Originally published on CoreProse KB-incidents
Policy debates about “pre-approval” for AI models feel abstract—until you’re trying to ship an LLM stack into a regulated customer’s environment.
Sam Altman has urged the US government not to require prior approval for AI models, warning this could freeze innovation. For US builders, the practical issue is: what does Washington already expect from your eval pipelines, logs, and architecture—and how much would a real pre-approval regime actually change?
1. How US AI Governance Actually Works Today (Without Pre-Approval)
The US has no EU-style AI Act and no single AI statute. It uses a decentralized, sector-specific strategy driven by agency guidance, enforcement, and voluntary commitments. [1]
This means:
- No single “AI regulator”
- Different rules for health, finance, employment, education, and government use
- Heavy reliance on soft law: frameworks, guidelines, best practices
💡 Implication: You are already in a compliance regime; it’s just fragmented. [1]
Executive orders, not a unified law
Federal AI policy is led mainly by executive action, especially Biden’s 2023 AI Executive Order. It is directional, not a technical rulebook. [7]
Key features:
- EOs guide federal agencies but can be reversed by future presidents
- Emphasis on safety testing, reporting, and civil-rights safeguards, not detailed technical specs
- Private obligations often flow through procurement, grants, and agency rulemaking rather than the EO text itself [7]
⚠️ Fragility: An EO can vanish with one new order; that is very different from statutory pre-market authorization. [7]
The Trump-era pivot: deregulation and “winning the AI race”
Trump-era policy, crystallized in the 2025 AI Action Plan and related orders, tilted toward deregulation and infrastructure build-out. [3][4][11]
They:
- Frame AI as a global race the US must win
- Direct agencies to remove regulations that “unduly burden AI innovation” [4][11]
- Warn that health AI rules may inhibit innovation, while still noting risks to trust and equity from less premarketing evaluation [3]
📊 Contrast: Biden: risk management and rights. Trump: speed, infrastructure, and cutting “red tape.” [3][4][11]
OMB’s 2025 memo: governance over pre-clearance
The 2025 OMB memo on “Accelerating Federal Use of AI” tells agencies to adopt AI aggressively but with safeguards for civil rights, civil liberties, and privacy. [5]
Focus areas:
- Governance processes and risk management
- Internal oversight roles and AI inventories
- Public trust and transparency—not model-by-model pre-licensing [5]
The patchwork you’re really operating in
Layered on top of EOs and memos is a web of:
- Sector regulators (FDA, CFPB, EEOC, etc.)
- State and city AI laws (Colorado, California, Illinois, NYC) on transparency, bias, privacy, accountability [1][10]
- Voluntary frameworks like NIST’s AI RMF that regulators increasingly reference [1]
💼 For engineers: A model + pipeline can be compliant in one jurisdiction and at risk in another six months later. [1][10]
2. What “Pre-Approval for AI Models” Would Mean in Practice for Engineers
Strong-form pre-approval means you cannot deploy a frontier model or major update until a federal authority reviews your technical docs, evals, and risk assessments. [7]
Think of a hybrid between:
- Medical device premarket review
- FedRAMP-style authorization for cloud services [3][12]
⚠️ Working definition: Pre-approval = a mandatory gate before real users see a new version, not just after-the-fact enforcement.
Mapping to existing compliance patterns
If you sell into US federal agencies, you already see analogous patterns:
- FedRAMP demands machine-readable evidence (OSCAL), defined controls, and ongoing monitoring [12]
- “Significant change” events (e.g., new model weights) can trigger re-assessment and more evidence [12]
- Evaluations function as operational evidence tied to release gates, not just benchmarks [12]
Pre-approval would formalize this and widen it across models and sectors.
💡 Design hint: Treat inference, retrieval, tooling, and training as separate risk surfaces with their own eval tracks. Federal guidance is moving AI authorizations this way. [9][12]
Enterprise implications: evals as first-class artifacts
Current governance guidance already nudges enterprises to:
- Tie releases to explicit evaluation thresholds
- Continuously monitor accuracy, drift, bias, and misuse in production [9][12]
- Version models, prompts, guardrails, and datasets as separate but linked compliance objects [12]
Pre-approval would shift these from “best practice” to mandatory.
Open-weight models: the square peg
Open-weight models clash with centralized oversight. Once weights are out, anyone can:
- Fine-tune on unvetted data
- Merge with other checkpoints
- Deploy in opaque environments
Research notes that open weights can be irreversibly copied and modified, making traditional risk management far harder. [2]
📊 Regulatory puzzle: What exactly is “approved”—the base checkpoint, or every downstream variant that diverges after hours of LoRA fine-tuning?
Agents and tools: what exactly is being approved?
For agentic systems, behavior depends on:
- Base model
- Orchestration and planning logic
- Tooling surface (APIs, RAG, actuators)
- Guardrails and escalation paths [8][12]
Any realistic pre-approval scheme must decide if it is approving:
- The model alone
- The model + reference system card
- Full workflows (e.g., a claims automation agent)
⚡ Engineering takeaway: If pre-approval comes, system-boundary diagrams, agent policies, and guardrail tests will weigh as much as raw model eval scores. [8][12]
3. Innovation vs. Risk: Lessons from Existing US AI Policy
Biden’s 2023 AI EO tries to balance innovation with human rights, anti-discrimination, and social justice, reflecting an ordoliberal view: markets are free but bounded by rules to prevent abuses. [6]
In this frame:
- Innovation is welcome, but not at the expense of fundamental rights
- Government sets conditions for fair competition and protects vulnerable groups [6]
💡 Policy signal: The debate is not “innovation vs regulation,” but “which guardrails support sustainable innovation.” [6]
US vs EU: why no AI Act-style authorization (yet)
Compared with the EU AI Act, Washington prefers flexible, risk-based governance over blanket authorization. [1]
Drivers include:
- Fear of chilling early-stage innovation
- Reliance on sector-specific approaches (health vs finance vs hiring) [1]
- Preference for voluntary frameworks, guidance, and procurement levers over broad bans [1]
Health AI as a microcosm
Trump-era health AI policy illustrates this tension. It warns that regulation can inhibit AI innovation in care delivery. [3]
Yet it also notes:
- Less premarketing evaluation can weaken clinician and patient trust
- Poor validation on diverse populations can deepen inequities [3]
📊 Lesson: Cutting pre-approval shifts risk to trust, equity, and liability—not to zero. [3]
“Remove red tape,” but keep certain safeguards
The Trump AI Action Plan and EOs stress:
- Removing regulations that “unduly burden” AI
- Accelerating data center and infrastructure approvals
- Ensuring federal procurement avoids tools seen as ideologically biased [4][11]
At the same time, OMB’s 2025 AI memo still demands strong protections for civil rights, civil liberties, and privacy in federal AI. [5]
⚠️ Prediction: Any serious pre-approval debate will be framed as civil-rights and public-trust policy at least as much as an innovation question. [5][7]
4. The Hidden Compliance Burden Already Facing AI Teams
Most engineering teams are far below the governance maturity that a pre-approval system assumes. Surveys show only about 30% of organizations have generative AI in production, and fewer than 48% monitor for accuracy, drift, and misuse. [9]
📊 Gap: AI is still treated like a pilot rather than a monitored critical system. [9]
The cost of getting it wrong
The same research finds: [9]
- 99% of organizations report financial losses from AI-related risks
- 64% report losses above $1M
- Average losses around $4.4M
- Non-compliance with AI regulations is the top risk, affecting 57% of orgs
Anecdotal experience shows misaligned LLM pilots can trigger audits, delay launches, and force retrofitted documentation when regulators update guidance mid-project. [9][10]
Patchwork as a moving target
The US state and sectoral patchwork emphasizes:
- Transparency (disclosing AI use)
- Bias and fairness controls
- Data privacy
- Accountability and auditability [1][10]
Because rules change quickly, a design compliant on day one can drift into non-compliance purely because the law moved. [1][10]
⚠️ Reality check: A federal pre-approval layer would sit on top of this complexity, not replace it. [1][10]
Toward continuous authorization
In federal cloud practice, “good” looks like: [12]
- Treating guardrails and safety policies as explicit, testable controls
- Versioning models and prompts with eval-gated promotion
- Using “significant change” notifications tied to model updates and new tools
This effectively creates continuous authorization for AI services without a formal model pre-approval statute. [12]
For LLM agents, ethical guardrails, clear responsibility, and detailed logging already act as internal approval gates: if you cannot explain and replay agent decisions, risk and audit teams will block deployment. [8][9]
💡 Net effect: Pre-approval would centralize a burden many teams already feel informally and reactively. [8][9][12]
5. Strategic Guidance for Builders in a Pre-Approval Debate World
Regardless of what Congress or figures like Sam Altman decide, the prudent engineering assumption is that some mix of pre-approval and ex-post audit is coming for large models, high-risk domains, or government-facing systems. [7]
Build pipelines for scrutiny by default
Design your stack so an external reviewer could understand and audit it without heroics:
- Treat evals, logs, and change histories as primary artifacts, not byproducts
- Maintain clear system-boundary diagrams, agent policies, and guardrail test suites
- Align release gates with documented evaluation thresholds and “significant change” triggers
Conclusion: You are already operating in a de facto AI governance environment. A formal pre-approval regime would raise the bar and centralize oversight, but the core asks—traceability, risk evaluations, continuous monitoring, and explainable system design—are the same pressures that forward-leaning AI teams should be building for today.
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