The Great Acceleration: What Today’s Nobel Laureates Know About Tomorrow

Part I – The Vision

Is Our Economic Future Behind Us?

By Joel Mokyr, Project Syndicate | Nov 29, 2016

With the global economy yet to recover from the 2008 economic crisis, concern about the future – especially of the advanced economies – is intensifying. But, with scientific progress surging forward, there is plenty of reason to believe that technological advances will continue to surpass our wildest dreams – and drive growth.

CHICAGO – With the global economy yet to recover from the 2008 economic crisis, concern about the future – especially of the advanced economies – is intensifying. My Northwestern University colleague Robert J. Gordon captures the sentiment of many economists, arguing in his recent book The Rise and Fall of American Growth that the enormous productivity-enhancing innovations of the last century and a half cannot be equaled. If true, advanced economies should expect slow growth and stagnation in the coming years. But will the future really be so bleak?

Probably not. In fact, pessimism has reigned over economists’ outlooks for centuries. In 1830, the British Whig historian Thomas Macaulay observed that, “[i]n every age, everybody knows that up to his own time, progressive improvement has been taking place; nobody seems to reckon on any improvement in the next generation.” Why, he asked, do people expect “nothing but deterioration”?

Soon, Macaulay’s perspective was vindicated by the dawn of the railway age. Transformative advances in steel, chemicals, electricity, and engineering soon followed.

When it comes to our technological future, I would expect a similar outcome. Indeed, I would go so far as to say, “We ain’t seen nothin’ yet.” Technological advances will create a tailwind of hurricane-like proportions to the world’s most advanced economies.

My optimism is based not on some generalized faith in the future, but on the way science (or “propositional knowledge”) and technology (“prescriptive knowledge”) support each other. Just as scientific breakthroughs can facilitate technological innovation, technological advances enable scientific discovery, which drives more technological change. In other words, there is a positive feedback loop between scientific and technological progress.

The history of technology is full of examples of this feedback loop. The seventeenth-century scientific revolution was made possible partly by new, technologically advanced tools, such as telescopes, barometers, and vacuum pumps. One cannot discuss the emergence of germ theory in the 1870s without mentioning prior improvements in the microscope. The techniques of x-ray crystallography used by Rosalind Franklin were critical to the discovery of the structure of DNA, as well as to discoveries that led to over 20 Nobel prizes.

The instruments available to science today include modern versions of old tools that would have been unimaginable even a quarter-century ago. Telescopes have been shot into space and connected to high-powered adaptive-optics computers, to reveal a universe quite different from the one humans once imagined. In 2014, the builders of the Betzig-Hell microscope were awarded a Nobel Prize for overcoming an obstacle that had previously been considered insurmountable, bringing optical microscopy into the nanodimension.

If that is not enough to quash technological pessimism, consider the revolutionary instruments and tools that have emerged in recent years – devices that would never even have been dreamed of a few decades earlier. Start with the computer. Economists have made valiant efforts to assess computers’ impact on the production of goods and services, and to measure their contribution to productivity. But none of these measures can adequately account for the untold benefits and opportunities computers have created for scientific research.

There is no lab in the world that does not rely on them. The term in silico has taken its place next to in vivo and in vitro in experimental work. And entire new fields such as “computational physics” and “computational biology” have sprung up ex nihilo. In line with Moore’s Law, advances in scientific computation will continue to accelerate for many years to come, not least owing to the advent of quantum computing.

Another new tool is the laser. When the first lasers appeared, they were almost an invention in search of an application. Nowadays, they are almost as ubiquitous as computers, used for seemingly mundane daily uses ranging from document scanning to ophthalmology.

The range of research areas that now rely on lasers is no less broad, running the gamut of biology, chemistry, genetics, and astronomy. LIBS (laser-induced breakdown spectroscopy) is essential to the protein analysis on which so much research in molecular biochemistry depends. Recently, lasers enabled the confirmation of the existence of gravitational waves – one of the holy grails of physics.

Yet another technological innovation that is transforming science is the gene-editing tool CRISPR Cas9. Already, sequencing genomes is a fast and relatively cheap process, its cost having dropped from $10 million per genome in 2007 to under $1,000 today.

CRISPR Cas9 takes this technology to a new, truly revolutionary level, as it enables scientists to edit and manipulate the human genome. While that idea may give some people pause, the technology’s potential beneficial applications – such as enabling essential crops to withstand climate change and water salination – cannot be overestimated.

Furthermore, digitization has lowered access costs for researchers substantially. All research relies on access to existing knowledge; we all stand on the shoulders of the giants (and even average-size figures) who came before us. We recombine their discoveries, ideas, and innovations in novel – sometimes revolutionary – ways. But, until recently, learning what one needed to know to come up with scientific and technological innovations took a lot more work, with countless hours spent scouring libraries and encyclopedia volumes.

Nowadays, researchers can find nanoscopic needles in information haystacks the size of Montana. They can access mega-databases, where they can find patterns and empirical regularities. The eighteenth-century taxonomist Carl Linnaeus would be envious.

Our scientific knowledge is surging forward, leading to innumerable new applications. There can be no doubt that technology will forge ahead as well, in scores of expected and unexpected areas. It will bring economic growth, albeit perhaps not the kind that will register fully if we continue to rely on our outdated standards for national income accounting.

Joel Mokyr, a 2025 Nobel laureate in economics, is Professor of Economics and History at Northwestern University.

Part II – The Evidence

What AI Means for Growth and Jobs

By Philippe Aghion, Simon Bunel, and Xavier Jaravel, Project Syndicate | January 14, 2025

While many commentators warn that AI will undermine employment and offer only modest productivity gains, empirical studies continue to suggest otherwise. With the right policies in place, the technology holds immense potential to drive both growth and employment.

PARIS – Some prominent economists argue that the revolution in artificial intelligence – particularly the rapid development of generative AI – will have only moderate effects on productivity growth but unambiguously negative effects on employment, owing to the automation of many tasks and jobs. We disagree on both counts.

When it comes to productivity growth, AI’s impact can operate through two distinct channels: automating tasks in the production of goods and services, and automating tasks in the production of new ideas. When Erik Brynjolfsson and his co-authors recently examined the impact of generative AI on customer-service agents at a US software firm, they found that productivity among workers with access to an AI assistant increased by almost 14% in the first month of use, then stabilized at a level approximately 25% higher after three months. Another study finds similarly strong productivity gains among a diverse group of knowledge workers, with lower-productivity workers experiencing the strongest initial effects, thus reducing inequality within firms.

Moving from the micro to the macro level, in a 2024 paper, we (Aghion and Bunel) considered two alternatives for estimating the impact of AI on potential growth over the next decade. The first approach exploits the parallel between the AI revolution and past technological revolutions, while the second follows Daron Acemoglu’s task-based framework, which we consider in light of the available data from existing empirical studies.

Based on the first approach, we estimate that the AI revolution should increase aggregate productivity growth by 0.8-1.3 percentage points per year over the next decade. Similarly, using Acemoglu’s task-based formula, but with our own reading of the recent empirical literature, we estimate that AI should increase aggregate productivity growth by between 0.07 and 1.24 percentage points per year, with a median estimate of 0.68. In comparison, Acemoglu projects an increase of only 0.07 percentage points.

Moreover, our estimated median should be seen as a lower bound, because it does not account for AI’s potential to automate the production of ideas. On the other hand, our estimates do not account for potential obstacles to growth, notably the lack of competition in various segments of the AI value chain, which are already controlled by the digital revolution’s superstar firms.

What about AI’s implications for overall employment? In a new study of French firm-level data collected between 2018 and 2020, we show that AI adoption is positively associated with an increase in total firm-level employment and sales. This finding is consistent with most recent studies of the firm-level effects of automation on labor demand, and it supports the view that AI adoption induces productivity gains by helping firms expand the scope of their business.

This productivity effect appears to be stronger than AI’s potential displacement effects (whereby AI takes over tasks associated with certain types of jobs and workers, thus reducing labor demand). We find that the impact of AI on labor demand is positive even for occupations that are often classified as vulnerable to automation, such as accounting, telemarketing, and secretarial work. To be sure, while certain uses of AI (such as for digital security) lead to positive employment growth, other uses (administrative processes) do tend to have small negative effects. But these differences appear to stem from different uses of AI, rather than from inherent characteristics of the affected occupations.

All told, the main risk for workers is that they will be displaced by workers at other firms using AI, rather than by AI directly. Slowing down the pace of AI adoption would likely be self-defeating for domestic employment, because many firms will be competing internationally with AI adopters.

While our interpretation of the data shows that AI could drive both growth and employment, realizing this potential will require suitable policy reforms. For example, competition policy must ensure that the superstar firms that dominate the upper segments of the value chain do not stifle entry by new innovators. Our own study finds that AI adopters are predominantly much larger and more productive than non-adopters, suggesting that those already on top are positioned to be the biggest winners of the AI revolution.

To avoid increased market concentration and entrenched market power, we must encourage AI adoption by smaller firms, which can be achieved through a combination of competition policy and suitable industrial policy that improves access to data and computing power. To enhance the employment potential of AI and minimize its negative effects on workers, broad-based access to high-quality education, together with training programs and active labor-market policies, will be crucial.

The next technological revolution is already underway. The future of entire countries and economies will hinge on their willingness and ability to adapt to it.

Philippe Aghion, a 2025 Nobel laureate in economics, is a professor at the College de France, INSEAD, and the London School of Economics.

Simon Bunel is an economist at Banque de France.

Xavier Jaravel is Professor of Economics at the London School of Economics.

Copyright Project Syndicate