Today's tech landscape sees major advances in AI capabilities, but with fascinating tradeoffs. While new breakthroughs in video generation and language models promise more efficient and capable AI systems, researchers are discovering that making these models faster and more compact may come at the cost of their core abilities - raising important questions about the balance between accessibility and capability in our AI future. Links to all the papers we discussed: VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models, Inverse Bridge Matching Distillation, ACECODER: Acing Coder RL via Automated Test-Case Synthesis, QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search, Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search, Can LLMs Maintain Fundamental Abilities under KV Cache Compression?