Some general science news:

• The New York Times ran an interactive article this week that shows what we all know. This past year was a very bizarre funding environment. The article focuses on NIH and NSF, but the major points are generalizable. The combination of circumstances (DOGE, general administrative turmoil, uncertainty and legal cases about indirect costs, the lack of a real budget followed by a late continuing resolution, plus the government shutdown and continued lack of real budgets) has been extremely disruptive, resulting unquestionably in less science and engineering research being funded by the US government than in many years.
• Conversations I’ve had with program officers at two agencies have conveyed that everyone thinks it is very likely that there will be another shutdown in January, when the present spending authority expires. To put that another way, there is very little confidence that actual spending bills appropriating real budgets for NSF, DOE, NIH, etc. will pass the House and Senate, with some reconciled conference version getting filibuster-proof support in the latter, before then. This uncertainty means that right now it’s going to be nearly impossible for the NSF, for example, to make much in the way of awards in the meantime, since they have no budget and can’t plan on a year-long continuing resolution.
• There has been an executive order announcing the Genesis Mission, which is going to be a large federal AI+science project. The goal is to "accelerate the AI and quantum computing revolution and to double the productivity and impact of American science and engineering within a decade", according to undersecretary of energy Dario Gil. Broadly, the plan is to have AI/ML agents developed (presumably by private contractors or private/public partnerships) and trained on vast datasets (ones already in existence in, e.g., national labs and public repositories). At the same time, a list of Grand Challenges will be defined (within the next 60 days), with the idea that these AI agents will be used to address these (and demonstrating application of the AI "Platform" toward at least one challenge within 270 days). Any stated support for science and engineering research is welcome. I hope that this ends up bearing fruit in terms of real research advances, and that university researchers can contribute effectively. (I worry about a framework for massive taxpayer-funded financial support of for-profit AI companies, privatizing financial/IP benefits from publically funded datasets. Of course, I worry about a lot of things. Ask anyone who knows me.). Ideas about grand challenges would be fun to discuss in the comments.
• We had a great physics colloquium this week from Steve Fetter at the University of Maryland about the continuing threat of nuclear weapons. Very sobering. One fact that I gleaned: In terms of missile defense, the Next Generation Interceptor is likely to cost $660M per interceptor. That is something like 50 times the cost of a Russian ICBM. Something else to bear in mind: The Houston Food Bank, one of the largest and most effective in the US, has an annual budget of about $64M. The amount of resources consumed by nuclear arms since 1945 is just staggering.