The Historian Revenge, Why the 2025 Nobel Prize is a Wake-Up Call for a World in Crisis

The Historian Revenge, Why the 2025 Nobel Prize is a Wake-Up Call for a World in Crisis

In the rarefied world of economics, the annual Nobel Prize announcement is more than an award; it is a signal, a statement of what the global academy deems most pressing. The 2025 prize, awarded to Joel Mokyr, Philippe Aghion, and Peter Howitt, sent a powerful and timely message: the future of our prosperity depends on understanding its deep, historical roots. While the trio’s work on innovation and growth has been celebrated, it was a wry, almost melancholic quip from Mokyr that echoed beyond academic circles: “economic historians don’t win the prize.” This statement, made at the moment of his ultimate professional vindication, is more than a jest; it is a key to understanding why our modern policy debates so often falter, and how the wisdom of the past can guide us through the tumultuous present.

We live in an age of unprecedented technological change, geopolitical upheaval, and social fragmentation. Our instinct is to look forward, to seek novel solutions in algorithms, fiscal stimulus, or regulatory crackdowns. Yet, the 2025 Nobel laureates remind us that these challenges are not new in their essence. They are variations on the oldest of human themes: how societies adapt, learn, and create the conditions for widespread well-being. By honouring Mokyr, an economic historian, alongside Aghion and Howitt, the architects of modern Schumpeterian growth theory, the Nobel committee has implicitly argued that the map for our future is, in fact, a palimpsest, with the writings of the Industrial Enlightenment still clearly visible beneath the digital ink of the 21st century.

The Social Plumbing of Progress

At the heart of Mokyr’s work, sharpened by scholars like David Hounshell, is a paradigm-shifting idea: modern growth is a social technology before it is a mechanical one. The Industrial Revolution was not merely a sudden eruption of gadgets—the spinning jenny, the steam engine. These were the symptoms, not the cause. The true revolution was the creation of a “civic machinery” that allowed useful knowledge to circulate, collide, and combine.

Imagine 18th-century Britain not as a land of isolated inventors, but as a nascent network society. The printing press democratized access to technical diagrams. Coffee houses became the social media platforms of their day, where merchants, scientists, and philosophers exchanged ideas. Learned societies and “dissenting congregations” provided forums for debate outside the stifling control of established institutions. Guilds, often dismissed as mere cartels, were also vital repositories of “tacit know-how,” where apprenticeships and shop-floor heuristics formed the living “codebase” of progress.

This framework provides a devastatingly effective lens through which to view our own technological moment. We are obsessed with the hardware of Artificial Intelligence—the chips, the models, the algorithms. But the true determinant of AI’s impact will be its social software: the regulatory frameworks, the educational systems, the norms of data sharing, and the institutions that govern its development and deployment. Where will the modern equivalents of the coffee house emerge? In open-source consortiums? In interdisciplinary research labs? Or will they be stifled by corporate secrecy and patent walls, the modern analogues of ossified guilds that “throttled entry” to protect their privilege?

This is where Mokyr’s narrative dovetails perfectly with that of his co-laureates, Aghion and Howitt. Their “Schumpeterian growth framework” provides the dynamic microfoundations for Mokyr’s historical panorama. They model how innovation rents attract entrepreneurs, how incumbents “litigate and lobby” to protect their turf, and how policy can either “harden moats or protect the process that makes churn productive.” Mokyr shows us how societies, centuries ago, built the first working engine of sustained growth. Aghion and Howitt provide the manual for how to keep it tuned under the immense pressures of global competition and technological disruption. The engine misfires when experimentation is costly and entry is blocked; it hums when institutions tilt toward contestability and diffusion.

Three Modern Test Cases: AI, Debt, and Inequality

The true power of this historical-economic synthesis is its immediate relevance to the most intractable problems gripping the world today.

1. Artificial Intelligence and the Future of Work:
The dominant public narrative around AI oscillates between utopian hype and dystopian fear of mass unemployment. Mokyr’s work cautions against such simplistic conclusions. Historical technology shocks, from the power loom to the personal computer, have rarely been one-for-one job killers. Instead, they reprice competencies and reorganise tasks. The spinning jenny didn’t eliminate the need for textile workers; it devalued the skill of hand-spinning and increased the demand for machine operators and maintenance.

The critical social question, therefore, is not if there will be jobs, but transition management. Who bears the cost of moving from old tasks to new ones? This is where the Aghion-Howitt injunction to “protect processes, not incumbents” becomes critically policy-relevant. Instead of subsidizing dying industries or specific companies, the focus must be on building agile, resilient workers. This means:

• Portable benefits that are tied to the individual, not the employer.

• Credible skills bridges through lifelong learning accounts and modern apprenticeship programs.

• Interoperability and data portability in digital markets, preventing platform lock-in and empowering workers to own their professional reputations and data.

These are not mere social policies; they are the essential social technology for ensuring that the creative destruction of AI is productive, not merely destructive.

2. The Spectre of High Public Debt:
In an era of swollen government balance sheets, a fatalistic narrative has taken hold: either we embrace crippling austerity or we face fiscal collapse. History, however, “cures exuberance and fatalism alike.” As Mokyr’s studies of the Dutch Republic and Great Britain show, these states did not become credible borrowers—able to finance long-term projects and roll over obligations—through austerity alone. They built civic capacity. They developed efficient tax systems, representative institutions that ensured accountability, and legal frameworks for enforceable contracts.

This institutional foundation created trust. Lenders believed that the state had the long-term capacity to honour its debts because it was backed by a robust social contract, not just a promise of future cuts. The lesson for today is stark: fiscal sustainability is institutional, not merely arithmetic. A nation can have a perfect, balanced budget on paper, but if its institutions are corrupt, its tax system is unfair, and its citizens do not trust the government, its fiscal foundation remains weak. Conversely, a country with higher debt but strong, trusted institutions has a much greater capacity to navigate fiscal challenges.

3. The Persistent Challenge of Inequality:
The history of guilds, as illuminated by Mokyr, offers a masterclass in how privilege often hides behind claims of quality control and public safety. Medieval guilds argued that their restrictive practices were necessary to maintain standards, much like modern professional associations or large tech platforms argue that their gatekeeping ensures security and quality. The counter to this is not reckless iconoclasm, but contestability.

The goal is to lower the costs of entry and diffusion so that insiders’ rents are bid down by demonstrable capability, not by pedigree or incumbency. In the digital age, this maps directly to:

• Pro-competitive procurement that gives startups a fair chance.

• Open standards that prevent large companies from locking users into their ecosystems.

• Strict limits on self-preferencing by dominant platforms.

The cheap pamphlet and the coffee house of the Enlightenment broke the monopoly of the established press and the university. Today, open-source software, modular AI tools, and decentralized digital networks could serve the same democratizing function.

Digitisation and the Reality Check of History

Finally, the historical perspective provides a crucial vaccine against the hype cycles that dominate tech discourse. The work of Sussex economic historian Nick Crafs (as noted in the text) revises our understanding of the British Industrial Revolution, showing that General Purpose Technologies (GPTs) like steam, electricity, and ICT appear in macroeconomic data surprisingly late. The reason? They require a vast web of complementary investments and firm reorganization. The steam engine was transformative not when it was invented, but when it was integrated with new factory layouts, supply chains, and worker skills.

This is a vital lesson for the age of AI. We should not expect productivity miracles to appear in the national accounts overnight. The true gains will come slowly, as businesses restructure, as new skills are learned, and as new social and legal norms are established around the technology. Similarly, Jared Diamond’s wider lens—incorporating geography, ecology, and diffusion barriers—reminds us that technology is never a pure, abstract force. It is embedded in landscapes, cultures, and existing path dependencies. An AI model that works in Silicon Valley may fail in rural India, not because of a deficiency in the algorithm, but because of a lack of complementary infrastructure, skills, or institutional trust.

Conclusion: The Machinery Over the Model

So, why does Mokyr’s quip—“economic historians don’t win the prize”—still feel true, even as he stands as a living counterexample? Because prizes, like productivity statistics, are lagging indicators. The frontier of economic research has, for decades, been dominated by a quest for mathematical rigour and clever “identification strategies” to establish causal claims. This is valuable work, but it often operates on a shorter time horizon.

Yet, the problems that preoccupy the public—automation, debt, social mobility, geopolitical realignment—are historical in essence. They are not clean, controlled experiments. They are messy, decades-long causal stories “with actors who learn, bargain, and sometimes entrench.” This is the comparative advantage of economic history. It is the lab where the complex interactions of rules, culture, and technology are tested as the ultimate drivers of, or brakes on, human progress.

The deeper point, therefore, is not about choosing one economic model over another. It is about prioritizing machinery over the model. The fundamental question for every society is this: Are we protecting the engine itself—the open, critical, and experimental process that allows new ideas to travel from discovery to diffusion? Or are we, in a fit of short-sightedness, leaning toward merely protecting the owners of the last great engine?

History’s answer, as documented by Mokyr and his intellectual forebears, is blunt: prosperity is the exception, not the rule. It is not a natural state nor an inevitable outcome of technological change. It must be argued for, built, and defended—institutionally and incessantly. The 2025 Nobel Prize is a powerful reminder that in forgetting that long-run truth, we risk engineering our own decline.

Q&A: Unpacking the 2025 Nobel Prize in Economics

Q1: The article emphasizes that growth is a “social technology first.” What does this mean in practical terms for a government trying to foster innovation today?

A: It means shifting focus from solely funding R&D or offering tax breaks to a handful of tech giants, to actively building the ecosystem that allows ideas to spread and combine. Practically, this involves:

• Investing in “connective tissue”: Funding public data repositories, supporting open-access publishing, and creating physical and digital spaces for interdisciplinary collaboration (modern “coffee houses”).

• Reforming education: Moving beyond rote learning to emphasize critical thinking, creativity, and the ability to learn new skills—the foundational competencies for a dynamic economy.

• Ensuring contestable markets: Implementing strong antitrust policies to prevent incumbents from using their market power to stifle new entrants, ensuring that innovation, not lobbying power, determines success.

Q2: How does the “Aghion-Howitt framework” specifically advise governments to handle job displacement from AI?

A: Their framework advises governments to avoid protecting specific, dying jobs or companies (the “incumbents”). Instead, policy should protect the process of creative destruction by making the economy more agile and workers more resilient. This means:

• Supporting workers, not jobs: Implementing wage insurance, portable health and pension benefits, and robust unemployment assistance that helps people transition between roles.

• Funding reskilling, not nostalgia: Creating public-private partnerships for credible, industry-relevant training programs in growing fields, rather than retraining people for versions of their old jobs.

• Promoting labour mobility: Encouraging geographic and occupational mobility through housing policy and by recognizing skills and credentials across sectors.

Q3: The text states that “fiscal sustainability is institutional, not merely arithmetic.” Can you explain this with a modern example?

A: Consider two countries with similar levels of public debt-to-GDP:

• Country A has a history of political corruption, an inefficient and unfair tax system where the wealthy often evade taxes, and low public trust in government.

• Country B has strong, independent institutions, a transparent and broadly perceived-as-fair tax system, and a high degree of public trust.

Despite having the same “arithmetic” debt level, Country B is far more fiscally sustainable. International lenders will demand lower interest rates to lend to it because they trust its institutional capacity to manage the debt. Its citizens are more likely to accept necessary fiscal adjustments because they believe the government will spend the money wisely. Country A, lacking this institutional credibility, faces a much higher risk of a debt crisis, as lenders and citizens alike lack confidence in its long-term governance.

Q4: The article draws a parallel between historical guilds and modern tech platforms. What is the common thread, and what is the modern solution?

A: The common thread is the use of a justified concern (quality, safety, network stability) to justify restrictive practices that ultimately serve to entrench incumbents and extract rents. Medieval guilds controlled apprenticeships; today’s platforms control APIs and data access.
The modern solution, analogous to the “cheap pamphlet,” is to enforce contestability through regulation:

• Mandating Interoperability: Forcing large platforms like social media networks or app stores to allow users to communicate with those on other platforms and to transfer their data.

• Banning Self-Preferencing: Preventing a company like Amazon from giving its own products an advantage in its marketplace search results.

• Promoting Open Standards: Supporting non-proprietary technical standards that allow new companies to build compatible products and services, preventing “lock-in.”

Q5: The piece suggests that economic history is a “vaccine against both euphoria and despair” regarding technology. How does it achieve this?

A: It provides a long-term perspective that tempers extreme emotions:

• Against Euphoria: It teaches us that technologies like AI are General Purpose Technologies (GPTs). History shows GPTs take decades to show up in broad productivity data because they require massive, slow, complementary changes in business processes, skills, and institutions. This cools the hype that AI will instantly revolutionize everything.

• Against Despair: It shows that technological disruption is not new. The Luddites feared the power loom, and societies adapted. Past transitions were painful, but they ultimately led to new industries and higher living standards. This perspective counters the doom-laden narrative that AI will inevitably lead to permanent, mass unemployment. It reminds us that the outcome is not predetermined by the technology itself, but by the social and institutional choices we make in response to it.