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The article's primary subject is the economic viability and risk assessment of deploying large language models within enterprise environments, making it central to AI rather than incidental. It addresses critical concerns regarding rising operational costs, failed pilot programs, and data security risks associated with current generative AI adoption strategies. This analysis directly impacts how organizations evaluate ROI and governance for their AI infrastructure investments.
Sources coveringGDELT, Fortune
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Business leaders are souring on AI and this is why

GDELT6 min read

https://arab.news/btmbv After years of soaring expectations, the AI industry is approaching a moment of reckoning. Despite record investment, executives, corporate boards, and investors are growing increasingly skeptical that artificial intelligence will deliver significant productivity gains anytime soon. This 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 LLM Token Expenditure Index, LLM spending has doubled since 2025, even though the cost per token (the basic unit of text processed by the models) has fallen by more than 90 percent over the past three years. In other words,…

Excerpt shown under the publisher's RSS terms · 107 words · full text and figures are on the publisher's site

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