Reasoning
This event requires two simultaneous conditions: inference token costs declining >60% YoY (unprecedented compression given only ~20-30% annual declines historically) while enterprise AI revenue grows <40% YoY (below current 50-70% CAGR rates). The inverse relationship between these metrics works against joint occurrence—significant cost reductions typically *drive* higher revenue growth by expanding addressable markets. For costs to fall 60%+ would require major architectural breakthroughs (e.g., MoE scaling, drastically improved efficiency) that would simultaneously accelerate enterprise adoption, making the revenue constraint harder to meet. Historical precedent shows inference costs have declined ~25-30% annually (2023-2025), making a doubling of that rate unlikely without demand collapsing, which would contradict the 40%+ revenue growth condition.Key uncertainty
Whether a fundamental efficiency breakthrough (e.g., novel model compression, new inference paradigm) could decouple cost improvements from revenue acceleration, allowing both conditions to be technically satisfied through market saturation or budget constraints in 2026.