Tag: automation

Japan-Watching: Economics in the Age of AI

A Thought Experiment Envisioning A “Fully Automated Society”

from REITI, by IKEUCHI Kenta [池内 健太], Senior Fellow (Policy Economist)

In a world where human labor has become unnecessary because of AI, what should be the focus of the study of economics? In the future, if AI and robots have become capable of producing all goods and services necessary for our society, what kind of economic problems could remain?

Of course, a fully automated society is not predicted for the near future. This article imagines such an extreme type of society as a thought experiment designed to consider economic systems in the age of AI. Here, a fully automated society refers not only to one in which corporate production is automated, but also to one in which the legislative, administrative, and judicial functions of government are substantially supported—and in some cases automated—by AI.

I started thinking about this matter when I heard from a researcher acquaintance that a paper concerning AI’s impact on employment had caused quite a stir on X, and I decided to read it out of curiosity. The paper discussed the possibility that AI-driven job cuts could reduce workers’ incomes, weaken consumer demand, and ultimately backfire on firms themselves (Falk and Tsoukalas 2026). Companies may earn higher profits in the short term by taking advantage of AI to cut back on personnel costs. However, if most companies follow that same approach, overall market demand would weaken because workers are also consumers.

The purpose of this article is not to question the validity of that paper’s argument. Rather, the focus is on what economic problems would remain in a future society if AI not only partially replaces human labor but also produces most goods and services.

Scarcity Will Continue to Be a Problem

There has already been extensive research regarding the impact of AI on employment. For example, Acemoğlu and Restrepo (2019) argued that while automation may replace existing human jobs, it may also create new ones. Moreover, the possibility that technological advances could free humans from labor and greatly alleviate economic problems was discussed long ago by Keynes (1930).

If AI and robots become capable of producing goods and services on a sufficiently large scale, would economic problems disappear? That would not necessarily be the case. Even in a fully automated society, scarce resources such as land, location, natural environments, energy, and rare metals will remain limited. Moreover, social status, influence, and political decision-making power are deeply connected to human intentions and perception, and cannot simply be delegated to AI. More land will not become available simply because more people wish to live in convenient urban locations. Quiet natural environments, advanced healthcare resources, social attention, and political influence also cannot be maximized for everyone at the same time.

Therefore, even in a fully automated society, some economic problems will remain. However, the main focus will shift from the “problem of insufficient ordinary commodities” to “how to allocate fundamentally scarce resources.” As production capacity increases, the value of true scarcity only becomes clearer.

In that case, the roles of markets and prices will still exist. Prices are not merely figures that allow for corporate earnings; they convey information about which resources are scarce and to what extent, how much demand there is for those resources, and what supply constraints there are. That perspective connects to a classic argument made by Hayek (1945), who argued that prices function as a mechanism through which dispersed information can be aggregated.

However, if AI becomes deeply integrated into the market, the concept of price itself may change. At present, prices serve as one-dimensional signals representing the levels of various factors, such as scarcity, quality, demand, supply, environmental impact, and future risks, expressed in terms of a single metric, that is, monetary value. If AI agents become capable of processing large volumes of information on behalf of consumers and companies, it is possible that multi-dimensional market signals that convey information concerning all those various factors, including quality, environmental impact, congestion, delivery time, reliability and social impact may come into use. Narita (2025) also discussed the possibility that the roles of money and prices may change, with more diverse evaluation standards becoming involved in economic coordination.

For People to Enjoy Affluence

In a fully automated society, how people participate in the market and society will become more important than ever. If the premise that people earn income through labor becomes obsolete, it will be necessary to develop a mechanism whereby purchasing power is distributed to everyone. In this context, universal basic income (UBI) may be reframed not only as relief for the unemployed, but as a form of fundamental purchasing power used by people to express their preferences. Managi [馬奈木 俊介] (2025) also pointed out that governance over the equitable distribution of the benefits of AI is essential.

Moreover, UBI may not be limited to simple monetary payments. In the future, UBI may take the form of a system combining other benefits as well, including energy use quotas, rights of access to basic healthcare services and education, and rights to refuse or control the use of personal data. In a fully automated society, UBI would therefore be a matter not only of how much to provide, but also of what kinds of access to guarantee and over what time horizon.

Another important issue is whether it is appropriate to treat people merely as consumers. In a fully automated society, the need for people to work for a living may diminish. However, even without such a necessity, humans will likely still possess the desire to create or to be creative. It is human nature to try new things and to try to surprise or impress other people. The spirit of fun and curiosity, a desire for self-expression, an inquisitive mind, and an appetite for challenges are deeply and fundamentally connected to human nature. Therefore, when designing a future UBI system, it will be important to treat people not merely as consumers but as agents who can participate in creation and exploration.

Additionally, the question of who owns and controls AI systems, robots, foundation models, and computing infrastructure is also a major issue. Even if a certain level of income is distributed to everyone, there may remain a power gap between those who control AI systems and robots and those who merely have access to them, in place of the income gap that currently exists in society.

All of the above-mentioned points for debate are relevant to the study of economics. How scarce resources should be allocated, how to guarantee people’s range of choices, and how to design ownership and controlling rights are problems central to economics. A fully automated society is not a near-future prediction. However, this extreme thought experiment serves as a useful guide for considering economic systems in the age of AI. Economics in the age of AI is not about discarding the intellectual legacy of economics, but about inheriting it and extending it toward a new society.

References

June 12, 2026

World-Watching: Old Problem, Modern Solution: Emerging Technologies for Anti-Corruption

[from Asia-Pacific Economic Cooperation, 29 July, 2025]

by Emmanuel A. San Andres and Glacer Nino A. Vasquez

Harnessing new tools to strengthen transparency and accountability can help APEC economies combat corruption and build public trust.

The Code of Hammurabi is one of humanity’s oldest surviving legal texts. Etched in basalt nearly four millennia ago, one of the many crimes it proscribes is corruption by a judge, for which the punishment is a hefty fine—“twelve times the fine set by him in the case”—plus removal and perpetual disqualification from office. Today, laws are published online rather than on stone tablets, but corruption remains a scourge across societies.

Thousands of years later, the fight against corruption continues. Corruption scandals continue to make headlines across the region, affecting both public and private institutions. Whether involving procurement fraud or illicit finance flows, these cases underscore how quickly trust can erode when institutions fail to adapt. The need for preventive systems, powered by data, backed by law and enabled by technology, has never been more urgent. Across APEC, the principles of transparency, accountability and integrity remain central to strong public institutions. As economies become more interconnected and more data-driven, emerging technologies are offering new ways to advance these goals.

APEC economies have long relied on oversight mechanisms such as audits, procurement rules, and internal checks to prevent, detect and prosecute corruption. These tools have been effective in fighting corruption, and they remain essential. But at the same time, new technology has also opened new pathways for corruption: The discreet meeting at a coffeeshop may now occur over an encrypted messaging app, and the cash-filled envelope replaced by a cryptocurrency transfer.

As corrupt actors grow more technologically sophisticated, so too must anti-corruption efforts. APEC economies are not new to digital solutions—e-government and e-procurement portals have reduced opportunities for hidden transactions. Beneficial ownership registries and asset tracking systems make it easier to prosecute and penalise incidents of corruption when they do occur. But emerging technologies offer even more powerful tools to prevent, detect and deter corruption.

For example, artificial intelligence and machine learning (AI/ML) enable real-time monitoring, risk scoring, pattern detection, and predictive analytics. These tools can support monitoring and investigation by automating document review and evidence gathering. AI/ML can also enhance institutional capacity through adaptive, personalized training systems.  Meanwhile, advanced data analytics can support the review of large volumes of data, revealing patterns of corrupt activity and informing decision-making. When data from different sources are connected, it becomes easier to understand corruption risks early and act with greater precision.

Blockchain—the technology that enables cryptocurrencies—can be used to create immutable, transparent ledgers for government transactions, supply chain monitoring and secure identity management, making it harder to conceal corrupt activity. Remote sensing and facial recognition technologies also offer potential in compliance monitoring and anomaly detection.

However, implementing these emerging technologies have their share of challenges and risks. The effectiveness of AI/ML systems is only as good as the quality, integrity and objectivity of the data they are fed; biased inputs can produce biased outcomes. Blockchain technology is very energy-intensive, which may hinder its scalability and availability. Facial recognition raises serious concerns over privacy and due process, enabling widespread surveillance without individual consent.

These trends mirror growing international momentum around the digitalization of integrity systems. International organizations are helping lead the way: the OECD is leveraging AI and big data to detect corruption risks and improve compliance, while the World Bank’s Governance Risk Assessment System [archived PDF] uses analytics to uncover fraud in public procurement, with pilots already underway in Brazil. As stewards of major anti-corruption conventions, these institutions are turning innovation into accountability. For APEC economies, this alignment offers a timely opportunity to shape global standards while advancing domestic reform.

It is also important to recognize the central role of human and institutional elements in anti-corruption efforts. Emerging technologies are not a silver bullet; they will only be effective if they are well integrated into government processes and are aligned with the skills of the people who need to use them. Training and capacity building will be essential to bridge capability gaps, while a committed leadership will be needed to implement the legal reforms and oversight structures needed to ensure effective adoption.

Buy-in from anti-corruption stakeholders across government, the private sector and civil society is also crucial to this pursuit. Technologies like AI/ML and advanced analytics require large volumes of reliable data, requiring cooperation and information sharing. Public understanding and trust, ethical use of data and equitable access to technology are all essential to ensuring long-term success.

APEC economies are at different stages of readiness to adopt these emerging technologies. While some economies have yet to develop adequate digital infrastructure, human capital and institutional structures, others are already in a position to expand or integrate more advanced anti-corruption tools into their day-to-day processes. Capacity building, information sharing and dialogue can help narrow this gap while learning from the experiences of those ahead.

This is where regional cooperation can make a difference APEC provides a platform for knowledge sharing, capacity building and policy cooperation. The Anti-Corruption and Transparency Experts Working Group could provide a venue for a collaborative strategy to mainstream emerging technology in anti-corruption work, while building technical capacity for economies that need it. Likewise, the upcoming APEC High-Level Dialogue on Anti-Corruption Cooperation provides an opportunity to reaffirm values and shared commitments in the fight against corruption.

Corruption has existed since the dawn of civilization. As methods to commit corruption have evolved, so must the tools to combat it. People and institutions will always remain at the heart of anti-corruption efforts, but with the right governance and safeguards, emerging technologies can be game-changers in fighting corruption and recovering its proceeds, whether it’s in Babylonian sheqels or in bitcoins.

Emmanuel A. San Andres is a senior analyst, Glacer Niño A. Vasquez is a researcher at the APEC Policy Support Unit. For more on this topic, read the latest issue paper “Technologies for Preventing, Detecting, and Combatting Corruption” [archived PDF].

Education and the Long-Term: Automation As Example

The American Revolution: Pages From a Negro Worker’s Notebook

Chapter 2: The Challenge of Automation

“Since 1955 and the advent of automation, overtime has been detrimental to the workers. Again and again workers have been faced with the decision to work overtime or not to work overtime, and the decision has usually been: ‘To hell with those out of work. Let’s get the dollar while the dollar is gettable.’ The amazing thing is that this has nothing to do with the backwardness of these workers. Not only can they run production and think for themselves, but they sense and feel the changes in conditions way in advance of those who are supposed to be responsible for their welfare. But with all these abilities there is one big organic weakness. Over and over again workers in various shops and industries, faced with a critical issue, only divide and become disunited, even though they are well aware that they are being unprincipled and weakening their own cause as workers. Since the advent of automation there has not been any serious sentiment for striking, particularly if the strike was going to come at the expense of material things that the workers already had in their possession, like cars, refrigerators, TV sets, etc. They were not ready to make any serious sacrifices of these; they would rather sacrifice the issue. Between the personal things and the issue, they have chosen the personal. Most American workers have geared themselves to a standard of living that is based on a five-day week plus—either in the form of overtime or another job, part or full time. And any time this standard of living is threatened, it is a personal crisis, which means that more and more decisions are being personalized and individualized rather than collectivized and socialized.”

(The American Revolution: Pages From a Negro Worker’s Notebook, James Boggs, Monthly Review Press, 1963, page 33)

As far back as 1963, with President John Kennedy in office, James Boggs (a Detroit autoworker) was already quite aware of automation and its challenges.

A “meta-intelligent” education means we learn from any sources available including “angry pamphlets” without worrying about the ideological blinders or fireworks because our desire is not to engage in polemics but to “extract signals” from a noisy world.

Chapter 2 of James Boggs’s pamphlet is called “The Challenge of Automation” and begins: “Since 1955 and the advent of automation, overtime has been detrimental to the workers…”

This immediately tells you that automation is a very long-run historical trend and should be seen in a larger sweep with history as your searchlight.

Indeed the famous German classic The Weavers by Gerhart Hauptmann is about machines as a threat to employment:

The Weavers (German: Die Weber, Silesian German: De Waber) is a play written by the German playwright Gerhart Hauptmann in 1892. The play sympathetically portrays a group of Silesian weavers who staged an uprising during the 1840s due to their concerns about the Industrial Revolution and replacement by machines and automation.

In 1927, it was adapted into a German silent film The Weavers, directed by Frederic Zelnik and starring Paul Wegener.

A Broadway version of The Weavers was staged in 1915–1916.

To dismiss all such movements and revolts as Luddite-like is not useful since it sweeps legitimate problems under the rug.

This includes Ernst Toller’s classic The Machine Wreckers (German: Die Maschinenstürmer). Two of his early plays were produced in this period: The Machine Wreckers (1922), whose opening night in 1937 he attended, and No More Peace, produced in 1937 by the Federal Theatre Project and presented in New York City in 1938.

All of these critiques of machines and automation are part of a long-term historical overview of machines and jobs and in our time, robotics and AI, etc which should be analyzed as a trajectory and arc where “machine wreckers” à la Hauptmann or Toller are understood empathetically and realistically and not dismissed as vandals.

Tangled Up Multifactorial Causes

Workers’ Shrinking Share of the Pie

The current issue of the Federal Reserve Bank of Richmond periodical, Econ Focus (second/third quarter 2019) has a good article, “Workers’ Shrinking Share of the Pie” [Archived PDF] which has the following “conclusion:”

So what explains the recent decline in labor’s share? Unfortunately, it is difficult to untangle the separate roles of automation, globalization, and changes in market power. Automation has likely played a role, but its independent impact is hard to gauge, due to the difficulty in differentiating the recent wave of automation from previous episodes in which labor’s share of national income held steady. Globalization appears to have been a strong contributor—a claim that is buttressed by the near simultaneity of the rise in U.S. trade with China and the decline of labor’s share. A variety of evidence also points to firms’ increased pricing power in product markets and workers’ weakened bargaining power in labor markets. In product markets, information technology and globalization appear to have increased the pricing power and profitability of certain dominant firms. And in labor markets, the insecurity engendered by automation and globalization may have helped to weaken workers’ bargaining power. In short, from the perspective of workers, multiple forces have come together to narrow their slice of an expanding economy.

(second/third quarter 2019 Econ Focus, Richmond Fed, page 17)

It so happens that Professor Robert Lawrence of Harvard (Kennedy School) in his very careful analyses, shows how American economic “numbers” in recent years are consistent with internal American numbers from decades ago and this implies the overall transformation of the American economy was primarily from within (“endogenous”) and not till very recently, from without (“exogenous”). Pre-exisiting internal American trends might be the dominant cause for many decades.

The variable or axis “endogenous versus exogenous” (internal pushes or external pulls?) adds a whole dimension to the discussion and we have to therefore avoid mono-causal explanations.

We live not only in a multifactorial world but in one where “inside pushes” can be confused with “external pulls.”