Aswath Damodaran, a widely followed valuation expert and finance professor, has examined the viral AI doomsday scenario presented by Citrini Research and concluded that the probability of such an extreme outcome is low. Applying his analytical “3P test,” Damodaran assessed whether the scenario is plausible, probable, and priced into markets, ultimately determining that the narrative does not meet these criteria.

The Citrini Research thesis has gained traction across financial circles for its warning that rapid advances in artificial intelligence could trigger massive economic disruption, potentially displacing large segments of the workforce and destabilising industries. While Damodaran acknowledges that AI will significantly reshape the global economy, he argues that the apocalyptic framing overstates the immediacy and scale of the risk.

The “3P test” framework used by Damodaran is commonly applied in valuation analysis to evaluate market narratives. The first step asks whether a scenario is plausible in theory. The second assesses whether it is probable within a reasonable timeframe. The third examines whether financial markets have already priced in the risk. According to Damodaran’s assessment, while AI-driven disruption is plausible, the extreme outcomes suggested by the doomsday narrative appear unlikely in the near term and are not supported by current evidence.

What is changing, however, is the pace at which artificial intelligence is moving from experimental applications to core business processes. Damodaran notes that industries heavily reliant on software, automation and data processing are likely to experience the earliest impact. Software development, financial services, and analytical professions are among the sectors where AI tools could significantly enhance productivity or alter traditional job structures.

Why this matters for markets is that technological disruption typically unfolds gradually rather than through sudden systemic shocks. Historically, major technological shifts—from the internet to industrial automation—have reshaped industries over years or decades. Damodaran’s argument suggests that AI should be viewed through a similar lens: transformative but not instantly catastrophic.

From a policy and labour market perspective, the long-term implications remain substantial. As AI capabilities expand, certain job categories may experience structural decline while new roles emerge around data, algorithm management and AI integration. This transition could create adjustment challenges for the workforce, particularly in knowledge-intensive sectors previously considered relatively insulated from automation.

For investors and companies, the discussion highlights the importance of distinguishing between narrative-driven speculation and measurable economic change. While exaggerated forecasts can influence short-term sentiment, sustainable valuation shifts typically occur when technology adoption translates into observable productivity gains or revenue models.

Market Impact on India

For India, the debate is particularly relevant because of the country’s large technology services industry. AI-driven automation could reshape demand patterns for IT services, potentially reducing labour-intensive coding tasks while increasing demand for higher-value AI integration, consulting and data engineering capabilities.

Sector Impact

Technology services, financial analytics and software development are likely to face the earliest transformation as AI tools become more embedded in workflows. Conversely, sectors requiring physical infrastructure, human interaction or regulatory oversight may experience slower adoption cycles.

Bull vs Bear Scenario

The bullish perspective argues that AI will enhance productivity, create new technology ecosystems and unlock new revenue streams across industries.

The bearish view suggests that rapid automation could compress employment opportunities in white-collar sectors faster than economies can generate new roles.

Risk Section

Key risks include uneven workforce adaptation, regulatory uncertainty around AI deployment and concentration of technological power among a few global platforms. If adoption accelerates faster than institutional frameworks can adjust, labour markets and corporate strategies could face significant transition pressures.

Overall, Damodaran’s analysis reframes the AI debate away from immediate catastrophe and toward a longer-term structural transformation. While the doomsday narrative may be overstated, the underlying technological shift remains one of the most consequential developments shaping future economic models.