OpenAI has released a policy document titled “Industrial Policy for the Intelligence Age: Ideas to Keep People First”, presenting early proposals to guide the transition toward advanced AI systems, including “superintelligence.”
The document explains that AI has evolved from handling simple tasks to completing work that takes hours, with future systems expected to manage “projects that take months.”
This shift is set to reshape how work is done, how knowledge is created, and how organizations operate. OpenAI stresses that navigating this transition requires a “democratic process” with broader public participation.
Why a new industrial policy is needed
The report highlights that past technological shifts created long-term benefits but also disruption that required new policies and institutions. AI is expected to follow a similar path, but at a faster scale.
Key concerns identified include:
- Job and industry disruption
- Concentration of wealth and power
- Misuse of advanced AI systems
- Limitations of existing policy tools
The document clearly states that “incremental policy updates won’t be enough,” calling for more comprehensive approaches.
Core principles for the AI transition
OpenAI outlines three central goals to guide the transition:
- Share prosperity broadly — AI should improve living standards, reduce costs, and expand access to essential services.
- Mitigate risks — addressing challenges such as job displacement, misuse, and system control, with the principle that “as capability scales, safety must scale with it.”
- Democratize access and agency — ensuring AI is affordable, accessible, and gives users meaningful control.
These principles form the foundation for the broader policy proposals.
Building an open AI economy
The first section focuses on creating an “open economy” where AI benefits are widely shared.
Workers are expected to play a direct role in shaping AI adoption. Their involvement can help ensure that AI removes “dangerous, repetitive, administrative” tasks while improving job quality rather than reducing autonomy. This approach connects productivity gains with better working conditions.
At the same time, AI is seen as a tool to expand entrepreneurship by reducing operational barriers. To support this shift, the document suggests:
- Microgrants and flexible financing models
- Shared tools such as contracts and back-office infrastructure
- Training and support through worker organizations
Another major proposal is the “Right to AI,” which treats access to AI as a basic requirement similar to electricity or internet access. This includes not just availability, but also affordability, infrastructure, and training.
The report also highlights structural economic changes. As AI shifts income patterns, tax systems may need to adapt through:
- Greater reliance on capital-based taxation
- Exploration of taxes linked to automation
- Incentives for companies to retain and retrain workers
To expand access to economic gains, the document proposes a “Public Wealth Fund,” allowing citizens to directly benefit from AI-driven growth through shared returns.
Infrastructure plays a critical role as well. AI systems require large-scale energy support, and the report emphasizes grid expansion and efficient deployment. It also notes that data centers should “pay their own way” while contributing to local economies.
Productivity gains from AI are expected to create “efficiency dividends.” These could take different forms:
- Reduced work hours, including 32-hour workweek pilots
- Expanded benefits such as healthcare and retirement contributions
- Structured “benefits bonuses” linked to performance gains
The report also focuses on strengthening economic stability through adaptive safety nets. These systems should respond dynamically to disruption, supported by portable benefits that follow individuals across jobs.
In addition, AI is expected to increase demand for human-centered roles in sectors such as healthcare, education, and caregiving. Policies aim to support transitions into these areas while improving job quality.
Finally, the document highlights the role of AI in accelerating scientific discovery. By integrating AI into research workflows, it can speed up experimentation and expand innovation across a broader set of institutions.
Building a resilient AI society
The second section focuses on managing risks and ensuring long-term resilience as AI systems become more capable.
The document outlines several emerging risks, including cybersecurity threats, biological misuse, and systems acting in ways that are misaligned with human intent. It emphasizes that resilience must extend beyond development into real-world deployment.
To address these challenges, the report proposes strengthening safety systems through:
- Advanced threat detection and testing
- Continuous monitoring and evaluation
- Preparedness systems for large-scale risks
A key concept introduced is the “AI trust stack,” which includes tools to verify AI-generated content, track system actions, and support accountability while preserving privacy.
The report also calls for structured auditing systems to evaluate high-risk AI models and establish consistent safety standards. These frameworks are expected to focus particularly on frontier systems with greater potential impact.
In scenarios where systems cannot be easily controlled, “model-containment playbooks” are suggested. These would enable coordinated responses, drawing on lessons from cybersecurity and public health.
Governance is addressed across both companies and governments. AI firms are encouraged to adopt public-interest models, while governments are expected to define clear rules for AI use, ensuring safety, transparency, and accountability.
Public participation is another core element. The document calls for “representative input processes” and greater transparency so that AI systems reflect broader societal values.
To improve oversight and learning, the report proposes structured reporting systems for:
- Incidents
- Misuse cases
- Near-misses
Finally, it emphasizes the importance of global coordination. Shared frameworks, evaluation systems, and international collaboration are seen as essential to managing AI risks effectively.
OpenAI’s next steps
To continue the discussion, OpenAI has outlined several initiatives:
- Feedback collection via newindustrialpolicy@openai.com
- Fellowships and research grants (up to $100,000)
- API credits support (up to $1 million)
- Policy discussions at OpenAI Workshop in Washington, DC (May)
These efforts are intended to expand participation and refine the ideas further.
Outlook
The document is presented as a “starting point for discussion,” emphasizing that the transition toward “superintelligence” is already underway. It highlights that decisions made today will shape outcomes “for decades to come.”
Rather than offering final solutions, the report calls for continued collaboration across governments, companies, and society. The goal is to ensure that AI development remains “open,” “resilient,” and aligned with human priorities as its capabilities continue to evolve.