From 'Model Race' to 'Closing the Books': What Competition in AI Industry Is Now About – Answers from WAIC 2026

At the WAIC 2026 conference, experts from various sectors - including industry, capital, technology, and infrastructure - discussed the AI industry's shift from a race for model parameters to a pragmatic 'taking stock'. Key changes in 2026 include: the emergence of Claude Fable5, which caused panic in the cybersecurity field; a sharp increase in the cost of accelerators (rental and purchase prices have multiplied); the complexity of B2B applications; a threefold imbalance between investments (over $700 billion) and revenues ($50 billion) in infrastructure; an 8-10 times drop in the price per token alongside a rise in total token consumption; and a shift in investor attention from 'big stories' to real economic returns. Participants noted that models remain important, but their role is changing: large companies must invest in models, while startups should focus on product concepts. Agent technology (Agent + Harness) has enabled probabilistic models to solve deterministic tasks, especially in coding, where token consumption has surged. Priority areas for the coming year include: AI infrastructure, trustworthy AI (reliability and security), world models (physical data), trusted data in narrow domains (medicine, industry), and model inference speed (up to 15,000 tokens per second on 8B-parameter models). Experts agreed that benchmarks are still useful, but closed tests are more reliable than open ones.