The Coming AI Economic Revolution

In June 2023, a study of the economic potential of generative artificial intelligence estimated that the technology could add more than $4 trillion dollars annually to the global economy. This would be on top of the $11 trillion that nongenerative AI and other forms of automation could contribute. These are enormous numbers: by comparison, the entire German economy—the world’s fourth largest—is worth about $4 trillion. According to the study, produced by the McKinsey Global Institute, this astonishing impact will come largely from gains in productivity.

At least in the near term, such exuberant projections will likely outstrip reality. Numerous technological, process-related, and organizational hurdles, as well as industry dynamics, stand in the way of an AI-driven global economy. But just because the transformation may not be immediate does not mean the eventual effect will be small.

By the beginning of the next decade, the shift to AI could become a leading driver of global prosperity. The prospective gains to the world economy derive from the rapid advances in AI—now further expanded by generative AI, or AI that can create new content, and its potential applications in just about every aspect of human and economic activity. If these innovations can be harnessed, AI could reverse the long-term declines in productivity growth that many advanced economies now face.

This economic revolution will not happen on its own. Much recent debate has focused on the dangers that AI poses and the need for international regulations to prevent catastrophic harm. As important, however, will be the introduction of positive policies that foster AI’s most productive uses. These policies must promote technologies that augment human capabilities rather than simply replace them; encourage AI’s widest possible implementation, both within and across different sectors, especially in areas that tend to have lower productivity; and ensure that firms and sectors undergo necessary process and organizational changes and innovations to effectively capitalize on AI’s potential. To unleash the full force of an AI-powered economy, then, will require not only a new policy framework but also a new mindset toward artificial intelligence. Ultimately, AI technologies must be embraced as tools that can enhance, rather than undermine, human potential and ingenuity.

THE GREAT SLOWDOWN

The accelerating progress of AI comes at a pivotal moment in the global economy. For three decades, the massive growth of productive capacity in China and other emerging economies kept inflation in check, allowing central banks to lower interest rates to zero and inject very large amounts of liquidity into their financial systems. Those years are over. In many developed countries, growth is slowing and remains weak, in part as a result of the protracted battle with inflation that central banks are now fighting. And productivity growth has been ebbing since around 2005, with the falloff especially pronounced in the decade leading up to the COVID-19 pandemic. Labor productivity growth in the United States, which ran at 1.73 percent in the decade before the financial crisis, dropped by more than two-thirds to 0.53 percent, in the decade before the pandemic. Large service sectors—the areas of the economy that fall outside of manufacturing and trade that now account for almost 80 percent of U.S. employment—fared even worse, with pre-pandemic productivity growth of just 0.16 percent, almost zero.

Other factors have also created supply-side constraints in the global economy. In countries that account for over 75 percent of global economic output, aging populations have limited the growth of the labor supply, increasing dependency ratios—the number of nonworkers relative to the working-age population in a given country—and creating fiscal stress. Many large employment sectors, including government, health care, traditional retail, hospitality, and construction, have critical shortages of workers. And in some countries, such as China, Italy, Japan, and South Korea, overall labor forces are shrinking. Labor markets have also been transformed by the preferences of job seekers in advanced economies, who are choosing employment sectors—and frequently shifting between them—based on flexibility, safety, level of stress, and income. Meanwhile, geopolitical tensions, combined with the shocks of climate change and the pandemic, have led many companies and countries to “de-risk” and diversify their supply chains at great expense for reasons that have nothing to do with reducing costs. The era of building global supply chains entirely on the basis of efficiency and comparative advantage has clearly come to a close.

In short, without a powerful new productivity-enhancing force, the global economy will continue to be held back by slow growth and reduced labor supply, the persistent threat of inflation, higher interest rates, shrinking public investments, and elevated costs of capital for the foreseeable future. Against these headwinds, the costly clean energy transition—which will require an additional $3 trillion in capital spending each year for several decades, according to projections by the International Energy Agency—will be close to impossible to engineer.

These long-term global pressures are a key reason why the AI revolution is so important. It holds the potential for a digitally enabled surge in productivity that could restore growth momentum by easing the supply-side constraints—especially the shrinking labor pool in many countries—that have been holding the global economy back. But for this transformation to occur, the surge will need to have the right characteristics. It must be driven primarily by value-added growth, in which firms and sectors expand value-added output, thereby contributing to a rise in GDP, rather than simply by reducing inputs, such as labor, while keeping the growth in output weak or flat.

A DIFFERENT DIGITAL REVOLUTION

In some respects, the current tsunami of investment in generative AI seems surprising. After all, digital technologies have been transforming the economy in measurable ways for at least three decades. One explanation for the excitement is that unlike earlier digital innovations, the AI revolution has extended the impact of digital technologies well beyond so-called codifiable work—routine tasks that can be reduced to a precise series of instructions. Until recent AI breakthroughs, digital machines could not perform tasks that defied codification, such as recognizing an object as a cat.

In the areas that it touched, the digital revolution was dramatic. Tasks long performed by humans were suddenly taken over by machines. Activities such as bookkeeping, filing, and accounting, much of consumer banking, and the control systems for entire supply chains were partially and sometimes completely automated. In parallel, most information came to be stored and transmitted in digital form, making it cheaper and easier to access and use. An abundance of free and low-cost web-based services also transformed the consumer economy and social interaction.

But the economic impact of these changes, although substantial, was limited in scope. In the sectors where the technologies were widely implemented, productivity increased, much as it did after the first Industrial Revolution, when humans stopped digging trenches and turned instead to steam shovels. In certain areas, jobs declined along with the incomes of some middle-class earners in a phenomenon that has come to be known as “job and income polarization.” Nonetheless, there were many kinds of tasks that could not be automated, and the extent of digital takeover was limited. Above all, the technologies had little effect on knowledge industries and creative industries, such as medicine, law, advertising, and consulting, in which much of the value comes from specific expertise and the performance of nonroutine tasks.

The AI revolution has shattered the constraints of earlier digital technologies.

Now, the AI revolution has shattered those constraints. Through advances in machine learning and pattern recognition over the past 15 years, AI researchers have shown that digital machines can do much more. For example, many human activities that do not lend themselves easily to codification involve pattern recognition: finding and assembling facts and insights, detecting logical and conceptual structures embedded in language, synthesizing and reprocessing information, and drawing on experience, expertise, and tacit knowledge to provide answers to complex and nuanced questions. By using deep learning—multilayered neural networks that simulate the way neurons send and receive signals in the human brain—researchers have made swift advances in machine learning. And with enough data and computing power, this approach has been remarkably effective at replicating many of these pattern-recognition, predictive, and now also generative tasks. The result has been a stunning series of breakthroughs.

Even before the advent of generative AI, machine learning had produced a number of major innovations. A short list of these includes handwriting recognition, speech recognition, and image and object recognition. Many of these tools have been used in smartphones and numerous business and consumer applications. Consider Google Translate, which employs deep learning and is used by more than one billion people; it can already handle more than 100 languages, a number that AI researchers aim to soon expand to more than 1,000. AI has also assisted breakthroughs in a number of scientific fields. For example, AlphaFold, an AI system developed by Google’s AI lab, DeepMind, has been able to predict the protein structures of all 200 million proteins known to science. Researchers around the world are now using these structures to accelerate and assist their investigation of diseases and develop new treatments for them.

Perhaps the most striking development, however, has been the rise of large language models, or LLMs, which provide the basis for generative AI. What underlies LLMs is the Transformer, a deep-learning architecture that was introduced in a now famous paper by Google researchers in 2017. Transformers make use of a mechanism of self-attention to understand the connections and relationships between different words. Along with so-called embeddings—which map the relationships between words and use a unique neural architecture—the Transformer makes it possible for the model to learn in a self-supervised way. Once trained, the model can generate human-like outputs by simply predicting the next word or sequence of words in response to a prompt.

By training these new LLMs on billions, and now trillions, of words, and over long periods, they can generate increasingly sophisticated human-like responses when prompted. More important, their capabilities are not confined to any one sector or area of knowledge. Unlike many previous AI innovations, which were tailored to specific functions, the LLMs that underlie generative AI have a strong claim to be a truly general-purpose technology.

QUICK STUDIES

Generative AI has several features that suggest its potential economic impact could be unusually large. One is exceptional versatility. LLMs now have the capacity to respond to prompts in many different domains, from poetry to science to law, and to detect different domains and shift from one to another, without needing explicit instructions. Moreover, LLMs can work not only with words but also with software code, audio, images, video, and other kinds of inputs, as well as generated outputs—what is often referred to as “multimodality.” Their ability to operate flexibly among multiple disciplines and modes means that these models can provide a broad platform on which to build applications for almost any specific use. Many developers of LLMs, including OpenAI, have created APIs—application programming interfaces— that allow others to build their own proprietary AI solutions on the LLM base. The race to create applications for a huge diversity of sectors and professional disciplines and use cases has already begun.

LLMs are also noteworthy for their accessibility. Because they are designed to respond to ordinary language and other ubiquitous inputs, LLMs can be readily used by nonspecialists who lack technical skills. All that is needed is a little practice in creating prompts that elicit effective responses. At the same time, the models’ use of the vast material on the Internet or any other corpus for training means that they can acquire expertise in almost any field of knowledge. These two features give LLMs far more extensive potential uses than previous digital technologies, even those involving AI. In June 2023 alone, the ChatGPT website was visited by 1.6 billion users, a convincing signal of the low barrier to entry and the breadth of interest in the technology.

It is hard to make detailed predictions about potential future uses for LLMs. But given their unusual attributes, combined with continuing rapid technical innovations by researchers and the huge amounts of venture capital pouring into AI research, their capabilities will almost certainly grow. Within the next five years, AI developers will introduce thousands of applications built on LLMs and other generative AI models aimed at highly disparate sectors, activities, and jobs. At the same time, generative AI models will soon be used alongside other AI systems, in part to address the current limitations of those systems, but also to expand their capabilities. Examples include adapting LLMs to help with other productivity applications, such as spreadsheets and email, and pairing LLMs with robotic systems to improve and expand the operation of these systems. If these various applications are implemented effectively across the economy, a large and extended surge in productivity and other measures of economic performance seems almost certain to follow.

Among the most promising uses of generative AI in the broader economy are in digital assistant systems for the workplace. Consider an April 2023 study by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond on the impact of an AI digital assistant for customer service representatives in the tech sector. The AI assistant had been trained on a large collection of audio recordings of interactions between agents and customers, along with performance metrics for these interactions: Was the problem solved? How long did it take to solve it? Was the customer happy with the result? The AI assistant was then made available to some agents and not others.

The authors of the study identified two important results. The first was that productivity for the group with the AI assistants was on average 14 percent higher. The second, and even more significant, was that, although everyone in the group with the AI assistant had productivity gains, the effect was much higher for relatively inexperienced agents. In other words, the AI assistant was able to markedly close the gap in performance between new and seasoned agents, suggesting generative AI’s potential to accelerate on-the-job training.

Digital mapping tools have had a similar effect on London taxi drivers. London is an incredibly complex city to drive in. In the past, drivers took months and even years to learn the streets well enough to pass the city’s notoriously difficult taxi driver exam, known as “the Knowledge.” Then came Google Maps and Waze. These apps did not eliminate the differential between the veterans and the newcomers, but they certainly reduced it. This leveling-up effect on employee performance seems likely to become a general consequence of the advent of powerful AI digital assistants in many parts of the economy.

Given their demonstrable value, AI digital assistants will soon be performing a great assortment of tasks. For example, they will produce first drafts in media and marketing applications and produce much of the basic code needed for a variety of programming, thus dramatically speeding up the work of advanced-software developers. In many professions, an AI system’s ability to absorb and process vast amounts of literature at superhuman speed will also accelerate both the pace and the dissemination of research and innovation.

Another area in which nascent LLM applications could have a large impact is in ambient intelligence systems. In these, AI technologies are used in conjunction with visual or audio sensors to monitor and enhance human performance. Take the health-care sector. As a 2020 study in Nature discussed, an ambient intelligence system could use a number of signals and inputs—say, recorded discussions between doctors and interns as they make their hospital rounds, combined with a given patient’s charts and the updates to them—to identify missing actions or overlooked questions. The AI component could then produce a summary of its findings for review by the medical staff. According to some estimates, doctors currently spend about a third of their time writing up reports and the decisions made; such a system could reduce that time by up to 80 percent.

In the foreseeable future, ambient intelligence and digital assistants could improve efficiency and transparency in supply-chain management as well as help with complex human tasks. According to the McKinsey Global Institute’s June 2023 report, generative AI has the potential to automate activities that currently take up 60 to 70 percent of workers’ time. Not only would this provide a spur to productivity; it would also free up more human labor for the most advanced tasks and allow for more rapid innovation.

CREATIVE INSTRUCTION

Despite the promise of AI, much of the public debate about it has focused on its controversial aspects and its potential to do harm. To begin with, LLMs are not 100 percent reliable. Their outputs can sometimes reflect the bias of their training sets, produce erroneous material, or include so-called hallucinations—assertions that sound plausible but do not reflect the reality of the physical world. Researchers are trying hard to address these issues, including by using human feedback and other means to guide the generated outputs, but more work is needed.

Another concern is that AI could achieve wholesale automation of many sectors, triggering large-scale job losses. These concerns are real, but they overlook the barriers to full automation in many workplaces, as well as the compensatory job gains—some from growing demand for existing occupations, others from the rise of new occupations, as a result of AI, including generative AI. For example, research suggests that over the next couple of decades, some occupations—roughly 10 percent of all occupations according to some estimates—whose constituent tasks can almost all be automated, will likely decline. Other occupations, both existing and new, will grow. But the largest effect of AI on the economy overall, involving about two-thirds of occupations, will be to change the way that work is performed, as some constituent tasks—on average about a third—are augmented by AI. Occupations in these fields will not go away, but they will require new skills as people do their jobs in collaboration with capable machines.

Many commentators have also noted the dangers of giving AI systems too much control. As numerous examples have shown, generative AI platforms occasionally get things wrong or hallucinate—that is, make things up. For example, an LLM given a prompt to write an article on inflation not only produced the article but concluded with a list of additional reading that included five articles and books that do not exist. Obviously, in applications that require factual accuracy, made-up answers pose a major concern. Even when not hallucinating, LLMs can produce bad, seriously biased, silly, or obnoxious predictions that require human review. Thus, the careless or overly expansive implementation of generative AI could lead to the perpetuation of flawed information or even to malpractice.

AI digital assistants will soon be common in many workplaces.

Access to better training data may lower the risks of faulty outputs, but the problem is really a function of how LLMs work: even if trained on perfectly accurate data, the models can yield different and even contradictory answers to the same prompt simply because they are prediction machines operating in a probabilistic world. The mistake in all this is to think of LLMs as databases that simply store information. In fact, because of the probabilistic mechanism by which they learn and generate outputs from the material they are trained on, and their ability to associate ideas and concepts that may not have been associated before, their output cannot be wholly determined, even with perfect training data. For many companies and economic sectors, prudence will dictate that humans cannot be entirely written out of the script, at least not any time soon.

Moreover, in some areas of the economy, facts and accuracy are not as important as new ideas or creativity. Fashion designers have started to ask AIs to generate new clothing prototypes. AIs can generate music, write poems, make art, and draft the outlines of novels. As a source of inspiration, generative AI could become a useful tool. The concern for some is that AI could eventually replace the artist. It is too soon to know whether AI-generated content will find a serious following in the creative and performing arts. Our best guess is that it will be used more for assisting and providing inspiration than for producing finished works of art.

Given its remarkable capabilities and range, where will the main economic impact of generative AI occur? When Sundar Pichai, the CEO of Alphabet, Google’s parent company, was asked a version of this question, he responded that it would come in the “knowledge economy.” This seems exactly right. One could substitute the term “information economy,” but across fields from scientific research to software development and a host of service functions, the potential economic benefits of LLM-based applications seem extremely large.

WITH US, NOT AGAINST US

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 lagged, 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 AI 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.

Generative AI will cause far more jobs to change than to disappear.

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.