“We need global governance for AI. We also need the ‘AI race’ not to get out of control. And we need the two sides to share best practices on things that are useful for humanity.” – Daron Acemoglu – Nobel Laureate
Fears about an unconstrained technological arms race emerge whenever a general-purpose technology begins to reshape economies, security doctrines, and political power. Artificial intelligence is now in that category. It touches not only labour markets and wealth distribution but also the informational infrastructure of democracies, the conduct of warfare, and the balance of power between states and between citizens and corporations.1,2,3 The underlying problem is not simply that AI is powerful, but that its direction is currently set by a small set of actors facing strong incentives to move fast, centralise data, and prioritise automation-heavy business models, with only weak countervailing institutions to discipline that trajectory.1,2,3,10
From industrial revolutions to algorithmic rivalry
Daron Acemoglu’s broader work on institutions and technology argues that major productivity-enhancing innovations have never been politically neutral.2,3,13 Each technological wave has produced a conflict over who controls it, which tasks are automated, which groups gain bargaining power, and whether the resulting prosperity is broadly shared or narrowly captured.2,11,13 In earlier industrial transformations, factory owners, financiers, and state elites tussled over railways, electricity, and mass production. With AI, the protagonists are large technology companies, data-rich platforms, security establishments, and a handful of states that host the key compute and talent hubs.2,3,10
Acemoglu’s assessment of AI’s likely macroeconomic impact is deliberately sober. He estimates that over the next decade, AI will raise GDP by only around 1,1 to 1,6 percent, with an annual total factor productivity gain of roughly 0,05 percent.1,10,14 This projection contrasts sharply with narratives that envisage AI doubling growth or generating explosive productivity miracles. The empirical basis for his estimate is a task-based approach: only about 5 percent of economic activities, mainly a subset of white-collar data-processing and pattern-recognition tasks, can be automated profitably by current and near-term AI systems.1,5,10 Yet even this modest aggregate impact disguises potentially stark distributional changes, especially between labour and capital.5,10
That asymmetry between modest growth and significant distributional upheaval is the first structural tension behind calls for global governance and for restraining an AI race. If the gains are relatively small in aggregate but heavily skewed towards owners of data, algorithms, and computational infrastructure, then the race is not about shared prosperity so much as about who captures the new rents, and who gains informational and surveillance leverage over everyone else.2,5,10
The logic of an AI race
An arms-race dynamic in AI arises from several overlapping mechanisms. First, the technology itself exhibits powerful scale economies: model performance improves when firms can combine vast datasets, specialised talent, and massive computing clusters.3,6,10 That pushes actors towards bigger models, larger training runs, and deeper integration across services. Secondly, there are network effects and lock-in: platforms that deploy AI across search, advertising, cloud, and consumer services accumulate data feedback loops that entrench their lead.3,6
Third, AI has become strategically salient for both economic and security competition. States see leadership in frontier models, semiconductor design, and cloud infrastructure as a source of geopolitical leverage. A government that can deploy advanced AI for intelligence analysis, cyber operations, or autonomous weapons development may perceive falling behind as a security risk. That logic favours accelerating deployment even where safety, robustness, and distributional implications are poorly understood.3,18
This race logic is amplified by financial markets. Investors reward firms that promise large automation-driven cost savings and defensible moats around proprietary models and data. Acemoglu terms many of the resulting products “so-so technologies”: systems that slightly outperform humans on narrow tasks, or merely match them, but are adopted because they reduce wage bills or centralise control, not because they open rich new domains of human activity.10,14 Under this incentive structure, firms race to automate existing tasks, even if the effect on aggregate productivity is underwhelming, and even if workers face stagnant or falling real earnings.5,10
Global governance as an institutional counterweight
For Acemoglu, the central question is how institutions can redirect technological trajectories towards worker complementarity, shared prosperity, and democratic resilience.2,3,10 Global governance of AI is one proposed counterweight to the logic of the race. The idea is not simply to add another layer of bureaucracy but to shape incentives by establishing baseline rules on safety, transparency, labour impacts, and concentration of power, and by enabling cooperation between rival blocs where their interests overlap.
The governance challenge is unusually complex. AI systems are general-purpose tools with applications ranging from drug discovery and climate modelling to mass surveillance, automated influence operations, and autonomous weapons.3,5,18 Regulation confined within national borders cannot fully address cross-border risks such as model-enabled cyber attacks, global misinformation cascades, or destabilising shifts in military doctrines. Nor can individual states easily regulate highly mobile capital and cloud-based services without coordination, as firms can arbitrage regulatory differences.
Acemoglu’s broader writing on multipolar AI governance highlights three linked dangers of leaving direction-setting to a few big actors.3,18 First, excessive automation and centralised control of information, which could reduce worker autonomy and undermine democratic deliberation.2,3 Secondly, a narrowing of the informational ecosystem, as large models trained on centralised datasets become the primary gateway to knowledge, marginalising alternative sources and local contexts.3 Thirdly, a potential race to the bottom on safety, labour standards, and data exploitation if firms and states fear losing advantage by adopting stricter rules.3,18
Global governance structures, whether formal treaties, standards bodies, or linked national regulators, can alter this calculus by fixing minimum safety tests, disclosure norms, and labour-impact assessments, and by encouraging more decentralised and pro-worker technological paths.2,3,18 They can also facilitate joint monitoring of truly systemic risks, such as widely deployed models with capabilities that neither developers nor regulators fully understand.
Why “sharing best practices” is not a platitude
Calls for sharing best practices between “sides” are sometimes dismissed as diplomatic boilerplate. In Acemoglu’s usage, they are tightly connected to his view of technological direction and institutional pluralism.2,3,18 The “sides” are not only geopolitical rivals but also competing visions of AI’s role in society: one anchored in heavy automation, data extraction, and centralised control; another focusing on augmenting human capabilities, creating new meaningful tasks, and empowering workers and citizens.2,6,8,10
Best practices in this context refer to concrete design choices and regulatory mechanisms that align AI with social goals. Examples include models optimised for assisting human professionals rather than replacing them; data governance schemes that give individuals and communities meaningful control over how their information is used; workplace AI systems that enhance worker discretion instead of monitoring and micromanaging them; and democratic oversight structures that scrutinise public-sector uses of AI.2,3,6,8
The sharing element matters because these design and governance choices are being explored in many jurisdictions simultaneously. Some labour markets have stronger collective bargaining institutions; some regulators are experimenting with algorithmic accountability and audit requirements; others are testing rules for foundation models, transparency of training data, or human-in-the-loop requirements in critical decision-making.2,3,18 Systematic exchange of what works and what fails could help steer the global trajectory towards configurations that demonstrably improve worker outcomes, reduce inequality, and preserve civic space, rather than leaving each jurisdiction to reinvent the wheel or copy the most commercially aggressive models.
The economics of inequality in the AI era
Acemoglu’s empirical work on AI and inequality sharpens the urgency of these governance questions. Using a task-based model of production, he argues that AI will likely widen the gap between capital and labour income, even if its direct impact on wage inequality across demographic groups is more muted than earlier automation waves.5 In his framework, output is produced by a set of tasks performed by humans and machines. Automation replaces human labour in certain tasks, while “new task” creation introduces additional functions where humans have a productivity advantage.
Formally, if Y denotes output and T the set of tasks, then a simplified representation is Y = \int_T A(i) y(i) \, di, where A(i) captures the productivity of task i and y(i) its level of performance.14 AI-driven automation effectively raises A(i) for machine-performed tasks and reduces demand for human labour in those segments. When many tasks are automated without a commensurate creation of new human-centric tasks, the labour share of income tends to fall, especially for lower-education workers whose tasks are more easily codified.5,14
Acemoglu’s estimates suggest that, while AI exposure is more evenly spread across demographic groups than previous automation technologies, there is no evidence that it will significantly reduce wage inequality.5 Indeed, he finds that AI is likely to exert downward pressure on the real earnings of low-education women in particular, even as it provides moderate productivity gains in some low-skill tasks.5 AI may also generate “bads” such as manipulation algorithms and deepfakes, which have negative social value but can be privately profitable.5
These findings feed directly into his scepticism of narratives that treat AI as a technologically determined tide lifting all boats, and his insistence on governance structures that actively redirect innovation towards worker-complementing and socially valuable applications.2,3,10
Redirecting AI: from “so-so” to pro-worker technologies
Behind the call for global governance is a more specific agenda: redirect AI away from excessive automation and centralisation, and towards “machine usefulness” that complements human skills.3,6,8,10 Acemoglu distinguishes between automation that merely replaces workers in existing tasks and innovation that creates new, more complex tasks that humans are uniquely well placed to perform.2,8,10 The latter historically underpinned shared gains from technological progress, not simply the volume of machinery deployed.
In policy terms, he advocates a “balanced portfolio” of automation and new tasks.8 Excessive automation, especially when subsidised via tax systems that favour capital over labour, can generate job displacement without sufficient new opportunities, leading to lower labour-force participation and an expansion of low-quality, meaningless jobs.8,13 To counter this, he proposes measures such as equalising the tax treatment of labour and capital, using public funding to support “blue-sky” technologies that create new capabilities for workers, reforming data-ownership rules, and breaking up overly dominant tech platforms.3,4,8
Global governance enters here as a mechanism to coordinate these redirection efforts and avoid a scenario where jurisdictions that attempt to steer AI towards pro-worker uses are undercut by rivals who embrace high-automation, low-labour-cost models. Without some common floor of labour standards and shared norms about acceptable uses of AI in workplaces and markets, a regulatory race to the bottom remains a constant threat.2,3,18
Democracy, information, and the centralisation problem
The informational dimension of AI is central to Acemoglu’s concern about unregulated races. Large language models and related systems increasingly act as intermediaries between citizens and information.3,6 Their architecture inherently centralises knowledge: they ingest vast corpora of human-generated content, process it in proprietary infrastructures, and return outputs that can displace older, more decentralised forms of knowledge discovery and public debate.3,6
He warns that current incentives push AI towards tools for monitoring workers, reducing autonomy, and intensifying surveillance, rather than empowering individuals.2,6 In the workplace, AI systems can track keystrokes, assess performance in granular detail, and optimise task allocation in ways that strip workers of discretion and bargaining power.2 In the public sphere, models can be tuned to micro-target political messages, generate persuasive misinformation at scale, or filter content in opaque ways that shape collective perceptions.5,6,13
Global governance efforts cannot directly redesign each system, but they can set principles around transparency, auditability, and the preservation of pluralistic information ecosystems.3,18 Sharing best practices in democratic oversight, algorithmic auditing, and safeguards against state or corporate manipulation is thus not merely a technical exercise; it is part of a broader struggle over whether AI infrastructures entrench or rebalance existing power asymmetries.2,3,13
Objections, limits, and the politics of global rules
Ambitious calls for global AI governance attract scepticism from several directions. Market optimists argue that heavy-handed regulation will stifle innovation and prevent societies from enjoying potential benefits such as new medical discoveries, climate modelling breakthroughs, and cost reductions in essential services.4,6 They suggest that competitive pressures already push firms to build trustworthy products, and that national-level regulations, if necessary, are sufficient.
Another objection focuses on geopolitical realism. If major powers view AI as a strategic asset, why would they meaningfully constrain themselves through global regimes, especially on defence-related uses? Deep mistrust about cyber espionage, intellectual-property theft, and information operations can hinder cooperative arrangements, particularly in areas that touch on national security.18 Skeptics fear that governance frameworks will either be so weak as to be symbolic, or selectively observed by some actors and ignored by others.
Acemoglu’s response is pragmatic rather than utopian. He does not assume that global governance will eliminate competition or reconcile all interests.2,3,18 The aim is to define specific domains where cooperation is rational even for rivals: preventing catastrophic misuse, coordinating on safety standards for frontier models, avoiding destabilising military applications, and agreeing on baseline labour and data protections that reduce incentives for regulatory arbitrage. Beyond these minima, he expects contestation to continue, including about how far to push automation and how strongly to empower workers.
There is also a technocratic risk: that global AI governance becomes dominated by the same narrow set of corporate and governmental actors currently steering AI, merely shifting their influence into a new institutional arena. Acemoglu’s broader intellectual project emphasises the need for wider representation of workers, civil society, and marginalised groups in decisions about technological direction.2,8,13 Without that pluralistic input, global governance could entrench existing inequalities under the guise of expert management.
Why the framing matters now
The stakes of this debate are magnified by the timing. AI is still, by Acemoglu’s account, early in its economic impact: a modest boost to productivity so far, concentrated in a limited set of tasks and sectors.1,10 This means the direction is more malleable than it will be once technological and institutional path dependencies harden. Choices made over the next decade about taxation, labour law, data governance, competition policy, public R&D, and international coordination will heavily influence whether AI becomes primarily a tool for augmenting human capabilities or a lever for further concentration of wealth and power.2,3,10,13
Against this backdrop, the call for global governance, restraint of the AI race, and meaningful sharing of best practices is not a plea for slow progress, but a demand for a different kind of progress. It reflects the view, developed across Acemoglu’s work on institutions and technology, that prosperity and democracy are not automatic by-products of innovation.2,11,13 They depend on purposeful institutional design, contested political choices, and an ongoing willingness to align technological development with the needs and dignity of workers and citizens rather than the narrow goals of a small set of powerful actors.
References
1. “Nobel Laureate Daron Acemoglu on the ‘brainless’ AI discourse, the myth of capitalism and the Gen Z revolution risk” – https://finance.yahoo.com/technology/ai/articles/nobel-laureate-daron-acemoglu-brainless-090000758.html
2. Nobel Laureate Daron Acemoglu on ‘brainless’ AI discourse, myth of … – 2026-06-21 – https://fortune.com/2026/06/21/nobel-laureate-daron-acemoglu-ai-productivity-capitalism-democracy/
3. Unchecked Power of Big Tech Poses Risks as AI Reshapes Society – 2024-04-28 – https://www.hbs.edu/bigs/insights-ai-wealth-gap
4. ‘Redesigning AI’ with Professor Daron Acemoglu – 2024-05-21 – https://www.oxfordmartin.ox.ac.uk/videos/redesigning-ai
5. Daron Acemoglu: Too Much Faith in Government, Too Little in … – 2024-10-18 – https://www.aei.org/articles/daron-acemoglu-too-much-faith-in-government-too-little-in-markets-and-ai/
6. [PDF] The Simple Macroeconomics of AI Daron Acemoglu Working Paper … – https://www.nber.org/system/files/working_papers/w32487/w32487.pdf
7. AI Is Not Improving Productivity: Nobel Laureate Daron Acemoglu – 2026-02-24 – https://sloanreview.mit.edu/audio/ai-is-not-improving-productivity-nobel-laureate-daron-acemoglu/
8. Daron Acemoglu: Can we have pro-worker AI? – YouTube – 2024-06-19 – https://www.youtube.com/watch?v=XByHfJrYThY
9. Daron Acemoglu on Artificial Intelligence – Social Science Space – 2024-09-03 – https://www.socialsciencespace.com/2024/09/daron-acemoglu-on-artificial-intelligence/
10. Daron Acemoglu on Artificial Intelligence – Apple Podcasts – 2024-09-04 – https://podcasts.apple.com/gb/podcast/daron-acemoglu-on-artificial-intelligence/id524122804?i=1000668249187
11. Daron Acemoglu: What do we know about the economics of AI? – 2024-12-06 – https://economics.mit.edu/news/daron-acemoglu-what-do-we-know-about-economics-ai
12. How Power and Progress by Daron Acemoglu changed my view on AI – 2025-08-15 – https://www.linkedin.com/posts/howardhyu_i-just-finished-power-and-progress-by-mit-activity-7362083281896415232-i_mH
13. AI is devouring centuries of human knowledge – Instagram – 2026-04-22 – https://www.instagram.com/reel/DXb7M8qNWqo/
14. GW Hosts Nobel Laureate Daron Acemoglu for a Discussion on AI … – 2025-07-28 – https://gwtoday.gwu.edu/gw-hosts-nobel-laureate-daron-acemoglu-discussion-ai-and-inequality
15. A Simple Explainer of Acemoglu’s Simple Macroeconomics of AI – 2025-04-16 – https://causalinf.substack.com/p/a-simple-explainer-of-acemoglus-simple
16. Daron Acemoglu: What do we know about the economics of AI? – 2025-01-02 – https://www.reddit.com/r/artificial/comments/1hry0kz/daron_acemoglu_what_do_we_know_about_the/
17. Who would you say is shaping the direction of AI today? Economic … – 2026-05-16 – https://www.facebook.com/nobelprize/videos/who-would-you-say-is-shaping-the-direction-of-ai-today-economic-sciences-laureat/1436114798285535/
18. Daron Acemoglu – NBER – https://www.nber.org/people/daron_acemoglu
19. The Need for Multipolar Artificial Intelligence Governance | 8 | The N – https://www.taylorfrancis.com/chapters/oa-edit/10.4324/9781003571384-8/need-multipolar-artificial-intelligence-governance-daron-acemoglu
20. Who would you say is shaping the direction of AI today? Economic … – 2026-05-16 – https://www.facebook.com/nobelprize/posts/who-would-you-say-is-shaping-the-direction-of-ai-today-economic-sciences-laureat/1391200006375158/
