The muh freeze peach crowd are about the worst defenders of free speech I've ever met.

Speech is powerful and dangerous, which is why it is important and needs to be free. Pretending otherwise is to diminish it and erase the need for it to be protected.

Why haven't you stopped watching _all_ corporate content - broadcast TV, cable TV, whatever the DSS dish companies are doing, Amazon, Netflix, blah blah blah

I hope you're not _paying_ for any of it...
And I hope you're not consuming very much of it...

It's bad for you. The people who make it are idiots, but they are crafty idiots. They know how to hurt you even if they don't know much else.

The idea that we have to watch their content is just baked into the cake, and nobody can escape it.

I was talking to a mechanic once. We were complaining about the news, and how it was all fake, they were all liars, etc.

I asked, "why not stop watching it?"

He responded: "but then I wouldn't know what's going on"

I replied, "you just said they are lying to you. So how does watching them tell you whats going on?"

(then everyone clapped)

He didn't answer. But I don't think he changed his habits.

Next, lets talk about Americas insane addiction to a Sportsball industry that regularly abuses us...

>not [seeking] an argument

Oh! Oh! I'm sorry! This is slashdot, in Room 12. This is Abuse!
For arguments, you want 12A, next door!

>people don't hire on merit
>Which candidate would you feel most comfortable working with for 40+ hours a week?

Why do you think "merit" and "works well with the employees I already have" are unrelated?

>Of course the candidate with a similar socioeconomic profile as your own would be preferred:

I suspect that's often true, but certainly not universal.

For example, allegedly, some people want slaves and want to lord themselves over their slaves. So they would never hire people like themselves, SES wise; they would only hire people where it was clear (in their own minds) that there was SES or other status daylight between themselves and their "underlings" So I'm not sure there's a universal law of hiring. I agree that "hiring someone like me" is a common -- but not universal -- pattern.

(Oddly enough, in the USA in 2026, I think you'll find that white males are the _least likely_ to use the "someone like me" hiring strategy - if for no other reason than decades of targeted enforcement against that that group for appearing to employ that strategy. Eg the Indian preference for hiring Indians, or the female preference for hiring females, is stronger than the white-male preference for hiring white-males. Maybe not in terms of hidden preferences, but in terms of actual hiring outcomes, yes, that's what the data will show)

Back to the non-universality of hiring strategies: A friend of mine with a tech business intentionally tries to hire mid-field performers. He doesn't want a rapid climber, because he cannot afford to keep them on staff once they understand what they are worth. He wants mid-field performers who are going to do solid, reliable work, and be content to work for him for a long time. So he doesn't hire on "merit" - he hires for "good enough, low turnover"

Other businesses have other strategies.

> but "not understanding cost" is a dangerous assumption to make.

One of the best, most succinct phrases in this discussion. Thank you.

We don't have to wait for AGIs to develop their own agency and build terminator robots. Industrial robots with no agency and with auditable and deterministic programming already kill humans. The industrial robot doesn't understand there's a human in the safety cage with it. It doesn't understand that moving through space occupied by a human has a cost. It doesn't know there's a human there and it doesn't know what a human is.

Designing automation that can never kill a person is already an unsolved problem with standard, non-intelligent technology.

I don't know if AI makes it harder or easier. On one hand, AI behaves, in effect, non-deterministically (even though at some level it is deterministic, it's not observably so).

OTOH, in organic intelligences, adding sensors, adding alignment, giving feedback - tend to produce humans who accidentally kill less frequently. So maybe something like the 3 laws will conceptually be part of the System Prompt of future AIs, and they'll figure out how to avoid HAL 9000 problems.

Anything that challenges your assumptions must be a joke. Therefore you never need to change your mind on anything!

NGL, I'm definitely liberal-leaning, yet I sincerely believed the "pay gap" was 99% explained by reproductive status and a general willingness to negotiate more aggressively for better pay (or similarly, willingness to apply for more aspirational jobs beyond the applicant's qualifications and experience level)>

The former is clearly protected under Title VII, but that doesn't change the physical reality of someone who left the workforce for five years having five years' less experience than their peers. The latter is a much harder social indoctrination (or maybe just a testosterone) problem.

This new study though... Holy shit, that's pretty black-and-white! Barring some non-obvious methodological issues, there's nothing here to argue with. We now have concrete data that even the label "male" or "female" applied to a genderless construct adversely affects perceived value.

Thanks for the interesting response.

The strongest man today; the hardest workers today... are not 10x or even 2x as strong or as physically productive as the best in human history. Depending on what you think about ancient man, we are possibly weaker and dumber than some of our ancestors.

The transmission of new ideas across memory, time, and place is the extra term in the equation of human achievement, civilization, and prosperity. It's not that labor doesn't matter. It's that ideas are like compound interest. Labor adds principal to our civilizational account balance. But Ideas compound it.

In the long term, ideas are all that matter.

I have the same reservations you do - powerful people will continue to want to corner and concentrate their power.

It's unclear how well they will be able to do that, however. The capability lag time between a paid frontier model and a self-hostable open-weights model is months.

The ability to train a good-enough model will also be commoditized. In my youth, we had distributed crypto challenges.... to get super-computing work done on mass democratized hardware of like-minded individuals. So in the future, we can fall back on analagous distributed model training efforts if some better alternative doesn't exist.

I am skeptical about a post-scarcity society and I am skeptical about man's "labor" ever going to zero. Of the infamous twelve possible AI outcomes, I am somewhere in middle. For biblical reasons, I believe the extinction of man is not going to happen, and I believe the utopia of man is also not going to happen.

Coll story bro. Got a citation for that "fact"?

Control of other people's speech is always about asserting power.

It's never actually about "reducing harm"; it's about asserting power.

Talking about it in "harm" terms is a clever hack: ... because most humans are sensitive to harm/harm reduction along their moral reasoning (see: Jonathan Haidt)

But "Never hurt my feelings" is an impossible bar, and it doesn't scale across a civilization. It cannot be a basis for law.

The attempt to abuse the "harm receptors" of political enemies is contributing to the mutual dehumanization across faction lines.

... the female AIs are more likely to quit in mid project to spawn a child process.

(Ducking and running.)

No one saw quasi-RH coming or many of the others here. So that is unlikely. And the last batch of 10 problems they released a few weeks ago which were impressive enough were all novel results. There have been issues with them using techniques or ideas that should be better credited to where some of those approaches are coming from. But that's the sort of thing that often happens at a preprint stage. When I referee a paper, if this happens, you just request they cite the relevant papers and move on. This isn't at all stealing things.

This is really not accurate. There's Fields Medal level work here with quasi-RH for example. And for Hadwiger-Nelson it took an approach that doesn't seem to be in in the literature. These things really are doing novel math. I've personally seen this is in a bunch of situations. Here's a personal example, much smaller than anything like these problems. I have a recent preprint with a student here https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Farxiv.org%2Fabs%2F2609.36068 (about 90% of this was done by her. She's very good.) But part of this came from when I ran a version of Proposition 13 in that paper through Claude just to clean up the draft of that bit before I sent it to her. Claude informed me (essentially unprompted) that the argument had a whole in it (in addition to pointing out grammar errors, unbalanced parentheses and some other embarrassing minor mistakes). I then fixed the hole, and gave it back to Claude. Claude thought for a few minutes, and then informed me that I had *not* fixed the hole, and the reason was that my version of the proposition was missing an entire infinite family which it constructed. The literature on this problem is small, and I'm very familiar with it. The family it produced is straightforward (see Prop 8), but definitely was not in the existing literature. So yes, these systems really can do novel math, and can even do so in an essentially minimally prompted fashion, in this case, explaining to the meat mathematician why his proof is wrong. My student and I then generalized Claude's family to Theorem 9 in that paper, but the AI definitely had an impact.

At some level this was actually not a good thing. I'm trying to encourage students to *not* rely on the AI for research so they develop basic research skills. So if the AI had not volunteered the family I wouldn't have had to tell her that the AI had discovered it, but honestly required noting it. So I had a conflict between intellectual honesty and being a good role model.

What does "anything that requires more than rearranging existing data" mean? Can you provide an example of an information processing device that does more than this?

Mathematician here specializing in number theory with a side-order of graph theory. I've only had time to start looking at two of them. First, is the Hadwiger-Nelson/chromatic number of the plane https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FHadwiger%25E2%2580%2593Nelson_problem proof. As far as I'm aware (and I could be wrong) the technique it uses here is not in the literature, so this is a genuine construction of a new technique. Second is the Erdos Egyptian fraction bound, and for that one it looks like the techniques are about what I'd expect, but I'm definitely still digesting both of these.

For the quasi-Riemann Hypothesis the striking thing is almost the opposite direction. It looks like the AI used standard complex analytic techniques to get the result. But many mathematicians have often thought for years that those techniques would not likely be strong enough to get this sort of result.

I know less about the Unique Games Conjecture https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FUnique_games_conjecture but having talked with some of the people there it looks like it took a somewhat standard set of ideas and then combined them with multiple just weird stuff and sort of took a hard left turn at one point for no clear reason and ended up at the result.

It is also worth noting that while many of these have Lean code confirming their correctness (quasi-RH for example) others do not. The three I mentioned above have all also been looked at at this point by human mathematicians who have not found issues; that's likely true for others, but those three I'm aware at least of people doing so. Not all the claims have Lean code though; a bit under half. One of the non-formalized problems also has been withdrawn due to what essentially amounts to a sign error https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fgithub.com%2Fopenai%2Fmath%2Fblob%2Fmain%2Fpreprints%2FAlgebraicity-of-Weil-classes-on-split-abelian-eightfolds-September-18-2026%2Fpaper.pdf. It is likely others will be withdrawn also by the end, but I'd be surprised if more than 10 are. And even if everything single one without Lean code turned out to be wrong (which seems very unlikely), this would still be an amazing set of math. I commented elsewhere that if a human mathematician had made the quasi-RH result they'd be likely a shoe-in for the Fields Medal, and another mathematician replied saying "delete likely."

Now a more editorial comment: There are legitimate concerns about what this is doing to mathematics. This sort of thing is very cool. But it also is part of a trend that may make it much harder to train young mathematicians or get them to exist at all. If the AIs are limited in how genuinely novel their ideas can be, then we may end up in a situation where we get a massive burst in math over the next few years, and then math stalls out because we don't have enough good really high caliber mathematicians (Not the mathematicians like me, but people like Serre, Tao, Scholze, Clausen,etc.) to come up with deeply new ideas that the AIs can build on.