All 21 firms reported active AI deployment or concrete plans; sectors include manufacturing (6 firms), finance, insurance and real estate (4) and business services (4).
6,000 → 15,000daily customer issues handled by 300 insurance service staff after AI deployment (+150%)
Structure-aware chunking is supported for nine languages (Kotlin, Java, Python, JavaScript, TypeScript, C#, PHP, Go, Rust); everything else falls back to line-based splitting.
32xstorage reduction from one-bit binary vectors versus 32-bit floats
Three scenarios: modest (impact like the internet), substantial (bigger than the internet or the railroad), extreme (likely requires recursively self-improving AI adopted quickly).
$34.1T / $36.3T / $44.4TUS GDP in 2030 at 2025 prices in the modest, substantial and extreme scenarios
Post-trained models are 3.6% less likely than their base versions to call back older applicants; eight of ten models shifted against age, median shift -4.7%, largest Granite 4 (0.0% to -10.0%).
5.6% to 17.3%global systemic exclusion rate, base models versus post-trained models
Modest: AI touches 4% of task instances by 2030; GDP +1.6%, growth 2.4% a year instead of 2%, labor share 60.0% to 59.4%, unemployment 3.9% against a normal 3.8%.
+8.3%GDP above the no-AI path in 2030, substantial scenario; the median US adult's answers imply +8.6%
Share of workers expecting AI to facilitate their work ranges from 42% to 86% by country; threat ranges from 18% to 46%.
42% to 86%range across countries of workers expecting AI to facilitate their work