AI Productivity Gains Have Yet to Spread Beyond the Technology Sector: Analysis
The mismatch between investor expectations and implementation timelines could have significant implications for AI spending and valuations.
News
- AI Productivity Gains Have Yet to Spread Beyond the Technology Sector: Analysis
- Abu Dhabi Expands AI Drive, Deploys Copilot to 35,000 Civil Servants
- Microsoft and G42's Inception42 Join Forces on Enterprise Agentic AI
- UK Foreign Secretary Warns World Cannot Wait for 'AI Hiroshima' Before Regulating AI
- UAE Banks Defy Rate Cuts With Double-Digit Profit Growth
- Sam Altman Floats Plan to Give US Govt 5% OpenAI Stake: Report
[Image: Chetan Jha/MITSMR Middle East]
Two years into the generative AI boom, investors are still waiting for productivity gains to materialize across the wider economy. While technology giants are already seeing measurable benefits from AI, economist Torsten Slok argues that the broader productivity revolution many anticipated has yet to take hold.
In a recent blog, Slok argues that while AI is already boosting profitability at large technology companies, most businesses outside the sector are yet to generate meaningful returns on their AI investments. That disconnect, he says, means financial markets may be expecting AI productivity gains that will take years instead of quarters to materialize.
“The key issue is the length of the ROI runway outside the tech sector,” Slok writes. “A mismatch between current earnings expectations and the actual time firms need to generate ROI on AI investments could have significant implications for many AI company valuations.”
His analysis pinpoints the widening gap between the technology sector and the broader corporate landscape. Using data from Bloomberg and Macrobond, Slok notes that profit margins for the Big Tech increased from roughly 15% in the first quarter of 2023 to about 25% by the first quarter of 2026. Over the same period, profit margins for the rest of the S&P 500 remained largely flat at around 10%, with the broader Bloomberg 500 Index showing a similar pattern.
The disparity shows that while software companies can often integrate AI directly into digital products and workflows, organizations in industries such as manufacturing, healthcare, financial services, and government face more complex implementation cycles. Regulatory compliance, data governance, legacy technology, and workflow redesign can significantly delay productivity gains and return on investment.
That slower adoption timeline, Slok argues, matters because equity markets have largely assumed that AI-driven earnings growth will spread quickly beyond the technology sector. If returns continue to lag expectations, companies may begin scaling back AI spending, leading to what he describes as a potential “painful repricing” of AI-related valuations.
His assessment echoes findings from a widely discussed study by researchers at MIT NANDA’s initiative, which found that only about 5% of organizations had realized significant returns from their generative AI pilot projects.
Slok also points to the growing corporate emphasis on token optimization—reducing inference costs and improving model efficiency—as an early signal that organizations are under pressure to demonstrate measurable business value rather than simply expand AI usage.
His broader outlook, however, remains optimistic. Slok has previously argued that AI is more likely to create jobs than eliminate them, citing the economic principle of Jevons’ Paradox, and believes the technology could ultimately spur a new wave of entrepreneurship. The challenge, he suggests, is less about whether AI will deliver productivity gains than how long businesses and investors can wait.
