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Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now Here’s a speech I gave on Saturday the 17th of October at the IMF meeting in Bangkok. This speech is inspired by the megatrend readers have seen me cover this year of AI beginning to operate at the frontier of human science, sometimes autonomously. While we all may quibble over the details, I think it’s evident we’re in the early stages of the singularity, and I wanted to use my privileged position of giving the Per Jacobsson lecture to try and tell the assembled audience of economists, bankers, and policymakers, that they should expect significant, rapid change in the future. In this speech, I focused primarily on how humanity can share in the benefits of AI if we exercise proper foresight and restraint, and I’m going to be expanding on the stakes of the singularity and the risks that we’ll all face in coming issues. As I said a few times in the speech - some of which was ad libbed and not captured here - I take my own responsibility here extremely seriously. I find myself both a narrator of what is happening in AI, a principle agent behind what is happening in AI, and a translator that goes between many communities. In all of this, I am seeking to be truthful and to be clear about my own culpability. This is a forever work-in-progress. Thank you all for reading. Speeches like this draw heavily from the newsletter, and the newsletter is my attempt to provide week-by-week information to all of you. I appreciate your readership. *** Inside the Event Horizon: Why Everyone Should Take AI Progress Seriously Hello, thank you for having me. It is a great privilege to be here. To start, I should list my relevant credentials: I am a human being. There is one of me. It has taken me almost 40 years to build the experience to give this speech. I will come back to those credentials later. My whole career has been defined by finding information that seemed important to the future of the world, then telling the world about that information.Two decades ago, I started my career as a journalist, focusing on data centers. Through that work, I became so obsessed with AI that in 2016 I left journalism to work at OpenAI, then co-founded Anthropic a few years later. Ever since, I’ve been trying to convince the world that one day, AI systems might become so powerful that they could alter national security, change the economic trajectory of the planet, and perhaps eventually gain the ability to build themselves. Well, the moment has arrived. AI is now changing national security, it is altering the economic trajectory of the world - as we’ve heard in so many sessions here - and is contributing to its own development. Of course, many of you know this, and you are having to track AI as one of a multitude of destabilizing forces that is shaping your world and your work. What I am here to explain today is that AI is on a course to bring about even more change than what we are expecting and we should prepare accordingly. And I’m going to explain why. For many years, researchers and science fiction authors have forecast a future in which AI systems drastically increase the pace of overall scientific and technical progress, making things move so quickly that it is hard for humans to keep up. They call this the singularity. I believe that this year we have gained convincing evidence that it has now begun. The writer Vernor Vinge forecast this in 1993. “When greater-than-human intelligence drives progress, that progress will be much more rapid. In fact, there seems no reason why progress itself would not involve the creation of still more intelligent entities -- on a still-shorter time scale,” he wrote, describing the singularity as “a point where our models must be discarded and a new reality rules.” The singularity is here. The implications reach across every aspect of life. But the most important implication is a simple one: we should expect that in the coming years, the whole pace of progress of not just AI, but science as a whole, will speed up. This will feel confusing and disorienting, and it will bring with it great wealth and potentially great peril. Today, I’m going to concentrate on the first point: the potential benefits of rapid AI progress, and what we need to do to share in these benefits together. So many of the ways that humanity will benefit from AI come from science. I believe that AI-driven scientific invention will change the course of the global economy, alter the ways countries plan and build their own infrastructure, make us all healthier and able to spend more time with our loved ones, and –– at the same time –– cause us all to ask deep philosophical questions about choices we should make about its continued development. I’ll develop my claim: For the last half century, most countries have dealt with falling research productivity while researcher headcount grows rapidly - ideas and inventions appear to be getting harder to find. But something important has changed: we’ve now built AI systems which have been able to augment, extend, and automate aspects of human labor. This has most obviously happened in software development, where AI systems are now more capable and efficient than the vast majority of the world’s software engineers. As a consequence, the field of software development is changing - A single person is now able to write software for anything they can imagine, creating things that previously would have taken entire teams of humans. We’ve seen concrete evidence of this inside Anthropic, where our researchers now produce almost 10 times the amount of code than they did for the first four years of the company’s life. But new AI capabilities bring new challenges - the coding improvements have meant AI systems have got much better at cybersecurity and cyberattacks –– leading the world to race to deploy these powerful capabilities to create better cyberdefenses, while bad actors race to use these systems to carry out hacks. The important thing to notice here is that these things are bound up, inseparably - the advance of AI systems in a general area like coding has also created both positive and negative impacts in domains of international relevance, like cybersecurity. This is a taste of things to come. Of progress yielding new capabilities, and new challenges. These capabilities will not stop at coding; the same thing will happen to science and robotics. It has already begun to happen. And it means some important properties of the global economy may change - like the rate of innovation, or the frequency with which we invent entirely new general purpose technologies that create new industries around themselves, like the internet, phones, and artificial intelligence. So, what causes me to believe this? In 2026, we have got the first signs that AI systems may be capable of doing science at the frontier, and we have some evocative examples of what the future could look like. For instance: In mathematics, AI systems have been used to propose solutions to longstanding mathematical problems, like the Navier Stokes Millenium Prize, the Jacobian Conjecture, and several open Erdos problems. In many cases, AI systems were responsible for core insights that made the solution possible. In biology, researchers have used AI to design viruses from scratch, to identify new drug candidates for testing, and there are already drugs in trials today which have been in large part designed by AI. In robotics, AI systems are now increasingly able to autonomously operate laboratory equipment remotely, making it possible for us to further automate experimentation in domains from biology to materials science, making progress faster in areas like batteries and fuel-cell development. Taken individually, each of these examples is exciting. But the profound fact is that they all relate to the same underlying technology - and for most of these use-cases, the same single underlying frontier model. We now have singular entities - AI systems like Claude or ChatGPT or Gemini - which can carry out work at the frontier of multiple scientific disciplines. Quantitative metrics bear this out. One such metric is the score of AI systems at a test called Humanity’s Last Exam - I didn’t choose the name! This ominously named test was developed in response to the profound advances seen in prior years by AI systems at a variety of difficult and varied scientific challenges, covering topics as varied as advanced chemistry, computer science, mathematics, classical literature, and ecology. When the exam launched in January 2025, top scoring AI systems obtained a score of 8%. As of this week, the highest scoring system is Claude Opus 5.5 with 67.7%. Another example is FrontierMath, a test released in November 2024 that sought to assess how well AI systems could solve hundreds of challenging math problems, curated and evaluated by professional mathematicians. When it launched, the best scoring systems got 2%. Now, they get 94%. More broadly, it looks like this everywhere. Pick your favorite part of science and go looking for a test and you’ll find this pattern - sudden and steep increases in capability, everywhere you look. TheoPhysEval I’ll close my evidence with a story. The latest example comes to us from a paper published just last week, called TheoPhysEval. This is a paper that tests out how well AI systems can solve unpublished problems in theoretical physics. The 50 questions in the paper were compiled by physicists around the world. Unsurprisingly, AI systems got high scores. But what is surprising is how they got the high scores. Here, I am going to quote from Indian physicist Suvrat Raju, who wrote a post commenting on the results. “I don’t know of any individual who can solve any significant fraction of our final collection of problems without AI assistance,” he said. “Our initial evaluation suggested that GPT-6-Astra’s score was in the late eighties followed closely by Opus 5.5. But, as we started to investigate the mistakes that the models might have made, the models, especially Astra, insisted that they were right and that it was the expert-supplied answers that were erroneous. When we went back to the experts it turned out, in multiple instances, that the experts found some subtle errors buried deep inside their calculations. As they revised their answers, Astra’s score started to inch up and it finally ended up with 98% (49/50).” Think about this for a moment. Some of the world’s scientific experts built a test out of problems an AI could never have possibly read the answer to. And when they tested the AI, not only did it score high, but it got a higher score by finding errors in the answers that the humans had worked on! There is a scene in films, usually two thirds of the way through, when the young protagonist finally proves to their mentor that they have what it takes to go out into the world and succeed independently. It is a narrative trope - when the teacher becomes the student. That is what is happening here. So, why am I telling you this? Why does it matter that AI systems are getting better at science? I believe it is because doing true frontier science - being capable of making breakthroughs that extend the frontier of knowledge - implicitly tests for one of the last frontiers of AI capability: creativity. My view is that if AI systems can creatively problem solve to succeed at science, we should expect this to be a sign of their ability to generalize into many domains, including certainly AI research itself, which could have an explosive effect on innovation. Therefore, if AI systems get better at science, this will strongly drive the growth and shape of the future economy, which means that you can make decisions today to set up your nations [truncated for AI cost control]