OpenAI Proposes Industrial Policy for the AGI Era

Industrial Policy for the Intelligence Age, OpenAI, April 2026

Executive Summary

OpenAI has released a landmark 13-page policy white paper titled “Industrial Policy for the Intelligence Age: Ideas to Keep People First”, outlining a comprehensive, forward-looking framework for governing advanced AI development and deployment. The document marks a significant evolution from Sam Altman’s earlier “Intelligence Age” vision — transitioning from philosophical aspiration to concrete, actionable policy proposals.

Key highlights include:

Explicit acknowledgment that economic gains from AGI may concentrate within a small number of firms — including OpenAI itself.
A suite of bold, human-centered proposals: Public Wealth Fund, four-day workweek pilots, Right to AI, adaptive safety nets, and portable benefits.
Robust governance mechanisms: mission-aligned corporate governance, model-containment playbooks, AI incident reporting systems, and an AI trust stack with verifiable provenance.
Dual-track architecture: Part I focuses on building an open economy, while Part II establishes foundations for a resilient society.

📌 This is not a final blueprint — it’s an invitation to democratic co-creation.

Introduction: Toward Superintelligence

“The promise of superintelligence is extraordinary. Just as electricity transformed homes, the combustion engine remade mobility, and mass production lowered the cost of essential goods, superintelligence will speed up scientific and medical breakthroughs, significantly increase productivity, lower costs for families, and open entirely new forms of work, creativity, and entrepreneurship.”

OpenAI frames superintelligence not as distant science fiction, but as an already underway transition. Unlike past technological shifts, AGI’s pace and scale demand proactive, collective action — grounded in democracy, pluralism, and constitutional safeguards.

Three Foundational Principles

Principle	Core Commitment	Key Mechanisms
Share Prosperity Broadly	Ensure AI lifts all living standards — not just shareholders	Public Wealth Fund, tax modernization, efficiency dividends
Mitigate Risks	Safety must scale with capability — no exceptions	Model-containment playbooks, real-time auditing, AI trust stack
Democratize Access & Agency	AI participation ≠ access to frontier models only	Right to AI, portable benefits, AI-first entrepreneurship support

Part I: Building an Open Economy

🔹 Worker Perspectives

Formal collaboration channels between workers and management to co-design AI deployment.
Prioritize AI tools that eliminate dangerous, repetitive, or exhausting tasks — enabling focus on higher-value work.
Enforce strict limits on AI uses that erode autonomy, intensify workloads, or undermine fair scheduling/pay.

🔹 AI-First Entrepreneurs

Support domain experts in launching startups via AI-powered overhead reduction (accounting, marketing, procurement).
Pair microgrants/revenue-based financing with “startup-in-a-box” infrastructure: model contracts, shared back-office services.
Empower worker organizations to deliver training, negotiate fair terms, and protect IP.

🔹 Right to AI

“Treat access to AI as foundational for participation in the modern economy — like global literacy, electricity, or internet access.”
– Expand affordable, reliable access to foundational models.
– Provide free/low-cost access points, especially for underserved communities, schools, libraries, and small businesses.
– Invest in AI literacy, connectivity, and workforce training — ensuring no one is excluded from AI-driven opportunity.

🔹 Modernize the Tax Base

Shift toward capital-based revenues: higher top-tier capital gains taxes, targeted AI-driven return levies, and automated labor taxes.
Introduce wage-linked R&D-style credits to incentivize firms to retain, retrain, and invest in workers.
Preserve funding for Social Security, Medicaid, SNAP, and housing assistance amid shifting labor-income dynamics.

🔹 Public Wealth Fund

Establish a sovereign-style fund providing every citizen — regardless of financial-market participation — with direct equity in AI-driven growth.
Seed through public-private collaboration; invest in diversified assets capturing value across AI innovators and adopters.
Distribute returns directly to individuals — democratizing upside and decoupling prosperity from pre-existing wealth.

🔹 Accelerate Grid Expansion

Launch new public-private partnership models to finance and fast-track energy infrastructure for AI data centers.
Address permitting delays, siting risks, and financing constraints — delivering infrastructure at speed and scale.
Guarantee taxpayer risk minimization and household/business energy-cost reductions.

🔹 Efficiency Dividends

Convert AI-driven productivity gains into durable worker benefits:
Increased retirement matches/contributions
Expanded healthcare coverage
Subsidized childcare & eldercare
Incentivize time-bound 32-hour/four-day workweek pilots, with output/service levels held constant — then convert reclaimed hours into permanent shorter weeks or bankable paid time off.

🔹 Adaptive Safety Nets

Modernize unemployment insurance, SNAP, Medicare, Medicaid, and Social Security to be fully functional, accessible, and responsive during rapid transitions.
Deploy real-time AI impact metrics: unemployment rates, regional displacement indicators, job-quality dashboards.
Trigger automated, threshold-based expansions: e.g., wage insurance, fast cash assistance, training vouchers — scaling up with disruption and phasing out as stability returns.

🔹 Portable Benefits

Decouple healthcare, retirement savings, and skills training from employer ties.
Use portable accounts — attached to the individual, not the job — pooling contributions across employers, gigs, education, and entrepreneurship.
Modernize retirement via pooled structures allowing continuous accrual across career changes — eliminating benefit gaps and preserving long-term continuity.

🔹 Pathways into Human-Centered Work

Scale investment in the care and connection economy: childcare, eldercare, education, healthcare, community services.
Leverage AI to reduce administrative burdens and personalize care — while preserving irreplaceable human connection.
Build government-supported pipelines: training programs, wage subsidies, and quality-of-work standards — especially in chronically under-resourced sectors.
Complement with a family benefit recognizing caregiving as economically valuable work — compatible with part-time, retraining, or entrepreneurial activity.

🔹 Accelerate Scientific Discovery & Scale Benefits

Build a distributed network of AI-enabled laboratories, integrating AI directly into experimental workflows:
Automating routine processes
Capturing high-fidelity data
Enabling rapid hypothesis → testing iteration
Scale real-world translation via:
Upgraded physical infrastructure & implementation systems
AI-literate scientist/technician/operator training
Broad deployment across universities, community colleges, hospitals, and regional hubs — not elite institutions alone.

Part II: Building a Resilient Society

🔸 Safety Systems for Emerging Risks

Develop AI-native tools for threat modeling, red teaming, net assessments, and robustness testing — focused on cyber, bio, and systemic harm pathways.
Build complementary protective capacity: rapid medical countermeasure production, strategic stockpiles.
Catalyze competitive safety markets via procurement mandates, standards, insurance frameworks, and advance-purchase commitments.

🔸 AI Trust Stack

Enable secure, verifiable signatures for AI-generated content and agent actions.
Develop privacy-preserving logging & audit systems — supporting investigation/accountability without pervasive surveillance.
Clarify internal accountability: define delegation, monitoring, and escalation protocols as system capability grows.

🔸 Auditing Regimes

Strengthen institutions like the Center for AI Standards and Innovation (CAISI) to develop internationally aligned auditing standards.
Create scalable market for third-party auditors/evaluators using procurement, insurance, and standards-setting levers.
Apply pre- and post-deployment audits only to the narrowest set of highest-risk models — preserving ecosystem diversity and startup innovation.

🔸 Model-Containment Playbooks

Prepare coordinated response protocols for scenarios where dangerous AI systems cannot be recalled:
Weights publicly released
Developers unwilling/unable to restrict access
Autonomous self-replication
Draw lessons from cybersecurity and public health: even partial containment meaningfully reduces impact.

🔸 Mission-Aligned Corporate Governance

Require frontier AI firms to adopt Public Benefit Corporation (PBC) structures with legally embedded public-interest accountability.
Mandate long-term philanthropic commitments and transparent benefit-sharing mechanisms.
Harden systems against insider capture via weight security, manipulative-behavior audits, and high-risk deployment monitoring.

🔸 Guardrails for Government Use

Codify strict legal standards for reliability, alignment, and safety in governmental AI deployments.
Leverage AI to enhance democratic accountability: create auditable digital records of AI-assisted decision-making.
Empower inspectors general, courts, and congressional committees with AI-enabled auditing tools.
Modernize FOIA frameworks to enable AI-assisted public oversight — while protecting sensitive information.

🔸 Mechanisms for Public Input

Make AI alignment democratic, legible, and accountable:
Publish transparent model specifications & evaluation methodologies
Anchor standards in democratic law and values
Institutionalize representative public input alongside traditional stakeholders

🔸 Incident Reporting

Establish non-punitive, learning-oriented reporting to a designated public authority.
Include near-misses: cases where models exhibited concerning reasoning, unexpected capabilities, or warning signals — even when safeguards prevented harm.
Balance transparency with protection of technical, national security, and competitive information.

🔸 International Information-Sharing

Strengthen CAISI as a trusted global technical body for frontier AI evaluation.
Launch a global network of AI Institutes, collaborating via shared protocols for:
Joint evaluations
Alignment research exchange
Coordinated risk mitigation
Evolve into a multilateral safety institution — with cross-border crisis communication channels and antitrust-safe information sharing.

Conclusion: Starting the Conversation

“We offer these ideas not as fixed answers but as a starting point for a broader conversation about how to ensure that AI benefits everyone.”

OpenAI emphasizes this document is intentionally early, exploratory, and iterative. It calls for:

✅ Inclusive, ongoing dialogue — engaging governments, companies, civil society, researchers, and families
✅ Democratic processes that give people real power to shape their AI future
✅ Global expansion — centering diverse cultural, societal, and governmental perspectives

To accelerate progress, OpenAI is:
– Hosting feedback at newindustrialpolicy@openai.com
– Launching fellowships & research grants (up to $100,000 + $1M API credits)
– Convening policymakers and experts at the OpenAI Workshop in Washington, DC (May 2026)

Original Source: Industrial Policy for the Intelligence Age (PDF)