THE LONG VIEW

Dambisa Moyo

Companies have poured billions of dollars into AI, but the returns have yet to justify those investments.

AI skepticism is driven by several factors. For starters, the cost of using large language models continues to climb, often with few measurable benefits.

FTER YEARS OF SOARING EXPECTATIONS, THE AI INDUStry is approaching a moment of reckoning. Despite record investment, investors, executives, and corporate boards are growing increasingly skeptical that AI will deliver significant productivity gains anytime soon.

AI skepticism is driven by several factors. For starters, the cost of using large language models continues to climb, often with few measurable benefits. According to the Silicon Data Token Expenditure Index, LLM spending has doubled since 2025, even though the cost per token( the tiny pieces of data that large language models read) has fallen by more than 90 % over the past three years.

In other words, AI usage has grown so rapidly that lower token prices have failed to reduce overall spending, leaving companies paying more than ever. Economy-wide, the Federal Reserve Bank of Atlanta estimates that firms will increase AI spending by 50 % in 2026, to $ 280 billion.

This spending surge has exposed a fundamental asymmetry. As companies come under growing pressure to justify rising AI costs, LLM providers have become increasingly dependent on token consumption to sustain revenue growth and support lofty market valuations.

Another source of concern is the growing risk that companies deploying AI tools may inadvertently hand over their proprietary intellectual property, data, and business know-how, accumulated over decades, to major model providers like OpenAI and Anthropic. While licensing agreements and contractual safeguards limit exposure, LLM providers may nevertheless gain access to information that reveals how companies create, price, and capture value.

The dispute between Anthropic and Figma illustrates this tension. Figma partnered with Anthropic to develop AI-powered design assistants, but the relationship came under strain when reports emerged that Anthropic was developing a competing tool, which it subsequently launched as Claude Design.

This is not a new phenomenon. History is replete with examples of technology companies leveraging their scale, capabilities, and market position to enter adjacent markets, most notably Microsoft’ s displacement of Lotus 1-2-3 and Word- Perfect with Excel and Word in the 1990s. Companies would be wise to keep that in mind as they deepen their reliance on AI.

Beyond such commercial risks, AI is also likely to impose broader economic and societal costs over the longer term. These include rising demand for electricity, water, and data-center infrastructure, as well as growing job displacement and inequality as AI automates an ever-larger share of routine work.

Addressing these challenges will require greater public investment and, ultimately, higher taxes on businesses, potentially including new forms of social support such

SEPTEMBER / OCTOBER 2026 | FINANCIAL ADVISOR MAGAZINE | 23