AI의 기하급수적 성장과 정책의 과제

Policy on the AI Exponential June 2026 In one of the side plots to The Lord of the Rings , two of the Hobbits attempt to rouse Treebeard—a wise but ponderous sentient tree—to defend his forest from an army that is cutting it down. The problem is that Treebeard operates at a very different speed than the Hobbits. It takes him a full day simply to say hello to another tree, so getting him and his peers to act fast enough is nearly impossible. The intersection of AI and our political institutions feels a bit like the Hobbits and Treebeard. AI is advancing at a lightning pace—in only four years, AI models have gone from barely being able to write a coherent line of code to writing most of the code at major AI companies . Similar gains have been made in biology, physics, math, finance, law, translation, and many other fields. AI’s scaling laws , which predict an exponential increase in general cognitive capabilities with increasing computing power, now have over a decade of empirical evidence behind them. If these scaling laws continue for only a year or two longer, we are likely to get what I’ve called Powerful AI, or “ a country of geniuses in a datacenter ”. By contrast, policy—and especially legislation—moves very slowly. Often this is for good reasons: governments have grave powers, and it’s usually for the best that they aren’t used too hastily. But the mismatch in timescale is nevertheless very painful: in the several years that it can take Congress to act, AI can go from an amusing toy to the full country of geniuses. Over the last few years since AI has become a major commercial technology, those of us who wanted to handle it responsibly have faced a dilemma. We could see clearly where the exponential was going: we strongly suspected that within a few years AI would be one of the rare technologies that fundamentally reshapes the entire policy landscape, in the same way that nuclear weapons reshaped geopolitics and the industrial revolution fundamentally reshaped every economic and social issue. But to those looking only at what AI could do at the time , it looked like a much more mundane technology—similar perhaps to the latest consumer app or cryptocurrency. It was hard to convince most policymakers and companies that anything other than a laissez faire attitude made sense. And to be fair, the fact that AI’s radical effects were not yet present, and that we didn’t know exactly what shape they might take, made it difficult to design the right policies even if there had been the will to act. Given the limits imposed by this situation, many safety advocates (including Anthropic) have so far been focused on advocating for policy actions that preserve optionality, tee up a fast reaction in the future, or give the world better insight into what is coming down the pike – things like transparency legislation, export controls on chips, and data collection on AI’s labor effects. These are not enough, but they have felt like all that was possible. In the last few months, however, the evidence of AI’s incredible power, as well as its risks, has become undeniable. Perhaps the most emblematic example is Claude Mythos Preview and the discovery that frontier models pose very real risks to cybersecurity, creating the potential for disruption of the financial sector, critical infrastructure, and national security. Mythos Preview scrambled the global cybersecurity landscape. But its broader significance is that it proves beyond doubt that AI models are now tools of global and national strategic consequence. The cyber risks that Mythos-class models present will not be the last that we must face. I believe that biological risks may soon follow, and that serious AI autonomy risks may not be far behind 1 . We now, globally and collectively, need to activate a slow and rickety policy apparatus to deal with risks and opportunities that are going to compound surprisingly quickly from here. Many policymakers are showing increased openness to taking action, and it's been encouraging to see our peers come around to the same positions we've been advocating for over the past few years. This is good, but I worry that these early actions are at least a year out of step with AI's rapid progress. This essay is an attempt to close that gap: to lay out where the exponential is now, and the collective action needed to meet the moment. I will focus on five perennial policy areas that need re-imagining in an AI world: regulation and public safety, macroeconomics and tax policy, scientific innovation, the balance of power between state and society, and geopolitics. I will speak primarily in terms of US policy since Anthropic is an American company, but most of my recommendations are also relevant to the rest of the world. Along with this essay, Anthropic is releasing a legislative proposal on frontier model testing and a policy framework for job displacement, for which we intend to provide substantial financial backing. We plan to do much more in the future, but we view these as first steps to signal our seriousness. 1. Regulation and public safety Every new technology or product has both beneficial and harmful uses, and therefore presents a dilemma between innovation and safety. Regulating products makes them less likely to cause harm and has played an important role in improving lives around the world, but it can also directly reduce their benefits and indirectly disincentivize innovation. There is also the Hayekian point that regulators often lack the information needed to make the right decisions about complicated economic tradeoffs, so that regulation is often both ineffective and burdensome. A related idea is the Collingridge dilemma , which states that the impacts of a technology are often hard to anticipate until it is too late to easily manage them. These dynamics loomed large for AI in 2023-2024. It was clear to Anthropic that AI might in the future be capable of producing biological weapons that could threaten millions, or autonomous misbehavior that in extreme cases could even threaten humanity itself. Less clear was the exact form in which the risks would appear, how best to test for them and mitigate them, and how they would play out in practice. There was therefore a high risk that legislation written ahead of time would end up being ineffective—creating pointless or low-value compliance requirements while missing the most crucial sources of actual risk 2 . Ultimately, we concluded that the right approach at that time was transparency . Developers of AI models should have to disclose their safety procedures and the tests that they run on their models and report on any critical safety incidents, so that the public and the scientific community could gain better visibility into risks as they emerge. When and if risks become more definite and their shape is more clear, then the evidence gained through transparency could be used to design smart legislation to precisely target the most concerning risks. Thus, in 2025, Anthropic supported transparency legislation, helping to pass SB 53 in California, RAISE in NY, SB 315 in Illinois (in early 2026), and advocating for a transparency standard at the federal level . However, now the risks are clearly here . It is time to go beyond transparency to more serious and binding regulation of AI. I believe the best analogy, at least at the current stage of the exponential, is to cars, airplanes, or drugs—powerful technologies essential to the modern economy, but capable of killing large numbers of people if designed or operated poorly. I therefore believe we should model AI regulation on agencies like the Federal Aviation Administration (FAA). Frontier AI models, like airplanes, should be required to go through technical testing and auditing, and their release should be blocked or reversed as a threat to public safety if they do not meet high standards of safety. I am grateful to see the Trump administration’s Executive O