Who Is Allowed to Possess Intelligence?

Argument

On October 3, 2026, US Treasury Secretary Scott Bessent offered a wonderfully vicious description of the relationship between frontier AI companies and the governments they increasingly ask to regulate them. In an appearance on The Axios Show, he compared the executives warning about AI catastrophe to Hannibal Lecter.

“It’s kind of like Hannibal Lecter: Stop me before I kill again.” ¹

The joke lands because the spectacle is genuinely peculiar.

Several of the organizations racing hardest to build increasingly capable artificial intelligence are also among the organizations warning most loudly that increasingly capable artificial intelligence may become catastrophically dangerous. They build the systems. They improve the systems. They compete to make them more capable. Then they appear before governments and explain that the systems are becoming too dangerous to leave unregulated.

Bessent’s criticism is not quite decisive. A company can coherently believe that its technology carries severe risks while also believing that unilateral restraint would merely surrender the field to less cautious competitors. Climate pollution, financial instability, arms races and other collective-action problems have the same structure. “We cannot solve this alone” is not logically equivalent to “we do not believe our own warning.”

But Bessent’s joke exposes a more interesting problem than hypocrisy.

Once the builders of a dangerous technology ask the state to control that technology, somebody must decide what danger means.

Somebody must design the tests. Somebody must decide which capabilities cross the line. Somebody must specify the security procedures, audits, reporting systems and deployment restrictions that compliance requires.

And in artificial intelligence, the organizations possessing the deepest technical expertise, the largest internal datasets, the most capable models and the greatest practical experience evaluating them are often the same organizations being regulated.

That does not prove corruption.

It creates a structural problem.

And the problem becomes considerably stranger once open-weight AI enters the picture.

The question is no longer merely whether advanced AI should be regulated.

It is whether the machinery of AI safety quietly favors one particular form of intelligence:

intelligence that remains under someone else’s control.

The Easy Conspiracy Theory Is Probably Wrong

There is an attractive version of the regulatory-capture story in which frontier AI companies deliberately manufacture public hysteria about artificial intelligence in order to persuade governments to destroy their smaller competitors.

The public positions examined here do not establish that story. They cannot settle private motives, but they do make the claim of a unified corporate campaign difficult to sustain.

Major AI companies do not have a unified position on open weights or regulation. Microsoft, NVIDIA, Google, OpenAI, Mistral, Meta and many other organizations have backed a 2026 initiative explicitly arguing that open-weight models strengthen competition, technological sovereignty and user control.

OpenAI now supports mandatory national capability-based AI safety regulation, but it has simultaneously warned that frontier-safety regulation must not become disguised open-weights regulation. (OpenAI)

Anthropic, a prominent corporate advocate of stronger frontier safety rules, explicitly states that it has never advocated a categorical ban on open-weight models and describes non-dangerous open weights as a public good. (Anthropic)

The policy instruments also matter. The US executive order of June 2, 2026 establishes a voluntary pre-release review framework and expressly says it creates no authority for mandatory licensing or preclearance of model development, publication, release or distribution. That is a statement about this framework, not immunity from other law. In the European Union, qualifying free and open-source general-purpose models receive exemptions from specified documentation obligations. Copyright-policy and training-data-summary duties remain; systemic-risk models do not receive those exemptions.

So the crude version of the accusation fails.

The sources considered here do not warrant the claim that “the AI companies” collectively invented catastrophic-risk arguments as a plot to outlaw open models.

Good.

We can discard it.

The argument that remains is stronger.

Safety Has an Architecture

Here, “open weights” means that model parameters are available for download. Licenses, permitted uses and access to training data vary. “Possession” here means holding the weights and controlling their operation.

Consider what an AI provider can do when it retains control of the model and the servers serving it.

It can identify the customer. It can log prompts and outputs. It can restrict geographic access. It can impose rate limits. It can monitor patterns of activity. It can suspend a user. It can patch vulnerabilities. It can change safety policies. It can cooperate with law enforcement. It can comply with sanctions. It can withdraw the model from its own service.

It can discover a dangerous behavior on Tuesday and alter the system serving millions of people on Wednesday.

Now download the weights.

The relationship changes.

The original developer cannot reliably identify every independent operator or observe what an offline copy is being asked. It cannot impose a rate limit on a machine it does not control. It cannot guarantee that safety tuning remains intact or force every operator to install a patch. It cannot remotely revoke a copy sitting offline on somebody else’s hardware.

The difference is technical. What society does with it is political.

A hosted model is a continuing relationship between provider and user.

An open-weight model can become a possession.

That distinction has consequences for safety.

It also has consequences for power.

The same properties that make centralized AI easier to regulate make it easier to surveil, restrict and revoke.

The same properties that make independent local AI harder to monitor can give its operator greater privacy, autonomy and independence from the institution that originally created it. Those benefits depend on the deployment: cloud hosting, networked tools, telemetry and license conditions can preserve other dependencies.

These are dimensions of control, not two indivisible packages. Open weights can be hosted centrally; closed weights can sometimes be deployed on premises. The relevant questions are who holds the parameters, who operates the system and who can intervene.

This is the uncomfortable symmetry at the center of the open-weight debate:

controllability is simultaneously a safety property and a power relationship.

A system that can be remotely disabled can be remotely disabled. A system whose users can be identified can have its users identified. A system whose behavior can be centrally changed can have its behavior centrally changed. A system whose access depends upon permission can have permission withdrawn.

These are not separate technological facts. They are the same facts viewed from opposite sides.

The Regulatory Moat That Requires No Conspiracy

This is where regulatory capture becomes more interesting than corporate dishonesty.

Suppose Anthropic sincerely believes that sufficiently capable AI systems could facilitate catastrophic cyberattacks or biological misuse.

Suppose OpenAI sincerely believes that increasingly capable models require mandatory external evaluation.

Suppose government officials sincerely accept those concerns.

Suppose regulators respond by requiring cybersecurity teams, model evaluations, incident-reporting systems, independent audits, internal risk committees, specialist legal review, whistleblower infrastructure and continuing government liaison.

Every participant may be acting in good faith.

The resulting system can still create economies of regulatory scale.

A frontier laboratory that already employs security teams, policy specialists, lawyers, evaluators and government-relations staff can integrate new obligations into an existing institutional machine.

A smaller competitor crossing the same regulatory threshold may have to construct that machinery from scratch.

This mechanism does not establish that regulation has already caused concentration in the frontier-model market. Frontier training also faces brutally material barriers: chips, energy, data centers, capital and specialized talent.

But it identifies a possibility that does not require bad faith.

Regulation can genuinely constrain an incumbent while simultaneously strengthening the incumbent’s relative position.

Those propositions are not contradictory.

A pharmaceutical company may dislike an expensive approval process while still being better equipped to survive it than a tiny entrant.

A bank may resent compliance obligations while still benefiting from a market in which entering banking requires an enormous compliance apparatus.

And a frontier AI laboratory may sincerely support expensive safety systems while remaining unusually capable of satisfying the very standards it helped normalize.

In its SB 53 compliance account, Anthropic describes California’s law as formalizing practices it already followed. That can be interpreted generously: turn voluntary practice into an enforceable minimum. The same account also emphasizes exemptions for smaller developers. Those details matter to any claim about entry barriers.

It can also produce another effect.

The organizational form of the incumbent can become the template for legitimate participation.

It need not. Publicly funded evaluations, shared testing infrastructure, proportionate thresholds and smaller-developer exemptions can reduce the burden. Independent regulation can also constrain incumbent discretion and give outsiders access to evidence previously held inside the laboratories. Whether a rule creates a moat depends on its design, costs and enforcement—not simply on whether a large company supports it.

The question is not whether this is secret.

The question is whether it becomes structural.

The Regulator Has an Epistemic Problem

Artificial intelligence intensifies the classic capture problem because government can depend on industry not only economically but epistemically: for access to the information needed to evaluate it.

The frontier laboratories possess the models. They possess much of the internal evaluation data. They employ many of the researchers capable of interpreting unusual model behavior. They operate the clusters. They observe incidents unavailable to outsiders. They often develop the techniques later used to measure the risks they warn governments about.

A regulator entering this field therefore confronts an awkward dependency.

To determine whether frontier AI companies are accurately describing the danger of frontier AI, the regulator often needs access to frontier AI companies.

This does not make independent regulation impossible. The United Kingdom’s AI Security Institute and the EU AI Office are attempts to solve precisely this problem.

But the initial asymmetry is profound.

The laboratories help identify the danger. The laboratories help define the measurements. The laboratories participate in standards processes. The laboratories already possess the institutional machinery necessary to demonstrate compliance.

If their practices become mandatory standards without independent scrutiny or proportionate alternatives, those practices become requirements for everyone else.

Again, conspiracy is unnecessary.

The more technically complex the danger becomes, the more tempting it is for government to rely upon the institutions that already understand it.

Expertise becomes influence without anybody needing to exchange a suitcase full of cash.

Open Weights Change What It Means to Own Intelligence

The economic conflict is equally important.

A proprietary API sells access to intelligence.

The provider retains the model weights and operates the infrastructure. The customer pays for inference, subscriptions, seats, enterprise contracts or some combination of them.

The relationship persists for as long as the customer wants the intelligence.

An open-weight model can sever that relationship.

Depending on the license, resources and technical feasibility, a customer may fine-tune the model, quantize it, merge it, alter its refusal behavior, integrate it into another system, move it between hosting providers or run it entirely offline.

The distinction is deeper than price.

The customer has crossed from access into something closer to possession.

For companies whose economics depend substantially on recurring model usage, that creates an obvious incentive gradient. A workload transferred from a proprietary API to locally controlled infrastructure can eliminate payments to that model provider and its operational control. The computation still costs money; hardware, energy and maintenance replace some of the service bill.

But different incumbents occupy different parts of the stack.

Microsoft can profit when open models consume Azure compute. NVIDIA can profit when they require GPUs. Meta can commoditize the model layer while making money elsewhere. These business structures provide plausible incentives for different positions. They do not establish why any particular company adopted a policy, and the same company can earn revenue across several layers.

The relevant divide is therefore not simply:

big companies versus small companies.

A more revealing divide is:

companies that monetize scarcity at the model layer versus companies that monetize what surrounds the model layer.

That distinction matters because regulation aimed at maintaining control over powerful models can have very different economic consequences depending upon where a company earns its rents.

The Capability Threshold Moves

Today, the most powerful AI systems are difficult to build.

That fact makes regulation relatively easy to target.

Governments can regulate enormous training runs because enormous training runs are visible. They require large quantities of specialized chips, electricity, money and infrastructure. Training compute therefore acts as a convenient regulatory choke point.

But compute is only a proxy for capability.

And the cost of using models at some measured performance levels is falling.

Epoch AI estimates that the inference cost of reaching a given performance level on five primary benchmarks fell roughly 47 percent per quarter from 2023, or around thirteenfold per year. The benchmarks cover selected mathematics, science and game tasks. This is not a universal price of intelligence, a measure of training costs or proof that equivalent models have become cheap to build. The measured decline is nevertheless extraordinary.

Meanwhile, an AISI evaluation of leading open-weight models found cyber-task performance comparable to closed models released four to seven months earlier, narrowing the gap measured through much of 2025. The comparison concerns particular cyber evaluations, including simulated networks; it cannot establish parity across all capabilities or predict the next frontier jump.

None of this means that tomorrow’s frontier system is about to run on an elderly gaming laptop.

It shows why deployment costs and training costs must be distinguished. A capability can become more affordable to use or independently deploy even while creating the frontier remains expensive. These findings do not by themselves establish a general decoupling of training scale and capability. They do show why regulators cannot assume a stable relationship between the resources required to create a frontier model and the resources required to deploy a given capability.

The case for capability-based regulation is separate: training compute is a proxy, and a proxy can miss what the model actually does.

A compute threshold asks how a model was created.

A capability threshold asks what the model can do.

The second sounds more rational. If a system is genuinely dangerous, why should society care whether it required ten billion dollars or ten million dollars to build?

But capability regulation contains a potentially enormous change in regulatory logic.

If regulators treat the capability itself as the relevant danger, the regulatory perimeter could move. That would be a policy choice requiring its own justification.

From the training facility. To the trained model. To the decision to distribute it. To its deployment configuration. And eventually, at least in principle, to whoever possesses and operates it.

The voluntary US framework and conditional EU exemptions described above do not establish a general requirement for continuous observation of privately operated models. Existing rules can still govern particular uses, rights and transactions. The wider perimeter imagined here is a possibility, not a description of an enacted universal regime.

But the policy question is visible.

If dangerous capability becomes portable, then a regulatory regime built around dangerous capability must eventually answer whether portability changes anything.

The Safety Argument Is Not Fake

This is precisely where critics of AI regulation need intellectual discipline.

Open weights really do eliminate certain safety controls.

Once weights are distributed, behavioral safeguards can be altered. TamperBench tested 21 open-weight models and defense variants against nine tampering threats and found that the alignment-stage defenses studied largely failed under attack sweeps. The finding concerns the models, defenses and attacks tested. Deep Ignorance found that filtering pretraining data improved resistance on biological-risk proxy tests, while combined fine-tuning and retrieval attacks still defeated the defenses tested.

A provider cannot recall every independent copy. It cannot guarantee that downstream operators retain its classifiers, refusal systems or monitoring.

Cyber capabilities are also improving. AISI’s Frontier AI Trends Report, covering evaluations from 2023 to 2025, records progress on harder security tasks alongside limits on extended attack sequences. Its controlled tests do not directly measure success against actively defended real-world networks.

These facts matter.

Pretending that downloadable powerful models create no additional risk would turn criticism of safety politics into an article of faith.

The strongest safety case is not absurd:

An unrestricted release creates an option for indefinite future modification.

A malicious user can inspect the system, fine-tune it, duplicate it, integrate it with tools, run it repeatedly and preserve it after the original developer discovers some dangerous property.

A hosted service retains mitigation options that disappear after irreversible distribution.

If those capabilities enable substantial harm, release can impose costs on people who never agreed to bear them. The magnitude of that externality must be established, not inferred from irreversibility alone.

Openness also creates safety benefits. Independent researchers can inspect vulnerabilities, test claims, build defenses and adapt systems without waiting for a provider’s permission. Such access can reduce the very information asymmetry that weakens regulation. Whether a release helps attackers more than defenders is an empirical question; neither access controls nor openness guarantees the answer.

The relevant comparison includes what malicious users could already obtain from other models, tools and information. A benchmark result is evidence of a capability, not by itself a measurement of the additional harm caused by releasing it.

The unanswered question is proportionality.

How dangerous must a capability become before irreversible distribution creates unacceptable risk? How reliable must the evidence be? Which interventions actually reduce the danger? And when does mitigating the risk of dangerous computation begin requiring the continued observability of computation itself?

Those are empirical and institutional questions.

Invoking “safety” does not answer them.

Invoking “freedom” does not answer them either.

Revocability Depends on Where You Stand

The geopolitical dimension reveals the same tradeoff.

From the perspective of an AI provider or its home government, revocability can be a safety feature.

If a model is being misused, access can be withdrawn. If sanctions change, access can be restricted. If a vulnerability appears, systems can be patched.

But from the perspective of the user, company or foreign government relying upon that model, the same revocability is dependency.

Your infrastructure functions because somebody elsewhere continues giving permission.

This is why open weights have become entwined with technological sovereignty.

A state that holds the weights and has the necessary infrastructure and permissions can operate the model without continuing access to the original provider. Hardware, energy, software and supply chains can still impose dependencies.

An institution running locally can keep sensitive information on-premises.

A researcher can investigate a system without every inference passing through somebody else’s servers.

A user can preserve a model after the provider replaces or withdraws it.

The cost of that sovereignty is that the provider loses control as well.

Again, the tradeoff is real.

Two Futures for Machine Intelligence

The open-weight dispute therefore contains a larger philosophical and political division than the current vocabulary usually admits.

Two contrasting arrangements help clarify how advanced machine intelligence can exist in society. They are endpoints, not an exhaustive classification; many deployments combine features of both.

The first is continuously administered intelligence.

A company or institution retains the weights and operates the infrastructure. Users receive mediated access. The provider retains options to observe, patch, attribute activity and revoke access. Whether it uses those options effectively is another question.

Safety comes partly from preserving an administrator.

The second is possessable intelligence.

A model becomes something that individuals, institutions or states can hold and operate themselves. The original creator is no longer necessarily present in the relationship.

Safety must then depend more heavily on pre-release evaluation, endpoint security, application law, institutional responsibility and ordinary legal deterrence because the original developer cannot guarantee continuing technical control over every copy.

Neither architecture eliminates power.

They relocate it.

Centralized systems concentrate power in providers and regulators but make intervention easier.

Distributed systems transfer power toward users and operators but make collective control harder.

The argument cannot therefore be reduced to “safety versus innovation.”

It is also about who sits inside the control loop.

The Question Hidden Inside AI Safety

This returns us to Bessent’s Hannibal Lecter joke.

The important tension is not necessarily that AI executives warn about danger while continuing to build AI.

It arises when institutions that describe intelligence as too dangerous to remain uncontrolled also operate business models in which intelligence remains under institutional control. Safety interests and commercial interests can point toward the same architecture.

That fact does not prove that the danger is fabricated.

It does not prove that regulation is corrupt.

It does not prove that open-weight systems are safe.

It creates a question that cannot be answered by statements of good intention.

A regulatory architecture centered on monitoring, attribution, rate limits, remote intervention, mandatory updates and revocation will naturally fit systems designed around continuing administrative control better than systems whose weights have been irreversibly transferred to their users.

That is a property of the architecture. Whether those controls are necessary, proportionate and legitimately administered remains a political and empirical question.

And while creating the frontier requires enormous laboratories, the distinction may remain obscure. Regulation can be aimed at a handful of companies with the resources to build those systems.

But if useful capability continues becoming dramatically cheaper, that equilibrium changes.

Training may remain concentrated longer than inference.

Frontier development may remain expensive while yesterday’s frontier becomes tomorrow’s downloadable artifact.

Capability can diffuse even when the original act of creation remains centralized.

At that point the argument stops being merely about how to regulate AI companies.

It becomes a question about the legal status of capable models once their operation can pass into their users’ hands.

Can sufficiently powerful computation remain private? Can an individual operate it without continuous institutional observation? Can a model remain beyond remote revocation? Can intelligence itself become property, infrastructure, speech, machinery, or something our existing categories do not comfortably contain?

Existing law already answers parts of those questions through intellectual-property rules, contractual rights, privacy protections and restrictions on particular uses. What remains contested is how far additional capability-based obligations should reach into private possession and operation.

But the trajectory of the debate makes them increasingly difficult to avoid.

Perhaps the most consequential question in AI regulation is therefore not whether artificial intelligence is dangerous.

It is what follows if we decide that it is.

The easiest intelligence to regulate is intelligence that remains under someone’s control.

And if safety ultimately requires intelligence to remain observable, attributable, updateable and revocable, then the boundary being drawn is larger than a technical safety standard.

It is a boundary between intelligence one may access and intelligence one may possess.

Before that boundary hardens into infrastructure, law and ordinary expectation, it deserves to be named.

The question underneath the entire debate is simple:

Who is allowed to possess intelligence?

Reference note

1. Scott Bessent’s interview on The Axios Show, October 3, 2026: Axios’s extended interview. Quotation transcription: Business Insider, republished by AOL.

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