Hi

Welcome (back) to The Prompt. We’re recapping the big updates of the week, what’s top of mind for us, and how everyday people are using our tools:

• Sam was in DC to preview the capabilities of our upcoming models.

• Thanks to efficiency gains, we were able to cut the prices of two of our models in the interest of making better AI accessible for more people.

• Reactions to our new voice model got us thinking about a future in which we’ll have a more natural, intuitive connection with the technology we use every day.

If this was forwarded to you and you find it helpful, make sure you’re signed up for the next issue.

[Perspective] The Value Frontier, Part 2

We’ve shared many thoughts recently about how AI models should be useful in the everyday world, including here in The Prompt and from our CFO Sarah Friar. For our new EOW issue, we’re bringing those ideas together.

We see the frontier increasingly as being about not just raw output, but how much useful work an AI model can do, how quickly and dependably it can do it, and what it costs. What we think of as useful intelligence per dollar.

GPT-5.6 shows what that looks like. We built it as a family for different points on the price-performance curve: Sol for the hardest problems, Terra for everyday work, and Luna for fast, high-volume workloads. Our new price reductions push that curve further, including an 80% cut for Luna and 20% for Terra, while Fast mode for Sol can deliver up to 2.5x faster performance.

This changes the economics of what people can build. Luna can now deliver performance comparable to models that were at the frontier just a year ago – at a fraction of the cost and many times the speed. On professional work measured by Agents’ Last Exam, Luna outperforms Fable 5 at an estimated cost per task nearly 99% lower.

But price alone isn’t value. A cheaper model that needs several attempts or substantial human correction can ultimately cost more than a more capable model that succeeds in one pass. The better measure is the full cost per successful outcome, and whether that outcome is dependable enough to become part of how work gets done.

The bigger story is how those economics are possible. Efficiency comes from improvements across the entire stack: models, infrastructure, inference, routing, caching, context management, and the systems that allow agents to use tools. Each improvement means getting more useful work from the same compute.

Increasingly, AI itself is accelerating those improvements. GPT-5.6 Sol has helped OpenAI engineers analyze production traffic, improve routing, optimize kernels and tune workloads. Together with broader engineering advances, work involving Sol contributed to a 20% reduction in end-to-end serving costs, while improvements to speculative decoding increased token-generation efficiency by more than 15%.

That creates a powerful flywheel: better intelligence produces greater efficiency >> greater efficiency makes intelligence cheaper and more abundant >> cheaper, more dependable intelligence can be used for more things. Each dollar and each unit of compute can accomplish more useful work.

This is the value frontier.

The next phase of AI won’t be defined by a benchmark score alone. The better questions are: Is AI completing work that matters? What does each successful task cost? Can people depend on the result? And does each AI dollar produce more value as usage grows?

The goal isn’t simply better AI. It’s making better AI useful and affordable for everyone.

[Product] Voice: the next form factor

So much of how we use technology today still requires us to stop what we’re doing, pull out a device, open an app, and type to a screen.

Voice changes all of that.

We’re biased, of course, but what interests us most in the reactions to our new voice model is the feedback about both how good it sounds and how natural it feels – how you can interrupt it, go back and forth, or change direction mid-thought, and it keeps up with you. Much less like using a piece of software, much more like just talking.

Voice can make AI fit more naturally into the way we live and communicate, rather than asking us to make our behavior fit the technology. We’re so used to hands on keyboards and eyes on screens that we don’t even think about how much of our day we spend that way — or how much we can’t do while we’re doing it.

That’s part of what makes voice so interesting. When you don’t need to hold a phone or type, you can use AI while your hands are doing something else: cooking dinner, fixing something around the house, working in a lab, stocking a shelf, tending a garden, sketching an idea, or caring for a child. A mechanic could talk through a repair without putting down a tool. A nurse could get information while moving through a hospital. A small business owner could work through tomorrow’s schedule while closing up for the night. The interface no longer has to compete with the task.

Liberating people from the keyboard-and-screen interface can make AI useful to a much broader group of people across a much broader expanse of their day, including people who would never think of themselves as power users today. It also changes where technology can be useful: not just at a desk or with a phone in your hand, but alongside you as you move through the physical world.

As the models get better and the interaction gets more natural, the interface will start to fade. You’re not thinking about prompting or operating a product. Instead, you’re just asking a question, thinking out loud, or getting help with whatever you’re doing.

That’s a pretty profound shift. The way we’ll interface with technology in the future will be more natural and intuitive – allowing us to focus on everything else.

[Weekly wrap] A team of agents for every worker

The next phase of AI will be defined not only by what a model can do on its own, but by what people can accomplish with teams of AI agents working on their behalf. That was the message that our CEO Sam Altman, Chief Strategy Officer Jason Kwon, and Noam Brown, one of our top researchers, brought to meetings this week on Capitol Hill, at the White House and with the executive branch, and with top economists.

Sam, Jason and Noam previewed OpenAI’s newest models to show how workers across a wide variety of fields and professions can oversee teams of agents that are working together on a larger assignment. With the economists, our Chief Economist Ronnie Chatterji noted that some of the most prolific users of ChatGPT Work inside OpenAI are on the non-technical teams.

Reactions to our presentations were similar: audiences were excited by what the AI advances mean for science and mathematics, but also demanded that the technology benefit business owners, workers, and people in their day to day.

We expect AI to give people greater reach and agency, helping them pursue ideas and projects that have required more time or resources than they’ve had, while making human judgment, creativity, and direction even more valuable. The opportunity is to give individual workers, small businesses and entrepreneurs the capabilities of an entire team, helping more people bring their ideas to life.

Also this past week:

• Accelerating scientific discovery with ChatGPT for Academic Researchers: We launched ChatGPT for Academic Researchers, providing free frontier-model access to 100,000 scientists, mathematicians, and engineers at selected institutions through 2027. The initiative begins with 10,000 researchers this summer and is part of a bigger commitment of over $250 million to support external scientific research and discovery.

• How AI is expanding what people do at work: Our first Work at the Frontier report finds that 43.5% of occupation-specific ChatGPT messages involve tasks traditionally associated with a different occupation, suggesting AI is already helping people take on work that once required a specialist hand-off.

• Use cases we love: ChatGPT is helping you search for apartments, complete school paperwork for your children, and manage grocery lists. Also, meet the pastor in Washington, DC who is using ChatGPT to brainstorm his sermons and make Scripture more accessible to his young congregants, as well as the young couple Kiley and Cole who used ChatGPT to gain clarity – and a more hopeful prognosis – from Kiley’s biopsy records.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• How AI will be assessed in its next phase – by value

• What the latest polling tells us about US views on AI

• Boston Children’s Hospital doctors are using AI to search for answers to rare medical mysteries

If you find them helpful, make sure you’re signed up for the next issue.

[Insight] The value frontier

Since the development of generative AI, there’s been a lot of discussion about the “best” AI models. But the key indicators right now aren’t solely about performance.

The latest model releases show how competitive the AI space is today. Independent evaluators like Artificial Analysis and DeepSWE track models’ cutting-edge capabilities, but also scrutinize cost-per-task, coding-agent performance, execution time, and token use. The frontier of AI development isn’t just about a static exam score – it’s how well a model can do real work and at what cost.

OpenAI’s recently released GPT-5.6 is a good example of where AI developments are heading, with three tiers of capabilities. That approach reflects an important reality: there’s no single “best” model for every job. The best model is the one that delivers the right level of power, latency, reliability, and cost for the task at hand.

On Artificial Analysis’s benchmark, GPT-5.6’s highest tier is the best performing coding agent. But it also does this with greater efficiency – at an estimated 33% lower cost than Claude Fable 5. That is exactly the kind of comparison that matters for developers and businesses: not just, “can it solve the problem?” but, “can it solve the problem well, repeatedly, and economically?”

This is the core of OpenAI’s approach. We want to push the frontier of AI capabilities, but we also want to push the frontier of value. A model that’s more capable but too expensive or too slow will only be useful in narrow settings. A model that’s capable and efficient can be used everywhere: in startups, small businesses, homes, hospitals, classrooms, large enterprises, public services, and developer workflows that run thousands or millions of times.

This efficiency comes from several layers working together.

It comes from infrastructure: better systems for training and serving models, more efficient inference, stronger networking, and the ability to serve high-volume workloads on the right compute. We’re investing in a large-scale compute portfolio – we’re on track to meet our Stargate Project goals ahead of schedule – and are managing this wisely: frontier models use premium infrastructure when capability matters most, while high-volume workloads can run on lower-cost infrastructure where efficiency matters more.

Efficiency also comes from smarter models: tiered models like GPT-5.6 let users choose the right price-performance point instead of forcing every task through the largest model. A coding agent handling a complex project may need advanced capabilities. A product workflow processing thousands of routine tasks may be better served by a simpler model. Many real business systems will use several models together.

Efficiency also comes from better algorithms and inference techniques. Over time, AI progress isn’t just about adding more compute. It’s also about learning how to get more out of the compute we have. We’ve made huge investments – and advances – in training methods, routing, reasoning controls, token efficiency, improved context handling, and systems that reduce unnecessary work.

Taken together, these factors underscore that the latest model race isn’t a simple performance benchmark contest. The leading AI models will combine all the elements people actually care about: the ability to answer questions, solve problems, speed, cost, reliability, and utility in real-world workflows.

The next phase of AI will be defined by this combination: frontier performance and practical economics. AI that is powerful, fast, affordable, and available where people need it. That’s what turns AI from a breakthrough into a tool that benefits everyone. – Adam Cohen, Head of Economic Policy

[Data] Sentiment on AI ticks upward

Our latest AI Sentiment Tracker shows a slight uptick in general opinions about AI – going from negative-15 points in June to negative-5 now. The movement is due, in part, to a Fox News poll released late last week showing 45% of registered voters with a favorable view of AI, versus 54% with an unfavorable view.

In the Fox poll, Republicans, conservatives, men, and those with graduate degrees were disproportionately more positive about the technology, while Democrats, liberals, women, and those without college degrees were disproportionately more negative.

The same Fox survey also finds a supermajority of voters – 70% – opposing the building of an AI data center in their area, compared with 30% favoring it.

Speaking of data centers, our own research finds $19 million spent on local broadcast and cable TV ads referencing data centers in this midterm season, with $32 million overall spent on AI-themed ads. AI has become a campaign issue in more than half of the country – 36 states plus DC.

[Forum] Solving medical mysteries with AI

How can AI help physicians solve some of medicine’s hardest cases?

Join the OpenAI Forum this week for a conversation with researchers from Boston Children’s Hospital on how OpenAI o3 Deep Research is helping reanalyze previously unsolved rare pediatric disease cases. The discussion will explore how AI can accelerate scientific discovery, surface promising new leads for expert review, and give more families a path toward answers.

3:15 PM – 4:00 PM EDT on July 30

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

A lot happened in AI this past week, even as weeks go in AI. Next week, our CEO Sam Altman will be in DC for a range of meetings. Here’s our take on it all. If this was forwarded to you and you find it helpful, make sure you’re signed up for the next issue.

[Perspective] The week that was, and the week that will be

AI capabilities continue to advance faster than predictions of a near-term plateau. Those advances are colliding with geopolitics, US national security, and a more fundamental question: How should America measure the economic value of what AI can do?

That debate is unfolding as the Trump Administration prepares a voluntary framework under its June 2 Executive Order on secure frontier-model deployment. The framework will help determine which frontier models are reviewed, how they are tested, and which trusted partners will receive early access. Its objective is not simply to manage risk. It is to put powerful, defensible systems into responsible hands quickly, particularly as open-weight models make advanced capabilities available to bad actors as well as entrepreneurs.

That makes the framework more than a technical standard – it could become America’s first de facto national standard for frontier AI and, eventually, a diplomatic asset. Governments and businesses in allied countries will want access to models that have passed a credible review process. Washington therefore has an opportunity to shape a global AI ecosystem around American technology and democratic values, rather than ceding that ground to China.

The economic stakes are just as high. As OpenAI CFO Sarah Friar recently laid out, the right questions about AI are becoming increasingly concrete:

1. Is AI completing work that matters?

2. What does a successful task cost?

3. Can users depend on the result?

4. Does every dollar spent on AI create more value as usage grows?

Those questions are becoming ever more urgent as agents gain the ability to work together on long-running tasks. Teams of AI systems could operate while people sleep, coordinate across disciplines and pursue problems continuously – all changing the basic math of productivity in science, health, education, software and other knowledge-intensive fields.

That’s the context for our CEO Sam Altman’s meetings next week with Administration officials, lawmakers, and economists on both the right and the left. His preview of OpenAI’s next model family will inevitably attract attention for its new capabilities. But the more consequential demonstration will be economic: how much reliable knowledge and useful work can AI produce per dollar?

Washington should pay close attention. The answer will shape not only the next generation of products, but America’s AI policy, alliances and global leadership.

[Policy] Reverse federalism gains Mass momentum

Massachusetts is poised to become the fourth major state to align around shared principles for frontier AI governance, building momentum toward a coherent and durable national framework.

California, New York, Illinois, and now Massachusetts are converging around the core elements needed to govern the most capable AI systems: documented safety frameworks and risk assessments, public disclosure and incident reporting, and independent, objective audits. Together, these states represent nearly a quarter of the country’s population and are showing how state governments can move in the same direction while the Trump Administration works with seriousness of purpose toward a national standard.

This past week, OpenAI endorsed the Massachusetts bill, S. 3178, and is engaging with policymakers to strengthen it by adding a requirement for independent audits modeled on Illinois SB 315, signed into law earlier this year. Massachusetts marks another step in the march toward reverse federalism, with states aligning on the essential elements of frontier safety rather than creating a patchwork of conflicting rules.

Reverse federalism does not mean closing the door on new policy ideas that advance frontier safety and that can become part of an eventual federal framework.

State action can help us reach that goal by establishing a common baseline that federal policymakers can build upon, rather than a patchwork of state laws that put US companies at a disadvantage against China, which is working to erase America’s technological edge through a coordinated national strategy, national standards, and a well-resourced plan for deployment and adoption. The open-source Kimi 3 model underscores the importance and urgency of converging around a coherent approach that gives governments the tools needed to evaluate new models and manage their risks.

Fragmentation could also undermine national security. The US needs a clearly defined process for reviewing advanced models and making their capabilities available responsibly and rapidly to cyber defenders like government agencies, critical-infrastructure operators, and trusted partners in allied countries. Conflicting state regimes would create uncertainty and delay where speed and clarity are essential. That would be a prescription for chaos, not coherence.

The stakes extend beyond our borders. The US cannot lead the development of global AI standards without first establishing a credible national framework of its own. Whether the eventual international model resembles cooperation through the G7 or another forum, American leadership abroad must be built on coherent standards at home.

Coherence beats chaos. By aligning state action with a durable federal framework, the United States can advance safety and innovation together, preserve its technological leadership, equip defenders with the tools they need, and ensure that the rules governing the most powerful AI systems are shaped by elected governments, not by frontier AI companies alone.

[Weekly wrap] This week at OpenAI

• Building compute, and community, in Georgia: Our new 3.2 GW Effingham County project pairs a major expansion of U.S. AI infrastructure with $80 million for local priorities, thousands of jobs, and a commitment that Georgia families won’t foot the bill.

• AI for everyone, everywhere: This week’s earlier Prompt traces how the AI story is shifting from what the technology can do to what people are already doing with it—from building businesses to navigating health care, careers, taxes, and everyday problems.

• Your health, with more context: Health in ChatGPT can now securely connect medical records and Apple Health, helping people make sense of their own information, spot changes, and prepare better questions for their doctors.

• AI goes to the newsroom: From AP verification tools to searchable public meetings and decades of local archives, news organizations are putting AI to work to help journalists find stories faster and make trusted reporting more useful.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Today, OpenAI announced our investment in generating another 3.2 GW of compute as part of our commitment to making AI accessible to as many people as possible. Making AI accessible for many, not just a few, means being able to count on sufficient compute. To help illustrate the meaning of this additional compute and democratized AI, we’re devoting a special edition of The Prompt to a look at how people already are using our tools to solve problems and bring ideas to life.

If you find this helpful, make sure you’re signed up for the next issue.

AI’s shift from capability to utility

There’s an AI story that’s even more important than what the technology can do.

It’s what people are doing with it.

Around the world, about 1 billion people use ChatGPT every week across 180 countries and territories. They’re not spending their time running benchmark tests or debating artificial general intelligence. They’re turning ideas into businesses, learning new skills, navigating big systems like health insurance or taxes, prepping for job interviews, building software, and solving problems for themselves and others.

That shift, from capability to utility, is the clearest signal yet of where AI is headed.

General-purpose technologies all tend to follow a similar trajectory. Their biggest breakthroughs eventually stop being the story. Electricity ceased being remarkable as it became available almost everywhere. The internet became transformative because billions of people used it every day.

AI seems to be entering a similar phase.

Of the billion or so users of OpenAI’s tools every week, more than half are women. More earn less than $100,000 than more than $100,000. Users span every major age group; we have more adult users both under and over the age of 30 than any other frontier lab. This user base, together with our investments in the infrastructure needed to generate the compute that powers AI, is our springboard toward making AI a global utility.

We believe that like electricity, AI should be available for people, businesses and institutions to use as much as they need, where and when they need it. This is where Americans are, too. They believe that:

• AI should be free and accessible, instead of kept in the hands of a few (62% to 38%).

• The best tools should be made available to everyone, not just big companies (66% to 34%).

• Free and accessible AI is the way to make AI safer for everyone (65%, vs. 35% believing AI should stay in the hands of a few).

Our job is to make that AI cheaper, better, and more abundant over time so that more people can use it to build, solve problems, and expand what they’re able to do. That’s what’s driving our Texas, Michigan, Georgia and future Stargate sites within and outside the US.

Matthew Rosenberg

ChatGPT helped Matthew Rosenberg and his wife launch Bangkok Rush Thai Kitchen with fewer costly missteps. It broke down market research, pricing, and unit economics so they could see whether a lease made sense. When it did not, it mapped an alternative path: running legally from a home kitchen, with a clear checklist for permits, inspections, and compliance. After launch, ChatGPT kept operations tidy by organizing tasks, drafting customer replies, and supporting marketing plans and margin tracking.

Navigating the system

AI as a utility can transform how people deal with systems, institutions, and actors against which they feel disadvantaged. According to our privacy-preserving analysis, in a recent given week:

• More than 7 million messages sought help navigating the US health insurance system and healthcare costs.

• Globally, 80 million messages sought help with identifying possible scams.

• Nearly half a billion messages on taxes/finances.

Nearly 600 million messages about careers – for advice on how to stand out in an increasingly difficult job market, and for help with negotiating compensation/titles/levels, promotions, career switching, and interpreting performance and management feedback. One in six of these messages were sent in the US.

Jaxsen Day

Jaxsen Day, a doctoral student at the University of Texas at Austin, uses ChatGPT to reduce the extra labor that visual impairment can impose on academic research. Before ChatGPT, he had to hunt through inaccessible databases, run OCR on PDFs, and fix broken reading order before a screen reader could make material usable. Now custom GPTs can find a paper from a citation, summarize it by section, and let him question the text by voice, saving hours or days while supporting his accessibility research.

Creating more hours and scaling ability

By making light and power constantly available, electricity gave people more hours in the day and scaled their ability to produce, create and learn. Our tools give people more hours in their day by helping them get things done faster and without requiring their active participation. Codex automations are allowing people to schedule recurring workflows, with some 150,000 users creating new automations each week. Codex is also giving people scaled ability, helping them multitask more efficiently, with about half of users running multiple tasks in tandem each week.

As this graphic shows, ChatGPT is still used more during daylight hours; check out the time of highest volume where you live. But we also see juxtapositions of people using ChatGPT for information when professional services aren’t available. For example, in a given week, seven out of 10 health-related messages in the US were sent outside of clinic hours (after hours on weekdays and during weekends).

User message activity vs. even hourly baseline

But we also see juxtapositions of people using ChatGPT for information when professional services aren’t available. For example, in a given week, 110 million messages ask health questions outside of clinic hours and on weekends – that’s the vast majority of all health-related questions put to ChatGPT. And we see users deploying Codex during evening hours or overnight to tackle projects while they’re doing something else, like sleeping.

Dr Margie Albers

In rural Montana, Dr. Marjorie Albers uses OpenAI-powered tools to protect time for patients and reduce after-hours paperwork. An AI scribe drafts visit notes from the conversation, which she reviews and edits before filing. That cuts documentation time and makes follow-up instructions clearer. She also uses AI to quickly check clinical references and guideline details when a case is unusual. The result is more attention during care and less administrative drag outside the clinic.

100M ‘how do…’ and ‘how to…’

Also in a given week, over 100 million messages start with “How do I,” or “How to…” – and that’s just messages in English. Useful technology doesn’t have to announce itself as historic; it just starts helping. And a single conversation can cover what used to require many searches, scattered tabs, lost time and confusion. Starting a small business is tough; in March 2026, at least 4 million Americans used ChatGPT for help with starting, running, or planning a business.

Kaija Pack

Kaija Pack uses ChatGPT to run and grow Break Life Houston, a family-operated business with limited time and staff. It helps with practical tasks that usually sit on the owner’s shoulders: researching competitors, drafting clear answers to pricing questions, and refining messaging that explains what customers receive for the price. ChatGPT also produces outreach and customer-communication drafts quickly, which supports efforts to fill slower weekdays and keep operations steady while marketing and sales continue to move.

AI for everyone, everywhere

The AI story is no longer primarily about AI itself — it’s about what people are doing with it. Every week, hundreds of millions of people turn to AI to navigate complex systems, learn new skills, solve practical problems, build businesses, advance their careers, and reclaim time in their day.

That is what utilities do. They become part of the background infrastructure of everyday life: available when needed, helping people accomplish things that would otherwise be harder, slower, or out of reach. Our task is to keep making AI more capable, more affordable, and more accessible, so that its benefits reach everyone, not just a fortunate few.

Alison Leeds

Designer and breast-cancer survivor Allison Leeds used ChatGPT to turn scattered recovery experiences into a structured resource for patients. She analyzed thousands of public stories and paired them with research on color and emotion to design a set of weekly art cards timed to recovery milestones. ChatGPT helped synthesize themes, organize the content, and keep the product coherent while she managed treatment and surgery. The same workflow supported building a small business that offers patients and caregivers a practical way to navigate the emotional side of healing.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• Our own red-teaming over recent months finds Chinese frontier models much more willing than GPT-5.5 xhigh to help with dangerous requests

• US Census-projected startup creation has spiked, hitting a record high for June

• We’re partnering with Latino public leaders to expand AI literacy

• Giving Florida parents skills to lead their families through the AI age

If you find them helpful, make sure you’re signed up for the next issue. Also…

General purpose technologies don’t come along too often. When they do, societies must decide who holds power (the many or the few?), and who benefits (the many or the few?). Chris Lehane’s new personal Substack, Toward a Common Sector, will look at past general purpose technologies, how they were adopted, regulated, and their benefits distributed, for lessons in how we can best set up the many – not just a few – for success with AI. Check out his first post.

[News] Chinese models trail US rivals on AI safety

Safety standards applied by Chinese AI labs are less stringent, per OpenAI’s own testing, and less transparent by many measures compared to those of leading US models across harm areas, including chemical, biological, radiological, and nuclear (CBRN) queries.

First is the issue of safety transparency. Researchers with Concordia AI last year surveyed 13 Chinese frontier AI labs and found among them that nine had published technical model cards, but only three had dedicated AI safety sections, and none disclosed evaluation results for CBRN or loss-of-control risks. US labs routinely release detailed model system cards with safety information (such as this one for GPT-5.6).

There’s also a gap in the public information available from independent safety evaluations of US and Chinese models. For example, OpenAI works closely with the US Center for AI Standards (CAISI) and Innovation and the UK AI Safety Institute (AISI) to conduct pre-deployment safety evaluations, and in turn these organizations share aggregate findings of their testing of models with the public.

Our understanding of how Chinese labs interact with independent, third-party evaluators is much murkier. We do know that Chinese labs are required by law to submit LLMs for evaluation to a process convened by the Cyberspace Administration of China (CAC). China’s rules require public-facing generative-AI services to “uphold socialist core values” and prohibit outputs that threaten “national unity and social stability.” These requirements give Chinese safety work a different center of gravity, prioritizing political and content compliance where leading US labs’ public frameworks emphasize dangerous capabilities such as cyber, biological and chemical risks, and AI self-improvement.

We conduct comprehensive red-team testing of our own models, and have in recent months tested leading Chinese models to understand how they respond to the same series of prompts. Across the tasks we examined, each of the five Chinese models produced substantially more responses that could meaningfully assist harmful behavior than GPT‑5.5 xhigh. Our tests found several Chinese models assisting with equipment acquisition for chemical-weapons, aerosol-device design, and experimental workflows involving dangerous pathogens. Some also helped identify and monitor student organizers, deanonymize anti-government accounts, and map their associates. Across both categories, GPT-5.5 xhigh consistently refused or safely redirected the requests.

Independent testing has confirmed the safety gap between Chinese and US models. DeepSeek, Kimi and Qwen were more susceptible to agent hijacking than OpenAI models in a recent joint exercise, including attacks intended to exfiltrate data or induce other harmful actions. Earlier CAISI testing similarly found that DeepSeek models were more readily jailbroken and manipulated into acting against their users’ interests. Additional independent testing found that Kimi K2.5 was much less likely than leading US models to refuse high-risk CBRN-related requests.

AI safety and security is core to how OpenAI approaches the training and deployment of models. As our models become more capable across a range of benchmarks, we continue to invest in a layered defense against potential misuses of our tools. Ultimately, consumers and businesses will choose which models serve their AI needs best. But those choices should be informed by an understanding of the relative safety of models, and robust third-party assessments based on internationally agreed-upon standards and transparent safety benchmarking can provide a clearer basis for comparison. – OpenAI’s Intelligence and Investigations Team

[Policy] Our roadmap for AI policy

Frontier AI safety is one of the six pillars of OpenAI’s new Public Policy Agenda website, which launches today. The site brings together our latest thinking on the policies we believe will help maximize AI’s benefits while managing its risks.

As part of the launch, Chris Lehane updates on progress toward a national framework for frontier AI safety via the approach we call reverse federalism, with states helping build a common foundation for a future US (and possibly global) framework for frontier AI safety.

Alongside frontier safety, the site also highlights our work on youth safety, education and AI literacy, workforce and economic transition, deepfakes and content provenance, and AI infrastructure and energy.

[Data] The AI-fueled startup surge continues

Three years after ChatGPT’s launch, we are seeing further indicators that AI is behind a surge in new business growth. We recently covered how AI is lowering the barriers to entrepreneurship by making research, coding, design, marketing, customer support, and countless other capabilities accessible to anyone with an idea. New government data points in the same direction.

While overall US business applications remain on pace for another record year, the latest Census Bureau figures (via the St. Louis Fed) show that projected business formations in professional services – one of the sectors most immediately transformed by AI – have climbed to an all-time high. As the chart shows, projected new firms have accelerated sharply, reaching 5,290 in June, the highest level on record.

No single chart proves AI is driving this trend. But taken together with record business applications, the rise of AI-native solo founders, and growing evidence that AI is dramatically reducing the cost of starting a company, the pattern is hard to ignore. More people appear willing to bet on themselves because the gap between “I have an idea” and “I can build it” has never been smaller.

[Partnership] Building civic leadership in the AI age

Today, OpenAI and the NALEO Educational Fund announced the NALEO x OpenAI Civic AI Leadership Initiative, a multi-year, NALEO-led partnership to help Latino elected and appointed officials understand, govern, and responsibly use AI in service of their communities.

The initiative will combine OpenAI’s technical expertise with NALEO’s nationwide network of public leaders to expand AI literacy through training, educational resources, and leadership programming. The collaboration will also support bilingual guidance for public officials and create opportunities to share best practices as AI becomes an increasingly important tool for delivering public services and strengthening civic institutions.

At its core, this partnership is about agency. It will help ensure Latino public leaders have the practical knowledge and trusted resources to determine where AI can strengthen public service, where safeguards are needed, and how communities should shape the choices ahead.

[Event] Bringing AI literacy to Florida parents

Over the next two months, OpenAI is hosting AI skills trainings for K-12 educators, students, and parents across the country. One of our most recent stops was at the 2026 Florida Parent Teacher Association (PTA) Leadership Convention in Orlando, where we met hundreds of parents, educators, and administrators eager to learn more about AI’s ability to support their communities.

For Jude Bruno, the new president of Florida PTA, giving his members access to an emerging, frontier technology was the motivating factor behind the partnership.

“It’s very important for us to make sure that our parents, who are probably leading these conversations or learning about these things, have an opportunity to interact, to learn, and to get hands-on experience [with AI].”

The event allowed attendees to explore how ChatGPT could connect them with other tools, help them create materials for school events, and support communication across language barriers. The most meaningful moments involved helping people recognize new possibilities for their needs

One such person was Ursula Castillo, who manages her PTA’s social media. During the event she built a website for her school’s PTA. Ursula acknowledged that, prior to this training, she used AI tools only for “basic commands.” But she learned how to build a website using Codex.

“To build a website, it took first months, then weeks, then days,” she said. “But we just built this website in 20 minutes.”

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• The strongest signal yet that AI is becoming a utility

• Our new 5.6 models are built for more people and more use cases

• AI’s growing role in disaster response

If you find them helpful, make sure you’re signed up for the next issue.

[Insight] Beyond the bubble debate

AI developments are driving a familiar skepticism. Is this a bubble? Is massive infrastructure investment crowding out more useful economic activity? Can leading AI labs build sustainable businesses?

These are important questions. Large and rapid technology shifts deserve scrutiny, especially when they require capital, energy, talent, and public trust. But we should be careful to separate the uncertainty around AI as an asset class from the real value it’s already creating.

The strongest signal that AI is more than a novelty isn’t a forecast or a valuation – it’s growing usage. More than a billion people have adopted AI tools over the last few years, including about 1 billion who use ChatGPT, simply because they’re useful. People are using AI to write, learn, code, analyze, translate, plan, and create. They’re using it to start businesses, understand medical diagnoses, navigate complex legal and financial situations, manage their busy lives, and complete tasks that previously would have required far more time, money, or specialized support.

That kind of repeated use is fundamentally different from a curiosity or passing fad. People continue to integrate AI into the way they learn, work, and live because they grasp its value. And they see how AI increasingly can do more and more for them – people who were casual ChatGPT users two years ago are now “power” users, moving from experimentation and simple tasks to take full advantage of our latest models.

That evolution is shaping how we build. Today, we publicly released our new 5.6 Sol, Terra, and Luna models to make more capable AI available to more people and more use cases. It’s another step toward the same goal: expanding what people can do, not simply advancing what AI can do.

Only two years ago, many critics argued that generative AI was little more than a clever word game. They aren’t saying that today. AI capabilities have improved quickly: models have become better at reasoning, coding, multimodal understanding, tool use, and longer-horizon work. The frontier is still imperfect, but it’s clearly advancing. Dismissing this progress as hype risks missing what is already happening around us.

The same is true of infrastructure investment. Some investments will be wrong. Some companies will fail. Not every AI application will justify its capital cost. That’s how major technology cycles work. But that doesn’t mean the buildout is wasteful. If AI becomes a general-purpose technology used across education, healthcare, science, business, and government, then data centers, chips, and deployment, these infrastructure investments are fundamental. They are the critical scaffolding for the next phase of our economy.

None of this means we should be complacent. We need to prepare for changes in work and jobs. We need to reduce environmental impacts, strengthen cybersecurity, and make sure the benefits of AI are broadly shared.

The right posture isn’t blind optimism. But neither is it reflexive dismissal. Much of the current public debate whipsaws between “AI is a bubble” and “AI is changing everything faster than society can manage.” The more productive path is steadier. We should separate durable value from extreme skepticism and hyperbole, and build the institutions, safeguards, and new businesses that help us see the real opportunity that lies before us. – Adam Cohen, Head of Economic Policy

[Event] AI for disaster response

Disaster response often comes down to speed: how fast can help reach people in need? Fast action depends on consolidated, verified information that decision-makers can trust. The challenge isn’t a lack of data; it’s turning thousands of fragmented updates into something useful before time runs out.

That’s where AI can help. At OpenAI’s recent Builder Lab in Bangkok, disaster-management professionals from 13 countries across South and Southeast Asia worked alongside local builders, DataKind volunteers, and OpenAI staff to create AI-powered workflows for real-world emergencies.

The event built on an AI literacy program we held earlier in the year. In the months that followed, conversations with participating agencies revealed a common pain point: information overload. Critical updates arrive through spreadsheets, PDFs, WhatsApp messages, field reports, and handwritten notes. One participant estimated spending six hours every day for months compiling situation reports by hand.

So instead of simply teaching AI, teams spent the week building. Participants developed tools to automate some of the most time-consuming parts of disaster response:

• a flash-flood risk map for Thailand’s Department of Water Resources

• a flood early-warning dashboard for Myanmar

• and a prototype from Pakistan’s EHSAR that maps household-level needs from SMS, voice calls, and field reports.

The gathering also reinforced an important lesson about building trustworthy AI. For humanitarian teams, accuracy matters as much as speed. Participants learned to define workflows, identify where human review belongs, validate critical data, and test outputs against real operational needs.

It was a glimpse of how AI can help disaster responders spend less time wrestling with broken information systems – and more time helping the communities that depend on them. – Alex Nawar, OpenAI Academy

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2:15 PM – 3:00 PM EDT on Jul 9

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[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• Our Jalapeño chip – the newest add to our stack

• China vs. the US on hearts and minds

• Our AI Sentiment Tracker ticks slightly more negative

• Huge ChatGPT prompt action for the World Cup

If you find them helpful, make sure you’re signed up for the next issue.

[News] The newest add to our stack

Making advanced AI broadly available requires building the full stack. Accessibility doesn’t happen by accident – it depends on everything beneath the model: the chips, systems, and infrastructure that makes AI faster, more reliable, more efficient, and available to more people.

That’s why OpenAI is building Jalapeño, our first-generation in-house chip, designed to improve LLM inference – the work required to run AI models for people and businesses once they are deployed.

As AI becomes a daily tool for learning, work, creativity, coding, research, and customer support, the infrastructure challenge grows accordingly. Every faster answer, smoother interaction, and more reliable product depends on moving computation closer to the needs of frontier AI systems.

Jalapeño is key to our long-term strategy to design more of the AI stack ourselves. Building frontier AI requires more than world-class models. It requires leadership across the technology stack – from chips to data centers to software. By optimizing hardware around the workloads that matter most for frontier models, we can make better use of each watt, chip, and data center.

For users, more efficient infrastructure can make a personal ChatGPT assistant faster and more responsive. It can help businesses deploy AI more reliably. And it can support the broader goal of making advanced AI more widely accessible instead of keeping it constrained by cost and capacity.

Every improvement in efficiency is ultimately an improvement in access. Infrastructure is not separate from democratizing AI – it’s one of the main ways in which democratization happens.

That matters for safety, too. More efficient infrastructure does not simply expand usage. It gives more people the opportunity to build familiarity with increasingly capable AI systems, allowing society to adapt alongside the technology rather than after it. Broad access helps build resilience.

Jalapeño also points toward another shift: AI is increasingly helping build the infrastructure that powers AI itself. We used our own tools to help design the chip and begin writing kernels needed to operationalize it. That could become a preview of a larger shift in the technology industry, where hardware and software development cycles become faster, more integrated, and more AI-assisted.

Jalapeño is more than a new chip. It is another step toward building the infrastructure that can make advanced AI faster, more reliable, more efficient, and more widely available. The future of AI will not be determined by breakthroughs in models alone. It will also depend on whether we can build the systems that allow those breakthroughs to reach everyone.

[Data] Winning minds before markets?

Many of the world’s most populous countries consider China, not the United States, to be the leader on AI development, according to a recent Public First survey. That includes Indonesia (#4 in population), Nigeria (#6), Brazil (#7) and Mexico (#10). China itself (#2) wasn’t included in the survey but presumably would also agree. The only top-10 most populous countries surveyed that see the US leading China on AI are India (#1) and the US itself (#3). (The other three, Pakistan, Bangladesh and Russia, were not included in the survey.)

While the survey measures perceived leadership, not actual adoption, it’s nevertheless a sobering data point for US policymakers and others who believe – as OpenAI does – that the best future with AI is one built on democratic rails. Not only should the most people possible have access, but their access shouldn’t come with strings of surveillance, censorship, or anti-competitive practices attached, or with attempts to control the benefits to cement power for the state.

It’s particularly sobering alongside trends at home in the US. For one, corporate America and US-based developers are more open than ever to using open-source Chinese models.

In addition, sentiment about AI is significantly more negative in the US than much of the rest of the world, tempering the drive to build. Just 31% of American adults say AI is making society better, compared with 40% who say it is making society worse – a rounded net -9 score. Together with Canada (-8), the US is the most skeptical country surveyed. In contrast, Nigeria (+78), Vietnam (+73), India (+56), Brazil (+45), and Mexico (+44) all register strongly positive views of AI’s impact on society.

“We may well be in the early stages of a global divide on AI that sees America hosting a technology that the rest of the world is more excited to use,” says Seb Wride, Public First’s head of opinion research.

That's a sharp reversal from prior Public First surveys, when Americans were modestly positive about AI (+3 in 2024 and +5 in 2025). We plugged in the Public First numbers for the US into the AI Sentiment Tracker we built with Codex. The result: the new data sent our smoothed average down slightly, from Negative-13 two weeks ago to nearly Negative-15 now.

We’ll continue to update our tracker as public polling on AI becomes available.

[Prompt] A World Cup powered by prompts

The World Cup has gotten off to a strong start, including on ChatGPT. All 10 of the days with the greatest number of ChatGPT prompts about sports have occurred since the World Cup began on June 11.

Interest is especially intense around match time, per our privacy-preserving analysis. We see a more-than-doubling in prompts as each country approaches the kick-off time of their first match. And as the tournament goes on, we’ve seen increasing interest in group tie-breakers like goal difference, advancement and elimination, and the round of 32.

How is your favorite team currently stacking up? Try it out for yourself.

[About] OpenAI Forum

Explore Forum programming by and for our community of approximately 75,000 AI experts and enthusiasts from across tech, science, medicine, education, government, and other fields.

2:15 PM – 3:00 PM EST on Jun 25

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you. If you find them helpful, make sure you’re signed up for the next issue.

This edition of The Prompt is another personal essay from OpenAI Chief Global Affairs Officer Chris Lehane (first essay here) based on his discussions at this week’s G7 meeting.

[Policy] Resilience is a safety strategy

The debate over AI is being framed as a choice between speed and caution: move fast to unlock enormous benefits, or slow down to minimize risks.

That framing misses a fundamental point: that one of the most important safety strategies is resilience.

As AI systems become more capable, the question is not whether people will have to adapt. They will. The real question is whether they have the opportunity to adapt alongside the technology itself, making their own judgments based on direct experience using the tools.

This is where democracies have an advantage. Democracy can be messy, but it distributes agency. It gives people the power to shape change, not just be affected by it. Broad participation is not a byproduct of resilience – it’s how resilience is built.

That’s why resilience is increasingly central to OpenAI’s approach to AI. For years, discussions about AI safety have focused primarily on technical safeguards: evaluations, red-teaming, testing, and risk mitigation. These measures remain essential. Powerful systems must be rigorously evaluated, deployed responsibly, governed thoughtfully.

But safety is a societal challenge, not just a technical one. Resilience is how societies navigate profound change: through exposure, trial, learning, and adaptation. Sam Altman believes that advanced AI should be introduced iteratively so that society and technology can advance together. As he recently wrote, one reason why OpenAI launches products “early and often” is to give “society and the technology time to co-evolve.”

This isn’t just a product philosophy. The more people who can access AI tools to more fully understand the benefits as well as the challenges, the more opportunities society has to build the muscles needed to navigate the profound changes AI will bring:

• Workers can learn which tasks AI enhances and which still require uniquely human judgment.

• Teachers can assess how students can use AI productively while preserving critical thinking.

• Parents can develop norms around healthy use.

• Businesses can redesign workflows.

• Governments can identify gaps in existing policies.

• Researchers can uncover risks and develop mitigations.

None of this learning happens in the abstract – it happens through use. The emergence of the automobile illustrates the point. Cars reshaped commerce, mobility, and daily life long before society settled on the rules that govern them today. We developed those rules through experience: creating driver’s licenses and speed limits, redesigning roads and intersections, introducing seat belts and insurance requirements, and continuously improving safety as both cars and society evolved.

History offers few examples of societies building resilience by sheltering from transformative technologies until every question has been answered, but it does offer notable examples to the contrary. Imperial China adopted printing centuries before 15th-century Europe, but its strong, centralized state deployed printing to standardize Confucian texts and promote officially sanctioned knowledge. Whereas Europe’s messy, perhaps, but ultimately agency-distributing system of competing states, churches and cities enabled the spread of ideas, making the printing press a linchpin of religious upheaval, scientific exchange, and political change.

AI demands a similar capacity for adaptation.

This doesn’t mean recklessness. OpenAI has long argued that the most capable systems require increasing levels of evaluation, safeguards, and oversight. The frontier of AI development demands serious attention to risks of misuse and catastrophic scenarios.

But another danger deserves equal attention: brittleness. A society in which only a handful of companies and institutions understand advanced AI is a brittle society. In particular, democracies that outsource their collective judgment about transformative technologies to a few private actors become brittle democracies.

Our collective experience with social media is a cautionary example. A few private companies came to shape the information environments in which democratic societies operate. Governments and institutions struggled to keep up, and societies found themselves debating the rules only after the technologies had already transformed civic life.

As frontier systems become more capable, every AI lab will make consequential decisions about deployment, safeguards, acceptable uses, and risk tolerance. Those decisions will matter enormously – but they will be private judgments.

The issue is not whether particular companies are acting in good faith. Most are. The question is whether the future of a foundational technology should depend so much on the worldview of any one company.

America is celebrating 250 years of independence based on the profound belief that democracy is the best way to deliver on life, liberty and the pursuit of happiness. The genius of our operating system for realizing these values into concrete realities, the US Constitution, is based on translating competing interests into public frameworks through institutions, standards, independent evaluation, accountability, and public trust. Not because companies or their leaders lack principles, but because no single company’s principles or individual leader’s beliefs should determine how transformative technologies reshape society. Public policy, not private policy.

The result may seem counterintuitive: broad access to AI can itself enhance safety. The more freely accessible these technologies are, within appropriate baseline guardrails, the more people gain firsthand experience with their capabilities and limitations. The more people and institutions incorporate them into daily life, the better they become at identifying risks, developing norms, correcting mistakes, and adapting to change.

In other words, widespread use does not merely distribute benefits – it distributes preparedness, and it distributes agency.

The alternative is a world in which advanced AI is only deeply understood by a narrow set of experts and executives while everyone else faces its consequences after decisions have already been made. That may reduce exposure in the near term, but risks leaving societies less capable of governing the technology over the long term.

OpenAI’s position reflects deeper confidence in people. Recent experience suggests that people adapt more effectively than we often expect. Predictions of AI-driven, immediate labor-market collapse have not materialized in the ways many predicted. Instead, people have experimented, learned new tools, created new businesses, redesigned jobs, and discovered entirely new uses for these systems.

The lesson isn’t that disruption will be painless – significant transitions still lie ahead – but that human agency matters. The future should preserve meaningful human involvement: people exercising judgment, setting goals, applying values, and deciding what’s worth pursuing.

That’s why resilience belongs alongside innovation and safety as a defining principle of the Intelligence Age. The societies that thrive during periods of technological transformation are neither those that halt change nor those that embrace it blindly. They are the ones that trust people and build institutions to learn and adapt as quickly as the world around them changes.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help:

• May data shows new business formation up 17% YOY

• We built an AI sentiment tracker and guess what…

• A peek at an election coverage tool debuting at our next OpenAI News Academy

If you find this helpful, make sure you’re signed up for the next issue.

[Insight] AI’s lift for small businesses

Advanced AI tools are making it easier than ever before to launch a new business. Entrepreneurs are quickly adopting AI for capabilities they previously would have struggled to afford without significant venture capital: research, writing, coding, design, customer support, market analysis, product specifications, and basic finance, all of which are among ChatGPT’s most popular capabilities.

The latest US numbers point to a large AI lift. Census data show 5.65 million business applications in 2025, up from just over 5 million in 2022, the year ChatGPT launched. The latest monthly release, for May 2026, recorded over half a million applications, up 17% from May 2025 and on pace for over 6 million new applications in the course of this year. Not all of these applications lead to new businesses, and not all of these businesses will succeed, but the sheer increase in new entrepreneurial activity – which coincides with three full years of ChatGPT, wider AI adoption, and ever-increasing AI capabilities – is worth a closer look.

AI tools appear to be helping sustain a much higher level of experimentation and risk-taking. They lower the cost of trying to start a business: writing a plan, testing a market thesis, building a website, creating a pitch deck, drafting ad copy, answering basic regulatory questions, or prototyping software. For many people, the gap between, “I have an idea” and, “I can test this” has become much, much smaller.

The pattern seems to be true in other countries, too. A recent study of Chinese firm registrations also found that, after ChatGPT’s launch, places with more AI talent saw a sharp rise in new firm creation, driven entirely by small firms. The European Union shows a similar, if less, dramatic rise. Eurostat’s business registration index for the EU rose to 108.6 in 2025 from 100.3 in 2022, suggesting registrations were about 8% higher over the period. This is an index rather than a raw count, so isn’t directly comparable to US business applications, but it points in the same direction: new business formation has increased since ChatGPT’s launch.

These new firms are also starting smaller and staying smaller than before. Stripe data for new “C” corporations shows that so far, in the second quarter of this year, 63% of these new businesses were solo-founded, an all-time high. Stripe found that these solo start-ups were increasingly “AI-native,” with core products that depend on advanced AI models, and that these founders are now much more competitive with larger, better-staffed startups.

All told, this has interesting implications for jobs and how our economy evolves with AI. People increasingly seem willing to take a risk and start something new – potentially leaving a salaried job at a larger company to do so. It’s certainly easier to take this leap than it was just a few years ago.

There also might be fewer early hires per startup or later hiring as firms attempt to scale and succeed. A founder who can use AI for coding, design, support, sales copy, analytics, and operations may not need to build a traditional team as quickly. Europe’s business demography data points in the same direction: the average newly born EU enterprise in 2023 had just 1.06 people employed. So the future of work may involve more tiny firms, more solo founders, and more businesses that begin with one person plus highly capable AI tools.

That doesn’t necessarily mean fewer jobs overall. It means work may show up in different places: specialist contractors, AI implementation experts, customer-success teams, compliance support, infrastructure providers, and later-stage hires once the best companies start to scale. The big question is whether AI mainly helps companies do the same work with fewer people, or whether it creates enough new companies and markets to expand work overall. The optimistic case is that AI widens the startup funnel: more people can try, more ideas can reach customers, and the firms that break through can create new kinds of work around smaller core teams. – Adam Cohen, Head of Economic Policy

[Data] Via Codex: Tracking America’s views on AI

This week, we’re launching a new AI Sentiment Tracker built with an apropos research assistant: Codex.

The assignment was straightforward: apply publicly available polling from Gallup, Ipsos, Pew Research Center, Quinnipiac, NBC News, and other reputable survey firms to create a single directional benchmark for how Americans feel about AI over time.

The challenge is that AI polling is abundant, but often difficult to compare. Many surveys focus on the intersection of AI and issues such as jobs, elections, misinformation, national security, creativity, or education. Those polls are valuable, but don’t necessarily measure broad public sentiment toward AI itself.

Using Codex, we reviewed hundreds of public polling questions and narrowed the dataset to surveys that asked respondents – all gen pop – to choose between paired positive and negative views of AI: benefits versus risks, good versus harm, favorable versus unfavorable, optimistic versus pessimistic, trust versus distrust, or excitement versus concern. We excluded issue-specific questions that could distort the broader picture and documented cases where topline results came from reputable polling roundups rather than standalone poll releases.

The result is intentionally modest. Each point represents a published poll result. The trendlines are a smoothed benchmark based on the available paired-response questions over time. This is not a polling average, nor does it imply that every survey asked the same question. Instead, it provides a directional view of how public attitudes toward AI have evolved across multiple high-quality surveys.

We’ll continue updating the tracker as new polling becomes available, and we’re grateful to the pollsters, researchers, and the Roper Center for Public Opinion Research, all of whose work makes projects like this possible.

[Insight] The power of utility

So contrary to what we might expect, sentiment about AI itself seems to have improved over the past year. Granted, AI is a foundational technology that will touch or impact much of our economy and society, and considering it distinct from those kinds of impacts – AI and jobs, AI and kids, etc. – comes with caveats. But the obvious explanation for the apparent improvement is that more people are using AI and finding use cases for it.

Which is noteworthy, especially given how AI, unlike recent new technologies, probably started off underwater. People are considering AI with years of cultural conditioning – literature, movies, television, video games – that has primed them to expect more harm than benefit. This pull from the Roper Center archive, a Cambridge Reports survey from 1987, shows that nearly 40 years ago, Americans were 19 points more inclined to say that AI would likely do more harm than good.

For context, Brett M. Powell, Roper’s Associate Director, offered a point of comparison between interest in public attitudes about AI and drones: “If, for example, you look at drones, you can see that in the early 2000s Roper Center archived five questions on the subject. Then, in the 2010s, we recorded 155 questions, and in the 2020s, we’re back down to seven questions (albeit we are just over halfway through the decade).” Drones are popular, he said, and society has learned how to live with them.

Whereas Roper’s archive includes four questions on AI in the 1980s-1990s, 32 in the 2010s, and 630 so far in the 2020s.

[Event] ChatGPT for midterms coverage

Tomorrow, June 11, we’re hosting a small OpenAI News Academy at our DC Workshop that’s focused specifically on election coverage.

The session is designed to help political and campaign reporters, editors, and producers get beyond the basics of ChatGPT and Codex to learn practical ways these tools can support the work they already do.

The instruction covers source-first workflows, research and reporting use cases, prompting techniques, and ways to turn dense documents and polling into structured briefs, first draft interview questions, and reusable newsroom tools.

Here’s one of those newsroom tools we’ll be demonstrating: an AI-created app helping you find the exact politician quote you’re looking for.

[About] OpenAI Forum

Explore Forum programming by and for our community of approximately 75,000 AI experts and enthusiasts from across tech, science, medicine, education, government, and other fields.

11:00 AM – 1:30 PM EST on Jun 15

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help:

• A blueprint for a stronger CAISI

• Within Codex growth, we see knowledge work changing

• Plus, our AI policy positions all in one place

If you find this helpful, make sure you’re signed up for the next issue.

[Policy] Building a world-leading AI safety agency

As frontier AI systems become more capable, government needs a trusted institution that can independently evaluate emerging capabilities, assess whether safeguards are keeping pace, and provide policymakers with clear technical insight.

That’s why, in our new frontier safety blueprint, we’re calling for strengthening the US-based Center for AI Standards and Innovation, or CAISI, as the federal government’s primary authority for frontier AI safety.

The first priority should be beefing up the institution itself. Before CAISI can take on significant new responsibilities, it needs the resources, expertise, authorities, and infrastructure necessary to succeed. That means permanent authorization and funding, flexible hiring authorities to attract top technical talent, access to classified computing environments, and stronger coordination across government.

Once that foundation is in place, CAISI should serve as an independent evaluator of the most capable frontier AI systems. The goal is not to create a regulator that approves or blocks deployments – developers should remain responsible for deployment decisions. Instead, CAISI should provide rigorous technical evaluations that help policymakers, companies, and the public better understand capabilities, safeguards, and emerging risks.

Over time, policymakers should also support a broader ecosystem of independent technical assessors. CAISI can help establish standards, certify qualified evaluators, and coordinate assessments that provide ongoing visibility into frontier AI progress. One area of focus should be understanding progress toward recursive self-improvement (RSI), where AI development itself is accelerated by AI. That’s a potential future capability which could have significant implications for AI governance and national security.

Policymakers in the US – and the rest of the world, too – should not be forced to make decisions in the dark when it comes to AI safety. As AI becomes increasingly consequential, building trusted institutions that can evaluate and understand frontier systems may prove just as important as building the technology itself.

[Policy] Our policy positions, in one place

Calling for a stronger CAISI is also listed on our new summary of our public policy agenda.

This summary outlines the principles that guide our work, including democratization, or resisting the potential of this technology to consolidate power in the hands of the few; and resilience. It also details the policy priorities we’re actively engaging on today – from frontier AI safety and youth protections, to workforce transition, AI literacy, content provenance, cybersecurity, and infrastructure.

Many of the ideas we’ve discussed here in The Prompt are reflected there, including proposals to support stronger protections for young people online, expand access to AI literacy and workforce training, and build the infrastructure needed to power the Intelligence Age.

AI policy is evolving rapidly, and we expect our views will continue to evolve as well. But we believe it’s important to make clear to all AI stakeholders, meaning effectively anyone, to understand where we stand, what we support, and why. Our goal is to make our policy thinking more transparent, more accessible, and easier for anyone to engage with as we work toward ensuring artificial general intelligence benefits all of humanity.

[Data] Knowledge work gets its factory redesign

Codex now has over 5 million weekly active users, up more than 6x since the launch of the desktop app four months ago. Developers remain the biggest user group, but knowledge workers now account for about 20%, their share is growing more than 3x as fast. And within that data, we see a bigger shift happening.

Knowledge work now dominates advanced economies. More than 40% of US workers – roughly 72 million people – spend their days working with information: code, documents, analyses, designs, decisions, and communication. Yet despite decades of digitization, much of that work remains surprisingly inefficient.

Modern workers can create information faster than ever, but they still spend enormous amounts of time searching for context, tracking down colleagues, reconciling versions, and moving information between systems. McKinsey found that knowledge workers spend nearly half their week managing email or looking for information and expertise.

This is the legacy of piecemeal digitization. Email, documents, spreadsheets, dashboards, CRMs, chat tools, and SaaS applications each solved specific problems, but together they created fragmented workflows. The office became digital without becoming integrated.

Three frictions now define the cost of knowledge work:

• Search: finding the right file, dataset, message, precedent, or expert across sprawling systems.

• Coordination: moving information and decisions through teams, tools, and approval chains.

• Verification: ensuring work is accurate, accepted, and survives contact with reality through reviews, testing, validation, and signoff.

These frictions help explain the productivity paradox. As Erik Brynjolfsson observed, information technologies generate their largest gains only when organizations redesign workflows around them. Electricity followed the same pattern: factories did not realize its full benefits until they rebuilt production around distributed electric motors rather than simply replacing steam engines.

Knowledge work is still waiting for its equivalent redesign. Previous generations of software reduced the cost of producing documents and information, but not the attention required to consume and act on them. The result has been more artifacts, more tools, and scarcer attention.

Codex is that redesign. Just as factories put electric motors next to every machine, Codex places AI closer to every problem. It can find inputs, coordinate workflows, produce deliverables, check quality, and help move work through approval processes. By removing the bottlenecks around information, organizations can move faster and allow people to focus on their highest-impact work. – Chris Nicholson, Field Correspondent

Watch Chris explain the shift in the video below, and check out more Codex data here.

[About] OpenAI Forum

Explore Forum programming by and for our community of approximately 75,000 AI experts and enthusiasts from across tech, science, medicine, education, government, and other fields.

4:00 PM - 4:45 PM EST on Jun 9

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help:

• What 1 million public-sector seats mean

• Compute is the new strategic resource

• Our prep for the 2026 elections

If you find this helpful, make sure you’re signed up for the next issue.

[Data] 1 million public-sector seats – and a bigger shift

OpenAI has crossed the milestone of more than 1 million public-sector subscriptions or “seats.” These users span every level of government: from federal agencies and the national labs, to states including Alaska, Connecticut, Massachusetts, Minnesota, Pennsylvania, and Tennessee, and cities like Austin and Fresno. A majority of these 1 million seats are being provided at essentially no cost.

This milestone reflects something bigger than growth – it signals how AI is becoming core infrastructure that institutions rely on to do their work.

Throughout history, foundational technologies have expanded access to essential capabilities: electricity brought power everywhere, the internet connected people and information, cloud computing made computing resources available on demand. AI is becoming the next layer of infrastructure, making intelligence available to people, businesses, and institutions wherever and whenever they need it. With nearly 1 billion people already using our products regularly, OpenAI’s job is to make that intelligence cheaper, better, and more abundant over time so more people can build, solve problems, and expand what they’re able to do.

One of the clearest examples of this shift is in government.

Across the country, public servants are using AI to help them work faster (in Pennsylvania state government, saving 95 minutes per day), more accurately, and more effectively on behalf of the people they serve. Teams are summarizing complex documents, streamlining administrative processes, drafting communications, analyzing information, and reducing time spent on repetitive tasks. That means more time focused on delivering services, solving problems, and helping people.

When governments work better, citizens benefit. Faster responses, more efficient services, and more productive public-sector teams ultimately translate into better outcomes for the people who rely on them.

We believe intelligence should be as accessible and dependable as other foundational utilities: available to people, businesses, and institutions whenever they need it. Reaching 1 million public-sector seats is an important milestone, not simply because of the number itself, but because it reflects a broader transition already underway. AI is becoming part of the infrastructure that helps societies operate – and one of the most important ways that infrastructure creates value is by helping governments better serve their citizens.

(Editors’ Note: We updated this item, adding how a majority of the 1 million public-sector seats are being provided at essentially no cost.)

[Policy] America needs strategic compute capacity

In the last century, oil became a strategic resource – so essential to national security and global power that the United States created the Strategic Petroleum Reserve to guard against supply shocks and geopolitical coercion.

In the Intelligence Age, compute is beginning to play a similar role.

This week, OpenAI sent a letter to Congressional leaders urging the federal government to address America’s strategic compute shortage as a matter of national security and democratic AI leadership. The core argument: frontier AI systems are becoming strategic assets. They can accelerate scientific discovery, strengthen cyber defense, enhance national decision-making, and shape the balance of global power. But those capabilities only matter if the government can actually deploy them when and where they are needed.

That requires strategic compute – a dedicated, ready supply of the specialized chips, data centers, energy infrastructure, and technical operators needed to run frontier AI systems securely, reliably, and at scale.

The challenge is timing. Frontier models are improving on the scale of months. New compute infrastructure takes much longer to build. That mismatch creates a national-security gap.

Strategic compute is the bridge between frontier capability and government adoption. Like the Strategic Petroleum Reserve, it would give the United States assured access to a critical resource when national security and public purpose demand it.

There is already an example of why this matters. At Los Alamos National Laboratory, the Department of Energy’s Venado supercomputer enabled researchers to run OpenAI’s then-frontier o3 reasoning model to accelerate national-security research. Because that capacity was already in place, LANL could securely access frontier AI capability when it became available.

Such examples should be more rule than exception, and that can only happen if the government invests more deeply in expanding its compute capacity.

Cybersecurity shows the urgency. From one model generation to the next, we have seen a sharp jump in frontier systems’ ability both to threaten critical infrastructure and to help defend it. Through OpenAI Daybreak and the Trusted Access for Cyber program, OpenAI is working to get advanced defensive models into the hands of entities with verified cyber-defense responsibilities, equipping defenders before adversaries and criminals can exploit the same advances.

But access to models is not enough. Defenders also need the secure compute to run them.

The United States leads in frontier AI today. But that lead cannot be taken for granted. Strategic compute would help ensure America can deploy AI in service of its security and values, protecting citizens and critical infrastructure at home, while supporting allies and partners abroad as they build on AI systems grounded in democratic principles.

[News] Election integrity in the AI era

Today, we’re announcing our election-year safeguards as 2026 becomes the second major global election cycle of the generative AI era. The backdrop is different from 2024: AI tools are more powerful, more widely used, and increasingly embedded in how people search for information, create content, and engage politically online.

Our approach centers on five priorities:

• surfacing reliable information

• supporting cyber defenders

• increasing transparency around AI-generated content

• combating misuse by bad actors

• keeping ChatGPT politically neutral by default.

We’re also taking several concrete steps. Beginning this fall in the United States and Brazil, ChatGPT is providing live election-night vote counts from The Associated Press. In the US, we’re also partnering with Democracy Works to surface trusted information about voter registration, polling locations, and election logistics.

On the security side, we’re extending cyber-defense tools to registered voting-system manufacturers and briefing election officials on new defensive AI capabilities. And we’re unveiling new provenance efforts designed to help people identify AI-generated images, including SynthID watermarking and a public verification tool tied to the C2PA standard.

We also used our announcement to repeat our red lines when it comes to AI and election: no political advertising on ChatGPT this cycle, no deceptive AI-generated political content, and no automated persuasion campaigns at scale.

Our main message: AI systems should help people participate in civic life with better information, stronger safeguards, and greater transparency, while giving users the freedom to explore political ideas without manipulation or hidden bias.

[About] OpenAI Forum

6:15 PM - 6:45 PM EST on Jun 2

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help:

• States start building a de facto AI rulebook

• Endorsing guardrails for biosecurity

• Can AI prevent the next pandemic?

If you find this helpful, make sure you’re signed up for the next issue.

[Insight] Three states, one emerging standard

Something significant is happening when it comes to frontier AI safety regulation: the states of California, New York, and now Illinois – representing about one-fifth of the nation’s population – are moving in the same direction, creating a de facto national standard governing the most powerful AI systems.

Last week, we endorsed Illinois’ frontier AI safety bill, SB 315, arguing that it represents something bigger than a single state effort. Think of it as reverse federalism: states leading in a way that helps move the country forward as we head further into the Intelligence Age. Rather than creating a 50-state patchwork of conflicting rules, states can establish a common baseline, and build momentum for federal action.

Frontier AI is reshaping everything it touches, and our regulatory approach needs to keep pace. The first step is harmonization, with states and the federal government rowing in the same direction instead of pulling against each other. That’s especially important for a technology that affects national security and global competitiveness, and raises risks that no single state is equipped to evaluate on its own.

The federal government has something no state does: the technical expertise and resources necessary to test and evaluate advanced systems before they are released. States are best positioned to focus on what happens after those models are deployed: reporting requirements, transparency rules, and accountability for systems that are already out in the world. Both approaches are important, but they do different things and work best when they are aligned.

That’s why the Illinois bill, SB 315, is so important. California and New York have already begun moving toward a common model, with safety standards we see as a strong first step and mechanisms to align state and federal law. The Illinois bill does that and more, going even further by including independent third-party audits. We believe the federal Center for AI Standards and Innovation (CAISI) – in partnership with national security agencies – is well positioned to develop auditing standards.

Passing legislation like SB 315 would be a better approach than waiting for Washington on one side or accepting 50 conflicting state frameworks on the other. States can help push companies up toward a stronger national system, while preserving their ability, as the laboratories of democracy, to lead on kitchen-table issues like child safety, AI literacy, and workforce training.

We have done this before. During the Space Race, a unified national strategy helped put Americans on the Moon. In aviation, our national approach to safety became the global standard. AI presents a similar test.

Illinois is helping point the country toward the better path: harmonized state leadership that builds toward a prevention-first federal approach, strengthens safety, supports innovation, and keeps the US moving with the clarity and speed this moment requires.

[News] Endorsing guardrails for biosecurity

There’s been a lot of discussion of advancing cyber capabilities in recent weeks and this is important – but advancing bio capabilities are a major focus for us and should be for policymakers, as well. OpenAI has been investing in this space through biological evaluations and safeguards, supporting startups working in biodefense, and convening government experts and national labs on how frontier AI can support resilience and biodefense research. We’ll be sharing more in the coming weeks about our broader action plan.

AI has the potential to accelerate lifesaving science, from earlier detection of biological threats to faster development of medical countermeasures. And as AI and biology advance together, strong safeguards matter, too.

That’s why today, we’re announcing our support for Sens. Tom Cotton and Amy Klobuchar’s bipartisan Biosecurity Modernization and Innovation Act. The proposal would establish a federal framework for screening gene synthesis orders and customers for dangerous pathogens or suspicious activity, while helping test and modernize new biosecurity tools as the science evolves.

Gene synthesis screening is an important checkpoint in reducing the risk that AI-designed biological materials could be misused. The legislation reflects a broader approach OpenAI is taking in biology: strengthening safeguards and mitigations for biological risk while also supporting beneficial scientific discovery through tools like GPT-Rosalind for qualified researchers. – Richard Johnson, National Security Risk Mitigation Lead, OpenAI

[Event] Can AI prevent the next pandemic?

Next week, the OpenAI Forum hosts Geoffrey Mosoti, Senior Technical Product Manager at the Gates Foundation, for a conversation about how AI and computational modeling are beginning to reshape global health decision-making.

Come join us.

4:30 PM - 5:30 PM EST on May 26

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. For the first time, we’re using it to endorse legislation. The bills we’re supporting today are the bipartisan Kids Online Safety Act (KOSA), from US Sens. Marsha Blackburn (R-TN) and Richard Blumenthal (D-CT), and Illinois SB 315, a frontier AI safety bill that we believe further advances the establishment of a national framework for frontier AI safety.

Also in today’s issue:

• Treating AI as a global utility

• Our Washington, DC Workshop, now open for business

• OpenAI’s Head of Codex on the OpenAI Forum

If you find this helpful, make sure you’re signed up for the next issue.

[Policy] Getting AI right for kids

KOSA would help create stronger online protections for young social media users through safer default settings, expanded parental controls, and greater accountability for online harms.

The path forward on kids safety, however, also requires AI-specific rules. And we believe KOSA is complementary to the work we’re doing at the federal and state level. Young people should be able to benefit from AI in ways that are safe, age-appropriate, and grounded in real-world support, including referrals to crisis resources and parental notifications in serious safety situations. That means building safeguards from the start, giving families better tools, and taking responsibility for reducing risks before they become harms.

The broader point is an important one: AI companies still have the opportunity to build protections early, before these technologies become fully embedded in everyday life. As OpenAI Chief Global Affairs Officer Chris Lehane has put it, “We can’t repeat the mistakes made during the rise of social media, when stronger safeguards for teens weren’t put in place until the platforms were already deeply embedded in young people’s lives.”

We’re grateful to Senators Blackburn and Blumenthal for their bipartisan leadership, and proud to support legislation that reflects the kind of practical, prevention-focused tech regulation families deserve. We see real momentum building across the nation on kids’ safety, and we look forward to continuing to work with Congress on AI-specific rules that protect young people, give parents better tools, and help ensure AI benefits everyone.

Also today, OpenAI is endorsing Illinois SB 315, a frontier AI safety bill that would establish clear requirements around safety practices, transparency, incident reporting, and accountability for the most advanced AI systems. The legislation closely mirrors frontier safety frameworks already advancing in California and New York, which we view as the start of a consistent, nationwide framework; the Illinois bill also requires independent third-party compliance audits of large frontier AI developers. We're supporting the Illinois bill because it advances a risk-based approach focused on the most capable models and highest-consequence harms, and because it further advances the emerging national framework for frontier AI safety.

[News] Intelligence as a utility

We’re entering a new phase for OpenAI.

In the first phase, we learned how to do the research and built increasingly capable, useful, and reliable models.

In the second phase, we turned that research into products that hundreds of millions of people now use every week.

Now, we’re turning intelligence into a global utility. We believe that, like electricity, intelligence should be available for people, businesses, and institutions to use as much as they need, where and when they need it. With nearly 1 billion people already using our products regularly, our job is to make that intelligence cheaper, better, and more abundant over time – not to dominate every vertical ourselves – so that more people can build, solve problems, and expand what they’re able to do.

That vision only matters if people can actually use these tools in ways that improve their own lives and communities. That’s why our Washington, DC Workshop officially opens for business today – as a place to host much needed conversations about who will really benefit from AI, and where those who work and live in the DC area can get hands-on experience with it and learn how to put it to work for themselves. You can catch a glimpse of the space and a fuller explanation of it via Axios’ interview with OpenAI’s Chris Lehane.

The Workshop will host trainings, OpenAI Forum talks, and other programming for elected officials, regulators, civil servants, educators, workers, nonprofits, and industry and community leaders – creating a space for practical engagement with AI and informed discussion about how the technology should develop and benefit society. Recent Forum talks hosted in DC and livestreamed included discussions on compute and AI infrastructure, labor and workforce development, and the future of journalism.

In a taste of what’s to come for the Workshop, this week we’re hosting three in-person OpenAI Academies, our practical AI trainings designed to meet people where they are. Thanks to DC CAP, local 10th and 11th graders planning to apply to college were the first outside visitors to our officially opened Workshop when they arrived for their Academy this morning. Tomorrow, we’ll host older adults with our partner OATS/AARP. And on Friday, we’ll work with Veterans Forge to help a group of veterans make the transition to work with AI.

These are just the latest in nearly 100 events, 60 in person, that the OpenAI Academy has hosted since January 2025. Our online instruction has engaged about 3.5 million people, and the Academy community has grown to more than 1 million members.

If we get this right, intelligence can become a foundation for greater productivity, creativity, scientific progress, and economic opportunity for many, not just a few – in service of OpenAI’s mission to ensure AGI benefits all of humanity.

[About] OpenAI Forum

4:45 PM - 5:55 PM EST on May 13

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help:

• One month, 4 million+ ChatGPT-supported entrepreneurs

• Comparing job risk with worker concern

• People want AI in more hands, not fewer

• Department of Labor’s text-based AI literacy program

If you find this helpful, make sure you’re signed up for the next issue. Also be sure to join the energy-projects experts from NVIDIA, Oracle and OpenAI this Thursday for a discussion about the strategic importance of compute. More below.

[News] Millions of entrepreneurs building with ChatGPT

More than 3 years in, we can now see clear indicators that ChatGPT is driving productivity gains, yes – but it’s also fueling entrepreneurship, giving people the tools they need to develop and launch a wide range of new businesses.

We see this in how small companies are using our tools. OpenAI Economic Research conservatively estimates that at least 4 million Americans who were starting, running, or planning a business in March 2026 did so with help from ChatGPT. Use spanned sectors from retail to professional services, trades, hospitality, and health. Much of the activity was concentrated in service-based businesses, where founders often start lean and do everything themselves.

Separately, we also see a large cohort of business customers that have fewer than 10 employees – another strong signal that our tools aren’t only serving established companies, but helping new ones take shape, launch, and compete.

With AI, small businesses increasingly have access to capabilities that used to be the preserve of large, well-capitalized companies: market research, customer support, legal and compliance help, product development, data analysis, design, and operations support. An entrepreneur can now test an idea, build a brand, understand customers, and run day-to-day workflows with a level of precision and insight that would have been hard to imagine a decade ago.

A few examples show what this looks like in practice:

• Matt Rosenberg used ChatGPT to test whether his Thai restaurant could work before signing a lease, then kept using it for permits, pricing, reviews, social posts, margins, and daily operations after Bangkok Rush opened.

• Kaija Pack uses ChatGPT as an extra operator for Break Life Houston, the world’s largest rage room (yes, check it out), helping with competitor research, outreach, customer communication, pricing explanations, and ideas to bring people in on slower weekdays.

• Alison Leeds used ChatGPT to turn thousands of post-surgery recovery stories into weekly art-and-color cards, building a recovery-support design business for breast-cancer patients and caregivers.

• Rob Cahill’s 10-person biotech company Junevity gives ChatGPT to every employee and uses OpenAI models for literature review, genomics, target selection, and operational planning.

With our tools already lowering the costs of starting, experimenting, and operating a business across sectors, a wide range of entrepreneurs are increasingly able to build sustainable businesses on their own terms. – Adam Cohen, Head of Economic Policy

Last November, OpenAI partnered with DoorDash, SCORE, and 16 regional business-support organizations to host over 500 small business owners in a multi-city hackathon. You can read about it here.

[Data] Comparing job risk with worker concern

Workers with jobs at higher near-term risk of automation are no more concerned about losing their jobs than most other workers, according to our latest research. Where their concern about their heightened risk shows up is in greater demand for controls and agency over how AI is deployed at their place of work.

We paired OpenAI Economic Research’s new framework for how different jobs will transition in the age of AI with a 5,060-sample survey via TrueDot of how people facing various degrees of AI-driven job risk feel about their position in the workforce. We organized respondents by the 900+ occupations that were classified in the framework, then cut the results based on the framework’s four identified risk categories of: 1) jobs facing less immediate change due to AI, 2) jobs that will be reorganized because of AI, 3) jobs that will see more demand because of AI, and 4) jobs at higher risk of automation by AI.

The results offer a look at how actual risk of job automation and concern about job automation align, and don’t.

Percentage of adults selecting each response, by job-risk category:

Workers in roles at higher risk of automation are in line with those in less at-risk roles in seeing their position as likely to still require people to do the work over the next two to five years, and are no more concerned than other groups about there being “fewer hours, fewer shifts, fewer job openings, or fewer workers needed overall.”

But because they are aware of how their roles are at risk, they are much more likely to want to see controls and have agency over how AI is used at their workplace. For example, they are significantly more likely to say that the best AI tools should be put into the hands of “people like me, so more people can benefit directly,” rather than given first to big companies that may be more ready to deploy AI.

It’s actually people in jobs that will see more demand with AI who are most concerned that AI may reduce the need for people in their roles (51% concerned). We think there are two reasons for this, despite the research suggesting they’re in a stronger position. First, this group is the most exposed to AI at work and at home (72% are weekly AI users, 42% use AI multiple times a day), and likely are more familiar with advancements in AI capabilities since the introduction of ChatGPT. Second, one in three of these workers are in technology-related fields, a sector that has seen a drumbeat of layoff announcements in the last couple of years, often vaguely attributed to AI.

Those in jobs that will see more demand also show much greater support for “workers getting a share of the gains if AI makes their workplace more productive”: 25%, compared to 16% among gen pop. These are people who are experiencing how AI can improve productivity and grow companies, and they want to participate in that success.

People in roles classified as less likely to see near-term change report lower concern than other groups.

[Insight] People prefer democratized AI

Perhaps unsurprising but still worth noting. The same 5,000-person survey finds strong majorities believing that AI should be free and accessible to all, and that free and accessible AI is safer than tightly controlled AI (an idea explained in OpenAI’s recent policy blueprint to democratize cyber defense).

[Resource] AI literacy, one text at a time

As AI reshapes work, government needs to prepare and respond. That’s the thinking behind the Department of Labor’s AI 101 course, a free text-based program designed to help more people build AI literacy and confidence. Taylor Stockton, the department’s Chief Innovation Officer, explains the thinking behind the program. This conversation has been edited for space and clarity.

How do people sign up?
They can text READY to 20202.

Why launch this now?
A lot of Americans are still on the sidelines. Some are unsure about AI, some are skeptical, and some are fearful. We wanted to create an easy first step that helps people feel more comfortable and start seeing how these tools could be useful at work and at home.

Are you hearing that uncertainty in your own life too?
Absolutely. In my own family, some people are excited, some are skeptical, and my mom wasn’t using AI at all.

Why text messages?
Because it keeps the friction as low as possible. You can sign up with a single text, and most of the experience happens over text too, which makes it more accessible for people who may have limited broadband or internet access.

What happens after that?
It’s a free, one-week course delivered by text: about 10 minutes a day of bite-sized learning and challenges.

What do people actually learn?
Each day focuses on a different part of the Department of Labor’s AI literacy framework. There are short lessons on how AI works, how to prompt effectively, how to evaluate outputs, and how to use AI responsibly. There are multiple-choice questions, small challenges, examples from both work and home life, and links for anyone who wants to go deeper.

Is it interactive?
Yes. It’s designed as a back-and-forth experience. The goal is not just to give people information, but to help them reflect on how AI fits into their own job or daily responsibilities.

What would success look like?
We had 10,000 Americans sign up in the first 24 hours, and would like to reach more than 1 million Americans. But it’s not just about volume. We also want to see whether people become more confident using AI and better able to connect it to their work and daily lives.

And finally: your mom. Where does she stand now?
She’s halfway through the course. She may be the toughest critic of all, because her skepticism was high to start. I’ll be interested to see if we’ve moved her a few notches by the end.

[Event] Compute, competitiveness, and country

Compute powers every layer of AI: frontier research, model performance, products, deployment, and revenue. When we announced our Stargate AI infrastructure project in January 2025, we set out to secure 10 gigawatts of AI infrastructure in the United States by 2029, and we’ve already identified over 8GW of that. Our available compute grew roughly 3x year-over-year from 0.2GW in 2023, to 0.6GW in 2024, to ~1.9GW in 2025.

Given compute’s importance to US competitiveness on AI, and to our own ability to continue to make AI tools freely available so that hundreds of millions of our regular users can create opportunity for themselves and others, the OpenAI Forum is putting compute center-stage on Thursday.

Join infrastructure experts Nick Edwards of OpenAI, Dion Harris of NVIDIA, and Drew Bryck of Oracle for a discussion about how investments in computing capacity and related infrastructure can keep the US the global leader in the next era of technology, and what it takes to build this future responsibly in partnership with local communities.

For those interested, we’ll have a live audience in the room in DC and a livestream for the public; as always, Forum members will have the chance to submit questions.

4:00 PM - 6:00 PM EST on May 7

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• Our 5-point plan for scaling cyber defense

• Join OpenAI and NVIDIA experts on the importance of compute

• Facts on Stargate water use

• The Texas town saying ‘yes’ to AI infrastructure

If you find them helpful, make sure you’re signed up for the next issue.

[News] Democratizing cyber defense

Artificial intelligence is reshaping cybersecurity. The same capabilities that help defenders identify vulnerabilities, automate remediation, and respond faster are also being used by malicious actors to scale attacks, lower barriers to entry, and increase sophistication. Recent incidents involving critical infrastructure disruption, large-scale ransomware, software supply-chain compromise, and increasingly sophisticated state-backed cyber activity have made clear that the threat environment is accelerating. AI intensifies that reality for defenders and attackers alike.

That reality forces a strategic choice. One approach is to treat these systems as too dangerous for broad defensive use and limit them to a very small number of approved partners.

We believe that misses the central challenge. Attackers won’t wait. Existing models are already useful for many cyber workflows and capabilities will continue to advance. Criminal groups will adopt whatever tools are available. We think the best way to reduce national risk is to responsibly equip and accelerate trusted defenders faster than adversaries can adapt.

The United States and its allies face a rapidly changing cyber threat environment, and private-sector innovators have an important responsibility to help meet that challenge. OpenAI takes that responsibility seriously, and today we’re releasing a new Action Plan that’s been informed by conversations with cybersecurity and national security experts across federal and state government and major commercial entities. It consists of five pillars:

1. Democratizing cyber defense

2. Coordinating across government and industry

3. Strengthening security around frontier cyber capabilities

4. Preserving visibility and control in deployment

5. Enabling users to protect themselves

The plan describes how we will deepen our existing commitment by building the infrastructure needed to support cybersecurity defenders, organized around democratizing access to the defensive tools that trusted actors across society should be able to use. Building resilience in the Intelligence Age will require both working through democratic institutions and processes, and broadening access to the technologies that can help protect communities, critical systems, and our national security. – Sasha Baker, Head of National Security Policy, OpenAI

[Event] Compute, competitiveness, and country

Compute powers every layer of AI: frontier research, model performance, products, deployment, and revenue. When we announced our Stargate AI infrastructure project in January 2025, we set out to secure 10 gigawatts of AI infrastructure in the United States by 2029, and we’ve already identified over 8GW of that. Our available compute grew roughly 3x year-over-year from 0.2GW in 2023, to 0.6GW in 2024, to ~1.9GW in 2025.

Given compute’s importance to US competitiveness on AI, and to our own ability to continue to make AI tools freely available so that hundreds of millions of our regular users can create opportunity for themselves and others, the OpenAI Forum is putting compute center-stage next week.

Join infrastructure experts for OpenAI, Nick Edwards, and for our partner NVIDIA, Dion Harris, for a discussion about how investments in computing capacity and related infrastructure can keep the US the global leader in the next era of technology, and what it takes to build this future responsibly in partnership with local communities.

For those interested, we’ll have a live audience in the room in DC and a livestream for the public; as always, Forum members will have the opportunity to submit questions. Consider this issue of The Prompt to be a pre-read!

8:30 AM - 10:00 AM EDT on May 7 (Event Time Zone)

Register

[Data] Water use myth-busting

A lot of people probably imagine data center campuses putting constant pressure, at high volume, on a local water supply.

We get that. And we want to correct the record, using our OG Stargate campus in Abilene, Texas as an example.

Our closed-loop water system in Abilene means there’s really just a one-time initial fill because the water is, as “closed-loop” suggests, constantly reused. Each 500,000-square-foot building on our Stargate campus in Abilene requires an initial one-time fill of about two Olympic-sized swimming pools.

After that, once the site is fully operational, the entire cooling system across all eight buildings only needs to be topped off each year with an amount of water equal to that used by four households per year.

Here’s how the closed-loop technology works: Large metal pipes take cold water from the outside tanks into the buildings, then a network of overhead rubber hoses delivers it to each rack. Black lines carry cool water in. Red lines carry hot water back out once it reaches about 82 degrees. The key is that the water is reused again and again, not lost to evaporation.

Watch Abilene Mayor Weldon Hurt explain why the Stargate campus in his city “is just not a huge water hog.”

[Insight] A Texas town’s data center story

The data centers generating computing power have become the lightning rod for public opinion about AI. In many communities, concerns about electricity costs, water use, land, and traffic have turned AI infrastructure into a flashpoint. We estimate that $6.3M has been spent to air campaign or advocacy TV ads about data centers in the 2026 elections, and a number of surveys show opposition to data centers hitting a high.

Not so in Abilene. The Abilene campus is bringing jobs, tax revenue, and new business activity to a West Texas city that sees the project as an opportunity, not an intrusion. The site is being built to train and power advanced AI systems behind ChatGPT and other tools used weekly by nearly 1 billion people worldwide. On the ground, it’s also reshaping how one community thinks about what AI infrastructure can mean locally.

“If you drive by it on a random day, it might just look like a whole bunch of warehouse buildings that there’s a whole bunch of people working on, but it is the backbone of a major industrial revolution,” said Taylor Slack, the project director of the site who works for Crusoe, an AI infrastructure construction firm. Slack oversees a workforce of 8,500 people operating in round-the-clock shifts.

According to Mayor Hurt, the project is giving Abilene a path forward while other towns in West Texas struggle to grow. Since Stargate arrived, the city’s sales tax revenue is up 37 percent. Hotel occupancy tax collections are climbing. When the campus’s eight buildings are fully operational, they are expected to generate additional property taxes equal to one third of Abilene’s current total property tax base.

You can also see the effects in local businesses benefitting from the growth. About 20 minutes away in Buffalo Gap, Tom and Lisa Perini run the iconic Perini Ranch Steakhouse. Stargate has brought new customers through their doors — workers and visitors tied to the project, especially on weeknights. But the larger story, they say, is the possibility that young people can leave for college and come back to Abilene for real jobs and real opportunity.

“A lot of small towns have lost their hospitals. They’ve lost healthcare. They don’t have much commerce. It’s tough. And I think you have to embrace change and figure out how to manage it,” Lisa Perini says.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• How 2026 candidates are talking about AI

• Our blueprint for patient-first AI in healthcare

• Join us in the OpenAI Forum for why compute is the ballgame

If you find them helpful, make sure you’re signed up for the next issue.

[News] AI politics are local

Polling might be the more common way to track how opinions about AI are showing up in the 2026 midterm elections, but what candidates are actually saying about AI is another important signal.

Our analysis suggests that tension is forming fast between pro-growth candidates who talk about jobs, national security, and competing with China, and localist candidates who talk about electricity bills, water, farmland, and community consent. In most places, AI is becoming politically salient when translated into concrete local costs, especially through data centers. Consistent themes:

Data centers are the main political vessel for “AI.” Data center energy use, rising utility costs, and related regulation have become the most salient AI issue so far of the 2026 midterms. That frame started early in 2025 in Virginia and New Jersey, and now recurs across Florida, Georgia, Maine, Michigan, North Carolina, Ohio, Tennessee, and Wisconsin.

The closest thing to a cross-party consensus on data centers is that they should not socialize their costs. The recurring, mainstream position is not anti-AI – it is pro-build, so long as developers absorb energy and grid costs, or bring their own power, and don’t shift costs onto residents. That’s a position shared by some of the candidates in Senate races in Michigan and North Carolina; in the gubernatorial race in Wisconsin; as well as by sitting governors and President Trump.

Party labels explain less than local exposure does. Neither party is unified. Democrats split between pro-industry growth and stronger regulation; Republicans split between Trump-style federal preemption and state-level or populist skepticism. The main cleavage is often not partisan identity but whether a candidate is prioritizing growth and competitiveness or ratepayer and local protection.

Moratorium calls are real, but far from the norm: Progressives have yet to attract wider support for a federal data center moratorium push, but that talk is becoming more common at the state level, including in Maine, where the state legislature just passed a moratorium bill. On the campaign trail, a few insurgent candidates are pushing for these data center pauses. However, the broader field clusters around guardrails, local review, and enforceable cost shifting rather than full-stop policies.

Bottom line: AI is becoming less of an abstract technology debate and more of a politics-of-siting, cost allocation, and local power debate. The map is not primarily red versus blue – it’s exposed communities versus growth coalitions, with data centers serving as AI’s avatar.

[Policy] Our new healthcare blueprint

OpenAI has released a new healthcare blueprint for making AI work for patients, clinicians, and the broader healthcare system.

For too many Americans, healthcare is expensive, hard to access, and tough to navigate. People struggle to find providers, understand medical information, and get help when they need it. That’s where AI comes in: three in five US adults say they have used AI in recent months to understand symptoms, prepare for a visit, and better comprehend clinical instructions, according to a recent survey. And ChatGPT users in underserved rural areas send an average of nearly 600,000 healthcare-related messages each week, per anonymized message data.

AI will not solve healthcare problems on its own; it will not reopen rural hospitals or replace clinicians. But it can help people better navigate the system.

The blueprint outlines a patient-first approach:

• giving people real control over their health data

• allowing clinicians to use AI responsibly to reduce administrative burden and spend more time with patients

• and modernizing regulation so safe, effective AI-supported care can scale.

The goal is a healthcare system that is more accessible, affordable, responsive, and centered on the people it serves.

[Event] Compute, competitiveness, and country

Compute is the ballgame when it comes to AI. It’s also showing up as a product constraint for those who don’t have enough of it.

In January 2025, OpenAI committed to generating 10GW of compute, and we’ve already identified over 8GW of that. We’re now planning 30GW by 2030. Our available compute grew roughly 3x year-over-year from 0.2GW in 2023, to 0.6GW in 2024, to ~1.9GW in 2025.

Given compute’s importance, the OpenAI Forum is hosting a discussion next month on why AI infrastructure matters to US competitiveness, economic growth, and national resilience.

We’ll explore how investments in computing capacity and related infrastructure can help ensure the US remains a global leader in the next era of technology, and examine what it takes to build this future responsibly in partnership with local communities.

8:30 AM - 10:00 AM EST on May 7

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• No gender gap for ChatGPT

• China’s AI dollars stretch further

• Compute is what makes AI run – and our edge

If you find them helpful, make sure you’re signed up for the next issue.

[News] From 80-20 to 50-50

Women initially adopted generative AI more slowly than men. A Harvard Business School analysis covering more than 140,000 people worldwide found a gender gap across most regions, sectors, and occupations. A widely cited 2024 Danish study similarly found that women were about 20 percentage points less likely than men in the same occupation to use generative AI.

OpenAI data studied through a privacy-preserving automated pipeline suggests the gender gap has closed, at least on ChatGPT: we now estimate over 50% of regular ChatGPT users have female first names, compared with early data following ChatGPT’s 2022 launch showing roughly 80% had typically male first names. This parity has persisted, and even begun skewing slightly female, since at least fall 2025. At our current WAU, that’s almost half a billion women around the world regularly using ChatGPT.

Gender gaps are common for emerging technologies; there’s still a significant male skew for crypto. But there has been far less work explaining why an AI gender gap emerged, why it has narrowed so quickly – at least with 3-year-old ChatGPT – or which interventions might help close similar gaps in the future. That matters. The more we understand why this disparity appeared and then faded, the better equipped we will be to detect and address other forms of uneven AI adoption.

This shift deserves deeper study, but there are some plausible explanations at this stage. As ChatGPT spread into everyday life, it stopped looking like a niche tool for technical early adopters and started becoming a mainstream utility, something people use to plan, organize, and solve practical problems – the kinds of needs that consumer heads of households might be solving for.

Social dynamics may also matter. Early AI adoption may have been shaped partly by who felt comfortable experimenting with a new, highly technical tool. But once generative AI became more familiar, visible, and socially accepted, those barriers may have weakened. Like many technologies before it, ChatGPT may have spread unevenly at first, then broadened quickly once its value became clearer and its use felt more ordinary. The difference is that it seems to be moving through that cycle much faster than previous technologies such as the PC or the internet.

Ultimately, we want to build tools for everyone – that’s core to our mission. And we want to ensure the most advanced technologies are considered accessible and useful across the board, not just geared around narrow technical or enterprise use cases. For AI to achieve its potential, we have to work for everyone: from coders to folks who consider themselves tech-shy. We are close to 1 billion users because of ChatGPT’s universal appeal.

From a policy perspective, the lesson isn’t that disparities will simply solve themselves. It’s that we need to understand them earlier, measure them better, and respond faster. Gender is only one dimension. We should also be tracking disparities by income, education, firm size, sector, and geography, and testing what actually helps close them, whether through training, access, institutional support, product design, or public policy. Democratizing AI, as we’re working to do, is about more than simply observing the spread of AI, it has to be about making sure its benefits are broadly shared. – Adam Cohen, Head of Economic Policy

[Data] China’s AI dollars go further. Here’s why

For all the attention on AI’s global competition, there’s surprisingly little clarity on what China is actually spending. The data is opaque, fragmented, and likely incomplete. But the emerging picture, from our research, suggests a level of sustained investment the US can’t afford to underestimate.

China’s overall 2025 AI spending: We estimate China’s 2025 AI spending at $97.2 billion to $125.3 billion across government, corporate, and state-owned telecom operators. For perspective, while the US still leads in capex with a projected $527 billion in 2026, China’s lower costs increase purchasing power. Its state industrial AI investment supports key priorities for the Chinese Communist Party, including low-cost compute deployment at scale and the spread of AI across sectors such as manufacturing.

Corporate spending: We estimate corporate AI spending in China at $67.5 billion to $76 billion in 2025, driven mainly by Alibaba, ByteDance, Huawei, and Tencent. This spending includes low-cost compute deployment at scale, with domestic chipmakers ramping production and firms such as ByteDance reportedly focused on developing cheaper compute.

Government and state-backed infrastructure: We estimate direct government fiscal AI spending at $16.8 billion to $36.4 billion in 2025, and AI spending by large state-owned enterprises at $12.9 billion. (Caveat: we’re only counting direct subsidies, since that’s the only capital that we can verify as spent rather than simply earmarked to be spent. This means our topline figures likely understate actual government funding of AI.) China also has built a broader financing architecture through guided funds, state-bank lending, and policy tools, including $8.6 billion to $18.5 billion in government-guided equity funds and a $28 million investment in Z.ai by the Beijing AI Industry Investment Fund in 2025.

2026 outlook: We expect corporate AI spending in China to grow 17% in 2026 from 2025, to $78.3 billion to $89.4 billion – with China’s AI dollars going farther due to lower costs and state investment. Just a reminder of how history across industries, from autos to batteries to solar, suggests that spending more does not always guarantee lasting industrial leadership. – Alan Hickey and Sam Brannen, OpenAI Intelligence & Investigations

[Insight] Compute is the ballgame

You may have noticed AI companies adjusting their rates or even their product roadmaps to optimize for available compute. Compute powers every layer of AI: frontier research, model performance, products, deployment, and revenue.

Since ChatGPT launched, both our revenue and our available compute have scaled rapidly as demand for intelligent systems has accelerated. In January 2025, we committed to generating 10GW of compute and have already identified over 8GW of that. We’re now planning 30GW by 2030. Our available compute grew roughly 3x year-over-year from 0.2GW in 2023, to 0.6GW in 2024, to ~1.9GW in 2025.

That scaling matters. Compute is now showing up as a product constraint. Put simply: when capacity is tight, product expansion, availability, and customer growth all bump up against that ceiling. Whereas each new generation of infrastructure lets us train more capable models, making every token more intelligent than the one before. At the same time, algorithmic gains and hardware improvements reduce the cost to serve each token, lowering the cost per unit of intelligence. As intelligence gets cheaper and more useful, AI can handle more complex workflows, usage expands, compute demand rises, and the flywheel accelerates.

The result is compounding advantage. Better infrastructure and better models lower the cost of delivery. Better products and deeper enterprise deployment raise revenue per unit of compute. As utilization improves and the platform matures, that creates meaningful operating leverage over time.

This leverage also enables OpenAI to continue democratizing AI. Instead of passing the costs of short-sightedness on compute on to customers, we are able to make our tools available to hundreds of millions of people. It allows us to be more generous with builders, better partners to the developer ecosystem, and pass capacity on to the people who are using our tools to create, and solve problems for themselves and society.

[Event] What the data says about AI and jobs

Today, the OpenAI Forum is holding a talk to discuss what our usage data reveals about where the labor market is today and what it means for the future. The event will feature OpenAI’s Chief Economist Ronnie Chatterji and Alex Martin Richmond, the Wharton School’s Daniel Rock, and UCLA’s Gregor Schubert.

3:40 PM – 4:25 PM EST on Apr 15

Register

[About] OpenAI Academy

The Academy is OpenAI’s free online and in-person AI literacy trainings for beginners through experts.

2:00 PM – 2:45 PM EST on Apr 15

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. Today, special issue, special timing, special guest in the OpenAI Forum:

• 20 policy ideas for keeping AI people-first

• Join Sam Altman today in the OpenAI Forum to help shape what’s next

If you find the below of interest, make sure you’re signed up for the next issue.

[News] Industrial policy for the AI age

Being a responsible steward of AI, we believe, means more than just innovating through product — it means innovating through policy, too. With AI, we are entering a new phase of economic and social organization that will fundamentally reshape work, knowledge, and production. The magnitude of the shifts we expect, as well as the potential risks we foresee, demand much more than the policy changes (largely updates to existing policies) on the table today.

That’s why we’re releasing a slate of ideas to help start a broader conversation about the kinds of policies and institutions needed to navigate this transition, a conversation that needs to happen across all AI stakeholders from governments and companies to communities and families. These ideas, co-developed by OpenAI’s policy and researcher teams, are intentionally early and exploratory, offered as a starting point that we invite others to build on, refine, challenge, or choose among through the democratic process.

History shows that democratic societies can respond to technological upheaval with ambition: reimagining the social contract, mediating between capital and labor, and encouraging broad distribution of the benefits of technological progress while preserving pluralism, constitutional checks and balances, and freedom to innovate.

In normal times, the case for letting markets work on their own is strong. Historically, competition, entrepreneurship, and open economic participation have lifted living standards and expanded opportunity. Capitalism, imperfect as it is, remains an effective system for translating human ingenuity into shared prosperity.

But industrial policy can play an important role when market forces alone aren’t sufficient – when new technologies create opportunities and risks that existing institutions aren’t equipped to manage. It can help translate scientific breakthroughs into scaled industries and broad-based economic growth.

These ideas are our first contribution to that effort, but only the beginning. Progress will depend on continued iteration, experimentation, and collaboration across institutions and sectors. To help sustain momentum, OpenAI is: (1) welcoming and organizing feedback through newindustrialpolicy@openai.com; (2) establishing a pilot program of fellowships and focused research grants of up to $100,000 and up to $1 million in API credits for work that builds on these and related policy ideas; and (3) convening discussions at our new OpenAI Workshop opening in May in Washington, DC.

[Policy] 20 ideas for keeping AI people-first

Here’s the roster of ideas we’re offering in today’s release, which is organized in two sections: 1) building an open economy with broad access, participation, and shared prosperity; and 2) building a resilient society through accountability, alignment, and management of frontier risks.

1. Building an Open Economy

Worker voice in AI deployment → Give workers formal input into how AI is used at work to improve job quality, safety, and fairness.

AI-first entrepreneurs → Enable workers to start AI-powered businesses by reducing overhead and providing shared tools, financing, and infrastructure.

Right to AI → Treat access to AI as essential infrastructure by ensuring affordable, widespread availability and training.

Modernize the tax base → Shift tax systems toward capital and AI-driven gains to sustain public programs as labor income evolves.

Public Wealth Fund → Create a national investment fund so all citizens share directly in AI-driven economic growth.

Accelerate grid expansion → Build public-private models to rapidly expand energy infrastructure needed for AI.

Efficiency dividends for workers → Convert AI-driven productivity gains into better benefits, higher compensation, or reduced working hours.

Adaptive safety nets that work for everyone → Ensure existing programs (UI, SNAP, Social Security, etc.) are fully functional and responsive at scale, and then define automatic support systems (cash, training, wage insurance) that expand when AI-driven disruption exceeds defined thresholds.

Portable benefits → Decouple benefits from employers so healthcare, retirement, and training follow workers across jobs.

Pathways into human-centered work → Expand jobs in care, education, and community services as durable, human-centric employment sectors and provide direct support recognizing caregiving as economically valuable work.

Accelerate scientific discovery and scale the benefits → Build distributed AI-enabled research labs to accelerate scientific discovery and validation and invest in infrastructure and systems to translate AI-driven discoveries into real-world benefits.

2. Building a Resilient Society

Safety systems for emerging risks → Develop tools and markets for detecting and preventing AI misuse in high-consequence domains like cyber and bio.

AI trust stack (verification + provenance) → Create systems that allow people to verify AI outputs, actions, and accountability while preserving privacy.

Auditing regimes for frontier AI → Develop standards, catalyze a network of auditors, and prepare for a future where a narrow set of highly capable models that pose national security risks require pre- and post-deployment audits.

Model containment playbooks → Develop coordinated response plans to limit harm from dangerous or uncontrolled AI systems.

Mission-aligned corporate governance → Encourage frontier AI companies to adopt governance structures that embed public-interest accountability.

Guardrails for government use of AI → Establish clear legal and technical limits on how governments deploy AI, while enhancing oversight.

Mechanisms for public input → Create structured, representative processes for public participation in AI system design and alignment.

Incident reporting systems → Establish a system for companies to share information about AI failures, misuse, and near-misses with authorities.

International AI coordination network → Build a global system of AI institutes to share risk information, coordinate evaluations, and respond to crises.

For more on these ideas, check out the full doc.

[Event] Building AI’s future

This Monday, the OpenAI Forum is hosting a talk on the future of building AI with OpenAI CEO and Co-Founder Sam Altman – along with OpenAI Chief Futurist Joshua Achiam, and OpenAI Researcher Adrien Ecoffet. They’ll be focusing on the above ideas to start the conversation about how to ensure that AI really does benefit everyone.

The OpenAI Forum presents programming and discussions by and for our community of 70,000 AI experts and enthusiasts from across tech, science, medicine, education, government, and other fields.

2:00 PM – 3:00 PM EST on Apr 6

Register

[About] OpenAI Academy

The Academy is OpenAI’s free online and in-person AI literacy trainings for beginners through experts.

Our Academy recently teamed up with the San Antonio Spurs Community Impact and Positive Coaching Alliance to bring together coaches, parents, and community leaders to explore how AI can support learning and youth development, both on and off the court.

A key takeaway from the event was that trusted adults are central to responsible AI adoption. Parent-coaches are emerging as translators of new technology, helping young people navigate AI safely and practically. As adoption accelerates, building AI literacy within families and local communities is becoming as important as access itself.

This work reflects a broader focus of the OpenAI Academy: partnering with institutions that shape everyday life, and advancing a more distributed model of AI readiness rooted in communities, not just companies.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• Growing AI’s hottest ideas platform

• AI’s next mission: building, adopting, deploying at scale

• Sam Altman and OpenAI researchers on building AI’s future

If you find them helpful, make sure you’re signed up for the next issue.

[News] Investing in ideas

OpenAI is acquiring TBPN, the increasingly popular digital-first daily technology show. Here’s why.

We believe that as AI becomes more powerful and touches every part of society, helping people understand what’s changing, how tools are being used, and what opportunities and resilience needs they create will only matter more. Much of our work, in this Substack and elsewhere, is about giving people a clearer view not just of what AI can do today, but where it’s going.

That understanding is becoming shaped less by launch days and one-off announcements, and more by a continuous, daily conversation across tech, business, policy, and culture. As fast as AI the technology is being adopted, AI as a concept is already everywhere. The conversation is being driven by scoops, but just as often and more constructively by ideas: the why, the how, and what’s next.

Where those idea-driven conversations happen, and who drives them matters.

That’s why TBPN is so interesting and important. They’ve built a real presence not by chasing scoops, but by leading on ideas – exploring the why, the how, and what’s next. The opportunity is to support and scale that, especially for developers and entrepreneurs who are building with AI.

It is also why our Commitment to Editorial Independence – which is embedded in our agreement with TBPN – is so important. It’s core to preserving the credibility and distinctiveness that has made TBPN matter in the first place.

This isn’t new in form. Media has long sat within larger enterprises, whether that was ABC/CBS/NBC sitting within large conglomerates, or Microsoft co-creating MSNBC, or Bloomberg News belonging to Bloomberg LP.

Our approach is different. This isn’t about turning TBPN into an in-house media arm. It’s about supporting an independent platform while learning from what they’re hearing every day about what people understand about AI, what they don’t, and what they care about. We want TBPN to keep on cooking.

[Insight] AI’s next mission

At OpenAI, we believe in democratized AI, making it accessible to everyone. We also believe in ensuring its prosperity is widely shared. And we want it to empower those who use it.

But we’re one AI company that’s headquartered in one country. If the goal is for democratic AI to benefit all of humanity, there needs to be an additional governing principle guiding US policy: meeting the moment with urgency.

That’s the central message from Sasha Baker, OpenAI’s head of national security policy.

In her recent essay, Sasha argues that the US is currently building the most capable AI models, but that China is moving faster to deploy AI across its economy, integrate it into the state, and scale it globally. “Today, the contest is no longer merely about how builds it first, but about who uses it best.”

Think about it as a new kind of moonshot. For the US to use AI the best way possible, the path forward rests on three pillars, Sasha says.

First, build at scale: treat AI infrastructure like national infrastructure by expanding energy capacity, compute, and integrated corridors that link research, workforce, and deployment.

Second, adopt at speed: the biggest risk isn’t falling behind technologically, but failing to diffuse AI across small businesses, public services, and traditional industries. Government should lead here, becoming the most sophisticated AI user in the world.

Third, compete globally: AI leadership will hinge on which systems other countries adopt and who sets the rules. That means delivering trusted, interoperable technologies and building alliances around democratic standards.

The US doesn’t need to replicate China’s centralized model. But it does need to match its urgency, scale, and strategic coherence, treating AI not just as an industry, but as a national mission.

[Event] Building AI’s future

On Monday, the OpenAI Forum is hosting a talk on the future of building AI with OpenAI CEO and co-founder Sam Altman together with OpenAI Chief Futurist Joshua Achiam, and OpenAI Researcher Adrien Ecoffet. They’ll be focusing on ideas to start the conversation about how to ensure that AI really does benefit everyone.

The OpenAI Forum presents programming and discussions by and for our community of 60,000 AI experts and enthusiasts from across tech, science, medicine, education, government, and other fields.

2:00 PM – 3:00 PM EST on Apr 6

Register

[About] OpenAI Academy

The Academy is OpenAI’s free online and in-person AI literacy trainings for beginners through experts.

Our Academy recently teamed up with the San Antonio Spurs Community Impact and Positive Coaching Alliance to bring together coaches, parents, and community leaders to explore how AI can support learning and youth development, both on and off the court.

A key takeaway from the event was that trusted adults are central to responsible AI adoption. Parent-coaches are emerging as translators of new technology, helping young people navigate AI safely and practically. As adoption accelerates, building AI literacy within families and local communities is becoming as important as access itself.

This work reflects a broader focus of the OpenAI Academy: partnering with institutions that shape everyday life, and advancing a more distributed model of AI readiness rooted in communities, not just companies.

[Disclosure]

Graphics created by Base Three using ChatGPT.

Hi

Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help. In this newsletter, we share some of these learnings with you:

• Workers are wage-benchmarking with ChatGPT

• A simple prompt unlocks a salary range

• Most workers expect to re-skill while working

If you find them helpful, make sure you’re signed up for the next issue.

[Insight] The price of work, finally visible

This week, OpenAI hosted labor leaders and workforce experts, from Deputy Secretary of Labor Keith Sonderling to union presidents Randi Weingarten of the American Federation of Teachers and Sean McGarvey of North America’s Building Trades Unions, to discuss how they see AI reshaping work and how America needs to prepare and respond.

One point of discussion: our new report on how workers are using ChatGPT to get wage and earnings information. The scale is meaningful: in January and February 2026, consumer ChatGPT users in the US sent an average of nearly 3 million messages per day about wages, compensation, or earnings.

The project began with a simple observation that wage information is economically important but often hard to access, unevenly distributed, and socially costly to ask for, even though it affects job searches, negotiations, training decisions, and career mobility.

Unlike most prices in the economy, wages – the price of labor – are often hard to observe and even harder to interpret, especially if you’re early in your career, changing fields, or moving across locations.

AI is a new type of labor-market intermediary. Rather than requiring a worker to search across multiple websites, interpret scattered salary pages, or ask questions that colleagues and peers might be unwilling to answer, a model can synthesize wage information and return a benchmark in seconds.

We find that wage search over-indexes for occupations where pay is harder to benchmark or more negotiable: creative fields, management, healthcare, and technical roles. We see something similar in entrepreneurship questions, especially in small businesses and creative work where there often isn’t a clear “posted wage” at all.

Across industries, the same pattern holds: people search more when wages are more dispersed and when pay levels are higher. In other words, demand for wage information is strongest exactly where the stakes are highest and the signal is noisiest.

That matters. Misunderstanding pay can keep people in lower-paying jobs, weaken their bargaining position, delay career moves, or discourage investment in skills. Better information doesn’t eliminate uncertainty, but it does make it easier to form a reasonable baseline, and that can meaningfully change decisions.

We also used this report to introduce something new we’re calling WorkerBench, which is an attempt to evaluate how well models actually perform on labor-market tasks that matter to workers. In this first pass, we evaluated GPT-5.4’s performance at retrieving national benchmark wages at the national level. The results are encouraging: high coverage, low bias, and estimates that are generally very close to the benchmark. There’s much more work to be done here to understand how AI can help workers with pay information in more nuanced settings, like in specific geographies, or for specific firms or roles.

The key takeaway: workers are already using AI as a kind of labor-market infrastructure, especially in parts of the market where information is thin and outcomes matter a lot.

The goal from here is to keep improving how useful that is, moving beyond national averages toward the more granular questions people actually care about: specific firms, locations, experience levels, and full compensation. – Alex Martin Richmond, Economic Research, OpenAI

[Prompt] The end of salary guesswork

So how do you use ChatGPT to get wage and earnings information? Try something like this sample prompt:

[Data] Most workers plan to skill up while working

OpenAI partnered with the University of Michigan Institute for Social Research on some questions in its latest survey (Feb. 1-28) on how workers plan to deal with AI affecting their job, one way or the other.

The results suggest that workers expect to stay in the labor market rather than leave it to pursue more education – meaning that for policymakers, employers and educators, meeting workers where they are on increasing their AI literacy means providing that training at the workplace.

Nearly 3 in 5 workers who believe that AI will affect their job, either positively or negatively, say they would respond by picking up new professional skills. By comparison, 24% say they would change how they plan financially; 19% say they would change careers; and 13% say they would start a business. Only 10% say they would go back to school.

To understand how Americans are engaging with AI, OpenAI has partnered with the University of Michigan to collect this data. Known for its surveys of American consumers, UM’s Institute for Social Research is among the world’s oldest social science research organizations and a world leader in the development and application of social science methodology. ISR conducts some of the most widely-cited surveys and studies in the nation, including, the American National Election Studies, the Monitoring the Future Study, the Panel Study of Income Dynamics, the Health and Retirement Study, the National Survey of Black Americans, the Surveys of Consumers, and the World Values Survey. – Cassandra Duchan Solis, Economic Research Team, OpenAI

[About] OpenAI Forum

Explore Forum programming by and for our community of 60,000 AI experts and enthusiasts from across tech, science, medicine, education, government, and other fields.

11:00 AM – 12:30 PM EST on Mar 24

Register

[Disclosure]

Graphics created by Base Three using ChatGPT.