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I feel the same way about education, but as someone who received an education, I'm biased. I like knowing things and understanding things.

Studies on education and well-being don't really validate us though. They generally find a positive correlation between education and happiness when you don't control for other factors. But better educated people are usually from a higher social class, have better income, are healthier, are less likely to have been abused, etc. When they control for these factors, the correlation often goes slightly negative.

(If ASI does all the labor, education would no longer cause better income or health, so we want to remove those confounders.)

There are a number of proposed explanations for this, and it doesn't prove causation. But the data isn't on my side.

Also, I think there are two kinds of learning, easy learning and hard learning, and most people will only do easy learning without extrinsic motivation.

Easy learning is fun. It's why there are so many edutainment videos on YouTube and TikTok. Learning cool facts, or reading an educational book (without doing exams or problems) fall into this category.

Hard learning requires more effort. An example would be learning Cal II. You have to study and practice, and you likely won't get it right away. People do hard learning all the time, but usually because they have to.

Before online courses were a thing, friends or family would sometimes tell me they wish they could go back to college and take some courses they were interested in. Now we can, for free, at our convenience. But they don't. We'll watch endless edutainment videos on YouTube, but we'll only enroll in Cal II if we think we may need it.

If future AI can do everything, the extrinsic motivation is going to disappear. People will naturally tend towards easy learning. Maybe that's good enough. Easy learning will help us get funny jokes, recognize references, and appreciate good movies. But you don't need school for this type of learning. It's everywhere, and people seek it out because it's fun.

> still, i think getting ASI to distribute itself properly is going to be harder than people give credit

I'm guessing you mean getting AI to allocate resources and distribute wealth fairly without employment? Assuming we don't get a doomsday scenario, I see two likely possibilities. If a democratic government has control, people will vote to give themselves wealth. We end up with something like UBI, and people vote with their wallets to decide what the market (controlled by AI) should produce.

The other option is we get a dictator. With ASI and robotic armies, the factors that normally protect us from dictatorships would be gone. Currently, if the president wanted to declare himself a dictator, he would need support from a lot of people. At the very least, the military would have to back him. And you need your citizenry to be productive. They make all your food and machines and services. But if the army is composed of deadly autonomous drones instead of people and one person has authority over the army, they basically have absolute power and they don't need other people for anything. The factories keep running. The drones gun down anyone who resists. We're left at their whim, and we better hope they aren't cruel.

The hell? Where did you get that from?

If AI takes off, there will be nothing to adapt to. Anything a smart (and motivated) person can do, a smart (and motivated) AI will be able to do faster and cheaper. We might end up permanently retired and just spend our time entertaining ourselves.

Neither of those are gatekeeping.

That's why I said,

> Words get their meaning from how they're used in the wild, and dictionaries are descriptivist, not prescriptivist.

But I didn't explain that clearly. What I mean is that definitions are always imprecise because language is imprecise and dictionaries (or people) try to keep definitions reasonably short and simple. Dictionaries are trying to describe what the words really mean in the wild. They aren't the source of that meaning.

For example, if we look up Merriam-Webster's definition of "game (noun)", none of the definitions really work for a game like Solitaire without also including an activity like reading a book.

The two relevant definitions are

1. a physical or mental competition conducted according to rules with the participants in direct opposition to each other

2. activity engaged in for diversion or amusement

(there are a bunch of other definitions as well, like animals you hunt)

The second definition is too broad and the first clearly excludes Solitaire. If you were deciding what a game is based on the dictionary definitions, you'd either think watching TV is a game, or that Solitaire isn't. But that's not the case.

Charging money for products isn't considered gatekeeping. So from a descriptivist perspective, it isn't, even if it seems like it should be, because the word isn't used like that. Banning a group of people from an activity is gatekeeping. If mathematicians said only mathematicians can prove theorems and correct proofs from non-mathematicians won't be accepted, that would be gatekeeping. But they don't do that.

If you want to use a broad definition of gatekeeping that includes virtually all human activity, then the response to any accusation of gatekeeping could be "well, everything is gatekeeping". That road just leads to pointless and confusing conversations.

1. This goes against the prevailing view of neuroscientists. Quantum indeterminacy is generally thought to be irrelevant to brain function.

2. Quantum randomness is the very opposite of control over your behavior. Instead of doing what you want, your neurons randomly misfire sometimes. That describes an illness where you lose control over whatever is misfiring. For quantum effects to represent your will, they couldn't be truly random (nor even statistically random), and that goes against everything we know about quantum mechanics.

It's not gatekeeping unless you're using your own esoteric definition that's different from how everyone else uses it. Words get their meaning from how they're used in the wild, and dictionaries are descriptivist, not prescriptivist. Anyone is allowed to buy the book, so it's not considered gatekeeping, even if some people may not be able to.

If you define any obstacle to an activity as gatekeeping, then literally everything can be considered gatekeeping and the term loses all meaning. Some people can't read, so offering the books for free would still exclude them. If you include an audio book, some people still can't understand the language. And both charging and not charging money for products would simultaneously be gatekeeping under that definition, since not charging places more financial burden on the producer. Not everyone can afford to print books and give them away for free.

According to the article you linked, there is no evidence at all showing OpenAI plagiarized Buckmaster and Alpoge. Buckmaster just speculated that since they used OpenAI's product, it's possible OpenAI looked at their conversation. Which OpenAI flatly denies.

And then your article says:

> On 13 October 2026, Scientific American published an article surveying several mathematicians stating that "the resemblance between the two groups’ methods now seems merely skin-deep."

But the AI companies didn't gatekeep knowledge. They released all their math results.

Unless you mean they're charging money for a product that contains knowledge. But that's not gatekeeping, just like selling a book isn't gatekeeping. Gatekeeping is when a group tries to prevent others from participating in a field or activity.

> But it's the job of an elected official to make that determination only after they have considered their options in a proportional-to-population way.

I don't know what this means, and there's no way to operationalize this where "do what's right only if the right lobby represents enough members" is better than simply "do what's right".

What you're advocating is going to do the exact opposite of what you're hoping for. It will increase the power of the rich and special interests.

It's common for a large special-interest lobby to face no comparably organized opposition, even when its preferred policies harm a much larger number of people. Look at the National Association of Realtors or the National Automobile Dealers Association. These are large, well-organized lobbies that promote their own special interests and harm the rest of us. There is no anti-realtor lobby.

Right now, anyone can write to or call up their representative and argue their position. That's lobbying. You don't need to get X signatures or form an organization with at least X members for the right to be heard by your representative. Putting up barriers like that isn't going to hurt the lobbies. It's going to restrict the opposing voices.

(To be clear, I'm not saying there would be an explicit threshold where a lobby needs a certain number of members. But no matter how you implement this proposal, if it has any effect, then there is some implicit threshold where that number of supporters pushes the lobby over the edge so their position is considered.)

All the money in politics - the campaign donations and the Super PACs - exist to try to get votes. Campaigning costs money. If you start requiring a threshold of votes, signatures, or members before a political position can be considered, you are handing more power to the rich and powerful. The well-funded NAR would get their policies by default unless we could organize a bigger lobby with more than their 1.5 million members.

Requiring votes, signatures, or members won't solve this. People cannot stay informed on every little issue and every special interest group. These groups are well funded, and they will campaign for their positions. Often their arguments sound convincing. Often they're misleading or dishonest. And they win. This isn't hypothetical. It happens all the time.

We absolutely want politicians to listen to lobbyists in a way that is proportional to some parameter besides the number of people they represent. The should listen to who is right, not which lobby represents the most members. Self-interested groups lobby for bad legislation all the time. When the harm to society is diffuse, they don't always have a large lobby opposing them.

...they did.

You're misremembering. The first known use of AGI was in 1997, but that was a single, mostly unknown use in one paper. It wasn't until at least a decade later that the term started entering mainstream use after being independently reinvented in the 2000s. AGI just wasn't a term in the 90s.

It wouldn't cost $10 for their lifetime use if they used cheap models instead. Mistral Small 3 24B is probably substantially better than their Qwen 3 0.6B trained model and would give you about 500,000–2,000,000 English to Bash translations. If they're a heavy user, they might use $0.30 in total, ever.

They also used Astra for the coding, which they can't get on a $10 subscription. And then there's the actual training cost.

Given that they're just using it to avoid the googling for bash command syntax, I'm not sure they'll save in the end against the 140k training examples they generated.

No it's not. 1-2 seconds is what we find in practice for real drivers responding to unexpected hazards.

Your close following distance example doesn't show anything. A normal braking event on the highway doesn't require a fast reaction. If you're driving 100 km/h and you're following 1.5 seconds behind the car in front of you (about half the recommended following distance) and they brake to 80 km/h, you have about 9 seconds to slow down or switch lanes. That's plenty of time.

The risky scenario is if the car in front of you has to do a full, hard emergency stop and you're following too closely. That's rare, and collisions are common when it happens.

If you know the car in front of you is going to brake because you can see the traffic ahead slowing down, that's not fast reaction time. That's just you seeing cars slow down and reacting at a normal speed. An AI has just as much time to react to that as you do.

This is well studied by researchers, and 1-2s is the typical reaction time for real drivers with real muscle memory when reacting to an unexpected hazard on the road.

There are a lot of reasons people could sometimes react faster (for example, if they anticipate the hazard, or if they're just above average in reaction speed), but one to two seconds is the reaction speed we find most of the time.

The fastest human reactions aren't to unexpected road hazards. We have a much faster reaction speed in tests where you just have to click the mouse each time the screen flashes. Our reactions are fastest when you know in advance the event is about to happen. But this isn't relevant for road safety.

The typical perception-to-reaction latency of an alert driver to a hazard is about 1-2 seconds. 250 ms when you're waiting for an event and know how to respond. For example, like a batter in baseball waiting to swing.

Are you asking for the latency or throughput?

In humans, it's about 200–250 ms for a visual cue where you already know how to respond and you're ready, but you don't know exactly when it'll happen. It can be a fair bit longer if you need to identify what you see and choose how to respond. Typical perception to reaction time estimates for drivers when there's an unexpected hazard on the road are 1-2 seconds.

With GPT-6 Luna (which is what the parent comment was talking about), that would come to 3.2¢, assuming GPT-6 used the same number of tokens.

I don't think you can guess more precisely than an order of magnitude from trying each once on one task.

This isn't right. You're comparing cost per token, but DeepSeek V4 Flash uses more tokens. Artificial Analysis found GPT 6 Luna to be significantly cheaper than DeepSeek: https://artificialanalysis.ai/models/comparisons?compare=dee...

They did not hardcode that, because:

a) There's zero evidence of them doing so

b) Some models released before the car wash problem was discovered would consistently get it right

c) Hardcoding it is pointless. No one is seriously asking that. It's just a trick question. Hardcoding one trick question won't fix its weakness at other simple trick questions.

d) Since it went viral on the internet, the next time they updated the knowledge cutoff, the LLM would likely be aware of the trick. It will fix itself without the labs doing anything special, even assuming the new models weren't smart enough to naturally figure it out.

JimDabell said they didn't come up with that thought themselves. They never implied the poster didn't mean it. sm-silversight responded, "How do you know it's not his/her thought?" You can reasonably interpret them as asking how we know they didn't come up with that thought independently.

If we go with your interpretation instead, then sm-silversight simply misread the comment they replied to and their post doesn't follow. No one doubted OP felt sincere about not wanting to read AI.

In this comment section alone, 5 people have used some variant of that aphorism, and it's used constantly on hn and other sites. They've heard it.

> Things like "You're right, but for a stronger reason than you said:"

I also get this a lot, but almost always with "and for a stronger reason than you said", which makes it sound more like it's reinforcing me than one-upping me.

Looking through my recent transcripts, I found this pattern 12 times, with every time using "and" instead of "but".

Eg:

- "Confirmed, and it's worse than a pause quirk."

- "I think the ban is right, and it's a stronger idea than what I'd proposed."

I did find one instance with "but", but it was a genuine disagreement rather than 1-upping or presenting strengthening evidence: "I would cap it, but for a different reason than fairness."

Buckmaster (the mathematician) and Alpöge used and credit AI substantially for their proof. Even if OpenAI did copy their ideas, it still wouldn't show that this didn't come from AI improving.

OpenAI's proof is substantially different and I don't think anyone has claimed otherwise. The accusation is that they used the same avenue of attack, and it's an uncommon one, and that makes it suspicious that they may have taken the idea.

You're conflating two very different meanings of "the speed of light". Confusingly, "speed of light" can refer to the universal constant, c, which does not change in glass, or to the speed that light travels in a medium. Here, we're talking about c. Relativistic effects are based only on c. The Three Body Problem trilogy plot device is also about c.

Why?

1. I didn't say LLMs have made any breakthroughs in math, not because they haven't, but because it's irrelevant to my point. The parent comment is using the same argument academic research opponents have long used against research. The vast majority of research fails to meet their bar. How would your daily life be different if we had no humanities papers published since 2023? Or math?

2. You can google this in 10 seconds and see a dozen results in math. This is not a good-faith demand.

There are several hundred thousand mathematicians producing hundreds of thousands of new results in math each year. Why can't most people name any human contributions to mathematics from the past decade? What is the finished result that affects everyday life? Do you consider all those human mathematicians to be useless?

Most of the biggest breakthroughs in mathematics, breakthroughs that win Fields Medals like sphere packing in dimensions 8 and 24, have no applications in everyday life. Probably the only new mathematics results that people notice affecting their daily lives are the ones that enabled AI.

Nevermind mathematicians. What about the millions of programmers? Are they all hype too because people pre-2023 were griping on hn that software is buggier than ever and people are still using the same operating systems as always? Why couldn't the 30 million human programmers make something better in the past decade?

You set your bar so high that all the world's human experts in math and programming combined would fail to meet it.

The top LLMs in 2024 were Sonnet 3.5 and GPT 4o. You couldn't have expected those much weaker models to be making breakthroughs in math. The models that are making breakthroughs haven't been around very long.

The Kevin Buzzard post linked at the top says they budgeted £1M over 5 years for a smaller proof.