OpenAI's GPT-5.6 Sol, released in 2026, is the first model with "self-iterative efficiency," optimizing its own service stack without relying on manual engineering. This reduces service costs by 20% and increases efficiency by 15%. OpenAI's inference costs in Q2 2026 are projected at $1.2 billion, and full deployment of GPT-5.6 Sol could save $960 million annually (20% × $1.2 billion × 4 quarters). A 15% increase in token generation efficiency means the same GPU cluster can handle 15% more requests or reduce new purchases; with plans to purchase 500,000 H200 units in 2026, this could be reduced to 425,000 units, saving $5.1 billion. Funding is shifting from a "computational arms race" to an "efficiency arms race." The industry is in a "post-scale era," moving from a "parameter quantity competition" to a "unit cost intelligence" competition. This represents a technological substitution leading to a reconstruction of the value chain, with value shifting towards "efficiency tools" such as compilers, inference engines, and model compression.