Impact and Compliance Guide: Artificial General Intelligence Development Moratorium - {Jurisdiction/Date}

Executive summary and key findings

Bottom line: There is no binding AGI moratorium; 1 U.S. federal proposal was removed in July 2025; 16 firms have voluntary pause pledges; immediate legal impact is 0% compute halted and $0 in mandated funding cuts.

This executive summary assesses the artificial general intelligence development moratorium landscape as of November 2025. No jurisdiction has enacted a legal development pause (0 worldwide), and the only prominent U.S. bid—a 10-year federal preemption of state AI laws—was removed in the Senate in July 2025 after clearing the House earlier in the year. Internationally, 16 companies from 10 countries have signed AI Safety Summit “Frontier AI” commitments to pause deployment if risks cannot be mitigated; these are voluntary and not binding law. Immediate legal impact on compute or funding is therefore 0% of capacity halted and $0 in mandated funding cuts; meanwhile, 12+ U.S. states continue to advance AI regulation that vendors must track.

Scope and affected entities: Any future AGI moratorium under discussion would target training and scaling of frontier models that meet specified risk thresholds, covering developers, model providers, cloud and chip vendors that supply covered compute, and publicly funded researchers using it. Immediate compliance deadlines: None specific to a moratorium today; monitor phased EU AI Act obligations through 2025–2026 and U.S./UK Safety Summit follow-ups. Near-term operational impacts: codify pause criteria, evaluation gates, and disclosure workflows so that deployment can be suspended quickly if internal or external triggers are met. Risk versus opportunity: while a moratorium could lower systemic risk, the absence of binding rules today enables proactive governance without forced downtime.

• Build an enterprise-wide moratorium playbook: asset inventory, decision rights, and an auditable pause switch tied to red-team thresholds.
• Align evaluations and documentation to EU AI Act GPAI/systemic-risk expectations and U.S./UK Safety Summit commitments.
• Add funding and capacity gates to 2025 plans (board-approved holdbacks, supplier clauses) that activate only if a moratorium is enacted.

Quantitative snapshot (as of Nov 2025)

Industry definition and scope: What the AGI development moratorium covers

A precise definition of an AGI development moratorium, with legal touchpoints, technical thresholds, covered and exempted activities, and jurisdictional reach for compliance mapping.

In AI regulation, an AGI development moratorium pauses specified activities tied to an AGI definition and objective technical triggers. For SEO clarity: AGI definition and development moratorium are framed across jurisdictions by differing legal texts and policy proposals. While binding statutes rarely define AGI explicitly, they operationalize scope via compute thresholds and capability triggers (e.g., multi‑domain autonomy, self‑improvement). This section delineates legal anchors (EU, US, UNESCO), technical metrics (FLOPs, parameter counts, dangerous capability tests), covered phases (development, training, testing, deployment), explicit exemptions (safety evaluation, low‑risk academic work), extraterritorial reach, and interaction with export controls and sanctions so that a product or research activity can be classified in scope or out of scope with justification.

Definitions used by major jurisdictions (AI regulation; AGI definition; development moratorium)

Binding texts seldom define AGI; they regulate proxy categories and triggers used by a moratorium.

Comparative definitions and triggers

Technical thresholds and triggers

• Compute: EU political texts have cited ≥10^25 training FLOPs for systemic-risk GPAI; US EO sets reporting at ≥10^26 training FLOPs.
• Model scale: Policy briefs often flag parameter counts above ~100B as presumptive frontier thresholds (non-binding).
• Capabilities: multi-domain autonomy, tool-use and orchestration, robust model self-improvement or code synthesis that elevates capability, bio/cyber dangerous capabilities, persuasive manipulation at scale.
• Operational signals: broad task generality, plug-in/tool ecosystems enabling domain transfer, autonomous agent frameworks.

Scope of covered and exempted activities

• Covered: pretraining, large-scale fine-tuning/RLHF/RLAIF that increases general capability; synthetic data scaling; integrated agentic tooling; deployment (API, on-prem, edge); weight release; significant evals that intentionally raise capability; provisioning of compute for qualifying training runs.

• Exempted (typical proposals): safety evaluations and red teaming without capability increase; alignment research; incident response; security patches; academic research below thresholds with safeguards; model cards and documentation work.

Jurisdictional reach, export controls, and sanctions

• EU: applies to providers/importers/distributors offering in the EU or whose outputs are used in the EU.
• US: EO-based reporting duties for US entities; export controls restrict provision of advanced AI chips, EDA tools, and certain services to listed countries/persons; proposed IaaS identity rules extend to foreign users training large models.
• Sanctions/IEEPA: OFAC/BIS restrictions can bar collaboration, compute, or model access for sanctioned parties.
• Interaction: a moratorium can incorporate export-control triggers (e.g., prohibiting training above thresholds for sanctioned jurisdictions) and require KYC for high-compute customers.

Annotated examples: in scope vs out of scope

Market size and growth projections: economic impact of an AGI moratorium

Baseline market size for AI remains on a steep CAGR trajectory through 2026, but an AGI moratorium would compress near-term demand for compute, R&D, and venture capital. We quantify conservative and severe scenarios to inform planning around AI regulation impact and compliance deadlines.

Moratorium scenarios with quantitative impacts (2025 unless noted)

Baseline market size and growth

Across major trackers, the AI market size is expanding rapidly. Gartner estimates total AI spending at $987.9B in 2024, $1.48T in 2025, and $2.02T in 2026, implying strong double-digit growth as AI’s share of IT spend rises from 19.6% (2024) to 27.2% (2025) and 34.1% (2026). IDC places AI-driven datacenter outlays at $692.1B in 2024 and $1.09T in 2025, approaching 40% of datacenter budgets by 2026 as GPU/TPU buildouts accelerate. Grand View Research sizes global AI software and services at $279.2B in 2024 and $390.9B in 2025, with a 31.5% CAGR to 2033. VC data (PitchBook, Crunchbase) shows >$50B deployed to AI in 2023, led by foundation-model rounds, with 2024 pacing higher.

Moratorium scenarios and quantitative adjustments

We model two AGI moratorium cases relative to the baseline: a conservative 12-month pause on training/deploying AGI-class systems with interim compliance deadlines, and a severe 24-month pause with tighter model thresholds and export controls. Assumptions draw on past regulatory shocks (e.g., GDPR-driven IT reprioritization, fintech/crypto crackdowns) and capex deferral behavior in uncertainty.

• Compute procurement: -18% (conservative) to -35% (severe) on AI datacenter spend as hyperscalers delay GPU clusters tied to frontier models; maintenance/MLOps for narrow AI continues.
• Total AI spending: -12% to -25% as software revenues slow, offset partly by governance tooling spend (+30% to +60%).
• VC funding: -30% to -55% as later-stage rounds and frontier labs face uncertainty; earlier-stage applied AI sees less compression.
• Recovery path: 2–4 quarters lag post-lift as backlogs clear; long-term CAGR resumes but from a lower base.

Industry knock-on effects and timing

Healthcare: slower rollouts of LLM-driven diagnostics and clinical documentation in the near term; compliance and validation vendors benefit. Finance: model risk, governance, and audit spend rises while GenAI copilots in front-office are gated; net employment shifts to compliance and controls. Defense: autonomy, perception, and edge AI (non-AGI) continue; decision-support LLMs in command-and-control pause, shifting funds to evaluation and red-teaming. Over 1–3 years, innovation rebounds as safety toolchains mature, but near-term revenue and hiring concentrate in assurance and compliance functions.

• Suggested charts: bar chart comparing baseline vs scenarios by segment; waterfall showing drivers from compute pullback, R&D reallocation, VC contraction, and compliance uplift.
• Key planning cue: align budgets to compliance deadlines to mitigate disruption while preserving optionality for post-moratorium acceleration.

Key players, market share, and stakeholder mapping

A moratorium on AGI development would reshape incentives for AI vendors, cloud platforms, chip suppliers, labs, and public-sector actors, concentrating compliance obligations where market share and compute intensity are highest.

An AGI development moratorium would concentrate risk and compliance costs on the largest AI vendors and their upstream infrastructure providers while advantaging cloud and chip incumbents with capacity, legal resources, and audit tooling. Cloud leaders control the bulk of compute: AWS holds 30–32% market share with an annual run rate near $124 billion; Microsoft Azure has 20–25% and roughly $120 billion; Google Cloud has 11–13% and about $54 billion (Synergy Research/Canalys, 2024–2025). Together, the Big Three supply about 68% of infrastructure capacity supporting frontier training, making them pivotal gatekeepers for compliance deadlines, logging, and access throttles.

On specialized hardware, NVIDIA maintains roughly 80–85% share of data center AI accelerators in 2024 (industry estimates, Omdia/Mercury Research), while AMD is scaling into the 10–20% range with MI300-series momentum. Supply allocation policies by these firms could effectively enforce or relax moratorium constraints. Frontier labs are highly exposed: OpenAI reported a $3B+ revenue run rate in late 2024; Anthropic is approaching a ~$1B run rate in 2025; DeepMind’s spend is embedded in Alphabet’s AI capex. These entities would face direct pause requirements and third-party audit duties.

Non-profits and universities (e.g., MIT, Stanford, Tsinghua) rely on cloud credits and shared clusters; they would encounter gating via provider policies but lower legal exposure. Governments (US, EU, UK) hold the highest influence via licensing and export rules, with the EU AI Act phasing compliance obligations through 2025–2026. Advocacy groups (e.g., Center for AI Safety, Algorithmic Justice League, EFF) will shape scope and exceptions. Incumbents can amortize compliance tooling and shift to productized APIs; startups may pivot to smaller models, synthetic data R&D, or partner-hosted training to manage compliance overhead and capital constraints.

Net effect: compliance raises fixed costs, advantages scaled clouds and GPU vendors, and pushes experimentation toward controlled sandboxes while preserving innovation via safe, auditable pathways.

1. 1) AWS — 30–32% cloud market share; ~$124B run rate (2024–2025). Highest exposure as primary compute gatekeeper; sources: Synergy/Canalys.
2. 2) Microsoft Azure — 20–25% share; ~$120B run rate. Strong policy leverage via enterprise controls; sources: Synergy/Canalys.
3. 3) Google Cloud — 11–13% share; ~$54B run rate. Fastest growth in AI workloads; sources: Synergy/Canalys.
4. 4) NVIDIA — ~80–85% AI accelerator share (2024). Export controls and allocation policies are critical; sources: Omdia/Mercury.
5. 5) AMD — ~10–20% AI accelerator share; MI300 ramp. Alternative supply for compliant capacity; sources: industry reports.
6. 6) OpenAI — $3B+ revenue run rate (late 2024). Highest direct frontier-training exposure; company disclosures/media.
7. 7) Anthropic — ~ $1B 2025 run rate. Dependent on partner clouds for compliance tooling; media/partner filings.
8. 8) DeepMind (Alphabet) — exposure embedded in Alphabet AI capex; aligns with Google Cloud controls; company filings.
9. 9) US/EU regulators — set licensing, export, and audit regimes; EU AI Act deadlines 2025–2026.
10. 10) Research universities/consortia — dependent on cloud credits and shared clusters; lower legal, high operational exposure.

Stakeholder mapping: exposure, influence, mitigation posture

Cloud: AWS, Microsoft Azure, Google Cloud

AI vendors relying on hyperscalers will face tightened provisioning, logs, and safety SLAs; moratorium compliance can be embedded at the tenancy and region levels.

Chips: NVIDIA, AMD

Allocation, export, and driver-level controls make GPU vendors de facto enforcement points; AMD’s share gains add competitive, compliant capacity.

Labs: OpenAI, DeepMind, Anthropic

Frontier labs bear direct pause risk; strategic responses include staged training, partnerships with compliant clouds, and domain-specific smaller models.

Governments, academia, advocacy groups

Regulators set compliance deadlines; universities and non-profits face operational gating via providers; advocacy groups influence carve-outs and audit norms.

Competitive dynamics and market forces under a moratorium

Analytical assessment of competitive dynamics, regulatory arbitrage, and AI governance effects of a moratorium, with indicators on startup funding, M&A, and compliance costs.

A time-bound moratorium acts as a regulatory shock that re-weights Porter-style forces toward incumbents with capital, compliance infrastructure, and distribution. Entry barriers rise as firms must finance AI governance functions (model evaluation, red-team testing, incident response, legal) before revenue. This tilts rivalry from speed-to-scale toward speed-to-compliance: the R&D race shifts to internal safety benchmarks and audit readiness. Incumbents convert fixed compliance outlays into strategic assets—embedding risk controls in procurement standards, bundling AI assurance with cloud and data services, and locking in partners via indemnities. Startups face higher cost of capital and delayed market testing, raising failure risk or forcing strategic sales; historical analogs in telecom and biotech show similar post-moratorium consolidation, though correlation should not be conflated with causation.

Scenario: during a 12-month moratorium on frontier model deployment, large incumbents keep training but gate external releases to pilots under sandbox rules. They invest $20–40M in audit pipelines and third-party certifications. When the pause lifts, enterprise buyers standardize on these certifications, creating compliance lock-in. Startups, unable to finance $3–10M annual governance run-rate, must partner on the incumbent’s platform or exit. Open-source dynamics partially counterbalance by enabling experimentation outside the frontier threshold, yet restrictions on weight releases can push projects to jurisdictions with lighter rules, intensifying regulatory arbitrage and geographic shifts in data centers and talent. Antitrust risk rises if a few firms set de facto compliance norms; regulators may need conduct remedies (data access, compute separation) to prevent oligopoly. Net effect: market concentration increases near the regulated frontier, while innovation migrates to open, smaller models and to regions optimizing permitting, energy, and audit regimes.

• Barriers to entry: upfront AI governance costs and certification delays favor well-capitalized incumbents.
• Rivalry and R&D race: competition pivots from model size to auditability, eval performance, and governance maturity.
• Supplier power: compute and specialized talent providers gain leverage; reserved capacity becomes a competitive moat.
• Substitutes/open-source: smaller, open models and retrieval pipelines substitute for frontier access during the pause.
• Buyer power and standards: enterprise procurement embeds compliance checklists, advantaging firms with accredited pipelines.
• Geographic arbitrage: firms shift training runs, data centers, and hiring to jurisdictions with clearer or lighter regimes.

Quantitative indicators: startups, M&A, and compliance costs

Porter-style view of competitive dynamics under an AI moratorium

Competitive dynamics pivot toward firms that can convert fixed governance costs into market standards. Expect fewer effective rivals at the frontier, greater reliance on platform partnerships for startups, and higher switching costs embedded in compliance tooling.

Regulatory arbitrage and AI governance implications

Divergent thresholds for what counts as a regulated model and differences in audit recognition will drive cross-border deployment strategies. Harmonized AI governance benchmarks and audit portability can mitigate a race-to-the-bottom while preserving contestability.

Technology trends, disruption, and technical thresholds

A technical overview of technology trends shaping AGI moratorium design, with measurable thresholds that trigger oversight, grounded in compute, model scale, benchmarks, and safety mitigations.

Technology trends most relevant to an AGI moratorium center on scaling, multimodality, emergent capabilities, and rapidly improving compute efficiency. Emergent capabilities refers to abrupt gains on complex tasks that appear as models cross certain scale thresholds; while some papers argue emergence can reflect measurement artifacts, regulators increasingly anchor oversight to objective proxies such as training FLOPs, parameter count, and standardized benchmarks. A notable case study is the 2023 US Executive Order 14110, which introduced reporting requirements tied to high-compute training runs (order 1e26 operations) and safety evaluations for dual-use systems after frontier models demonstrated strong performance on exams and coding tasks. This illustrates how technical thresholds map directly to regulatory triggers.

Concrete data points: contemporary large models commonly span 10–100B dense parameters or mixture-of-experts with similar active parameters; training compute for frontier systems ranges from roughly 1e24 to 1e26 FLOPs, with runs lasting weeks on clusters of 1,000–10,000+ accelerators. Estimated single-run costs for state-of-the-art models in 2024 are in the $10M–$100M range, depending on hardware, software stack, and data pipeline. Hardware roadmaps (e.g., H100/H200 to Blackwell-class GPUs and MI300-series to successors) imply 2–3x annual effective throughput gains via FP8/FP4 training, HBM3e capacity, and interconnect improvements. Algorithmic efficiency continues to compound (data curation, curriculum learning, MoE routing, and optimized attention), often yielding 3–10x lower compute for a given capability level across 1–3 years. Open-source releases (e.g., 7–70B-class weights) enable 1e22–1e23 FLOP fine-tunes that can rapidly close capability gaps.

Likely moratorium indicators combine: training-run compute (e.g., 1e24–1e26 FLOPs), parameter or active-parameter counts (50B–200B+), strong benchmark performance (e.g., high MMLU, GSM8K, HumanEval, ARC-C), and agentic features (autonomous tool use, code execution, web browsing, long-horizon planning). Regulators also track cluster size, energy use, and fine-tuning compute on released weights. Compliance actions scale with risk: pre-registration of large runs, mandatory red-teaming and reporting, audits for dangerous-capability evals, and deployment constraints on agentic integrations.

Mitigation technologies include differential privacy fine-tuning, structured red-teaming and adversarial evaluation, interpretability via sparse autoencoders and causal tracing, safety-relevant logging, and capability controls on tool access. To support discoverability and sourcing, use anchor text such as emergent capabilities arXiv 2023–2024 survey, compute scaling laws and FLOPs cost estimates, multimodal agents safety evaluations, and differential privacy for LLMs. This ties technology trends, emergent capabilities, and AI safety to measurable thresholds that guide moratorium triggers and compliance decisions.

• Measurable triggers to track: single-run training compute, parameter/active-parameter counts, benchmark thresholds, agentic feature flags, cluster size and accelerator-days, fine-tuning compute on released weights.
• Risky capabilities cited by regulators: autonomy in tool use and code execution, self-improvement loops, high-stakes decision-making with human-level or superhuman performance on domain benchmarks, and scalable content generation.

• Recommended anchor text: emergent capabilities arXiv survey
• Recommended anchor text: compute scaling laws and FLOPs cost estimates
• Recommended anchor text: multimodal agents safety evaluations
• Recommended anchor text: sparse autoencoders interpretability
• Recommended anchor text: differential privacy in LLM fine-tuning
• Recommended anchor text: red-teaming methodologies for foundation models

Example moratorium trigger indicators (illustrative)

Regulatory landscape overview and comparative analysis

Authoritative, comparative assessment of AI regulation relevant to an AGI development moratorium, with enforcement mapping, compliance deadlines, clause-cited sources, and a cross-border matrix to guide compliance officers.

Across major jurisdictions, no binding statute currently imposes an explicit AGI development moratorium, though one international instrument recommends time‑bound moratoriums in high‑risk contexts. Regulation (EU) 2024/1689 (EU AI Act) entered into force on August 1, 2024, with staged compliance deadlines: bans on unacceptable‑risk practices from February 2, 2025; obligations for general‑purpose AI and governance from August 2, 2025; most remaining duties by August 2, 2026. Article 5 AI Act enumerates prohibited practices; enforcement is through national competent authorities and the European Commission’s AI Office, with administrative fines up to 7% of global annual turnover for the most serious breaches. Citation: EU AI Act, Art. 5; OJ L, 12 July 2024; implementation schedule per Commission notices.

United States federal law contains no horizontal AI statute or moratorium; governance pivots on Executive Order 14110 (2023) directing testing, reporting, and safety standards (e.g., Sec. 4 on dual‑use foundation models) and OMB Memorandum M‑24‑10 (2024) imposing agency AI inventories and risk controls with phased compliance deadlines into 2025. Enforcement arises via existing authorities (FTC Act Sec. 5, sector agencies), not an AI‑specific penalty regime. The UK’s pro‑innovation framework relies on regulator guidance (DSIT Government Response, 2024) with the ICO, CMA, and others applying extant law; it contains no moratorium language. Canada’s Artificial Intelligence and Data Act (AIDA, Bill C‑27, Part 3) is proposed: the Minister of Innovation would oversee compliance, with administrative monetary penalties and offences carrying up to the greater of $25 million or 5% of global revenue; scope would reach systems made available in Canada. China’s Interim Measures for Generative AI Services (2023) are in force; oversight by the CAC and co‑regulators, with Article 17 referencing penalties under cybersecurity and data laws; no moratorium.

Internationally, UNESCO’s Recommendation on the Ethics of AI (2021) invites states to consider time‑bound moratoriums for high‑risk applications where necessary to protect rights, alongside risk‑proportionate regulation; the Council of Europe Framework Convention on AI (2024) establishes cooperation obligations but no moratorium. Statistics: 0 of 5 national jurisdictions surveyed enact explicit AGI development moratoriums; 3 of 5 assert express extraterritorial or market‑access reach (EU, Canada draft, China); 2 international instruments mapped provide structured cross‑border coordination commitments (UNESCO recommendation; Council of Europe AI Convention). For AI regulation planning, prioritize jurisdictional moratoriums surveillance, cross‑border data and model provisioning, and compliance deadlines. Internal linking recommended to EU, US, UK, Canada, China subsections for operational applicability.

Comparative matrix of jurisdictions with legal status and enforcement powers

Extraterritorial scope and cross-border coordination

EU AI Act applies to providers and deployers outside the EU when outputs are used in the Union, enabling market-access based extraterritoriality (AI regulation via product safety logic). Canada’s AIDA would similarly capture systems made available in Canada. China’s Generative AI Measures regulate services provided to users in China, irrespective of provider location. The Council of Europe AI Convention requires cross-border cooperation and mutual assistance once ratified and implemented. UNESCO’s Recommendation promotes international coordination and, where necessary, time‑bound moratoriums to manage systemic risk. Together, these instruments create heterogeneous jurisdictional moratoriums dynamics without a harmonized AGI pause mandate, underscoring the need to map data flows, model access, and service availability against compliance deadlines.

Jurisdictional moratoriums, timelines, and enforcement schedules

Use this compliance calendar to track confirmed and proposed compliance deadlines, regulatory reporting schedules, and phased enforcement by jurisdiction. No active U.S. federal AGI moratorium is in effect as of July 2025.

This section consolidates jurisdictional moratorium status, phased enforcement dates, grace periods, grandfathering clauses, and mandatory notification and regulatory reporting milestones. It is written as an action-ready calendar to build 90-day plans per country or city. Where items are proposed, they are labeled as such; only confirmed government or agency dates are listed as confirmed.

Gantt chart suggestion: Plot rows for EU AI Act (prohibitions, GPAI, high-risk), U.S. OMB M-24-10 (CAIO, inventory, annual reporting), NYC AEDT (audit cadence), China GenAI (effective, filings), and Colorado AI Act (effective date). Show start-end bars with milestones at 2024-08-01, 2025-02-01, 2025-08-01, 2026-02-01, and recurring annual reporting cycles. Add filters by country and timeframe for quick anchor navigation: EU 2024–2027, US Federal 2024–2025, NYC 2023–ongoing, China 2023–ongoing, Colorado 2024–2026.

Moratorium note: The U.S. Senate removed a proposed federal 10-year moratorium on state AI law enforcement in July 2025, so state and city rules continue to apply. Prioritize state/city compliance deadlines where applicable.

Confirmed and proposed compliance dates with sources

European Union — phased AI Act enforcement and transitional rules

Grace and grandfathering: prohibited uses have no grace period; GPAI obligations start 12 months after entry into force; high-risk obligations apply after 36 months. Transitional arrangements allow preparation via codes of practice and sandboxes before binding dates.

• 2024-07-12: AI Act published in the Official Journal (source: Council of the EU).
• 2024-08-01: Entry into force; start counting phased application windows.
• 2025-02-01: Prohibited practices enforceable; 2025-08-01: GPAI transparency starts; 2027-08-01: High-risk obligations start.

United States (Federal agencies) — OMB M-24-10 regulatory reporting

Grace and waivers: agencies may issue time-limited waivers with notification to OMB; maintain public AI inventories and risk assessments on a recurring basis.

• 2024-03-28: OMB M-24-10 issued; establishes governance controls and reporting cadence.
• 2024-05-27: Deadline to designate Chief AI Officer (60 days from issuance).
• 2024-12-01: Publish AI use-case inventory and risk information; annual updates thereafter.

NYC (USA) — AEDT bias audits and notices

Transitional status: initial grace period ended at enforcement. Ongoing: annual independent bias audit; 10 business days advance notice to candidates; publish audit summary.

• 2021-12-11: Law enacted (Local Law 144 of 2021).
• 2023-04-06: Final rules adopted by DCWP.
• 2023-07-05: Enforcement start; annual audits and notice obligations in effect.

China — Generative AI Measures and algorithm filing

Transitional and notifications: providers must complete security assessments and algorithm filings before launch; ongoing content moderation and incident reporting per CAC rules.

• 2023-07-13: Interim Measures for Generative AI Services released (CAC).
• 2023-08-15: Measures effective; filing and security assessment required before offering services.
• Post-2023-08-15: Continuous compliance with algorithm registration and safety responsibilities.

90-day action plan per jurisdiction

• EU: Map systems against prohibited, GPAI, and high-risk categories; implement GPAI transparency controls; schedule conformity assessment workstream to meet 2027 high-risk deadline; prepare incident reporting playbooks.
• US Federal: Confirm CAIO role; complete AI use-case inventory content and publication workflow; set quarterly regulatory reporting checkpoints; document waiver governance and 30-day OMB notification process.
• NYC: Engage independent auditor; schedule bias audit before next annual deadline; update candidate notices (10 business days), website disclosures, and data retention notices.
• China: Complete CAC algorithm filing package; conduct and document security assessment; update user T&Cs and content moderation processes; assign responsible contact for CAC inquiries.
• Colorado (planning ahead): Gap-assess developer/deployer duties under SB24-205; draft risk management and impact assessment templates; prepare consumer and regulator notification workflows before 2026-02-01.

Compliance obligations, reporting requirements, and enforcement mechanisms

Compliance playbook for an AGI moratorium outlining regulatory reporting, documentation standards, AI oversight expectations, and enforcement mechanisms. Use this to map internal controls to regulator expectations; seek legal counsel for jurisdiction-specific obligations.

This section summarizes expected compliance obligations under an AGI moratorium, drawing on patterns from the EU AI Act (2024), FTC AI-related orders, and data protection regimes (GDPR/CCPA). It is informational, not legal advice. Emphasis is on regulatory reporting AI, documentation standards, audit-readiness, and enforcement mechanisms so compliance officers can identify gaps and remediate.

Who must report and reporting cadence

Reporters: model developers, deployers/operators, compute providers, and data vendors supporting covered AGI-related activity. Cadence typically annual/biannual for plans and transparency, plus event-driven incident reporting within 24–72 hours.

• Event-driven: critical safety, security, or data incidents (24–72 hours).
• Periodic: transparency and risk-management updates (quarterly to annual).
• Pre-notice: frontier training runs exceeding compute/capability thresholds (e.g., regulator-defined FLOPs).

Mandatory reports and cadence

Documentation standards and evidence

Maintain contemporaneous, tamper-evident records sufficient for regulator verification. Evidentiary standards favor reproducibility, signed hashes, and traceable provenance.

• Risk assessment and safety case: hazard analysis, risk ratings, mitigations, residual risk acceptance.
• Computation logs: training/inference compute (FLOPs, PF-days), energy, hardware inventory, access logs.
• Data provenance: sources, licenses, collection dates, consent basis, lineage, and dataset snapshots/hashes.
• Model/system cards: capabilities, limitations, evaluations, guardrails, human-in-the-loop oversight.
• Safety tests: red-teaming, alignment evals, jailbreak resistance, misuse/dual-use assessments.
• Security controls: key management, isolation, SBOMs, dependency scans.
• Third-party audits: scope, methods, findings, remediation evidence (e.g., ISO/IEC 42001, SOC 2).
• Incident register: impact, root cause, corrective and preventive actions (CAPA).
• Retention: keep core records 5–7 years; incident and audit artifacts minimum 2–3 years.

Audit triggers and inspection protocol

Audits may be risk-based or for-cause. Expect document requests, supervised system access, interviews, and reproduction of key tests.

• Triggers: compute over regulator threshold, high-risk classification, material incident, failure to report, public complaints, or market-wide sweep.
• Protocol: evidence preservation hold, data room production, sampling of datasets, replay of evals, and third-party verifier engagement.

Enforcement mechanisms, penalties, and remediation

Non-compliance can lead to fines, suspensions, mandated deletions, and publication of orders. EU AI Act benchmarks: up to 7% of global turnover or €35M for severe violations; up to 3% or €15M for other breaches. GDPR analogs: up to 4% or €20M. FTC-style remedies may include algorithm/model deletion and disgorgement.

• Remedies: license suspension/revocation, product halts, daily penalties until cure, independent monitors.
• Appeals: administrative review (e.g., 30 days), then judicial review. Remediation via CAP with milestones, re-audit, and re-approval before restart.

Mock reporting checklist (fields)

• Entity ID and contact; regulator registration number
• Model ID, version/commit hash, release channel
• Risk class and intended use
• Compute consumed (FLOPs, PF-days), hardware profile
• Datasets and licenses; provenance hash and lineage link
• Safety tests performed, dates, pass/fail, scorecards
• Red-team findings and mitigations
• Security controls and SBOM reference
• Incident summary since last report
• Third-party auditor, scope, opinion, date
• Model/System card URL; governance approvals
• Signatories and accountability owners

Sample reporting timeline

1. T-10 business days: file training run pre-notice over compute threshold.
2. Day 0: incident occurs; preserve evidence.
3. Within 24 hours: preliminary incident notification.
4. Within 72 hours: detailed incident report and CAPA.
5. Monthly: submit compute and access logs summary.
6. Quarterly: transparency report and evaluation updates.
7. Within 15 days of substantial change: change notice.
8. Annually: third-party audit attestation and risk protocol refresh.

Impact on AI development and deployment strategies

A moratorium shifts AI governance from scale-first to compliance-first, reshaping R&D roadmaps, deployment pipelines, procurement timing, and vendor management under new compliance deadlines and contractual controls.

A moratorium will pivot roadmaps from capability expansion to risk reduction, with immediate emphasis on AI governance, auditability, and change control. Expect extensions to average R&D cycles (baseline 6–18 months) as programs re-sequence toward safety evaluations, documentation, and regulatory evidence. Deployment strategies under an AGI moratorium should introduce additional gates for model evaluation, traceability, and human-in-the-loop verification, prioritizing lower-risk analytics while pausing or re-scoping generative and regulated-domain use cases.

Procurement and contracts require rapid alignment. GPU lead times of 3–9 months heighten the need to freeze or amend purchase orders, secure flexible cloud GPU commitments, and add deferment rights. Vendor management must verify supply chain compliance (model provenance, training data rights, safety evaluation reports, export control assertions) and incorporate change-of-law and force majeure clauses with clear notice and mitigation timelines. Coordinate with counsel on contract interpretations; operationally, create a 30/60/90 day plan that sequences audits, re-scoping, and controlled restarts tied to compliance deadlines. See internal link: /compliance-playbook.

Operational benchmarks affected by the moratorium

Prioritized checklist (execute now)

1. Pause: freeze launches of high-risk or regulated-domain models; maintain essential maintenance only.
2. Audit: inventory active AI systems, data sources, and third-party dependencies; map to compliance deadlines.
3. Re-scope: shift capacity to lower-risk analytics and offline evaluations; define minimal viable compliant scope.
4. Procurement: freeze new GPU POs; add defer/delay rights; secure cloud GPU commitments with step-down options.
5. Contracts: activate change-control; prepare change-of-law and force majeure notices and mitigation plans.
6. Vendor management: obtain attestations on provenance, data rights, safety evaluations, and export control compliance.

Decision tree: pause or proceed

1. Is the use case regulated/high-risk (healthcare, finance, defense, biometric, generative)? If yes, Pause.
2. Does it require new data collection or personal data? If yes, Pause pending privacy and DPIA checks.
3. Are GPUs or external models critical and not contractually compliant? If yes, Pause or Limit scope.
4. Can the work proceed offline with synthetic/redacted data and safety evals? If yes, Limit scope.
5. Is it internal, low-risk analytics with existing controls? If yes, Continue with added audit trail.

30/60/90 day operational plan

1. Day 0–30: Issue moratorium guidance; classify projects; pause high-risk; conduct gap assessments; freeze GPU POs; notify key vendors.
2. Day 31–60: Re-scope pipelines; move to sandbox testing; amend contracts (change-of-law, reporting, audit rights); implement vendor compliance attestations.
3. Day 61–90: Restart approved pilots with limited scope; publish AI governance controls; track compliance deadlines; rebalance budget and capacity.

Contractual and procurement adjustments

Embed change-of-law clauses with 10–30 day notice, clear amendment processes, and cost/governance impact assessments. Include force majeure for regulatory moratoria with immediate notice and 5–10 business day mitigation plans. Add audit rights, model lineage documentation, and safety evaluation deliverables to SOWs. For procurement, incorporate deferment rights, allocation transparency, export-control assurances, and cloud fallbacks with elastic commitments to manage GPU risk. Strengthen vendor management by requiring provenance attestations, data rights warranties, SBOM or model BOM where available, and ongoing compliance reporting aligned to AI governance.

AI governance and risk management frameworks

A prescriptive guide to structure AI governance and risk management for moratorium compliance, grounded in NIST AI RMF 1.0, ISO/IEC 42001, ISO 23894, OECD AI Principles, and leading corporate models.

Design AI governance to satisfy a moratorium by integrating recognized frameworks and clear accountability. Use NIST AI RMF 1.0 (Govern, Map, Measure, Manage) to structure policy-to-control linkage; adopt ISO/IEC 42001 to implement an AI management system (AIMS) with auditability; apply ISO 23894 for risk processes; and align with OECD AI Principles for trustworthy AI. Anchor the model in a three-lines-of-defense structure with board oversight, management ownership, and independent assurance to ensure risk management and regulatory compliance.

Governance structure: The Board Risk Committee sets risk appetite, moratorium guardrails, and receives quarterly assurance. The Chief AI Officer (CAIO) is accountable for AI governance and the model registry, reporting to the CEO and BRC. First line (product owners, data stewards, security) designs and operates controls; second line (compliance/risk) challenges and monitors; third line (internal audit) provides independent reviews. Establish escalation thresholds: any AGI indicator trigger, severity-1 safety/security incident, compute or capability thresholds exceeded, or third-party finding rated high must be escalated to the CAIO within 24 hours and to the BRC within 72 hours.

Operationalization: Maintain a live model registry and a risk register capturing use case, inherent/residual risk, control set, owner, review date, data lineage, and AGI indicator status. Require pre-deployment safety cases, model cards/system cards, threat models, privacy and impact assessments, red-team reports, and eval results. Implement continuous monitoring (telemetry, drift, misuse signals), gated releases, change control, and incident runbooks. Detect in-scope AGI activity via capability probes (cross-domain generalization, tool-use autonomy, long-horizon planning), compute/scale thresholds, and red-team stress tests, with automatic workflow to suspend projects pending CAIO review during the moratorium.

Assurance and cadence: Integrate third-party audits (ISO/IEC 42001 certification readiness, NIST AI RMF conformance assessments, independent red-teams) pre-deployment for high-risk systems and annually thereafter. Training: role-based onboarding plus quarterly refreshers for operators and annual enterprise awareness. Track compliance KPIs and publish a quarterly scorecard to the BRC. Use the six-month plan below to achieve minimum viable AI governance and risk management aligned to AI governance frameworks, risk management AGI moratorium, and compliance KPIs.

Operational controls and documentation artifacts

• Controls: model inventory and registry, role-based access, data lineage, evaluation and test coverage, red-teaming, monitoring/alerting, change control, incident response, kill switch, vendor due diligence.
• Risk register fields: model ID, use case, stakeholder impact, risk taxonomy (safety, security, bias, privacy, resilience), inherent risk, controls, residual risk, owner, review date, AGI indicator score/status.
• Artifacts: safety case, model/system cards, threat model, data protection impact assessment, evaluation results, red-team reports, validation logs, deployment decision record, audit trail.

KPIs and dashboard

Track a concise set of outcome-oriented metrics to drive regulatory compliance and timely escalation.

Sample KPI dashboard

Roles and RACI

Define clear accountability using a three-lines-of-defense model and board-level oversight.

• Chief AI Officer: accountable for AI governance, registry, AGI indicators, reporting.
• Compliance Officer: policy owner, control monitoring, regulatory change management.
• Data Steward: data quality, lineage, retention and consent assurance.
• Product Owner: first-line control execution and safety cases.
• Security Lead: threat modeling, monitoring, incident response, red-team liaison.
• Internal Audit: independent assurance and third-party audit integration.
• Board Risk Committee: risk appetite, moratorium guardrails, oversight.

Governance RACI matrix

Six-month implementation plan

1. Month 1: Appoint CAIO and BRC mandate; adopt NIST AI RMF, ISO/IEC 42001 scope; publish AI policy and moratorium guardrails.
2. Month 2: Stand up model registry and risk taxonomy; define AGI indicators and compute/capability thresholds; establish escalation runbook.
3. Month 3: Baseline risk assessments for all models; create safety case and documentation templates; enable monitoring and logging.
4. Month 4: Launch red-team program for high-risk models; implement change control and kill-switch; deliver role-based training.
5. Month 5: Scope third-party audits; map controls to ISO/IEC 42001 and SOC-style criteria; remediate gaps.
6. Month 6: Go-live KPI dashboard and board reporting; conduct moratorium breach simulation; finalize audit schedule and continuous improvement plan.

Regulatory analytics, reporting workflows, and Sparkco automation opportunities

How Sparkco automation streamlines regulatory analytics and reporting under an AGI moratorium, with measurable efficiency gains, clear workflow design, and pragmatic integration points.

Under an AGI moratorium, organizations must evidence control over AI systems while adapting to evolving guidance. Sparkco automation brings regulatory reporting automation to the front line: ingesting multi-source evidence, mapping it to policy obligations, and generating audit-ready packages. The platform’s analytics dashboards and AI compliance tools surface gaps early, enforce workflow consistency, and maintain immutable audit trails—reducing manual overhead without sacrificing rigor.

Across Sparkco deployments and comparable RegTech case studies, teams report faster time-to-report, fewer defects, and lower compliance costs. Compliance managers can map a 6-step automated workflow (below) and estimate ROI using saved FTE hours, avoided penalties, and reduced remediation rework. A common before-and-after outcome: a model transparency report that once took 10 business days can be issued in 5, a 50% reduction enabled by automated ingestion, normalization, and templating.

1. Data sourcing: SIEM events, IAM entitlements, MLOps artifacts (model cards, training data lineage, computation logs), change tickets, vendor attestations, regulatory texts.
2. Ingestion: prebuilt connectors, APIs, secure file drop, and email capture with metadata extraction.
3. Normalization: schema mapping, deduplication, and policy tagging; optional PII redaction with role-based masking.
4. Regulatory mapping: obligation crosswalks to policies/controls; risk scoring and exception flags aligned to moratorium rules.
5. Evidence bundling: bind signed computation logs, approvals, test results, and lineage into tamper-evident evidence packs.
6. Reporting and distribution: auto-populated templates, workflow routing, e-signature, versioning, and immutable audit trails.

• Auto-populated templates: risk assessments, computation logs, DPIAs, incident and material change notices, model release checklists, control test summaries.

• Integration points: IAM (Okta, Azure AD) for entitlement evidence; SIEM (Splunk, Sentinel) for event feeds; MLOps (MLflow, Kubeflow, SageMaker) for lineage and metrics; ticketing (Jira, ServiceNow) for approvals; GRC suites (Archer, ServiceNow GRC) for policy/control synchronization.

• Implementation risks: data privacy leakage (mitigate with field-level masking and data minimization), false positives in control mapping (mitigate with confidence thresholds and reviewer checkpoints), change-management friction (mitigate with role-based task queues), regulator acceptance (mitigate with transparent mappings and exportable computation logs).

• Suggested anchor texts: Sparkco automation for AI compliance tools, Regulatory reporting automation playbook, MLOps-to-compliance pipeline blueprint, Audit-ready evidence bundling with Sparkco.

Efficiency metrics from Sparkco implementations and analogous RegTech case studies

Implementation roadmap, milestones, and compliance checklists

A prescriptive implementation roadmap and compliance checklist to operationalize an AGI development moratorium with 30/60/90/180-day milestones, resource estimates, KPIs, and contingencies for accelerated enforcement. Designed for rapid import into project management tools and jurisdictional tailoring.

Use this implementation roadmap to achieve regulatory readiness for an AGI moratorium. Sequence tasks to the critical path, assign named owners, and adapt timelines by firm size and jurisdiction. Integrate with ISO-style PDCA cycles, NIST AI RMF, and internal change control.

Prioritized step-by-step implementation roadmap

1. Days 0–15 (Critical path): Appoint executive sponsor and program manager; ratify moratorium policy, scope, and exception authority; freeze high-risk activities pending review.
2. Days 0–30 (Critical path): Perform initial risk inventory of models, datasets, compute, experiments, and vendors; create a system-of-record register with ownership and disposition.
3. Days 0–30: Documentation audit against ISO/IEC 27001, ISO/IEC 23894, and NIST AI RMF; map gaps; draft control objectives to enforce the moratorium.
4. Days 31–45 (Critical path): Process redesign—embed moratorium gates in change management, research approvals, procurement, and data access; define exception workflow and SLAs.
5. Days 31–60 (Critical path): Automation deployment—implement policy-as-code in CI/CD and MLOps; enforce compute quotas, job kill-switches, data egress blocks, and privileged access reviews.
6. Days 31–60: Training and comms—role-based training for R&D, IT, and vendors; obtain attestations; update onboarding/offboarding.
7. Days 61–90 (Critical path): Internal readiness assessment; assemble evidence library; remediate findings and verify closure.
8. Days 61–90: Third-party legal and technical pre-audit; adjust scope by jurisdiction; finalize audit plan.
9. Days 91–180: Continuous monitoring—dashboards, alerts, and metrics; perform internal audit; schedule external audit or certification.
10. Ongoing: Board and regulator reporting; quarterly scenario tests; refresh risk register; update controls for new laws or accelerated enforcement.

30/60/90/180-day milestones with owners

Resource and role estimates

Indicative resourcing for a mid-size organization; scale up or down based on scope and jurisdiction.

Resource estimates and notes

KPIs to measure readiness

• Inventory coverage: 100% of models, datasets, compute assets registered
• Unauthorized runs: 0 per month; time-to-kill runaway job under 5 minutes P95
• Access hygiene: 100% privileged access re-certified quarterly
• Exception SLA: 95% decisions within 3 business days
• Evidence freshness: 100% control evidence updated within 30 days
• Vendor attestations: 95% in-scope vendors signed to moratorium terms
• Audit findings: 90% closure within 30 days; no repeat findings next cycle

Contingency steps if enforcement accelerates

1. Activate emergency change freeze on AGI-adjacent experiments.
2. Raise compute thresholds to stricter defaults; require VP approval for exceptions.
3. Move to 24x7 monitoring for high-risk clusters; staff on-call rotation.
4. Engage external counsel for rapid legal interpretation and regulator liaison.
5. Advance external audit by 30–45 days; publish interim assurance letter.
6. Escalate board reporting to biweekly with KPI variance and risk heatmap.

Schema markup recommendation for timeline and checklists

For SEO and rich results, model the implementation roadmap and compliance checklist using schema.org types.

• Use HowTo with HowToSection and HowToStep for 30/60/90/180-day steps (properties: name, position, duration, startDate, supply, tool).
• Represent the checklist as ItemList (itemListElement of HowToStep or Action) with position and priority.
• Model owners with Role linking Person to Organization and actionResponsibility.
• Express KPIs as PropertyValue or QuantitativeValue with unitCode and value.
• Encode audits as Action or AssessAction with agent and resultReview.
• Expose schedule via Schedule (byDay, startDate, endDate) and link to the HowTo.

Sample 90-day plan with owners and deliverables

Future outlook, horizon scanning, and scenario planning

AGI moratorium policy trajectories over the next 1–5 years are uncertain. Using scenario planning and horizon scanning informed by think tanks and prior regulatory cycles, organizations can budget, monitor early indicators, and pre-position for risk and opportunity.

The future trends in AGI governance will likely oscillate between regulatory patchwork and periods of coordination, with the possibility of abrupt clampdowns after incidents. Drawing on horizon scanning from Brookings, RAND, and the Future of Life Institute, the scenarios below emphasize explicit assumptions, probabilities, and quantified implications so strategy teams can build contingency budgets and monitoring dashboards.

These are not predictions; they map plausible pathways to stress-test compliance roadmaps, vendor portfolios, and capital plans.

Scenario matrix 2025–2028

Assumptions are labeled; probabilities are indicative bands based on recent regulatory cycles and horizon scanning signals.

Probability vs impact with early-warning indicators

Early indicators and monitoring

Build a monthly dashboard combining policy velocity, safety metrics, and market signals to detect scenario drift early.

• Legislative velocity: count of enacted AI laws and budgeted regulators per region; key committee calendars.
• Standards adoption: ISO/IEC 42001 certificates issued; NIST AI RMF mappings referenced in policy.
• Safety incidents: reported severe AI incidents per quarter; liability insurance premium trends.
• Compute governance: export-control updates, licensing bills, GPU price and lead-time indices.
• Audit capacity: number of accredited AI auditors and red-team providers; assessment backlogs.
• Multilateral alignment: count of joint communiques/MoUs by INASI, G7, OECD, UN bodies.

Strategic moves for compliance teams and vendors

Actions balance risk and opportunity and are designed to be modular across scenarios.

• Scenario A: Maintain a living regulatory inventory; parameterize controls by jurisdiction; allocate 3–5% OpEx buffer; invest in policy-as-code and evidence automation.
• Scenario B: Fast-track unified certification; centralize model registries and cards; negotiate cross-border data-transfer and shared-audit rights with customers.
• Scenario C: Prepare moratorium-ready playbooks; pre-file compute disclosures; maintain fallbacks using small/on-prem models; shift roadmap to safety tooling and assurance services.

SEO keywords for long-form analysis

• future outlook AGI moratorium
• scenario planning AI regulation
• horizon scanning AGI policy
• future trends in AI governance
• AI compliance cost benchmarking
• global coordination vs regulatory patchwork
• AGI safety indicators and monitoring

Investment, M&A activity, and investor guidance

A moratorium on advanced AI/AGI would depress deal counts, bias value toward mega-deals, push valuation discounts via regulatory risk multipliers, and harden deal terms with conditional closings, earn-outs, escrows, and broader indemnities; investors should intensify regulatory diligence and target compliance enablers and distressed assets.

A moratorium on advanced AI/AGI would reprice investment and M&A by shifting risk from technology to regulatory execution. Refinitiv and PitchBook indicate AI/tech dominated 2023 with 27% of global deal value, while 2024 combined fewer overall transactions with more mega-deals: deals over $1B rose 17% even as total volumes fell 9%, and median EV/EBITDA in AI/tech stayed near decade lows. In a moratorium, expect deal counts to compress further (particularly sub-$1B), valuation dispersion to widen, and an explicit regulatory risk multiplier to enter models. Practical approach: apply a 5–20% discount rate increment or price haircut tied to (a) revenue share exposed to restricted training/inference, (b) concentration in sensitive sectors, and (c) dependency on constrained compute or cross-border data. This preserves option value while recognizing moratorium tail risk.

Deal structuring would further tighten. 2024 deal practice already favored contingent earn-outs and conditional closings in regulated verticals; high-profile discussions like Google–Wiz ($32B) highlighted regulatory pathways and conditionality. Under a moratorium, expect: broader regulatory condition precedents, MACs tied to formal pause triggers, larger escrows and indemnity caps for compliance liabilities, and stepped separability of restricted models. Diligence must elevate regulatory risk mapping, data/IP provenance, compute supply resilience, and safety/compliance history. Investors should build scenario trees (e.g., 6–18 month pause with/without grandfathering, jurisdictional asymmetry) and re-underwrite cash conversion, customer churn, and capex deferrals. Red flags include heavy revenue dependence on moratorium-restricted activities, opaque model lineage, non-compliant data pipelines, and fragile compute contracts. Opportunistic plays: distressed divestitures of model assets, compliance tooling (data provenance, model evaluation/assurance), compute-efficiency software, and providers of audit-ready MLOps. Coordinate closely with legal and financial counsel on regulatory risk allocation and cross-border issues to protect investment outcomes.

Impact on deal volumes, valuations, and deal structuring techniques (AI/Tech M&A 2022–2024)

Actionable due diligence checklist for AGI moratorium risk

• Regulatory mapping: identify moratorium definitions, scope thresholds (compute, capabilities), and jurisdictional exposure.
• Revenue sensitivity: % of ARR tied to restricted training/inference or safety-critical uses.
• IP and data provenance: rights to training data, model lineage, licensing encumbrances, audit trails.
• Safety and compliance: model cards, evals, red-teaming history, incident logs, regulator interactions.
• Compute resilience: contracts for GPUs/TPUs, reservation terms, export-control exposure, substitution options.
• Security and privacy: data localization, cross-border transfers, privacy DPIAs, third-party processor risk.
• Product roadmap: ability to pivot to smaller models or inference-only offerings during a pause.
• Contract review: key customer clauses (change-in-law, uptime SLAs), right to suspend restricted features.
• Financials: cash runway under paused revenue, capex deferral levers, covenant headroom.
• Governance: board-level AI risk oversight, compliance owner, escalation procedures.

Deal structuring levers and investor red flags

• Regulatory CPs tied to moratorium outcomes; tailored MACs referencing pause triggers.
• Escrows/holdbacks sized to remediation costs; indemnities for non-compliance and IP/data breaches.
• Earn-outs contingent on regulatory clearance or resumption of restricted activities.
• Price collars and ticking fees to share timing risk; RWI with regulatory exclusions addressed by specific indemnities.
• Red flags: >40% revenue from restricted uses, opaque data lineage, weak compute contracts, adverse regulator correspondence.

Opportunistic plays and SEO assets

• Opportunities: distressed model/data asset sales, compliance tech (provenance, evals, auditability), compute-efficiency and finetuning tools.
• Suggested blog titles: Navigating an AGI Moratorium: Investment and M&A Playbook; Pricing Regulatory Risk in AI Deals; Earn-Outs, Escrows, and AI Regulatory Risk.
• Suggested link anchors: investment M&A regulatory risk; AI moratorium diligence checklist; AI deal structuring techniques; AGI moratorium valuation multipliers.