If so, then would each of the pure states that constitute this mixed state be different branching multiverses, like different trees with their own branches?
Is it possible to have an impure state for the entirety of a completely isolated system, with known initial conditions?
For example, a quantum superposition of 10 photons with 11 photons, and so on.
https://arxiv.org/abs/2608.05044
>We show that a finite number of time-frequency shifts of a Schwartz function can be linearly dependent. This disproves the so-called HRT conjecture of Heil, Ramanathan, and Topiwala. In particular, we provide an example consisting of 12 time-frequency shifts.
I like Terence Tao's exposition on this topic, so I screenshoted those portions that caught my eye and chose to show those here:
https://terrytao.wordpress.com/2026/08/06/a-partial-digestion-of-the-hrt-counterexample/
For the awesome science behind this, checkout this webpage:
https://www.cmetracker.ai/science#validation
>Everything the tracker shows — every wedge, arrival time, and storm estimate — comes out of a small set of published physics and classical astronomy, seeded entirely by real measurements. This page walks through all of it, equations included.
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You can even replay historical solar storms such as the Carrington Event!
(Once again, I did not make this; I originally found it from this post. The actual developer is oldschoolscreenname -- thank them for making this awesome sun ejaculation simulation!)
The first thing I'd push back on is the premise: Gen Z doesn't uniformly hate AI. They're actually the generation with some of the highest rates of AI use—for homework, coding, writing, image generation, and search. What they often hate is what AI is doing to the internet and to creative work.
If I had to summarize the biggest reasons:
AI has flooded the internet with low-effort content. Gen Z grew up online. They can tell when Google results, TikTok videos, Pinterest pins, or blog posts are padded out by generic AI text. "AI slop" has become a real complaint because it makes finding authentic content harder.
It threatens creative careers. Artists, musicians, writers, voice actors, and designers are disproportionately young. Many see companies using AI to replace entry-level creative jobs while training models on existing creative works without permission.
Distrust of big tech. Gen Z is generally less enthusiastic about large technology companies than Millennials were in the 2010s. AI is often seen as another product rolled out by corporations promising to "change the world" while primarily cutting costs.
They worry about employment. Previous automation mostly threatened manufacturing. AI directly targets white-collar and creative work—the kinds of careers many Gen Z members are trying to enter. That makes the technology feel personal.
School has become an arms race. Students deal with AI-generated assignments, AI detectors that sometimes falsely accuse honest work, and teachers struggling to adapt. AI has complicated education in ways many students find frustrating.
Social media amplified the backlash. Ironically, algorithms reward outrage. Posts mocking obvious AI mistakes, bizarre AI-generated videos, or "look what AI ruined today" spread faster than mundane examples where AI quietly helps someone code or summarize a document.
That said, there is an interesting contradiction.
Many Gen Z users use AI every day while simultaneously saying they dislike "AI." Often what they really mean is:
they dislike AI replacing humans,
they dislike AI-generated spam,
they dislike companies forcing AI into every product,
but they do like AI as a personal productivity tool.
So the dislike is often directed less at the underlying technology and more at how it's being deployed.
One final observation: online discussion can make the backlash seem larger than it is. People who use AI to summarize notes or debug code usually don't post about it. People who encounter a terrible AI-generated ad or lose work to automation are much more likely to speak up. That creates a strong negative impression on social media even while AI adoption continues to grow.
From the article:
>The idea behind this construction was suggested by an LLM (OpenAI’s GPT- 5.6 Sol). The authors have verified the mathematical details and have written the argument in their own words. Computer algebra software (Mathematica, Maple) was used to verify computations and produce visualisations.
Its abstact which explains the conjecture, while briefly describing their counterexample:
>We exhibit a configuration of five point charges in Euclidean space whose electrostatic potential admits at least 24 critical points all of which are non-degenerate. Maxwell’s conjecture that the field of n point charges has at most (n − 1)^(2) critical points which are all non-degenerate is therefore false.
Edit: Just saw this was posted in the main sub as well: https://reddit.com/r/Physics/comments/1vbrzbe/the_maxwell_conjecture_is_false/
Like it's been asked before what would happen if time were negative instead of positive, but what if instead I substitute time t with -i*t, or t with some other complex value?
What I mean is that in the future there will be far less need for human theorists. On the other hand, there will likely be an explosion of experimental setup proposals, which will still need hands to realize these designs and perform the experiments. Or, do you think advancements in robotics will cut down on this type of work as well?
"Hey Gemini, help me cope. Why is what Sabine wrote here wrong?"
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Take a deep breath—doom-posting on X is practically a sport, and hyperbole gets engagement. The idea that AI will cause an "extinction-level event" for mathematics and theoretical physics mistakes automation of computation for the extinction of understanding. Here is why that perspective misses the mark and why theoretical physics and math aren't going anywhere:
When electronic calculators and symbolic algebra software (like Mathematica or Maple) emerged, critics worried mathematicians wouldn't be needed anymore. Instead, those tools eliminated tedious, error-prone grunt work and birthed entire new domains—like chaos theory, computational fluid dynamics, and modern cryptography. AI is the next evolution of tool-use: it automates complex algebra, suggests non-obvious conjectures, and navigates high-dimensional parameter spaces faster than a human ever could.
Physics is fundamentally distinct from pure computation:
Deep learning models operate on probabilities, whereas math and theoretical physics demand absolute rigor and self-consistency:
Rather than an asteroid hitting the dinosaurs, a far better analogy is the telescope hitting astronomy. AI will likely handle the brute-force search space—calculating millions of Feynman diagrams, searching string theory landscape compactifications, or crunching numerical simulations—freeing theoretical physicists to focus on conceptual breakthroughs and physical interpretation.
>The Bottom Line: AI will change how theoretical physics and math are done, but as long as we care about understanding the universe rather than just predicting data outputs, the theoretical physicist isn't going extinct.
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Edit:
Maybe X (formerly known as Twitter) will be extinct long before math or theoretical physics.
Found it on HackerNews; currently top post rn
Charts taken from here: https://www.cbsnews.com/projects/2026/how-much-water-ai-uses/. The units are in billions of gallons.
From the article:
>The heaviest demand on America's water isn't data centers or AI. It's from everyday uses such as growing food, watering lawns and flushing toilets.
Data sources used by the article:
Charts taken from here: https://www.cbsnews.com/projects/2026/how-much-water-ai-uses/
The units are in billions of gallons.
- Kelly J., Pham S., Reilly S., Manthey G., Cohen P. "How Much Water Does AI Really Use?" (2026, June 9). CBS News.
Data sources used by the article:
- Xiao, T., Nerini, F.F., Matthews, H.D. et al. "Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA" (2025, November 10). Nature Sustainability, 8, 541–1553. https://www.nature.com/articles/s41893-025-01681-y
- Lei et al. "The water use of data center workloads: A review and assessment of key determinants" (2025, June). Berkely Laboratory. https://eta.lbl.gov/publications/water-use-data-center-workloads
- Mialyk, O., Schyns, J.F., Booij, M.J. et al. "Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model" (2024, Febuary 14). Scientific Data 11(206). https://www.nature.com/articles/s41597-024-03051-3