AI for All: How Human-Centered Economics Can Guide the Future of Artificial Intelligence

Artificial Intelligence is often seen as a purely technological revolution. A race of algorithms, chips, and breakthroughs. But its true impact lies not in its code, but in its consequences. As AI reshapes industries, rewrites job descriptions, and redefines how we make decisions, we must begin asking a deeper question: not just what AI can do, but who it will do it for.

This is where economics, and more importantly, human-centered economics, comes in. The future of AI will not be determined solely by engineers and data scientists. It will also be shaped by the economists, policymakers, and thinkers who influence the incentives and institutions around it. If AI is to elevate humanity, it must be built on systems that prioritise equity, dignity, and access. In short, it needs an economic compass.

The Economic Risks of AI: A Disruption With Winners and Losers

Much like past waves of automation, AI carries the promise of greater productivity. But it also brings the peril of deepened inequality. Tasks once done by humans are increasingly handled by algorithms, from warehouse sorting to customer service to content generation. While new roles may emerge, the transition is not frictionless.

The World Economic Forum estimates that AI could displace 85 million jobs by 2025, even as it creates 97 million new ones. But those gains are unlikely to be evenly distributed. Highly educated, digitally literate individuals, often in wealthier countries, will benefit most. Those in low-skill, routine jobs face the greatest risk of displacement, with limited pathways to retraining.

Moreover, AI rewards scale. Tech firms that control data and computing power enjoy massive network effects. This centralisation of wealth and influence raises concerns not just of economic inequality but also of democratic erosion. A handful of platforms now wield disproportionate control over information, commerce, and labour.

The Invisible Hand Meets the Invisible Algorithm

Traditional economic theory assumes markets optimise resource allocation through the “invisible hand.” But AI introduces a new player: the invisible algorithm. Recommendation systems dictate what we buy, watch, or believe. Algorithmic pricing can alter what we pay in real time. Automated credit scoring determines who gets a loan and who doesn’t.

If these algorithms are biased, opaque, or designed to maximise only profit, they can silently reinforce systemic inequalities. For instance, facial recognition systems have been found to misidentify darker-skinned individuals at far higher rates. AI hiring tools have penalised applicants from marginalised backgrounds based on historical data patterns.

To combat this, ethical design must be paired with economic accountability. Algorithms should be auditable. Platforms must be subject to antitrust scrutiny. AI development should reflect a broader societal purpose, not just shareholder value.

Reimagining the Economic Model for an AI Age

Rather than reacting to AI’s disruptions, we must proactively redesign economic systems to steer AI toward human flourishing.

1. Redefining Growth and Value: Gross Domestic Product (GDP) struggles to capture the real value of AI tools, especially those offered freely, like ChatGPT or Google Maps. A future-facing economy must incorporate well-being, access, and inclusion as key metrics of value, not just output.

2. Universal Basic Income and Job Transitions: If AI makes some labour redundant, the economic surplus it generates could fund Universal Basic Income or job transition schemes. Countries like Finland and Kenya have piloted UBI models, showing positive effects on mental health, entrepreneurship, and social trust. These are all vital in a world of disruption.

3. Taxing Automation and AI Rent: Bill Gates once proposed a “robot tax,” taxing companies that replace workers with machines. While controversial, the idea reflects a growing consensus. AI’s economic benefits must be shared, not hoarded. Progressive taxation on AI-driven profits could fund public services and reskilling programmes.

AI for Good: When Technology Meets Human Need

When guided by inclusive economics, AI becomes a powerful tool for global good.

In Sub-Saharan Africa, AI-driven credit scoring allows informal workers with no banking history to access microloans. This boosts financial inclusion.

In rural India, AI chatbots provide maternal health advice in local languages, improving outcomes where doctors are scarce.

Organisations like the World Bank and UN Global Pulse use satellite data and AI to map poverty in remote regions. This enables smarter allocation of aid.

These are not science fiction. They are signs that when AI is paired with a human-centered economic framework, it can empower rather than exclude.

What Should Policymakers and Economists Do?

To ensure AI works for all, not just the few, economists must:

1. Design inclusive AI policies: Ensure digital infrastructure and AI access reach marginalised groups and the Global South.

2. Invest in open-source AI: Prevent monopolisation and promote collaborative innovation.

3. Expand how we measure success: Move beyond GDP to indicators like the Social Progress Index, Wellbeing Economy metrics, or Doughnut Economics.

4. Collaborate across disciplines: Economists, ethicists, computer scientists, and citizens must co-design AI policy.

   
   

The Role of Human-Centered Economics in Shaping AI

To conclude, Artificial Intelligence may be the most powerful general-purpose technology since electricity. But without a guiding philosophy, power alone means little. Economics provides the framework to decide who benefits, who is protected, and what we value as a society.

To shape a future where AI uplifts humanity, we must embed it in systems that are fair, inclusive, and sustainable. Human-centered economics is no longer a niche idea. It is a necessity. Because if AI is the engine of the future, economics must be its compass.