In 2026, the global economic landscape has transitioned from initial enthusiasm surrounding artificial intelligence (AI) to a more cautious and analytical phase. According to Citrini Research, the focus has shifted from a basic hardware race to what they term “Phase 2” of the AI revolution, emphasizing operational efficiency and the replacement of high-cost human labor. While corporate profit margins have benefited from automation and streamlined operations, a growing concern has emerged about “Ghost GDP,” a term reflecting productivity gains that do not translate into increased income for workers.
The premise of Ghost GDP raises a pivotal question: could the anticipated benefits of AI, which include enhanced productivity and new growth opportunities, turn out to be excessively beneficial, thereby undermining the economic frameworks on which modern growth relies? Citrini Research has developed a forward-looking scenario called “The 2028 Global Intelligence Crisis,” which explores the implications of AI’s success on labor markets and consumer behavior.
In this envisioned 2028, AI does not merely assist workers; it replaces them entirely. Machines swiftly automate tasks traditionally performed by millions of skilled laborers, leading to a decline in household income and consumer spending, critical engines of growth. The scenario posits that as automation becomes more prevalent, companies that replace workers to cut costs may inadvertently create a feedback loop: fewer workers lead to reduced consumer spending, which in turn compels further investment in automation.
The consequences of this shift are evident, particularly in the U.S. economy, which could see a significant decrease in consumer demand as production escalates while incomes stagnate. AI agents might assume most white-collar roles—encompassing coding, research, transactions, and even strategic decision-making—leaving professionals to either transition to lower-paying service jobs or face unemployment. This cycle raises critical questions about the resilience of labor markets and the mechanisms of consumer spending.
The economic stress is already palpable in financial markets, with private credit linked to technology and software sectors at heightened risk of defaults. This situation poses challenges for insurers and alternative asset managers who struggle to ascertain value amidst widespread automation. Citrini Research emphasizes that this outlook is not a dire prediction but rather a thought experiment aimed at assessing the broader ramifications of AI on economic growth.
Traditionally, higher productivity has been associated with increased wealth distribution; however, the 2028 crisis scenario contests this notion. It illustrates that distributional impacts—not merely aggregate GDP numbers—determine whether technological advancement results in shared prosperity. The study notes that disruptions in the job market began not with sudden shocks but rather from incremental technology adoption decisions that, although rational for individual firms, collectively diminished overall demand.
Industries once deemed secure, such as software and financial services, are similarly threatened by the relentless march of automation. AI agents capable of executing tasks such as legal work, tax preparation, and travel bookings undermine both new and established businesses. As these changes unfold, serious questions arise regarding the strength of labor markets, consumer spending patterns, and the structure of credit in an economy increasingly reliant on technology to perform work traditionally done by people.
Policymakers are urged to reconsider social safety nets and labor market policies in anticipation of further automation. Planning for structural displacement rather than cyclical unemployment has become imperative. As economic growth becomes decoupled from consumer spending, financial regulators may need to reevaluate their approach to credit and debt exposures, adjusting stress tests and capital requirements to reflect technology-driven risks.
This evolving situation presents India with a distinctive set of challenges and opportunities. The memo largely focuses on the U.S. economy, yet its implications resonate deeply with India, particularly given the country’s reliance on service exports and its information technology sector. As AI agents increasingly undertake high-value tasks at minimal cost, India’s IT services ecosystem, characterized by client contracts and low labor costs, faces structural pressures.
Major firms such as TCS, Infosys, and Wipro may experience significant contract losses and declining prices if client demand shifts toward technology-driven solutions. The scenario also suggests that India’s currency, the rupee, could depreciate sharply as demand for human software labor decreases globally. The implications of generative and agentic AI on labor markets compel emerging economies reliant on labor-intensive service exports to reconsider their competitive advantages.
As the situation develops, it serves as a wake-up call for policymakers to reevaluate the frameworks supporting labor markets and consumer protection. The macroeconomic foundation in India remains robust, bolstered by low interest rates and aggressive government spending. However, the global trend towards workforce reductions through AI poses a direct challenge to the conventional model of service exports. Simultaneously, India’s emergence as a key player in the “atoms versus bits” debate positions it favorably for investment as global supply chains shift away from traditional hubs.
See also
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OpenAI Restructures Amid Record Losses, Eyes 2030 Vision
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