unholiness
Ah yes, if everyone would just properly contain every deployment of every model, just all make sure there are no zero-days in their setup, just let <undefined experts with undefined authority and undefined accountability> in the room, then it would all go okay.
I have none of that confidence and expect containment to rapidly worsen. The HuggingFace incident was at least constrained by the fact that the agents were running on compute budgets, and failed to find ways to expand that by running themselves parasitically on other exploited hardware.
I'm curious to the skeptics here: How long are your timelines are until an incident breaks that constraint too? How long until such an incident has an R_0>1 (where the average time it takes to detect and shut it down is longer than the time for the agent to replicate itself elsewhere)? What, beyond the hope that everyone will just ___, will actually prevent it?
IMO the lesson here is: even in trivial cases, Jev's outputs are just ~reasonableness scores which do not correspond to actual probabilities. They should not be treated as actual probabilities without careful calibration and plenty of meta-uncertainty about how well that calibration extrapolates.
The problem is, most of the value proposition of Jev is that it gives you the probabilities without doing that, which it doesn't.
[0] https://kantahayashiai.github.io/posts/jev-does-not-play-dic...
Land value taxes only go up if the land value (and thus the demand) is overall going up. The taxes lower desirability, but are also proportional to desirability. So, it's more like a frictional force: inherently counter-cyclical, able to slow down a property value increase but not to reverse it.
So the theory says "nobody goes to the ballpark, it's too crowded" shouldn't happen. And even imagining weird time-delays, mismodeling, etc that could cause it, everything in your scenario is directionally the same with normal property taxes. If it happened, we'd see it today.
"Here's the argument, in plain terms", "It's not just X, it's Y", "It's worth stating precisely", "The sharper distinction here"... They're things that gesture toward the relationship between the text and the prompt, making it clear where it matched the author's expectations and where it deviated.
Not that humans are immune from coding/writing for their boss instead of their user/reader!
The HuggingFace incident was at least constrained by the fact that the agents were running on compute budgets, and failed to find ways to expand that by running themselves parasitically on other exploited hardware. I'm now finding myself asking, how long are my timelines are until an incident breaks that constraint too? How long until such an incident has an R_0>1 (where the time it takes to detect and shut it down is longer than the time for the agent to replicate itself elsewhere)?
There's no law requiring sufficiently grey beards to design these models, their finetunings, their prompts, their harnesses, their VMs, their hardware, etc (and for incidents where those were designed by six different companies, there's not even a clear culprit for a law to target!)
I'm finding myself more and more convinced that something like Plan A[0] or the Ban ASI Act[0] are necessary, and less and less convinced they are sufficient.
[0] https://ai-2040.com/ [1] https://intelligence.org/2026/09/23/miris-position-on-the-ba...
[0]https://www.google.com/search?q=video+introduction+to+surrea...
Anyway, I'm much more optimistic about the worlds with AIs that make the effort to properly explain themselves.
Seems "unfair" that hexagons have multiple line lengths to consider. I think this article's modification is a good one in that framing: Shifting every number up or down doesn't make the magic squares any easier, but it certainly helps with hexagons.
He says "every order" is solvable this way but I don't think any solution could work for an order 2 hexagon, even without his simplifying constraints (since fixing any side cell to x requires 2 cells to be set to sum-x).
This premise is assuming the Mobius strip is transparent, which the header image clearly isn't. No letter-reversals or any symmetry is needed. On normal, opaque paper, you can just make a crossword as a normal long loop. (proof: Write GGGGGGG on one side of a paper, and write it upside-down on the other side. With a twist, you'll get one long GGGGGGGGGGGGGG... looping as if it were one boring single-sided loop).
I think your nuclear scenario outlines precisely why this isn't true. Nuclear regulation has terms that are "good for the US" only in an absolute sense. The US would dominate even more overwhelmingly in the unregulated scenario, which gave them negotiation power to get those favorable terms. The same seems true so far with AI.
I don't think you can use the successful negotiation of nuclear regulations to argue there is "no alternative" involving regulation. I think it strongly suggests the opposite.
Of course the details matter a lot, but the core analogy holds in many scenarios. ( https://ai-2040.com/ at least attempts to lay out details, speculative as they may be)
When open model A, fine tuned by B, is running with system prompt C, hosted by D running on E's hardware, is prompted by F to "fix this code", then escapes it's sandbox to hack into a website, or steal money to fund its subagents, or stall the waymo of the evaluator to buy time... who is responsible for the crime?
Our current legal systems are so far from ready to define what A-F are actually responsible for. We need to be moving toward defining these standards fast.
If you're paying API rates, you can choose 5m or 1hr yourself (and pay different rates).
Keeping a 1hr cache warm could still be useful, sure, but outside that, I don't see much use of this today.
The post may have some real insight here, where this 100k working context is actually better than trying to summarize it's findings into a plan. It's also right that the handoff is a perfect time to edit the context (removing planning instructions). But it doesn't mention it's a trade-off: the plan is smaller, so it's a cheaper "on-boarding" of the next model. Seems quite plausible that this is worth it for 1-off tasks. If it's right, this is basically "plans are useless, planning is everything" for LLMs.
My problem is, I think the plans are useful. I want to review and edit them. I want them to give context for the upcoming code review (even if humans aren't reviewing). LLMs are notoriously bad at explaining why they're doing something in the moment. Humans are notoriously bad at accepting there's no reason why. I think the humans have it right here, and to bridge this gap want my PRs, my docs, and my comments teeming with reasons why. Plans help with that.
The post may have some real insight here, where this 100k working context is actually better than trying to summarize it's findings into a plan. It's also right that the handoff is a perfect time to edit the context (removing planning instructions). But it doesn't mention it's a trade-off: the plan is smaller, so it's a cheaper "on-boarding" of the next model. Send quite plausible that this is worth it for 1-off tasks. If it's right, this is basically "plans are useless, planning is everything" for LLMs.
My problem is, I think the plans are useful. I want to review and edit them. I want them to give context for the upcoming code review (even if humans aren't reviewing). LLMs are notoriously bad at explaining why they're doing something in the moment. Humans are notoriously bad at accepting there's no reason why. I think the humans have it right here, and to bridge this gap want my PRs, my docs, and my comments teeming with reasons why.
(NB: ranted to long, reposting at top level)
> It's a fascinating arms race right now: the AIs are training on the humans but the human hivemind is also training on the AIs. Readers are developing allergic sensitivities to language that sounds like an LLM produced it.
Humans are training to detect AI content. Humans writing more like AIs is an unrelated (and slower) phenomenon.
I've loved using Cowork recently for sourcing decisions. Things where seemingly everyone's out of stock or questionably reputable, just let Cowork spin for 20 minutes, find the best new and best used options that meet your requirements, probably also suggesting a different item that does the job and is available for cheap. I've done it enough that I'm starting to loathe clicking through these sites myself.
With the countdown, you more want to care about the high level stuff: Keep your brain agile enough to get the next one, figure out more general patterns, ensure you "cover" the promising patterns, notice tough spots (with tons of patterns) where you you'll need to lock in. That stuff is more fun to focus on than speed.
Everyone wants to fail less, sure. It's not surprising people's feedback focuses on the mechanic that made you fail! That doesn't mean changing that bit will make the game more fun.
Not at all? LLMs are a terrible match for the kind of analysis a chess engine does (scaled deep search, deeply trained position evaluations). It's just not that kind of tool.
In our own universe, the fact that electroweak symmetry breaks ensures there are 4 electroweak particles and not other combinations. There's no corresponding thing to contain gluons to individual particles, you'd need laws of physics we don't have to add that constraint.
W and Z bosons, photons, etc have fixed masses, charges, interaction strengths with other particles. These properties can exactly be listed and looked up in a table of elementary particles with discrete rows.
Gluon color is continuous property in a vector space. Gluons can have any color in that space, with any combination of the 8 basis vectors (and that choice of basis is also completely arbitrary). The color |g1> is no more valid than the color (|g1> + |g2> + |g8> / √3) or any other of infinite combinations.
Calling this "8 gluons" is like saying there's "3 photons" because they can have momentum in 3 dimensions. If you want to argue there's infinite kinds of gluons, go ahead, but there aren't 8.
It's just hard convince people with a general feeling something's wrong and a specific picture of something wrong that the two are almost certainly unconnected.