John Gulliver

Category: Artificial Intelligence

  • AI and Wealth Inequality: Who Benefits from the Technological Revolution?

    AI and Wealth Inequality: Who Benefits from the Technological Revolution?

    As artificial intelligence reshapes the global economy, a stark pattern emerges—one that would make even Victorian industrialists blush: the financial rewards of this technological revolution are flowing disproportionately to a narrow elite of AI owners, developers, and high-skill workers. This capital-biased technological change threatens to exacerbate existing wealth disparities, creating a two-tier society where the gains from increased productivity concentrate amongst those who control the technology, whilst large swathes of the population face stagnant wages or unemployment. One might say we’re building a brave new world, though Aldous Huxley might suggest we should have read the fine print (Piketty, 2014; Korinek and Stiglitz, 2019).

    The economics of AI differ fundamentally from previous technological shifts. Unlike industrialisation, which eventually created broad-based employment opportunities (albeit after considerable suffering), AI systems can scale indefinitely with minimal additional labour input. A successful AI model can be deployed millions of times at negligible marginal cost, generating enormous returns for its owners without proportional job creation—a feature rather than a bug, as Silicon Valley might say (Brynjolfsson et al., 2018). This dynamic favours capital over labour in unprecedented ways, as shareholders and executives of AI companies capture value that would traditionally have been distributed more widely through wages (Acemoglu and Restrepo, 2022).

    The concentration of AI capabilities amongst a handful of technology giants compounds this problem. Companies with vast computing resources, proprietary datasets, and top-tier AI talent enjoy substantial competitive advantages that smaller firms cannot easily replicate—creating barriers to entry higher than a medieval castle wall, and considerably more expensive to breach (Khan, 2017). This cements existing market power and ensures that AI-driven profits remain concentrated. Meanwhile, workers whose skills complement AI systems command premium salaries, whilst those in routine occupations face downward wage pressure or displacement, creating what economists politely term ‘skill-biased technological change’ and what affected workers might call something rather less academic (Autor et al., 2020).

    Geographic inequality adds another dimension to this divide. AI development clusters in a few technology hubs in advanced economies, channelling wealth to these regions whilst leaving others behind. Within countries, urban centres with strong educational institutions and technology sectors pull ahead, whilst rural and post-industrial areas struggle to participate in the AI economy (Florida, 2017). This spatial dimension of inequality can destabilise entire regions and fuel political discontent—a phenomenon we might charitably describe as ‘suboptimal for social cohesion’ (Case and Deaton, 2020).

    Addressing AI-driven inequality requires bold policy interventions that go beyond wishful thinking and corporate promises of ‘trickle-down innovation’. Proposals range from progressive taxation of AI companies and automation taxes to universal basic income schemes that redistribute productivity gains (Susskind, 2020; Atkinson and Luttrell, 2021). Strengthening worker bargaining power, investing in education and retraining, and ensuring broad access to AI tools and infrastructure also merit consideration. Without such measures, the promise of AI to improve living standards risks becoming a reality for only a fortunate few, whilst widening the gulf between society’s winners and losers—a gulf that may eventually require more than a metaphorical bridge to cross (OECD, 2021).

    References

    Acemoglu, D. and Restrepo, P. (2022) ‘Tasks, automation, and the rise in US wage inequality’, Econometrica, 90(5), pp. 1973-2016.

    Atkinson, R.D. and Luttrell, C. (2021) ‘Why and how to tax robots’, Information Technology & Innovation Foundation, May.

    Autor, D.H. et al. (2020) ‘The fall of the labor share and the rise of superstar firms’, The Quarterly Journal of Economics, 135(2), pp. 645-709.

    Brynjolfsson, E. et al. (2018) ‘Artificial intelligence and the modern productivity paradox’, in Agrawal, A., Gans, J. and Goldfarb, A. (eds.) The Economics of Artificial Intelligence. Chicago: University of Chicago Press, pp. 23-57.

    Case, A. and Deaton, A. (2020) Deaths of Despair and the Future of Capitalism. Princeton: Princeton University Press.

    Florida, R. (2017) The New Urban Crisis: Gentrification, Housing Bubbles, Growing Inequality, and What We Can Do About It. London: Oneworld Publications.

    Khan, L.M. (2017) ‘Amazon’s antitrust paradox’, Yale Law Journal, 126(3), pp. 710-805.

    Korinek, A. and Stiglitz, J.E. (2019) ‘Artificial intelligence and its implications for income distribution and unemployment’, in Agrawal, A., Gans, J. and Goldfarb, A. (eds.) The Economics of Artificial Intelligence. Chicago: University of Chicago Press, pp. 349-390.

    OECD (2021) Bridging Digital Divides in G20 Countries. Paris: OECD Publishing.

    Piketty, T. (2014) Capital in the Twenty-First Century. Cambridge, MA: Harvard University Press.

    Susskind, D. (2020) A World Without Work: Technology, Automation and How We Should Respond. London: Allen Lane.

  • AI and Job Displacement: Navigating the Future of Work in 2025

    The rise of artificial intelligence has sparked a profound debate about the future of employment—one that oscillates between apocalyptic visions of mass unemployment and utopian dreams of universal leisure (though probably not in equal measure amongst factory workers and venture capitalists). As we progress through 2025, the automation of routine tasks through AI systems has accelerated across industries, from manufacturing and logistics to customer service and data entry. Whilst these technological advances promise efficiency gains for businesses, they also pose significant challenges for workers whose roles are being transformed or, in some cases, made as redundant as a travel agent in the age of budget airlines (Brynjolfsson and McAfee, 2014; Frey and Osborne, 2017). The narrative of wholesale job destruction, however, requires nuance—and perhaps a deep breath. History demonstrates that technological revolutions typically create as many employment opportunities as they eliminate, albeit in different sectors and requiring different skill sets (Autor, 2015). The challenge lies not in the net number of jobs, but in the transition process itself, which can be about as smooth as a hedgehog in a tumble dryer. Workers displaced from routine positions often lack the skills required for emerging roles in AI development, data science, and human-AI collaboration. This skills gap represents one of the most pressing socioeconomic challenges of our time (Acemoglu and Restrepo, 2020). Governments and educational institutions are beginning to recognise the urgency of large-scale reskilling initiatives. Programmes focusing on digital literacy, critical thinking, and uniquely human capabilities such as emotional intelligence and creative problem-solving are being developed worldwide—though whether they can keep pace with AI’s evolution remains to be seen (World Economic Forum, 2020). The pace of technological change often outstrips these efforts, leaving vulnerable populations, particularly older workers and those in low-skilled positions, at risk of prolonged unemployment and economic marginalisation (OECD, 2019). The business community, too, must shoulder responsibility for managing this transition. Forward-thinking organisations are investing in upskilling their existing workforce rather than simply replacing human workers with automated systems—a strategy that not only mitigates social disruption but also maintains employee morale and capitalises on institutional knowledge (Manyika et al., 2017). The most successful companies of the AI era will likely be those that view technology as augmenting human capabilities rather than replacing them wholesale, though convincing shareholders of this approach may require an AI-powered persuasion algorithm. Ultimately, navigating the future of work requires a collaborative approach involving policymakers, educators, businesses, and workers themselves. Social safety nets may need reimagining, potentially including measures such as universal basic income or expanded unemployment benefits during transition periods (Standing, 2017). The question is not whether AI will transform the labour market—that transformation is already underway—but whether we can manage it equitably, ensuring that the benefits of increased productivity are shared broadly rather than concentrated amongst those who own and control the technology (Susskind, 2020).

    References

    Acemoglu, D. and Restrepo, P. (2020) ‘Robots and jobs: Evidence from US labor markets’, Journal of Political Economy, 128(6), pp. 2188-2244.

    Autor, D.H. (2015) ‘Why are there still so many jobs? The history and future of workplace automation’, Journal of Economic Perspectives, 29(3), pp. 3-30.

    Brynjolfsson, E. and McAfee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company.

    Frey, C.B. and Osborne, M.A. (2017) ‘The future of employment: How susceptible are jobs to computerisation?’, Technological Forecasting and Social Change, 114, pp. 254-280.

    Manyika, J. et al. (2017) Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. McKinsey Global Institute.

    OECD (2019) OECD Employment Outlook 2019: The Future of Work. Paris: OECD Publishing.

    Standing, G. (2017) Basic Income: And How We Can Make It Happen. London: Pelican Books.

    Susskind, D. (2020) A World Without Work: Technology, Automation and How We Should Respond. London: Allen Lane.

    World Economic Forum (2020) The Future of Jobs Report 2020. Geneva: World Economic Forum.