THE AI REVOLUTION AND A FUTURE OF FAIRNESS

by Dr Ekaterina Abramova, Adjunct Assistant Professor of Management Science and Operations at London Business School

The AI revolution is not on the horizon; it is already transforming how we work, solve everyday problems, and interact both with one another and with technology. From generative models to agentic systems capable of disrupting entire industries, artificial intelligence has advanced at a pace that few institutions, businesses, or governments are fully prepared for. What once felt like a distant technological possibility has become a structural force shaping labour markets and economies. As a result, one of the most pressing questions facing societies is no longer whether AI will change the world, but whether it will make it fairer. Increasingly the answer depends not only on the technology itself, but on the choices organisations and governments make about how its benefits are shared.

AI has the potential to unlock unprecedented prosperity. Yet history shows that technological revolutions rarely distribute their rewards evenly. Without deliberate intervention, the benefits of AI risk concentrating in the hands of a small number of large technology firms, highly skilled professionals and capital owners. This pattern has already emerged in earlier waves of digital transformation, where wealth and opportunity accumulated disproportionately in regions best positioned to adapt. For AI to foster equality rather than widen disparity, policymakers must treat inclusion as an ex-ante design principle rather than an ex-post correction.

The first crucial step for achieving fairness is improving the data that AI systems rely upon. Algorithms are only as representative as the information used to train them. When datasets exclude marginalised or underrepresented communities, AI risks reinforcing existing biases. Organisations and governments developing AI algorithms should prioritise collecting data from communities historically overlooked in policy design, such as rural populations, low-income groups, minority communities and those outside the formal labour markets. More inclusive datasets lead to fairer systems, more effective public services and policy decisions that better reflect the realities of entire populations, rather than just their most visible segments.

Another equally important aspect is how governments distribute the productivity gains and wealth generated by AI into broader societal benefits. Different regions are experimenting with alternative approaches. In parts of the Middle East, including the United Arab Emirates, economic gains from technological advancement are often channelled through state-led investment strategies rather than relying solely on traditional taxation and redistribution mechanisms. While VAT and other taxes exist, governments often reinvest a significant share of national income derived from natural resources and state-owned enterprises directly into infrastructure, public services, education and economic diversification. This approach builds long-term national capability by funding human capital development, strengthening digital infrastructure and fostering new sectors that create employment and opportunity.

Such strategies highlight an important principle: AI benefits do not need to be redistributed after inequality has emerged. They can be embedded in development strategies from the outset. By investing in education, digital skills and access to technology, governments expand the number of people able to participate in the AI ecosystem rather than merely compensate those left behind. China, for example, has made substantial investments in AI education and research capacity, recognising human capital as central to technological leadership. Every year 100,000 selected teenagers are funnelled into elite science talent streams across top high schools. These “genius classes” systematically train students to excel in international maths, physics, chemistry, biology and computer science competitions.

The pace of the AI revolution makes this challenge more urgent than previous technological transitions. Earlier industrial transformations unfolded over decades, allowing societies time to adapt institutions and labour markets. AI development in recent years has gained pace. Breakthroughs that once took years are now emerging within months, with new capabilities rapidly spreading across sectors from healthcare diagnostics and financial analysis to logistics and defence industries. This acceleration has been further intensified by the present-day AI race to achieve Artificial General Intelligence (AGI), amid a widespread belief that the first government to reach this milestone will gain a decisive strategic advantage. Organisations at the forefront of AI development are reluctant to slow for fear of falling behind geopolitical or commercial rivals. Meanwhile, many governments are hesitant to introduce AI regulation, concerned that premature constraints could hinder innovation and weaken their competitiveness in the pursuit of AI leadership.

However, the path forward requires a global perspective. While governments should encourage innovation, they must also recognise that AI technology will diffuse across borders. Hence governments worldwide should collaborate towards a global AI governing body, or at the very least, agree on minimum safety and fairness standards for AI deployment. The EU AI Act provides an important foundation by identifying unacceptably high-risk AI applications that should be prohibited. When forming such regulatory frameworks, governments should seek guidance from leading AI scientists to ensure they fully understand where the principal risks originate. Indeed, many prominent experts in the field argue that regulation is failing to keep pace with AI innovation.

Allowing AI technology to evolve without placing guardrails in place early risks embedding structural inequalities, particularly in labour markets, education access and capital distribution. Ultimately, the debate about AI and inequality is not primarily about algorithms; it is about governance. Technology reflects the priorities of the societies that deploy it. If policymakers treat AI purely as an engine of leadership and economic growth, its benefits will likely accrue to those already best positioned to capture them. But if AI development is guided by a clear commitment to inclusion through better data, wider access and sustained investment in human capital, it has the potential to expand opportunity on a global scale. As AI reshapes labour markets, workers will need opportunities to develop capabilities that complement intelligent systems rather than compete directly with them. Access to AI infrastructure, computing resources, data and digital connectivity must not be confined to a small group of corporations or wealthy regions.

The direction of the AI revolution is not predetermined. The question is not whether AI will transform our world, but whether governments and institutions will act quickly and thoughtfully enough to ensure that its benefits are broadly shared. In the race to build increasingly powerful systems, equal attention must be given to building the social and economic frameworks that will ensure the future is genuinely fair.