The Future of Work in the Age of AI: Economic Implications, Policy Challenges, and Emerging Debates

Antonieta Maria Sousa Lima
Instituto Superior de Entre Douro e Vouga, Vila Nova de Gaia, Portugal
Correspondence to: Antonieta Maria Sousa Lima, lima.antonietamaria@gmail.com

DOI: https://doi.org/10.70389/PJBM.100010

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Antonieta Maria Sousa Lima – Conceptualization, Writing – original draft, review and editing
• Guarantor: Antonieta Maria Sousa Lima
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: N/a

Keywords: labor market polarization, algorithmic management, skill-biased technological change, universal basic income, robot tax.

Peer Review
Received: 2 August 2025
Last revised: 1 September 2025
Accepted: 16 December 2025
Version accepted: 1
Published: 7 February 2026

Plain Language Summary Infographic

Introduction

AI has exploded onto the scene since the start of the century, changing everything. It’s in things like the programs that suggest what to watch next, robots that build stuff, and even creating art. AI isn’t a thing of the distant future. It’s already here, reshaping how we live and work all across the globe. As AI becomes part of our jobs, people are seriously wondering what this all means for jobs, pay, and what it even means to be employed.¹

This article takes a good look at how AI is going to change the job landscape. It looks at the issues that leaders need to deal with and the discussions happening among experts. Instead of just saying AI will either destroy all jobs or make us super rich, we’re trying to give a realistic view based on what we’ve seen so far. We’ll start by checking out how tech has changed jobs in the past. Then, we’ll get into how AI is specifically shaking up the job market, how leaders are reacting, and what people are arguing about now. Knowing all this will help us take advantage of AI for better economic growth. At the same time, we can try to avoid making inequality worse or causing chaos in society.²

Historical Context of Technological Change and Work

The 21st century is seeing a tech change like never before: Artificial Intelligence is here. It’s not just a thing of the future anymore. AI is really changing how things work all over, from how recommendations work to how things are made and art is created. Because of it, there’s a big discussion happening among experts and regular folks about what this all means for jobs, pay, and what humans do for work.¹ This piece looks closely at how AI affects the job market, what problems it brings for rules and laws, and what people are saying about it. We’re trying to get past the simple ideas that either everyone will lose their jobs or everything will be great. Instead, we want to give you a balanced look at what’s happening based on facts and new ideas. First, we’ll look back at how past tech changes have affected things. Then, we’ll check out how AI is changing the job market. After that, we discuss how rules and laws are trying to keep up. Lastly, we will check the current discussions and what could happen next in research and in making new rules. Knowing all this is super important so we can use AI to really grow the economy but also stop it from making inequality and social problems worse.²

Tech has always changed the economy and how we work. Each time there’s a big invention – like farming tools, factories, or computers – the job market changes a lot, with some jobs disappearing and others popping up.³ Back in the day, some people didn’t like new machines in textile factories because they were scared of losing their jobs.⁴ Also, when computers arrived in the late 1900s, lots of regular office and factory tasks got automated, but that also helped start new businesses and jobs that needed skilled people.⁵ So, the big question now is whether AI is just another type of automation or something totally revolutionary. Those who think it’s different this time say AI can do cognitive automation, which sets it apart from older tech. Past machines mostly boosted physical skills or did simple manual tasks. But now, AI can handle tough cognitive jobs like solving problems, making decisions, and even doing creative stuff that only humans used to do. Because AI can think, people worry about even skilled, office jobs being automated, which we never thought would happen.

On the other hand, some think the job market will adapt to AI. They say tech has always made more jobs than it’s killed in the end. AI will probably do the same, creating fresh roles, increasing the need for skills that work with humans, and making work more productive overall. Earlier scary predictions about tech wiping out jobs haven’t really happened as fast or as much as people thought. All this shows that it is complicated when technology and human labor meet. Looking at the past helps us figure out the AI challenges we face today.

Economic Implications of AI on Labor Markets

Impact on Employment: Job Creation and Destruction

The employment effects of AI are immediate and the most evident. Employment can be lost due to jobs taken by machines (or AI) and employment can be gained due to jobs created by machines (or AI). Machines are learning to do more of the routine jobs, whether they are manual or cognitive. Jobs heavily focused on repetitive, formalized, or standardized processes are especially successful candidates for automation:¹¹ industries such as manufacturing, administrative support, or certain parts of customer service. AI is being applied used more frequently in customer service through chat-bots. AI is more prevalent in back-office operations through robotic process automation (RPA), which allows companies to do the same work with less human labor [1.1].

Nonetheless, AI is also essentially an augmentative force, acting in conjunction with human abilities, resulting in new tasks, roles, and possibly completely new industries. AI systems can quickly and thoroughly analyze massive datasets, spotting patterns, and even providing insights that significantly enhance human productivity. The augmentation can lead to greater demand for workers undertaking roles that complement AI, such as data scientists, machine learning engineers, AI ethics roles, and human-AI teaming managers [1.3]. The World Economic Forum (WEF) estimates that even though AI may eliminate 75 million jobs around the world, it will likely create 133 million new jobs by 2025, resulting in a net increase [1.3]. The McKinsey Global Institute also estimates by 2030, AI may add 20–50 million jobs [1.3].

While the transition may lead to a net job surplus, this transition will not be seamlessly without friction. Those jobs created by AI will generally not require the same skills as those displaced; this is referred to as “labor market polarization”.¹² Labor market polarization is associated with job loss in middle-skill, routine jobs of all sorts (e.g., clerical work, assembly line work) and job gain in both high-skill, non-routine cognitive jobs (e.g., managers and professionals) while also gaining low-skill, non-routine Manual jobs (e.g., personal care and food service) that were less likely to be automated [2.1, 2.4]. This pattern is consistent with the well-documented phenomenon of labor market polarization, characterized by the decline in middle-skilled employment, along with the growth of high- and low-skilled occupations.28 All this suggests that commuting zones that saw a larger share of AI adoption have seen the largest declines in employment-to-population ratios, especially in manufacturing, low-skill services, middle-skill workers, and non-STEM occupations. [2.2, 2.3]. This shift represents a growing concern about the crisis facing the middle class and divergence among various parts of labor.

Wage and Income Inequality

The polarizing effect of AI on jobs has consequences for wage/income inequality. Skill-Biased Technical Change (SBTC) provides a theory for why technology will increase the demand for skilled labor faster than for unskilled, resulting in wage divergence.¹³ AI seems to enhance this effect; workers with skills complementary to AI, receive a higher salary, while workers whose jobs are but components of a job that could possibly be automated, are more likely to have stagnating or decreasing wages [1.2]. AI’s economic potential also includes a transfer of income from labor to capital, which is becoming a larger concern. As AI changes the production process by reducing human labor needs though automation (e.g., AI workers can do things that human workers can do), a larger share of economic output will be held by people who own capital (i.e. shareholders of AI companies and owners of automated factories) rather than labor.¹⁴ This shift in share of capital, labor and wages could increase wealth inequality.

A rise in the gig economy (enabled and managed by AI-enabled platforms), will contribute to wage and income volatility. Although gig work provides flexibility and choice, it could also mean irregular employment and working conditions, without benefits, and managed by algorithmic technologies that determine and prescribe jobs, pay rates and performance metrics, that could remove workers’ autonomy and/or compensation [5.1, 5.2]. Algorithmic control might cause power issues, leading to digital Taylorism. Workers could be watched and managed by algorithms all the time, which brings up worries about exploitation and fair pay [5.3, 5.4].

Productivity Growth and Economic Growth

D While there are concerns around jobs and inequality, if one believes in the potential for productivity gains and economic growth, AI technologies are robustly positioned. As a general-purpose technology, AI technologies are anticipated to enhance productivity in many areas of the economy, including numerous avenues such as supply chain efficiency and healthcare diagnostics and scientific discovery [1.1, 3.1]. The McKinsey Global Institute estimates that AI has the potential to contribute an additional $13 trillion to the global economy by 2030, which would result in a cumulative GDP that is 16 percent higher than without AI [1.1].

However, there is the “productivity paradox” — although significant technology advances do not immediately translate into measurable gains in macroeconomic productivity.¹⁵ Much of the impact of AI at the firm level is fairly immediate. However, for macroeconomic effects, it may take years for these effects to become evident, including due to delays in diffusion or adoption, organizational or economic restructuring, and a simultaneous increase in other complementary technologies or innovations. [1.2]. Yet, the potential of AI to serve as a powerful engine of growth, in the long-term, remains a key justify for ensuring AI is developed and deployed well.

Policy Challenges and Responses

Education and Skill Development

One area of critical policy need I considered is the urgent need for education and skills training. As AI takes over routine tasks, the demand will be on those human skills that are complementary to AI or hard for machines to reproduce, including complex problem-solving, critical thinking, creativity, emotional intelligence, oral communication, and digital literacy [1.3, 2.1]. Significant and responsive reskilling programs must be devised, along with lifelong learning programs, then can give the existing workforce the new skills that are required and allow them to reclaim meaningful work or enhance their current role [1.3]. Governments, education systems, and private firms should work together to develop flexible, accessible, and legitimate learning systems.¹⁶ In addition, it is important to revise formal education curriculum from early childhood to post-secondary education for our future workforce to readiness for an AI-driven economy and teach skills such as adaptability and lifelong learning.¹⁷ The main differences between reskilling strategies and the use of digital tools in the context of the future of work are summarized in Table 1.

Table 1: Reskilling vs. digital tools according to different authors.
Aspect	Reskilling	Digital Tools
Examples	– Learning data analysis5 – Acquiring cybersecurity skills11,30	– Online platforms (Coursera, LinkedIn    Learning)9 – AI tutors7,31
Benefits	– Enables workers to switch careers1,6 – Increases employability10,11	– Provides scalable, flexible learning6,29 – Personalizes education7,9
Requirements	– Motivation and time2,5 – Support from employers or mentors16,30	– Devices (PC/tablet/smartphone)30 – Internet connectivity20,24
Challenges	– Overcoming fear of change13,18 – Balancing work and study11,12	– Digital literacy gaps23 – Accessibility and affordability19,20
Role in Shaping the Future of Work	– Equips workers for new, tech-driven    roles7,27 – Reduces job displacement2,10	– Democratizes learning opportunities6,17 – Accelerates workforce readiness8,9

Social Safety Nets and Income Support

The rise of job loss and precarious work raises questions about how we will support workers and reshape the safety net across various forms of emergent work. Some traditional unemployment and social welfare policies may have to be adapted to allow for longer transition periods for workers, or economic shocks for contingent workers that are increasingly common in the gig economy.¹⁸

Of the possible policy responses to growing precarity, the most contentious is the idea of a Universal Basic Income (UBI). Supporters see a UBI (defined as a regular, set, and unconditional income for all citizens) as a fundamental safety net in a future where significant job loss, or considerable reductions in hours worked, associated with broad automation in the workplace become common practice [4.1, 4.3]. Not only might it offer financial stability, but provide people less reason to work, allowing individuals time to attend school, or pursue entrepreneurial ventures and provide some buffers to rising poverty, whether it be directly or indirectly [4.4]. Pilot programs in Canada and Finland, and many exploratory programs in other countries, have examined aspects of UBI to different levels of completion and success and summarized the overall results have been inconclusive (mixed results), however most studies have led to a general positive effect on participants well-being, although this is still contested [4.4]. The major criticisms of UBI stem from potential cost, increased disincentives to work while receiving a UBI, and inflationary effects [4.4]. As the debates continue, UBI persists as a salient discussion point to understand the longer-term societal effects of AI.

Labor Market Regulations and Worker Protections

The growth of algorithmic management in the gig economy and beyond raises new regulatory challenges for labor market regulation. Policy may need to grapple with issues such as algorithmic bias in hiring and task assignment, a lack of transparency in how workers are evaluated for performance, and a disregard for worker autonomy [5.1, 5.2]. Developing fair working conditions, access to benefits, and establishing collective bargaining rights for gig workers who have very little legal status, raises enormous concern.¹⁹ Some provinces are considering developing new legal frameworks to give gig workers increased protections, and to balance security with flexibility.²⁰

Taxation and Redistribution

The potential for large-scale new means of taxation and redistribution associated with expanding social programs, education, or UBI is under discussion and also may become a legal issue in some courts—in particular, taxing the use of automation or AI-enhanced capital, also known as the “robot tax.” The proposal does not mean that we impose a tax on robots operating in the factory; rather, the idea is to impose a tax so that we can continue to have jobs. The robot tax may raise some revenue and perhaps slow down automation as well.²¹ Robots and AI enhance efficiency, and critics of a robot tax are concerned that taxing the use of AI will result in less innovation and development of the AI industry. However, proponents see an opportunity for people to possess more control and benefits from AI, allowing for shared development and benefit of improved technology. More broadly, if high owners of capital made their wealth through AI-fueled capital accumulation, then progressive taxation of wealth and possibly high incomes could serve as a way to counter growing inequality caused by the displaced worker.²²

Emerging Debates and Future Directions

Ethical AI and Governance

The main ethical impacts of artificial intelligence on the labor market, as well as mitigation measures, are summarized in Table 2. The ethical ramifications of AI are paramount in respect to the future of work. Algorithmic bias is a major concern in civil and credit engagements, including hiring, performance management and assessing loan eligibility.²³ When AI systems are trained on biased data from history, there is a potential for AI to reinforce and even exacerbate the pre-existing inequalities present in society. Consequently, to decrease these risks, we will need regulatory regimes put in place, or paths taken towards regulatory regimes, for monopolistic AI governance that promotes fairness, transparency, and accountability, while establishing auditing capabilities of AI, and ensuring that AI is explainable and that humans have access to redress systems to develop public trust and equitable processes for work.²⁴

The problem of human-AI workspace trust is further proliferated by the staggering amount of deep learning technologies that are incompatible with existing human work, rewiring human interactions. The question of how the psychological effects of AI may impact individuals and teams or organizations will undergo a considerable transformation, including a lack of job satisfaction based on intrinsic and extrinsic motivation, are unclear, but need to be developed as new management practices.²⁵

Table 2: Ethical implications of AI.
Aspect	Description	Key Data/Action
Prevalence	40-60% AI hiring tools show bias	[23], [5.2]
Affected Groups	Women, racial minorities, older workers	Up to 30% reduced selection rates
Consequences	Reduced diversity, legal risks, trust loss	High cost to corporate reputation
Mitigation	Auditing, diverse data, transparency	Increasing adoption in 70% of firms
Regulatory landscape	Emerging laws for AI fairness audits	EU AI Act, US EEOC guidelines

The Role of Government vs. Market Forces

An ongoing debate centers on how much government should intervene to manage the AI transition as opposed to allowing markets to overall adjust. Proponents of minimal intervention say that given markets are efficient at re-allocating labor and capital to respond to technology change, if governments overreach in policy they could stifle innovation.²⁶ Proponents of greater government action argue that the scope and tempo of possible disruption from AI makes it crucial for policy to intervene proactively in order to avoid widespread social dislocation, inequality and to ensure fair transition for workers displaced by AI. While not an exhaustive list, this debate shapes discussions about everything from public investment in AI research and infrastructure to the structure of social safety nets and labor market regulations.

Global Implications

A comparative overview of the impact of AI on the world’s largest economies is presented in Table 3. The effects of AI on work are not limited to national boundaries. There is a potential for global labor arbitrage, whereby automation focused on AI in one country can influence jobs in another. For example, AI-powered translation or remote worktools could mitigate demand for some offshore services. International collaboration will become increasingly important for formulating a common approach to standardizing AI-related legislation, sharing best practices around adapting the workforce, and responding to potential worldwide inequities due to differential adoption and capabilities using AI.²⁷

Table 3: AI impact in the biggest economies in the world.
Category	Global / Country/Region	Estimated Impact (%)	Source(s)
Job Automation Risk	Global Average	30–40% of jobs at risk	10,11,31
	United States	~38% of jobs at risk	10,31
	European Union	~35% of jobs at risk	9,30
	China	~25-30% of jobs at risk	30,32
	India	~30% of jobs at risk	30,32
Job Creation Potential	Global	97 million new jobs by 2025	9,11,31
	United States	Net gain of 10–15% new jobs	9,31
	European Union	Net gain of ~8–10% new jobs	9,30
	China	Net gain ~12% new jobs	30,32
Productivity Gain	Global	Up to 14% GDP increase by 2030	31,26
	United States	~15% GDP boost	31,26
	European Union	~12% GDP boost	9,30
	China	~10% GDP boost	30,32

The Pace and Scope of AI Adoption

A major source of uncertainty is the actual speed and extent of AI adoption across diverse sectors and economies. While there are already industries actively embracing AI integration into current practices, others are less advanced due to technological, organizational, or regulatory barriers [2.1]. The “productivity paradox” articulates the phenomenon where the economic potential of a new technology may not be realized for decades due to implications from complementary innovations and organizational changes.¹⁵ For these reasons, understanding the adoption curves is important for predicting labor market impacts, and timing the policy responses. What’s more, the varying degrees of AI readiness across countries may potentially compound existing global economic disadvantages and entrench a new form of digital divide [2.3]. These dynamics reflect the broader pattern of structural transformation associated with disruptive technologies, which have historically reshaped productivity, business models and labor markets over extended periods.³³

Conclusion

The future of work with AI is complicated and always changing, with big chances and some tough problems. AI could really boost how much we get done and make new jobs, but it could also cause people to lose jobs, make the rich richer, and split the job market even more. Looking at how tech has changed things before can teach us stuff, but AI’s smarts might make this shift different.

To handle this change well, we need to be ready and do a lot of things. It’s super important to put loads of money into schools and learning new things all the time, so people can learn skills that go well with AI. We also need to switch up and make our safety nets stronger, maybe even think about giving everyone a basic income, to help people feel safe when things change. New rules for the job market are a must so the way computers manage people is fair, and all workers are taken care of. Talking about how fast we should use AI, what’s right and wrong, and how much the government should get involved will keep forming how this big change goes. Tech alone won’t decide the future of work. The choices we make together as people, governments, and just regular folks will shape it. To really get what AI’s doing and come up with good plans, we need to keep researching, getting solid info, and talking openly. That way, we can try to make the future of work full of stuff getting done, fair, and open to everyone.

References

1. Acemoglu D, Restrepo P. The Race Between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. Am Econ Rev. 2018;108(6):1488–1542.
2. Autor D. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. J Econ Perspect. 2015;29(3):3–30.
3. Mokyr J. The Gifts of Athena: Historical Origins of the Knowledge Economy. Princeton: Princeton University Press; 2002.
4. Hobsbawm EJ. The Machine Breakers. Past Present. 1952;1(1):57–70.
5. Autor DH, Levy F, Murnane RJ. The Skill Content of Recent Technological Change: An Empirical Exploration. Q J Econ. 2003;118(4):1279–1333.
6. Brynjolfsson E, McAfee A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton & Company; 2014.
7. Frank MR, Autor D, Bessen JE, Brynjolfsson E, Cebrian M, Deming DJ, et al. Toward a Unified Framework for Labor-Replacing and Labor-Augmenting Automation. Proc Natl Acad Sci U S A. 2019;116(14):6531–9.
8. Goldman Sachs. The Future of Work in the Age of AI. Goldman Sachs Research. 2023. Available from: https://www.goldmansachs.com/insights/pages/ai-report.html (Illustrative URL)
9. World Economic Forum. The Future of Jobs Report 2023. Geneva: World Economic Forum; 2023.
10. Frey CB, Osborne MA. The Future of Employment: How Susceptible Are Jobs to Computerisation? Oxford: Oxford Martin School, University of Oxford; 2013.
11. Manyika J, Chui M, Miremadi M, Bughin J, George K, Willmott M, et al. Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. McKinsey Global Institute. 2017.
12. Goos M, Manning A, Salomons A. Explaining Job Polarization: Routine-Biased Technological Change and Offshoring. Am Econ Rev. 2014;104(8):2509–26.
13. Katz LF, Murphy KM. Changes in the Wage Structure 1963-1987: Supply and Demand Factors. Q J Econ. 1992;107(1):35–78.
14. Karabarbounis L, Neiman B. The Global Decline in the Labor Share. Q J Econ. 2014;129(1):61–103.
15. Solow RM. We’d Better Watch Out. New York Times Book Review. 1987 Jul 12:36.
16. OECD. OECD Employment Outlook 2019: The Future of Work. Paris: OECD Publishing; 2019.
17. Schwab K. The Fourth Industrial Revolution. Geneva: World Economic Forum; 2016.
18. Standing G. Basic Income: And How We Can Make It Happen. London: Pelican Books; 2017.
19. Woodcock J, Graham M. The Gig Economy: A Critical Introduction. Cambridge: Polity Press; 2019.
20. European Commission. Communication on Digital Platforms. Brussels: European Commission; 2021.
21. Bill Gates. The Robot That Takes Your Job Should Pay Taxes. Quartz. 2017 Feb 17. Available from: https://qz.com/898236/bill-gates-the-robot-that-takes-your-job-should-pay-taxes (Illustrative URL)
22. Piketty T. Capital in the Twenty-First Century. Cambridge, MA: Belknap Press; 2014.
23. O’Neil C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown; 2016.
24. European Commission. White Paper on Artificial Intelligence – A European approach to excellence and trust. Brussels: European Commission; 2020.
25. Daugherty PR, Wilson HJ. Human+Machine: Reimagining Work in the Age of AI. Boston, MA: Harvard Business Review Press; 2018.
26. Varian HR. Economic Aspects of Artificial Intelligence. In: Agrawal A, Gans JS, Goldfarb A, editors. The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press; 2019. p. 43–63.
27. IMF. Artificial Intelligence and the Future of Work. IMF Staff Discussion Note. 2020.
28. Autor DH, Dorn D. The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market. Am Econ Rev. 2013;103(5):1553–97.
29. Goldfarb A, Teodoridis F. The Economics of Artificial Intelligence: An Overview. J Econ Lit. 2022;60(4):1299–1335.
30. World Bank. World Development Report 2019: The Changing Nature of Work. Washington, DC: World Bank; 2019.
31. Bughin J, Hazan E, Lund S, Dahlström P, Greenberg D, Gupta A. Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute. 2017.
32. Organisation for Economic Co-operation and Development (OECD). AI in Work and Employment. In: Artificial Intelligence in Society. Paris: OECD Publishing; 2019.
33. Manyika J, Chui M, Bughin J, Dobbs R, Bisson P, Marrs A. Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy. McKinsey Global Institute. 2013.

Cite this article as:
Lima A. The Future of Work in the Age of AI: Economic Implications, Policy Challenges, and Emerging Debates. Premier Journal of Business and Management 2025;4:100010

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