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NEA partner Tiffany Luck discusses on TechCrunch's Equity podcast how enterprises are still grappling with AI ROI, noting trends like tokenmaxxing and cost overruns at companies like Uber and Meta.
Discusses why 95% of enterprise AI projects fail due to governance, ROI, and deployment issues, and promotes a free book by a veteran practitioner covering frameworks and patterns.
Ed Zitron argues that AI lacks measurable ROI, highlighting cases of massive overspending and the inherent unpredictability of LLM costs. The article critiques the industry's inability to quantify returns, urging skepticism.
Despite $2.5 trillion in projected global AI spending in 2026, MIT's NANDA Initiative reports 95% of enterprise generative AI projects deliver zero measurable ROI, with a practitioner's first-hand analysis of 14 engagements pointing to misallocated budgets favoring model work over data infrastructure as the root cause.
A Gartner study reveals that AI-driven layoffs are not generating the expected returns for companies, with high ROI linked instead to using AI for workforce amplification rather than replacement.