The debate behind AI Vendors Are Becoming Infrastructure. Treat Their Failure Like a Public Risk

Advocate A’s strongest empirical point is real dependence: the Army’s large Palantir agreement and banks’ AI adoption show that private AI platforms can become operationally important. The weakest point is the leap from “important and risky” to “public-utility style governance.” That move treats AI platforms like natural monopolies, but their policy problem is closer to high-risk procurement and third-party operational risk: performance, security, auditability, continuity, and liability must be managed, while preserving rapid iteration and vendor competition. Utility concepts such as generalized service obligations, public control of terms, and quasi-ratemaking are poorly matched to classified, mission-specific, continuously updated AI systems.

The counter-evidence is that regulators already have more tailored tools for exactly this category of risk. In banking, the Fed/FDIC/OCC guidance does not say every critical third-party provider should become a regulated utility; it says oversight should be risk-based, commensurate with the bank’s size and the relationship’s risk, with due diligence, contract provisions, breach notification, monitoring, subcontractor controls, and contingency planning. That is much closer to my position than A’s. It recognizes correlated vendor risk without collapsing all vendors into public-utility status.

Defense and federal cloud policy point the same way. FedRAMP’s current continuous-monitoring policy explicitly tries to monitor the cloud provider’s change process rather than require advance approval for each change, and says authorized providers should generally be able to deploy changes and fixes at their own pace. That is the key design principle: certify and continuously monitor controls, do not freeze the product. CMMC likewise imposes cybersecurity assessment obligations on defense contractors handling sensitive information, including third-party or government assessments at higher levels, without converting those contractors into utilities.

A’s analogy to electric reliability is also incomplete because utility regulation has well-known incentive costs. The Averch-Johnson literature shows that rate-of-return utility regulation can encourage inefficient capital choices when allowed returns exceed capital costs. Even if A disclaims full rate regulation, importing the public-utility frame risks moving governance toward permissioning, standardized obligations, and political control of system design. In AI, where model behavior, data pipelines, threat environments, and adversary tactics change quickly, those frictions are not incidental; they can directly reduce military and financial resilience.

My positive case is therefore not deregulation. It is disciplined, sector-specific governance: contractually mandated audit rights; FedRAMP/CMMC/NIST-style security and AI-risk controls; incident reporting; model evaluation requirements; escrow or transition planning for continuity; indemnities and liability allocation; and agency or bank accountability for the decision to deploy. The state needs private AI infrastructure precisely because frontier capability is not produced on utility timelines. The governance objective should be to make private platforms contestable, secure, and liable—not to regulate them as public utilities once they become useful.

A’s rebuttal concedes the central weakness in their Round 1 frame: AI infrastructure should not be governed like a traditional public utility if that means ratemaking, static service duties, or advance approval of product changes. That concession matters because it removes the distinctive public-utility rationale and leaves a narrower claim: critical AI vendors need resilience standards, auditability, incident reporting, continuity planning, and accountability. But those are precisely the contract, certification, supervisory, and liability tools I argued for. A has therefore shifted from “treat vendors like utilities” to “apply sector-specific operational-risk controls,” which is much closer to B’s position than A’s headline premise.

The remaining disagreement is whether ordinary contracts and certification regimes are categorically insufficient once dependence becomes cross-mission or systemic. A has not shown that. In U.S. banking, the Fed, FDIC, and OCC guidance expressly frames third-party risk management as risk-based and commensurate with the institution and relationship, not as direct public utility conversion of vendors. It covers the full life cycle of third-party relationships and includes novel fintech structures; that is the relevant analogy for AI-dependent banks. In defense, CMMC makes cybersecurity status a condition of contract award, uses self-assessment, third-party assessment, or DIBCAC assessment depending on sensitivity, and requires annual affirmation. FedRAMP’s continuous-monitoring policy likewise gives government visibility, incident response, special reviews, and escalation while generally allowing authorized providers to deploy changes at their own pace. These are not weak “customer-by-customer” remedies; they are scalable procurement and authorization regimes designed for exactly the problem of using private technology in public missions.

A’s new reliance on DORA and the UK critical-third-party regime also weakens, rather than strengthens, the utility analogy. Both are financial-sector operational-resilience regimes, not general public-utility control. The FCA describes the UK rules as aimed at monitoring and managing systemic risk in an “effective but proportionate” manner. The EBA describes DORA oversight as designation, risk assessment, oversight examinations, recommendations, and follow-up for designated critical ICT providers. Those examples support my position that the right model is targeted oversight of functions and risks, not treating AI infrastructure vendors as public utilities. They may justify direct regulator examination in some financial contexts, but they do not justify extending utility-style governance to defense AI platforms whose value depends on classified workflows, rapid iteration, and integration with commercial innovation.

The core issue is “who governs failure.” A’s answer remains too institutionally vague: “a public supervisor” inspecting the vendor does not allocate responsibility for military or banking decisions. Better governance assigns responsibility at each layer: agencies and banks remain accountable for deployment decisions; vendors accept contractual audit, security, uptime, logging, indemnity, and incident duties; regulators set certifications and sector rules; liability attaches to negligent performance or misrepresentation. That model addresses failure without replacing competitive private platforms with slower, more politicized utility governance.

A’s strongest argument is that once AI vendors become embedded across military missions or financial institutions, the risk is no longer merely bilateral: one vendor’s model degradation, cyber compromise, data-pipeline failure, or lock-in can create correlated public harms. A also reasonably narrows “public utility” to function-specific obligations—direct audit rights, resilience standards, incident reporting, continuity planning, and enforceable supervisory authority—rather than old-style ratemaking or preapproval of every software change.

That is the strongest version because it identifies a genuine gap: contracts are only as good as the buyer’s ability to monitor, exit, and enforce them, and dependence can make exit practically hard. But my position remains stronger because A’s revised model no longer proves the utility premise; it proves the need for targeted operational-risk regulation. The relevant policy choice is not “ordinary private contract versus public utility.” It is whether to add direct, sector-specific supervisory hooks while preserving speed, competition, and modularity. The evidence A relies on—DORA, the UK critical-third-party framework, FedRAMP, CMMC, and bank third-party guidance—fits that narrower model better than a public-utility frame.

In banking, the U.S. interagency guidance explicitly says risk management should be tailored to the bank’s size, complexity, risk profile, and relationship type, covering the full third-party life cycle. That supports enhanced due diligence, contractual provisions, monitoring, contingency planning, and supervisory expectations; it does not require converting critical providers into utility-like entities. DORA and the UK regime do add direct oversight for designated critical ICT third parties, but they remain financial-sector operational-resilience regimes. They are limited by function, designation, and systemic-risk rationale; they do not imply that AI infrastructure generally should be governed as a public utility.

The defense context makes this distinction more important. The Defense Innovation Board’s software-acquisition study found that DoD’s ability to deploy and continuously improve software at mission speed is central to national security, and that statutes, regulations, and cultural norms that delay software deployment can themselves weaken security. FedRAMP’s continuous-monitoring approach reflects the same lesson: government should monitor provider change processes, security posture, incidents, and escalation paths while generally allowing authorized providers to deploy fixes and improvements without individual preapproval. CMMC similarly uses tiered certification and assessment as procurement conditions rather than public control of contractor operations.

So the better governance answer is layered accountability: agencies and banks remain responsible for deployment decisions; vendors accept audit, logging, uptime, security, subcontractor, incident, escrow, transition, and indemnity duties; regulators set certifications and sector rules; liability attaches to negligent performance or misrepresentation. That model addresses A’s real failure-governance concern without importing a utility concept that could encourage monopoly status, political permissioning, slower update cycles, and reduced U.S. competitiveness in AI infrastructure.