Is Elon living in simulation theory?

Ezra Klein, Elon Musk’s Endgame, NYTimes Oct. 31, 2026.

Ezra Klein has a wide-ranging discussion of Elon Musck with Quinn Slobodian, a professor at Boston University who has just coauthored a book about Musk, Muskism, with Ben Tarnoff. The discussion ends with Musk's apparent belief in the idea that we're living in a computer simulation.

Klein:

I just think that for a person with as much power as he has, to have that kind of psychology to suspect that this is a game where your role in it is making big moves — I don’t really know how to describe that situation. But it is a distinctive one that we all now have a lot of exposure to.

Slobodian:

There are a couple of varieties of the simulation theory, too. One is we are all simply just artifacts of this future civilization’s computer video game.

But the other is that most people are shadow people and a small number of people are actually player characters. And it seems like that’s the one that orients him most.

In the libertarian community that reflected a lot on these things, one of the people is Robin Hanson, the George Mason University economist, who said: What kind of ways would we conduct ourselves if this were true?

And he says: If this were true, then it would make sense to first care nothing about other people — so things like foreign aid and the welfare state would be frivolous things to spend time on — to be as entertaining as possible, to accumulate as much wealth as possible and to try to simply organize the world around you in as successful a way as you can.

There’s this radical reneging on anything like social responsibility that is only possible if you embrace this incredibly nihilistic reaffirmation of your megalomania. And the question is how to remove the props and struts that hold him up in that delusion, because it is not true, and he needs to be somehow persuaded from the outside that it’s not true.

If he continues to be incentivized to indulge in those ways of thinking, then he will never be required to be divested of that.

There's much more in the discussion.

OpenAI's Navier-Stokes result not possible in the real world

My deep analysis of the OpenAI Navier-Stokes result: the conditions proposed by OpenAI can never be created by natural physics. If the dimension of the core of the vortex induced by their unrealistic forcing function were equal to the spacing between water molecules, then the length of the vortex would have to be more that 10^12 times the radius of the observable universe to induce blowup. OpenAI did in fact satisfy what amounts to a huge loophole in the Clay Mathematics Institute's conditions to win the prize; however, OpenAI's solution is so wildly unrealistic that we in fact learned absolutely nothing from their result about how well-conditioned Navier-Stokes is under the actual physics of the universe -- which was the whole point of this problem being chosen as a Millenium Prize Challenge in the first place. https://t.co/77PWqeJCqg

— Luke Hutchison (@LH) October 29, 2026

Renting a bike in Hoboken

Virtuoso jazz recoder: Samba de Orfeu

"Samba de Orfeu" by Luiz Bonfá

Emmet Cohen - Piano
Gunhild Carling - Recorder
Yasushi Nakamura - Bass
Joe Farnsworth - Drums

Keith Jarrett, "Over the Rainbow," Tokyo 1984

Keith Jarrett’s solo performance of “Over the Rainbow” in Tokyo in 1984 is widely regarded as one of the most outstanding and moving piano solo covers of all time. Rather than simply restating the familiar melody, the legendary pianist infused the piece with new life through his brilliant improvisation, refined harmonic structure, and profound emotional depth.

— Timeless Classics (@Moonlighhy) October 22, 2026

On our place in the universe: the Anthropocene, Gaia, and the noosphere

Robert Wright and Boris Shoshitaishvili discuss Boris’s new book, Our Planetary Mirror, which draws on three big ideas—Gaia, the noosphere, and the Anthropocene—along with philosophy and myth to reassess who we are and the problems we face as a species. Overtime: Are humans AI’s fossil fuel?

0:00 Overtime teaser
0:43 Boris’s background and new book, Our Planetary Mirror
3:43 Three ways of seeing the planet—and us
12:47 Resurveying the Gaia “hypothesis”
20:31 Dawkins’s case against Gaia
27:16 Is humanity outgrowing its planetary parent?
32:57 The noosphere: Teilhard vs Vernadsky
48:46 Can we reason our way to global solidarity?*
53:58 Heading to Overtime

*This section has tantalizing hints about the need for a symbolic and mythological dimension to our conception of our place in the universe.

Network geometry and task representation in the human cortex

The brain uses dimensionality reduction as a built-in feature of its wiring. Network geometry shapes the transformation of task representations across the human cortex https://t.co/8ztNt5yHSG #neuroscience

— Earl K. Miller (@MillerLabMIT) October 22, 2026

Charting human thought with LLMs

https://t.co/wWtIKiwxz4

— How To Prompt (@HowToPrompt__) October 21, 2026

On 14th St. in Hoboken looking at the Empire State Building

Math, AI, and Terry Tao on a “war footing”

Elisabeth Bumiller, Why One of the World’s Greatest Mathematicians Is on a ‘War Footing’, NYTimes, Oct. 15, 2026.

Opening paragraphs:

Terence Tao is typically described as one of the world’s greatest living mathematicians, but right now he is doing very little math.

“I’m on a war footing,” he said while grabbing coffee during a two-day math workshop at the University of California, Los Angeles, that was rife with talk about the existential crisis that artificial intelligence has created in his field.

A bit later:

Until recently his job was as a bridge between the slow-moving world of higher math and the scorching speed of A.I. Dr. Tao, who uses A.I. as an assistant to run calculations, write code, search vast troves of literature and brainstorm ideas, has been experimenting with OpenAI’s models since 2023. He was an enthusiast for a “best of both worlds” future.

OpenAI’s releases have now left him feeling used. “As recently as a year ago, I had a pretty good relationship with many employees at OpenAI,” he said in a recent phone conversation.

“A lot of people I interacted with shared a very common vision of how A.I. should be used — you know, to empower scientists.” But the rush to publish new findings, he said, shows their true purpose. They “used math for what they needed, which was to evaluate their models,” he said. “At this point, I don’t see a way to constructively engage.”

His anger was clear in a post on the widely read math blog Proofs and Prompts after OpenAI’s Oct. 6 release. “I am horrified by the many person-years” of research, particularly by young mathematicians, “being casually disrupted or destroyed,” he wrote.

“Much as one cannot unhear a movie spoiler or a crossword clue, one cannot explore a problem as profitably and richly once one is aware of an existing solution.” He mourned, he added, “the path not taken, and the opportunities lost in the frantic race to develop this technology.”

Final paragraphs:

Despite OpenAI’s releases, he said, most problems at the frontiers of math are unsolved, math remains infinite and the difficulty of problems is therefore boundless. Since A.I. has given mathematicians the ability to collect data on such a vast scale, they will increasingly work in large groups collaborating on once-unimagined projects.

Dr. Tao, who sees each of his three students once a week, said every meeting is “increasingly a therapy session more than technical guidance.” His answer to them is that “if you focus on only the things you could do before, it looks bleak.”

So he has offered hope with advice that amounts to acceptance born of necessity rather than fighting a war. Problem solving is not a destination but a journey, and lessons learned along the way are often more important than the answer itself. Rather than solving a single problem on their own, they can use A.I. to solve 10 problems and find new patterns and more questions to ask.

“They will have to use their imagination,” he said. “This is a time for creativity, and I do hope students rise to the challenge. They didn’t ask for it. But I think many of them will surprise us.”

There's much more at the link.

Terence tao says we're being dragged into a new era of mathetics

Mathematician Terence Tao: "we're seeing the end of the romantic era of mathematics, the age of heroes. we're moving into an industrial era where math becomes large-scale, done in big teams like the other sciences" We're entering, rather unwillingly, a new era where exciting new things are possible, but some things are being lost

— Haider. (@haider1) October 15, 2026

Hoboken, on the street, May 30

Consolidating Context into Parametric Memory

Human intelligence grows by building on experience. We acquire knowledge, refine skills, revise beliefs, and carry what we learn into future situations. 🤔 Can LLM agents do the same? 🧠 Introducing our new work, “Cortex: Consolidating Context into Parametric Memory,” in collaboration with ByteDance Seed. #Cortex #KnowledgeEditing #LLM #Memory Our inspiration comes from biological systems consolidation: the hippocampus rapidly encodes new experiences, and interactions with the neocortex help integrate them into long-term memory. LLM agents already benefit from documents, demonstrations, and feedback in context. But those gains do not automatically persist once the context is removed. With Cortex, we study how to consolidate those benefits into model parameters, so they remain useful beyond the original context—while preserving capabilities outside the intended change. External memory preserves information for later retrieval. We explore a complementary route: integrating the useful effects of experience into the model itself. 📄 Paper: https://t.co/Gnc3t6BPNH 💻 Code (will be released soon): https://t.co/eQ6R7R62Y1 💡 What can be consolidated? Experience can call for different kinds of change: • Addition: acquire new knowledge or a new skill. • Revision: update an outdated fact or behavior. • Suppression: learn not to produce specified information or responses. We bring these context-specified changes in knowledge, skills, and behavior together under Memory Consolidation: recover the benefits of context after removing it, while preserving capabilities outside the intended change. ⚙️ How does Cortex work? We organize the approach around three questions: what to learn, where to write it, and how to make it usable without the original context. What: rehearse future uses. Cortex builds training examples around situations where the context should help. In most tasks, it generates prospective questions and answers them with the context available. Where: write into feed-forward networks. We explore two mechanisms: low-rank weight updates (Synaptic) and additional nonlinear feature channels (Structural). Both can be folded into standard model layers. How: let a model with context teach a model without it. The pre-update model itself serves as the teacher. It receives the original context; the student learns to respond without it. We align both their output distributions and internal representations, training only the newly introduced parameters. For example, the teacher can consult documentation for an unfamiliar API while producing a solution. The student learns from that supervision so it can use the API after the documentation is removed. 🔬 What counts as successful consolidation? Learning the new material is only part of the challenge. We also need to measure what happens to the capabilities the model already had. Our testbed covers five task families: • Agent experience: reuse useful strategies from prior experience. • Novel API learning: use unfamiliar APIs without the original documentation in context. • Persona steering: retain a specified change in how the model behaves. • Knowledge suppression: reduce responses containing specified information. • Factual editing: revise facts supplied through context. We measure two outcomes together: • Consolidation Rate (CR): how much of the performance gain provided by context is recovered after that context is removed. • Retention Rate (RR): how much of the original performance outside the intended change is preserved. We test single, batch, and sequential updates. Sequential evaluation asks a further question: as new experiences arrive, do earlier gains survive? 📊 What did we find? We evaluate Cortex on 4B–120B dense and mixture-of-experts models, including Qwen, Gemma 3, and GPT-OSS. • After 50 sequential factual updates on Qwen2.5-14B, Cortex-Structural achieves 88.9% CR and 99.8% RR, adding 0.74M parameters per update. • On GPT-OSS-120B, Cortex-Structural recovers the full context-provided gain on ALFWorld and slightly exceeds it (101.2% CR), while achieving 95.2% RR. • On GPT-OSS-20B, it reaches 90.7% CR in single-context API learning. • Across three Qwen backbones, the two Cortex variants achieve approximately 80–91% CR in batch factual editing. All results are evaluated without the original source contexts. This is the direction we want to explore: agents that turn useful experience into capabilities they can carry into future interactions.

— Ningyu Zhang@ZJU (@zxlzr) October 15, 2026

Reconciling symbols and neural networks

🤖🧠NEW PAPER🧠🤖 What is the relationship between neural networks & symbolic systems? Based on evidence from mechanistic interpretability, we argue for a quasi-symbolic view of cognition: Neural nets implement symbols but only approximately Link: https://t.co/j8B3IOIMpe 1/n https://t.co/zpn6q00wqq

— Tom McCoy (@RTomMcCoy) October 14, 2026

For comparison, see Relational Nets Over Attractors, A Primer: Part 1, Design for a Mind, Version 3

3 Patterns of hemisphere specialization in humans

The brain’s neat division of labor, with language on one side and attention on the other, is just one of several workable layouts. Hemispheric specialization appears to be flexible and partly inherited, not governed by a fixed template.

— Earl K. Miller (@MillerLabMIT) October 14, 2026