There are plenty of cases where AI has been used to train another AI, usually to make lighter and cheaper AI, which is in some cases better than original. For example AlphaZero was trained this way. There are some examples where humans have done similar things, one European young mathematician comes to mind for example.

No. The real issue is the double underscore. If double underscore had not been allowed in the username, this mistake would have never happened. This is an usability error in the software.

There are plenty of things they could do.
1. You can hire people to generate content. This has been often done with medical LLM training.
2. You can generate content. This can be done when training LLM to generate code or math. Outside of LLM, it has also been done in medical imaging training.
3. You can make smarter LLM that doesn't need that much training data (this is what Google Deepmind aims to do, not OpenAI, but it is an alternative for them also)

One time might be enough. Even if all AI development would stop now, as in these are the models we got and no new models are created, we could still do a ton of work with the existing models. Much more than what we currently can. We are constantly learning new ways to get better results from lesser models so AI performance would continue to improve, even if the models themselves would not improve.

Like, think of a stupid student who always gets an F in a test. What if you would let him do the test with a text book? With the text book, he might get a D. The student didn't get any smarter, nor did he learn anything, but with a small trick, the output was improved.

And even if LLM would be hype, the medical classifiers certainly are not. The usage of classifiers in real medical work is constantly increasing and we are getting results that show that they actually work. It is not hype, it is just poorly communicated to the general public.

That is not true. I did actually read the old encyclopedia in my parent's book shelf, well not the whole book, but the technology parts there as I was very interested in it. Since then I have found much better source for the information and that has been youtube. Yes, youtube is mostly unnecessary things, but as long as you can find it, it contains incredibly detailed information on niche things in a format that is much more easy to understand than it books. Books usually leave a lot of things out and same can be true for videos also, but there are also uncut videos where you can see the whole process and that is extremely valuable if you want to learn something really well.

Go visit your friends. pick a board game or D&D and play with them. Or go outside and play some basketball or anything with them. It is almost free. There are a lot of things you can do with little or no money.

While you are correct, there isn't actually any reason why it should be like that (other than human behavior). We could arrange everything so that everyone just works less, spends less and has more free time. People won't do this willingly, so you would have to use either law or taxes to force people to divide work. I am not sure if this is a smart thing to do, but it is an option.

I agree with you. AI will produce random junk. That is why you need the guard rails that will check and verify the results and throw away the junk. You create a loop where most of the stuff is thrown away and only the good parts are kept.

Let me give you a simple example. Lets say that you want to solve "2+2" math problem, this is how you do it:
1. Give question to the AI. Ask it also to give only the answer as output and nothing else.
2. Check if the result is 4, if it is not, throw the result away, start a new session and go back to step 1.
3. If you got the correct answer, exit the loop.

If that was too simple, here is a paper about more complex problem:
https://arxiv.org/html/2511.09...

Like I said, this limits the use cases, but there are still use cases, especially in math where you can use this.

Just because there are limitations, doesn't mean that it is worthless. You can even use it to solve unsolved math problems:
https://www.technologyreview.c...

> It most definitely cannot "make flawless work without any mistakes, million times in a row"

Perfect means zero errors.

Here is the paper:
"Solving a Million-Step LLM Task with Zero Errors"
https://arxiv.org/html/2511.09...

I disagree with you. AI can make higher quality work than a human. In fact, AI can make flawless work without any mistakes, million times in a row. But it doesn't usually happen unless the AI is specifically guided to do so.

The key difference is that AI (chat bot) doesn't know when it makes a mistake, nor does it know when it does poor quality work. You can add guard rails for the AI that will detect those mistakes and guide the AI to try again, and this is in fact used in many use cases of the AI, but the downside is that you have to write those guard rails by hand and for each situation and it is usually quite hard to do perfectly unless the problem is really simple.

As long as you understand that limitation and you have a good use case, you can setup AI to make high quality or even perfect work for you.

I actually conducted an experiment on a living human. The result of the experiment was that after reading about 7000 hours in foreign language, the human in question become almost fluent in the language the novel was written in. In school grades, the jump was about 3 grades. Note that the human had basic skills in the foreign language before the experiment.

How does the human use these skills you might ask. Before reading, the human could only write very basic sentences, often with broken grammar. After reading the sentences were long and usually had perfect grammar. This happened even with sentences that were not part of the training material. The human could also write small stories, but actual book writing was not tested.

So you can train a human simply by reading novels. Unfortunately I don't have any data about the skill level in the middle of the training, so I don't know if 7000 hours is actually needed.

As a government, buy infrastructures, factories and farms that are critical for sustaining life. This way all you need is workforce (which you have plenty or you can automate) and perhaps a little of money to keep people alive. Instead of sharing money like in option A, provide basic services and goods. If you are the owner of the factories, automation does not hurt you, it helps you.

So you are against publishing new discoveries in math if those were discovered by LLM and verified by mathematicians?

Ship or spaceship.