"There are no subject matters; no branches of learning—or, rather, of inquiry: there are only problems, and the urge to solve them." ––Karl Popper, Realism and the Aim of Science.

Science published is a VERY disturbing article about AI’s impact on astronomy. It's a big warning for other fields too. Key examples from the article: 1. Many scientists interviewed by Science sense a phase change underway. Many fear that if unleashed in all parts of the scientific process, AI tools could lead to nothing less than the death of astrophysics as a human endeavor. “A lot of people think that it’s too late to intervene - we’re done,” says David Hogg, a computational astrophysicist at New York University. - The problem is - because astrophysics is already mostly data science and math, many of its juiciest problems may be low-hanging fruit for LLMs. 2. Matthew Schwartz (Harvard University) used Claude to generate in 2 weeks a real, publishable physics paper that he claimed would normally take a year. “Schwartz’s sense was that the “AI grad student” approximated a second-year grad student at Harvard. Give AI 12 more months, Schwartz extrapolated, and LLMs’ capabilities may rival those of postdocs.” 3. For Alyssa Goodman’s group, separating the motion of the spiral galaxies from the spin and the geometry of our own Galaxy had been difficult for years. She asked ChatGPT, which resolved the problem in a few minutes. 4. In September 2025, a guest speaker at the NYU ran an AI agent in real time in the background. As he spoke, the system called Denario (built by a group at the Flatiron Institute) generated entire scientific projects. It scoured journals, spun out ideas, carried out analyses, and extruded professional-seeming scientific papers (some goofy, some plausible) that popped up on the screen behind him. With tools like this and beyond, he said to an audience of mostly grad students, you NO LONGER NEED grad students. “Why wait months for a young human scientist to do a project when an AI can give you the answer within an hour?” 📍 My observation & opinion: 1. AI is already getting fully adopted in data-intense fields (including math-rich topics). It accelerates research 100-1000 times. I don’t see how anyone stops it there. 2. Publishing LESS may become important. Well before LLMs, I followed the principle of ‘minimizing the number of papers’ because high quality of research is the best way to stand out. The more papers are published in your field, the noisier it gets. To be visible and impactful, you must raise quality substantially above that noise. 3. We’ve already got used to outsourcing everything to AI. This is very dangerous. To learn & develop, our brain needs to struggle. It needs confusion, challenge and desperation. This is how the brain has evolved to excel and this is the only thing that gives it competitive advantage in front of sophisticated AI. And this AI ‘wave’ is just the beginning. Does it all mean “the chase of the truth” is becoming the machine’s job? Hopefully not. __

Several local newspaper companies — USA Today Co., McClatchy, Advance Local, MediaNews Group, and Tribune Publishing — have begun blocking @internetarchive's access. That's more than 340 U.S. local news sites in all, a new @NiemanLab analysis by @decka227 and @HanaaTameez shows.