Whitepapers

Tech layoffs attributed to AI are largely a correction of COVID-era overhiring, with "AI efficiency" serving as narrative cover for stock-price-friendly restructuring. Challenger Gray tracked ~55,000 AI-cited US job cuts in 2025, yet Gartner found less than 1% were actually attributable to AI productivity gains. This paper disentangles attribution using company-level headcount data, BLS employment figures, and earnings call analysis.

Token consumption is growing at 320x YoY while per-token costs have fallen 300x since GPT-4 launch, yet enterprise AI spending surged 320%. This paper maps consumption across six dimensions: application category, model tier, architecture pattern, geography, buyer segment, and token type. Programming now exceeds 50% of all tokens, and agentic loops multiply consumption 10-40x per task.

AI-mentioning postings are up 134% since 2020 while total postings grew only 6%. The structural shift is spreading beyond tech: 51% of AI-requiring postings are now outside IT. AI skill holders earn a 28-56% wage premium depending on methodology, and entirely new role categories (CAIO, AI Engineer, Prompt Engineer) have emerged in the Fortune 500. This paper builds the longitudinal evidence base from Indeed, LinkedIn, Lightcast, and PwC data.

Enterprise AI adoption follows a J-curve, not an S-curve. Only 5-7% of organizations have reached full maturity across all major frameworks (MIT CISR, McKinsey, BCG), and the gap between AI access and AI competence is the primary barrier. Only 28% of employees can use their company's AI tools, yet organizations with mature training programs see 3.8x higher AI ROI. This paper compares maturity models, maps industry-specific adoption rates, and identifies why companies stall.