Politica sull'esponenziale dell'IA

<p># Policy on the AI Exponential</p><p>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.</p><p>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”.</p><p>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.</p><p>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.</p><p>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.</p><p>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 behind1.</p><p>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&#x27;s been encouraging to see our peers come around to the same positions we&#x27;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&#x27;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.</p><p>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.</p><p>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.</p><p>## 1. Regulation and public safety</p><p>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.</p><p>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 risk2.</p><p>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.</p><p>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 Order move incrementally towards a greater role for government in AI, though** **Anthropic’s proposal recommends even further action. Our proposal includes the following elements:</p><p>- Models above a threshold of compute should undergo mandatory testing by a qualified third party for their level of risk in four specific areas: cybersecurity, biological weapons, loss of control of AI systems, and automated R&amp;D that could accelerate these other risks.<br>- The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks. This power must be scoped to the above four specific risks and there must be protective measures against political favoritism or arbitrary decisions.<br>- Third-party evaluation could be done by a government agency (similar to the FAA) or a set of private organizations that are authorized and inspected by the government to evaluate models according to certain standards (a “regulatory markets” approach).<br>- AI companies that develop advanced AI models must have strong security standards that protect their model weights, should conduct regular red teaming and penetration testing, and should work with the government to defend against major threat actors.<br>- Safety incidents in the four critical areas must be reported promptly.</p><p>There may come a time, perhaps relatively soon, when we need to go beyond this, when the most powerful AI systems look less like airplanes or automobiles and more like weaponizable nuclear materials—a threat to humanity rather than “just” a threat to public safety. If that occurs, we may need more aggressive regulatory measures than those I have laid out3. But just as it was difficult in 2024 to target and apply the measures I’m suggesting now, I don’t think we should get ahead of ourselves. We should design policies for the dangers that are emerging today, while laying the foundations to ramp up our response even more quickly as new dangers appear.</p><p>## 2. Macroeconomics and tax policy</p><p>Governments have long faced the problem of how to encourage economic growth while also providing important public services and ensuring that the least fortunate are taken care of. An important (and generally correct) premise of these debates has been that *economic growth is fragile and difficult to achieve*—that while reducing inequality might provide important benefits, it has to be traded off against the economic drag of increased taxes or deficits.</p><p>I suspect that powerful AI may scramble this assumption. If AI achieves the ability to do most cognitive tasks far better than humans, it stands to reason that it could result in extremely rapid and robust economic growth via the acceleration of science, technology, and operational efficiency. The iterative ability of AI to build even better AI may supercharge that growth even further. But for exactly the same reasons, AI may also act as a more general economic substitute for human cognitive abilities than previous technologies have, while also altering the economy far faster than previous technologies have. Thus, it’s reasonable to think that AI could produce much larger disruptions to the labor market than previous technologies, and, potentially, more *enduring *disruptions. We risk ending up in a world where the economic tradeoff dial is stuck on the hypergrowth, hyper-inequality setting, and is potentially very hard to unstick from that setting. *The key challenge in such a world won’t be incentivizing growth, but finding a way for everyone to share in the benefits.*</p><p>Of the topics discussed in this essay, macroeconomics and enduring labor displacement are arguably the ones that have attracted the most public attention and the most misunderstanding, so I want to be extremely clear on two points.</p><p>_(testo troncato)_</p>