But absent informed engagement by policymakers, AI fails to transform the economy and supercharge global economic growth. Without copyright law changes, AI risks multiple scenarios for extinction

Despite its enormous promise, AI is unlikely to trigger an economy-wide jump in productivity, or to support sustainable and inclusive growth, if its use is left to market forces. Achieving AI's greatest potential benefits will require a proactive two-sided approach. One is anticipating and, to the extent possible, preventing the misuse or harmful effects of the technology. The other is promoting the uses of AI that most assist and benefit people, power the economy, and help society tackle its most pressing opportunities and challenges--by making it more accessible, ensuring its widespread diffusion, and encouraging its most productivity-enhancing applications. For the moment, preventing harm and damage has received the lion's share of attention. In May, more than 350 AI industry leaders signed an open letter warning that "mitigating the risk of extinction" from AI should be a global priority alongside preventing pandemics and nuclear war; many, including one of us (Manyika), signed the letter to highlight the precautionary principle that should always be applied to powerful technology. Others have warned of the risks of misuse by bad actors with various motivations, as well as unconstrained military applications of AI in the absence of international regulations. These issues are important and should be addressed. But it is wrong to assume that simply limiting the misuse and harmful side effects of ai will ensure that its economic dividends will be delivered in a broadly inclusive way. Active policies and regulations aimed at unleashing those benefits will play a major role in determining whether AI realizes its full economic potential. First, policies will need to be developed to ensure that AI complements rather than replaces human labor. In current practice, AI tools are often developed and benchmarked against human performance, leading to an industry bias toward automation. That bias has been referred to as "the Turing trap," a term coined by Brynjolfsson, after the mathematician Alan Turing's argument that the most important test of machine intelligence is whether it can equal or surpass human performance. To get around this trap, public and private research funding for AI research should avoid an overly narrow focus on creating human-like ai. For example, in a growing number of specific tasks, AI systems can outperform humans by substantial margins, but they also require human collaborators, whose own capabilities can be further extended by the machines. More research on augmenting technologies and their uses, as well as the reorganization of workflow in many jobs, would help support innovations that use AI to enhance human productivity. Another crucial priority will be to encourage the widest possible spread of AI technologies across the economy. In the case of the earlier digital revolution, a large body of research has documented highly uneven adoption across sectors and firms. Many large employment sectors tagged, leading to a drag on productivity. This pattern could easily be repeated. In the case of generative AI, small and medium-sized firms deserve special attention, since they may not have the resources to conduct the experiments and develop use cases. It is possible that reductions in the current high costs of A I development and research, as well as competition among the major developers, will lead to affordable AI applications that can be widely implemented, by keeping costs down and spurring entrepreneurial activity. But policymakers must be diligent in creating rules that ensure that such competition results in broad diffusion and use of the technologies. A related issue is how to accelerate the use of AI by the industries that stand to benefit from them most. In many cases, some stakeholders, including employees, will understandably focus on the risks and resist adopting AI systems. To counter this tendency, policymakers and companies will need to consult with all parties involved and ensure that their interests are taken into account. At a macro level, the employment and wage effects of AI adoption--including the disappearance of some jobs even as others grow--should also be addressed. Partnerships involving government and industry and educational institutions will be needed to help people adapt to the different skill requirements needed for working in an AI-assisted environment. Income support during the transition to an AI-augmented economy may be another key ingredient, particularly in occupations such as call centers and other customer operations in which ai could put downward pressure on wages and even cause net job loss. But despite fears to the contrary, the prospect of large-scale Ai-induced unemployment does not seem likely, especially given current labor shortages in a number of sectors. Those anxieties are based on the incorrect assumption that demand is fixed, or inelastic, and hence insensitive to price and cost changes. In such a world, productivity gains automatically produce employment reductions. In fact, although there are likely to be lots of changes in the characteristics of many jobs, as well as some job displacement, overall employment levels in the economy are unlikely to change much, assuming the economy continues to grow. Research suggests that under most scenarios, more jobs will be gained than lost over the next decade or more. A larger challenge will be addressing the uneven effects of the new technologies, both within and between countries. Within countries, productivity growth is likely to be concentrated in white-collar jobs rather than blue-collar jobs because of generative Ai's particular impact on the knowledge economy. To achieve a similar productivity surge in the industrial economy, however, will require additional major advances in robotics. Despite good progress on that front, technological challenges remain, with the result that automation and augmentation in manufacturing, logistics, and autonomous vehicles are proceeding more slowly. Such a divergence in productivity growth between the knowledge economy, the wide service sector, and industrial sectors could further contribute to unequal distribution of AI gains. Countries will also need to confront the uneven adoption of advanced digital technologies both among firms within the same sector and among sectors. For example, within sectors, so-called frontier firms, which are often the most nimble, have outstripped other firms in using digital technologies. Similarly, the high-tech and financial services sectors have been faster to adopt new technologies than has health care, creating unevenness that can become a barrier to economy-wide productivity gains. Internationally, the recent breakthroughs and innovations in AI have clearly been led by the United States, with China in second place. These two countries are also home to the AI platform companies with enough computing power to train advanced LLMS. By contrast, the European Union has fallen behind the United States and China in AI, cloud computing, and other related areas. The question, then, is how quickly advanced AI applications can be implemented throughout the global economy. Under the open model that prevailed for several decades after World War II, technology could spread quite rapidly across borders. But that world is no more. The complex and increasingly restrictive constraints on flows of technology and capital--whether from the war in Ukraine, sanctions, or rising tensions between China and the United States--have created new barriers to international diffusion. Because of its digital nature, AI technology will spread; in fact, it would be very hard to stop it from doing so. But ensuring that it does so in the right way will require new forms of international economic governance. Thus, even if it lags in AI research, the EU will adopt the technology and use it. But many emerging economies will also benefit from this technology, and for them, access may be slow and uneven. The extent to which AI can be developed and used in an equitable way worldwide will determine the magnitude of its effect on the global economy. THE REAL AI CHALLENGE AI, including its most recent addition, generative AI, has the potential to produce a large and decisive upswing in productivity and growth at a moment when the global economy desperately needs it. Among many current economic challenges are supply constraints, growing pressure on overindebted countries, demographic changes, and persistent inflation, all of which threaten to limit countries' ability to sustain prosperity. With its broad scope and its ease of use, generative AI could do much to counter these forces. Moreover, the Ai revolution has unleashed an intense period of experimentation and innovation that could add much more value to the economy. But to fully realize this potential will require equally intense attention to policy. Governments, companies, and researchers will need to prioritize augmenting human skills rather than replacing them. They will need to promote the use of the technology across the whole of the economy. And they will need to build an economy in which the use of AI systems is sensitive to the needs of workers themselves and in which shocks are minimized and the widespread fears of excessive automation are addressed--or they will likely encounter unnecessary resistance. The development of AI has reached a crucial juncture. The technology's fraught potential, to bring enormous human and economic gains but also to cause very real harms, is coming sharply into focus. But harnessing the power of AI for good will require more than simply focusing on existential threats and potential damage. It will demand a positive vision of what AI can do and effective measures to turn that vision into reality. For the most likely risk that AI poses to the world today is not that it will produce some kind of civilizational catastrophe or a huge negative shock to employment. Rather, it is that without effective guidance, AI innovations could be developed and implemented in ways that simply magnify current economic disparities rather than bring about a strengthened global economy for generations to come.