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Sam Altman’s remarks at the United Nations Security Council

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OpenAI CEO Sam Altman addressed the United Nations Security Council, discussing AI's potential for opportunity and the critical need for human control over powerful AI systems. He emphasized that even a small risk of catastrophe is unacceptable and urged against training models that cannot be demonstrably kept under human control. Altman highlighted a crossroads, advocating for a future where AI development is guided by democratic institutions to ensure the technology benefits humanity and empowers individuals.

Why this one

This marks the first time an OpenAI CEO has addressed the UN Security Council, signaling a new level of AI's integration into global policy discussions.

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PublishedOffset at this time: UTC+0Sep 23, 2026, 12:00 UTC

IngestedOffset at this time: UTC+0Sep 23, 2026, 22:01 UTC

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Sep 23, 2026, 12:00
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Sep 23, 2026, 22:01
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Today, OpenAI CEO Sam Altman addressed the United Nations Security Council on artificial intelligence. He discussed AI’s potential to expand opportunity, the importance of keeping powerful systems under human control, and the need for international cooperation on AI safety.

Remarks as delivered

Thank you, Mr. President and your Excellencies. I’m honored to be here and to be able to speak with you all at this important time.

We have been talking about artificial intelligence for years, but it feels different in recent weeks and months. Rapid model progress has made the timeline feel more compressed, the upside more tangible, but also the stakes and the risks more immediate.

We have a choice in front of us. AI can either be more like a new Renaissance of creativity and discovery, or more like a new Industrial Revolution of upheaval and disarray.

The Renaissance was a time when new tools, new ideas, and new institutions expanded what people believed they could do. AI can play that role for our time. I believe that people are much more capable than they imagine themselves to be, but have been held back by limited technology that was meant to empower but instead distracts and often steals attention.

The best version of AI is not about making people cogs in a giant machine, or optimizing every part of life until the human parts disappear. It is about giving people more agency in an ever more complex world: more ability to learn, to create, discover, build, participate. There are many things that AI cannot—and should not—automate.

AI should be built for people, not simply to turn the crank of the machines faster for their own sake. Some of the things people working to build AI have said are so dystopian that they sound like the plot of bad science fiction movies.

Anything this powerful is also intimidating. This moment is complicated as we feel both tremendous potential and very understandable anxiety at the same time.

These models can help people discover new knowledge: new medicines, better health care, stronger education, and build more productive economies. They can be new tools for people to pursue dreams and ambition that today feel out of reach. The complexity of life has increased so dramatically for the average person. AI can be a defensive tool against this complexity as much as it can be this tool of unprecedented discovery and innovation.

On the other hand, as AI systems become more capable and more autonomous, they could move faster than our institutions, concentrate power in too few hands, or make decisions that people no longer understand or control.

That concern becomes especially important as we approach systems that can improve themselves and future versions of themselves, often called recursive self-improvement. As the process of building AI becomes more automated, the pace of AI progress could accelerate rapidly. This moment calls for extreme care.

I largely agree with Professor Bengio. Although I’ll say two instead of three—I believe there are two ways that AI progress could go very badly that we have to avoid.

First, we could lose control of the future to AI. The risk is that it moves so fast that people can no longer follow what’s happening or intervene when needed. This would obviously be terrible.

The industry must not accept too much technological risk just because the benefits are too great and that they feel too important to slow down. Beating companies in a competitive pace is not a reason to make rash decisions. Nor do we believe we are locked in a race where we are unable to do that. We have unilaterally slowed down in the past. We will do so in the future. We do not want to build systems—we think it’s important that everyone avoids building systems—where we cannot make the alignment, monitorability, and safety guarantees that we must.

We need to understand what these systems are doing and have strong evidence that they will do what people intend, even as they get very, very smart. Actually especially as they get very very smart. It doesn’t matter whether people put the risk of catastrophe at 10%, or 1%, or 12%, or .1%. None of these levels are remotely acceptable. And we should not train models that we cannot make an extremely strong case that we will be able to keep under human control.

So that’s one way things can go wrong. In the other direction, these systems could concentrate too much power in too few hands. No one person or company or country should be able to use the most powerful AI models to impose their worldview on everyone else. A company or country that believes only it can be trusted with this technology can use that belief to justify almost anything else. We have to reject that logic, even when it’s convenient for any one actor.

I believe there is a pragmatic path through both of these. At OpenAI, we approach these questions with a clear goal: AI should give people more power over their own lives. More freedom to live lives the way they want. More ability to solve problems, big or small, and to improve their communities. We don’t want to fall into the trap of blind optimism. We also don’t want to fall into the trap of doomerism either. Getting pulled into either extreme will not help us successfully figure out the challenges in front of us, especially at a time when the world and its people face such enormous challenges that this technology can help solve. So we try to walk the middle path between these two poles.

For us, that means three principles.

First, as AI becomes more capable, people must remain at the center of AI decision-making. Alignment is not an abstract research question. It is the work of ensuring that these systems reliably remain under human control, reflect human values, and help people guide the next stages of development. We are not trying to, and must not, automate human judgment, or human values.

Second, the benefits of scientific progress and economic growth must be by people and for people. AI should help researchers make discoveries, doctors treat patients, teachers teach students, entrepreneurs start companies, communities solve problems that felt out of reach, and much more. The continuous story of human progress is not that tools replace human agency. It is that the right tools make people more capable, and I believe this is going to go much further than we believe possible.

Third, this technology must empower people individually. A great future will come from people realizing their potential and building value for themselves, their communities, their countries, and the broader world. We might be one of the builders of this technology, but we are not the heroes of this story. Our role is to enable people all over the world and to trust in the magic of humanity’s skill and faculties.

We now have models capable of discovering knowledge that we have never had before. Consider the progress we’ve seen in math. Three summers ago, our models were okay at grade-school math. Two summers ago, they were pretty good at high-school math competitions. Last summer, we reached a gold-medal level in the most prestigious international math competition. And just a few weeks ago this summer, one of our models solved one of the Millennium Prize Problems, the Navier-Stokes equations.

These equations are used for aircraft design, weather forecasting, and the study of blood flow. We are not solving math problems for their own sake, but to empower people to discover new knowledge and to use it to improve healthcare, raise standards of living, and expand everyone’s potential around the world. There remains so much to be discovered.

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