According to Jeff Dean on Twitter, Gemini's Vlad Feinberg presented slides highlighting critical scaling considerations for large language models (LLMs) that are directly relevant to AI and crypto trading strategies. Feinberg emphasized that traditional scaling law analyses often overlook practical factors such as inference cost, model distillation, and adaptive learning rate schedules, all of which directly impact the operational costs and efficiency of deploying LLMs in real-time trading environments (source: Jeff Dean via Twitter, vladfeinberg.com, 2025/04/24). For traders and quantitative analysts leveraging AI-driven strategies, understanding these overlooked parameters can help optimize algorithmic performance and reduce trading infrastructure overhead, improving profitability and risk management as AI integration in crypto markets accelerates.