X：Rohan Paul

Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter. This is the biggest jump we've made so far on coding and agentic work. Try it in Muse Code and our API. Next up 🍉 and Muse Spark open weights releases coming soon. https://t.co/XQQEDEJGD7

Satya Nadella: Microsoft’s latest Wisconsin AI data center keeps yearly water consumption no higher than that of 1 local restaurant. "The cooling loop is filled once and the data centre can operate effectively with zero water consumption. Daily water usage across a year is roughly equivalent to what a single restaurant would use" The mechanism is mainly about replacing evaporative cooling with closed-loop direct-to-chip liquid cooling, so water moves like coolant inside a sealed machine rather than being boiled off into the air. Hot GB200-class AI racks produce too much heat for normal air cooling, so cold liquid is pushed through pipes into the servers and across metal cold plates touching the hottest chips. The liquid enters the rack cool, absorbs heat from the chips through cold plates, then exits the rack at a higher temperature and carries that heat through pipes to a huge cooling system outside the compute floor. Microsoft says Fairwater sends that hot water to cooling “fins” beside the datacenter, where 172 20-foot fans blow air across the fins and dump the heat into the outside air. The important detail is that the air cools the water through metal surfaces, so the water does not need to evaporate the way many older datacenters use cooling towers. The cooled liquid then returns to the servers, repeats the loop, and keeps absorbing heat from the chips. In older data centers, heat is often removed partly through cooling towers. Hot water meets moving air, some water evaporates, and that phase change carries heat away. Effective, but it consumes fresh water continuously. But Firwater is a closed loop because the same coolant keeps circulating through sealed pipes: it absorbs heat from the chips, releases that heat through radiator-like fins, then flows back to the chips again. For Wisconsin Fairwater, Microsoft says more than 90% of the facility uses closed-loop liquid cooling, while the remaining portion uses outside air and switches to water only on the hottest days. ---- From "Microsoft" YouTube channel, (link in comment)

The Trump administration has now formally put the U.S. government behind OpenAI’s core fair-use argument in its copyright fight with The New York Times. some of the most conclusive statements they said in their filed document. - “the ‘training of AI models on copyrighted material,’ in and of itself, ‘does not violate copyright laws.’” - “For all these reasons, the United States has a strong interest in this Court rejecting any argument that training LLMs on copyrighted texts violates copyright law.” - “The fourth fair use factor … supports the conclusion that OpenAI’s model training using New York Times articles is fair use.” - “The copying of protected text articles as part of training an LLM is a use of a different kind or character that is ‘transformative—spectacularly so.’” - “In sum, the use of copies to train LLMs is extraordinarily transformative.” “Rules of law that make it significantly more difficult to develop a robust AI industry in the United States therefore threaten national security and give a competitive advantage to foreign adversaries who are not so encumbered.” - “In this litigation, the New York Times seeks to narrow fair-use doctrine to exclude the training of OpenAI’s large language models (LLMs). That result would be inconsistent with basic copyright law principles and severely hamper ‘the Progress of Science and useful Arts.’” “But it would be problematic—and legally incorrect—to impose broad copyright liability that would generally render training of AI models impermissible without licensing.”

A massive win for OpenAI (and for AI training in general) for its legal case against New York Times. The U.S. govt just formally backed OpenAI’s claim that copyrighted-text training is fair use, partly on national-security grounds. This is Washington’s first formal intervention in the wider wave of copyright lawsuits over AI training, though the filing is advisory rather than binding on the court. The U.S. Dept of Justice filed a statement of interest of the US formally arguing in the OpenAI copyright litigation that training LLMs on copyrighted texts should generally qualify as fair use; That distinction still leaves separate copyright questions around how training data was acquired and whether particular outputs reproduce protected passages. The Justice dept separated acquiring material, training on it, and generating outputs, then focuses its argument specifically on copying at the training stage. It argues that training serves a different purpose from publishing an article because an LLM uses text to learn statistical relationships and generate new responses. For market harm, DOJ says training itself does not substitute for the original work, so later AI-generated competition should not automatically make the earlier training unlawful. The administration also warns that blanket licensing requirements could raise barriers for smaller AI companies and put U.S. developers at a disadvantage against foreign competitors. The court must still decide fair use case by case, but adopting DOJ’s framework would shift much of the legal pressure from model training toward data acquisition and specific outputs.

Introducing Gemini 3.8 Flash, another jump in Gemini's agentic + coding capabilities, and our 3rd updated Flash model in only 6 weeks... This model has been a ton of fun to work with, excited to see what you all think! https://t.co/Cj07lCBtp8

JUST IN: Nvidia agreed to buy Hugging Face for $12.9B. At roughly $150M in reported annualized revenue of HuggingFace, thats 86X that run rate. - The Information. https://t.co/7tgqB3lmWi