New taxonomy reveals AI industry faces critical scaling walls as data depletes, costs balloon to $300M+, and six novel paradigms emerge to break through traditional limits by 2025.

LLMOrbit survey analyzes 50+ language models from 15 organizations (2019-2025) across eight dimensions, tracking architectural innovations and training methods

Three critical crises identified: data scarcity (9-27T tokens depleted by 2026-2028), exponential cost growth ($3M to $300M+ in 5 years), and 22x increase in energy consumption

Six paradigms breaking the scaling wall include test-time compute approaches, with models like o1 and DeepSeek-R1 achieving GPT-4-level performance through alternative methods