’s take is that AI (or more specifically AGI – Artificial General Intelligence) will be a technology that has world-changing implications. He takes the nuclear parallel seriously himself. His favorite book, according to Kevin Roose at the New York Times, is “The Making of the Atomic Bomb.”
But this analogy breaks in a couple of different ways.
A secret government project vs. a widely dispersed and publicly available technology
The Manhattan Project was a secret that could be maintained until its explosive revelation to the world. AI capability hasn’t been a secret. Large Language Models are based on a research paper published by Google back in 2017. LLM capabilities rapidly diffuse through academic papers, experimentation, and other creative approaches. Publicly available open source models are only a few months behind the cutting edge.
Anthropic is on an annual run rate of $20bn as of March 2026. The DoD contract was for $200m. These are significantly different numbers. The use of Anthropic’s models in enterprise and by “regular people” dwarfs the potential national security use cases.
This means the “nationalization window”, if it was ever open, may have closed before anyone noticed. The genie is out of the bottle, and anyone can now have their own AI assistant.
There is a counterargument worth acknowledging: access isn’t the same as control.
You can use Claude on your desktop, but running a frontier model is beyond the capabilities of consumer hardware. Training and running frontier models like Claude Opus requires datacenter-scale infrastructure that only a handful of entities can acquire. This means the real nationalization question is about access to compute and electricity, which is a narrower problem, but isn’t really being discussed apart from local opposition to datacenter construction.
Same capabilities in different dimensions, or “you can take Claude from my cold, dead hands.”
As a subscriber to the “Claude Max” plan from Anthropic, I have access to a model with capabilities (reasoning, planning, synthesis, persuasion) that are categorically similar to those used by the DoD.
Similar capabilities, different magnitudes, and used for different use cases. After all, a long-running safety concern from the likes of Amodei is terrorists using commercially available LLMs to build weapons of mass destruction.
Nuclear technology has civilian applications too: power plants! But nuclear weapons are not the same as nuclear power. These are different capabilities. With AI, the differentiation happens at the application layer. The same model that helps me think through this blog post could be used by the NSA to find security vulnerabilities. If you decide to restrict the use of one, you must restrict the use of the other.
To put it another way, any serious attempt to wall off military-grade AI capabilities necessarily implicates consumer access. That is not going to be a popular position given how quickly this technology has dispersed and become part of mainstream knowledge work. Now, the argument could shift considerably if we were to face a crisis caused by an AI-enabled attack.
In any case, the nationalization frame, however intellectually coherent, is not a realistic scenario.
The Breaking of the Accountability Loop
There is a deeper thread running underneath the contractual dispute.
Democratic governance rests on a transaction: citizens pay taxes, serve on battlefields, and sustain the economy. Elected officials represent their citizens. Rights, franchise, the welfare state, the GI Bill are what the state offers its citizens.
This loop has been fraying for years. Contractor armies, drone warfare, and now AI: each reduces the state’s dependence on broad citizen participation for force projection. AI is already being used for offensive operations. Gregory Allen describes a scenario in which AI agents could multiply the NSA’s offensive cyber capabilities, from thousands of human hackers to millions of AI hackers, enabling a debilitating first strike.
The trajectory of AI-enabled military capabilities points towards radically reduced dependence on human labor. Drones don’t unionize. Autonomous weapons systems don’t get tired or mutiny. When the state can project force without relying on citizen soldiers, the mechanism that historically forced political accountability from capital to labor begins to erode.
The Anthropic dispute is a preview of the challenges to come. The government is attempting to control AI capabilities while the people building the technology object to specific applications, and the people affected by those applications (mass surveillance, autonomous weapons) have no visibility and little understanding of the decisions being made.
The Trump administration’s approach of maximum velocity has gotten inside the OODA Loop (Observe, Orient, Decide, Act) of how government is supposed to function. Anthropic will sue the DoD over the supply chain risk designation and probably win the case eventually. Hegseth and Trump both used social media to issue directives before Anthropic was notified or before there was the chance of any legal due process. The calculation in this, and in many other instances, was to control the narrative and rely on the institutional processes being so slow as to become irrelevant.
(”We have always been at war with Eastasia”)
Thompson’s framing captures the forward-looking dimension of this. The economic asymmetry between the government and AI companies is only going to grow. The government’s economic leverage (carrots) is shrinking relative to the industry’s revenue and capital base. What remains are sticks: regulatory power, procurement threats, and national security designations. The worse the asymmetry gets, the more tempted the state is to use those sticks preemptively, before the companies are powerful enough to resist.
In a more stable political environment, this tension would play out through public debate and legislative action. Instead, it’s playing out through broadsides on X and Truth Social.
The Visibility Problem
Most people have no idea any of this is happening. Their exposure to AI is watching a video of babies doing crude standup on Instagram. The people watching AI move in real time are a small group: researchers, developers, and a small subset of AI-pilled knowledge workers paying significant money for access to frontier models.
This gap between an informed vanguard (”riding their models into personal singularities” to paraphrase technology writer Venkatesh Rao) and the general public itself is a democratic failure. Thompson’s assertion that politicians are held accountable for their decisions holds only if the general public has sufficient visibility and the mental models to do so. How many people know the capabilities of frontier models? To them, this whole dispute seems like a storm in an extremely nerdy teacup.
But the frontier models like Claude Opus keep getting better. And now, we are starting to see significant repercussions in the “real world” – from volatility in stock markets to job losses that are attributed to AI capabilities. Companies like Anthropic, OpenAI, Google, and Meta (along with hyperscalers such as Amazon and Microsoft) are locked in an escalating, expensive race to build more capable models and infrastructure. The rate of investment in AI now dwarfs ($36bn vs $700bn) that made in the Manhattan Project – even accounting for inflation.
So on one hand, we have the government and an AI company locked in a contractual dispute, and on the other, we have rapidly improving technology that could have a significant impact on the economy and on society.
The problem is that the people who get to decide whether they want this future don’t seem to have visibility or much of a say in this collision between a government that sees AI as both a weapon and a means of control and corporations that are gambling on an “AI or bust” future.
Heavy Lies the Crown
I agree with Thompson. Political accountability under uncertainty is what democracy is for. The crown should lie heavy on whoever wears it.
But who is wearing it?
The current administration isn’t integrating expertise. They rely on vibes, culture wars, and performative displays of dominance on social media to govern. Most of the electorate doesn’t have visibility to hold anyone accountable. And the guardrails – Congress, the judiciary, and the media seem unable to function at the speed that this technology and administration demands.
We are being asked to deal with the consequences of decisions made without the political and social infrastructure these times demand.
What would it take to build that infrastructure?
At minimum: a broad consensus-building effort between government and the technology industry, regulatory frameworks that address military AI procurement and civilian protections simultaneously, and international cooperation that acknowledges the global diffusion of these capabilities. This would be difficult under any circumstances. Given the “winner takes all” dynamics that dominate the AGI discourse, both among nations and among companies, I see very little sign of it happening.
And the cost of not building it is severe. Without institutional infrastructure, there are two default paths, and neither is democratic. The first is the trajectory the current dispute previews: the state asserts control through coercion rather than consensus, using national security designations and emergency powers to bring the technology to heel. Taken to its logical endpoint, that’s a future where frontier AI capabilities are captured by the state and deployed to entrench power, at home and abroad. The second is a future where the technology simply outruns all governance, where capabilities improve faster than any institution can adapt, and the question of who decides becomes moot because nobody decided anything. It just happened.
I hope for the best, but I am not optimistic.
Perhaps the next round of elections will bring some more measured approaches to AI regulations. Perhaps the “market” will decide that incinerating public goodwill in a rapid race to the singularity doesn’t make sense.
Perhaps we have already decided that the best course is to do nothing. To wait and hope for the best and enjoy our AI-generated entertainment while we can.
