"Magick is an influence on probability and minds.
Make a plan that could work without magick, then use magick to make that plan work better - such is Strategic Sorcery."
Do you believe in “magic”? According to Charles Cao, this feature of quantum systems might be what makes the fabric of space-time so bendy and springy.
https://t.co/DoLuozu0Lu
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.
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"Your brain has two modes. The Focused Mode: tight attention, prefrontal cortex engaged, grinding through familiar steps. The Diffuse Mode: it runs in the background when you relax. It is loose, wide, and wired for connecting ideas that sit far apart from each other."
An engineering professor who failed math her entire childhood spent years figuring out exactly what had been sabotaging her, and the answer was not low intelligence. It was a hidden mode her brain kept switching into that nobody had ever told her existed.
Her name is Barbara Oakley. The book is called A Mind for Numbers.
She failed math and science from grade school to the end of high school. Numbers felt like a language everyone else had been taught in secret.
So she ran toward the thing she was good at. She enlisted in the Army right after graduation, and the Army paid her to learn Russian at the Defense Language Institute in Monterey.
She got very good at Russian. Good enough to earn a degree in Slavic Languages, serve four years in Germany as a Signal Officer, and rise to Captain.
Then the wall appeared.
She watched her career options shrink because she could not handle the technical side of her own job. The people with math moved up and moved out. The people without it stayed stuck. So at 26 she did something that sounds insane. She left the Army and enrolled in engineering, starting from remedial math, sitting in classrooms with teenagers.
In between, she worked as a Russian translator on Soviet trawlers in the Bering Sea and as a radio operator in Antarctica. Today she is a professor of engineering at Oakland University with a doctorate in systems engineering.
The question that drove her for years was simple. What changed? She was the same brain that failed algebra. Why did it suddenly start working?
The clue was hiding in the one subject she had mastered. She noticed she had never learned Russian by staring at it. She practiced a little every day, walked away, came back, and the language quietly assembled itself between sessions. Math she had attacked the opposite way. Lock eyes with the problem. Push harder. Refuse to look away until it cracks.
It never cracked. And neuroscience explains why.
Your brain has two modes. The focused mode is the one you know. Tight attention, prefrontal cortex engaged, grinding through familiar steps. The diffuse mode is the one nobody teaches you. It runs in the background when you relax. It is loose, wide, and wired for connecting ideas that sit far apart from each other.
Oakley uses a pinball machine to explain the difference. In focused mode, the bumpers are packed tight. Your thought bounces in the same small circle, over the same ground, again and again. In diffuse mode, the bumpers spread out. The thought travels. It reaches parts of the brain the tight loop could never touch.
The trap has a name. The Einstellung effect. The first approach that comes to mind blocks every better approach behind it. The harder you focus, the tighter the loop, the more locked in you become. The grinding feels virtuous. It is actually the cage.
And every time her mind wandered off a math problem as a kid, she dragged it back, believing the wandering was laziness. The wandering was her brain trying to switch into the mode that solves things. She spent ten years fighting the half of her brain that wanted to help her.
You cannot run both modes at once. The diffuse mode only takes over when you genuinely let go. Which is why answers ambush you in the shower, on a walk, at the edge of sleep. Salvador Dali knew this. He napped in a chair holding a key over a plate, and the instant he drifted off, the key dropped, woke him, and he carried the half-formed ideas straight back into focused work. Edison did the same trick with ball bearings. Two of the most inventive minds in history were deliberately farming the mode the rest of us treat as slacking off.
The practical version fits in two sentences. Focus hard on the problem until you stall. Then stop completely, and let the other mode take the shift.
The break is not a reward for the work. The break is the work. It is also why cramming fails and procrastination is fatal. Diffuse mode needs hours and nights between focused sessions to build anything, and procrastination burns that time before the first session even starts.
Oakley failed math for ten years using one mode at full strength.
She became an engineering professor the day she started using both.
@ArchieHall@TheEconomist "But the centre has struggled, too, to concoct a creative and enticing policy offer."
The center must hold. But what is the center?
🚨: You’re closer in size to the entire observable universe than to the smallest possible scale of reality—the Planck length—by roughly 400 million times.
Let that sink in.
LLMs learn by predicting tokens. World models (JEPA, data2vec) learn by predicting their own abstractions. Which needs more data? For data with hidden hierarchy, we prove the gap is exponential. https://t.co/r2uuX0lBCu