Advocates of artificial intelligence (AI) often highlight its potential to drive continuous productivity gains and create an economy of superabundance, where material constraints on well-being are significantly reduced. Even without achieving artificial general intelligence, expectations that automation will lower costs and boost profits have fueled a wave of investment enthusiasm in AI technologies. However, prominent economists point to substantial uncertainties and challenges surrounding the broader economic and social implications of AI’s rapid advancement.

A key concern is the nature of the economic transition AI might instigate. Unlike previous major technological revolutions—such as the shift from agricultural to manufacturing economies, which eventually created new jobs after displacing many—AI may bring about a more difficult adjustment. While proponents suggest opportunities in arts, culture, and service roles catering to technological elites, questions remain about whether sufficient, well-paying, and fulfilling jobs will emerge for the broader workforce. The looming prospect of widespread job displacement raises doubts about the traditional link between technological disruption and employment creation.

One frequently proposed solution, universal basic income (UBI), has met with skepticism. Critics argue that UBI could undermine the dignity associated with work and point out that given the tech sector’s opposition to increased taxation, government payments are likely to be limited, offering little relief to most people. This dynamic contributes to concerns about a "dual economy," where immense wealth accumulates among a small number of tech billionaires while the majority face economic struggle. Meanwhile, the industry is said to resist tax policies that could fund stronger public services or more substantial social support systems.

The pace and nature of AI deployment further complicate the picture. Some experts warn of an AI bubble, contingent on three conditions: limited competition to preserve high profits, rapid technological rollout, and effective macroeconomic management. If competition intensifies, profit margins will shrink, challenging investor expectations but potentially distributing AI’s benefits more broadly. Conversely, reduced competition could entrench monopolistic profits but exacerbate inequality, with negative macroeconomic effects. The speed of AI adoption is equally pivotal; a rapid transition could cause severe job losses, while a slower rollout might frustrate investors and burst speculative bubbles.

There are additional worries about the quality of information feeding AI systems. As AI and social media platforms capitalize on content created by traditional information producers, these producers face declining resources and incentives, potentially degrading the inputs that AI relies on. This decline risks the proliferation of misinformation, deepfakes, and disinformation, undermining public trust—a foundational element for a functioning economy and society. The legal and regulatory frameworks currently in place, including intellectual property laws and accountability mechanisms, are often seen as inadequate for addressing AI’s unique challenges. Notably, legal standards like "fair use" and liability rules do not easily translate to AI’s autonomous operations, complicating efforts to assign responsibility for harm or misinformation.

Mental health impacts, particularly among younger populations, add further concern. Rising anxiety and depression correlate with shifts in technology usage, while AI’s contextual limitations suggest humans will retain an advantage in understanding nuance—at least temporarily. However, increasing reliance on AI-generated information might weaken social and intellectual networks that provide essential context, potentially degrading the overall quality of public discourse.

To address these multifaceted challenges, some experts advocate for a "progressive AI agenda" centered on three pillars: competition policy, regulation, and using AI-driven productivity gains to foster a better society. Strengthening antitrust enforcement could prevent monopolistic dominance and promote consumer benefits. Enhanced regulation would seek accountability for AI’s actions and address social harms without stifling innovation—a claim supported by comparisons to regulated technology development in countries like China. Finally, the agenda envisions harnessing AI’s productivity to fund improved public services and fair compensation for essential workers such as teachers, nurses, and caregivers, funded by increased taxation on AI beneficiaries.

The attitude of the tech industry toward this agenda is crucial. Some technology leaders have faced public backlash and rising political discontent, a reflection of broader societal fears around job security, inequality, and political power concentration. Environmental and economic opposition to AI infrastructure, such as data centres, further highlights public anxieties.

Overall, while AI holds the promise of transforming economies and societies for the better, realizing these benefits depends heavily on policy choices, regulatory frameworks, and political will. Managing this complex transition effectively will be a significant challenge, and the future trajectory of AI’s economic impact remains uncertain.