The article discusses the importance of data activation in enhancing the performance of large language models (LLMs), particularly in the healthcare sector. It highlights recent advancements in transforming structured medical data into usable formats for LLMs, emphasizing the need for effective reasoning methods to fully leverage the potential of healthcare data.

• Having proprietary data is no longer enough—the real advantage comes from "activating" it into forms LLMs can actually use before competitors catch up
• Tables2Traces converts structured medical data into reasoning traces via contrastive reasoning, notably boosting LLM performance on medical tasks
• Doctors have questioned the fidelity of these synthetic reasoning traces, and gains so far appear mainly in weaker models, raising doubts about scalability
• Despite heavy healthcare-focused LLM investment from OpenAI and Anthropic, the field is fragmented and the best method for transforming healthcare data (knowledge graphs, ontology grounding, etc.) is still unsettled

The article reviews significant trends and developments in the LLM space throughout 2025, highlighting breakthroughs in reasoning, the rise of coding agents, and the increasing use of LLMs in command-line interfaces. It notes the evolution of tools and models, including the impact of asynchronous coding agents and the normalization of YOLO mode for improved efficiency.

• Reasoning models became mainstream in 2025, significantly boosting LLM performance on complex tasks
• Coding agents surged in popularity, with asynchronous agents enabling developers to offload larger chunks of work
• "YOLO mode" (letting agents run commands without manual approval) went from risky novelty to normalized practice for efficiency
• LLMs increasingly moved into command-line interfaces, embedding AI more directly into developer workflows