trio8453

> it's actually just maths essentially mirroring the dataset

And your brain is just water and fat conducting electrical signals. Also, modern AI doesn't mirror the training dataset, that's not how LLMs work, it's only true for the initial pretraining phase. But that's besides the point.

What matters is what it can and can't do. Whether you label it as intelligence or not is pretty inconsequential long term.

The people whose job it is to measure this have explained it and it's linked above - because it changes employers' incentive to negotiate and makes them more reluctant to give any raises because those raises would be visible and trigger more raises being requested. This can reduce inequality but bring the averages down (some might argue that's an acceptable tradeoff).

There's a difference between coworkers talking to each other and the system being set up in a transparent way where everyone can see everyone else's salaries, these two aren't the same thing.

My point is that we should be able to distinguish between the studies that are dubious and the studies that are solid but go against out assumptions. And if our assumptions are that that economists are just protecting corporations, we'll end up rejecting claims that might be true, and end up with policies that sound like they protect the little guy but are actually harmful.

Freakonomics have a good episode including one of the authors of that paper - https://freakonomics.com/podcast/how-to-pave-the-road-to-hel...

It's very hard for people to accept that this might even be the case, many people just assume that transparency is better for workers.

I'll think about it if/when it happens - the space changes so quickly that whatever your plan is now, by the time you need the plan, everything will have changed so much that your current plan will be completely irrelevant.

And having to pay more and/or using slightly less capable models isn't a disaster, it's not like you need a frontier model for everything.

This was a more relevant conversation six months, since then OpenAI have been much more competitive on price for top models, Anthropic much less so but they're showing some signs of improvement in the 5.5 releases, and we've had an abundance of cheaper open source models.

The disruption in programming is more obviously tied to changes in productivity, changes in demand for programming work, impact on the whole economy.

In math there are more obvious examples of AI achieving things that humans have failed to achieve for decades without AI. But in practice the number of mathematicians is relatively tiny and most of the value they produce economically isn't tied to how many cutting edge proofs they produce. So the changes are massively disrupting but not as existential as they feel to some of the mathematicians - they'll still be able to do research, write papers, teach, give talks and everything else they've been doing; the changes will be in the tools available, ways of working and in the system of incentives in academia which will finally be forced to stop pretending that people's value comes from publishing papers (it was never true but the people who are most upset now are the ones who fell for that lie).

I don't see how OpenAI owe the level of interest or polish similar to human mathematicians to anyone.

1. OpenAI do model training on verifiable tasks that push the boundaries.

2. The work from the previous step produces groundbreaking results.

3. OpenAI can keep the unpolished results private, publish them or branch off a mathematics research arm which produces human-level quality of output. I think they should be free to not do that last one.

And it's been less than a month, it would be shocking for any of it to have practical value, even when we take into account the fact that most new mathematical proofs have no practical value whatsoever for a very long time. Take the previous few hundred proofs produced by humans recently and show me their practical value.

The novel thing is the production of proofs in a way that isn't coupled with the simultaneous expansion of human understanding. Mathematicians are massively affected in how they work and what their careers will look like but they're not victims that OpenAI has somehow maliciously targeted like some of the discourse seem to imply.

I get that it's disruptive to mathematicians but I'm also wondering what people would have OpenAI do differently -

1. Not train models to get better on certain verifiable tasks like math proofs?

2. Train models to get better, produce groundbreaking proofs and keep the results private?

3. ?

Hacker News really living up to its name these past few days.

Kind of insane how much of the front page is about security compromises today.

It's a valuable pattern because a lot of communication is about drawing distinctions and making delineations. Saying what a thing isn't (despite being close to it) gives you a more clear understanding of what it is.

I wouldn't put too much emphasis on pre-training data, there are so many steps afterwards in modern LLM development that completely change the output. Generally, if you're looking at LLM output in 2026 and thinking something along the lines of "this must be what humans were doing with their language in the training data", you need to pause and reconsider, because modern LLMs are much more than machines which repeat what was in the training data.

I'm not disagreeing with you - I give agents a lot of input and spend a lot of time iterating with them before I'm happy with the result. My point was only that I don't think that I'm somehow superior in design patterns or any other general part of coding just because I happen to be human or because I have years of experience.

I course-correct it all the time too, my point was only that I don't exclude it from the initial design.

I'm not the one who's claiming to be above the latest models in some aspect of coding in this interaction.

> I don't think you can deny that the current generation of models makes questionable decisions sometimes

Sometimes, not enough to believe you that you can do it better. And even if you could, to believe that it's overall worth it to work in that way.

The LLMs know design principles too, being an experienced developer doesn't make you as special as you might think.

Rust is quite complicated but people like it a lot. Python too in a way - it's quite large and complicated but it exposes the complexity in a very gradual way and you can ignore so much of the language and still get things done.

I agree with you about simple languages though. I really like Go - the "there should be only one way to do things" from the Zen of Python actually applies to Go in a way that it doesn't apply to Python at all.

Being anti AI doesn't keep your job around for longer though.

Your particular case is very uphill - there aren't that many people or companies that do this, very limited number of openings, you have no relevant experience, not in the pipeline for internships. Learning about databases and doing a bit of C++ and Rust is worth just about nothing in terms of helping you for these jobs.

Keep it up as a hobby, find open source projects in which you can make meaningful contributions (that alone can be very hard), spend years in it, then maybe you'll have a better shot - whether all of that's worth it is for you to decide.

If I were you, I'd reconsider why you even want this in the first place.

Asking as a non-JS person - what's the cost and effort to switch?

How is withdrawing a paper irresponsible?

Sides? Hate? This is all very emotional. Try to put the facts down plainly and see how ridiculous it is --

It's a product and if you're using it incorrectly, we can either

1. say so

2. pretend that you don't to get/keep you on "our side"? or not say is because you're skeptical or hate it? (how does that last bit even follow logically?

How is 2 better in any way for anyone involved? Why would you, as a paying customer, holding it wrong, want other people to keep that information from you?

> "You're holding it wrong." Is hardly a retort from a real paying customer having problems with their paid services.

It's very appropriate in the cases when you're holding it wrong. The fact that you're paying doesn't mean that you can't make mistakes or waste resources.

I don't think we'll get any more responsibility by bickering any time the word "agent" is used as the subject of a sentence.

> A concrete example, the ability to remember 10 digit phone numbers was really normal before smart phone, try to push your mind now to see if you are still able to do that?

But would you tell anyone in the early 2000s to keep practising 10 digit phone number memorisation knowing that it will be completely useless skill in the coming future? How do you know what telling OP to be more hands on and spend time on active practice isn't the same thing?

Why are you reducing the AI to ones and zeros and not reducing the dog to cells, water, proteins etc.?

I don't disagree with this, my point is that you can say "agents did a thing" and communicate something meaningful with that language, and that's separate from whose legal responsibility the whole situation is.

> An AI didn’t hack into a company, the engineer set an automated tool to.

What if I say that "my program crashed"? Is that language ok or would you pause to tell me that the program didn't crash and it's actually me who set the system that would eventually cause the crash?

Why does the commonplace "program did thing" language become a problem when the program is an agent? I think this somehow betrays more assumed anthropomorphizing on your part, not less; if you didn't anthropomorphize the agents, saying "agents hacked" would be as mundane as "my browser is playing a video".

It's not novel at all, we do it all the time. It's very common to say "program X did Y" without making the conversation about blame or responsibility. But when the program is an AI agent suddenly using it as a subject of a sentence and saying that "agents did X" becomes a sensitive topic for some people.

> When I read that "AI hacked into ..." I was like what? You mean someone instructed the AI to do that?

No one instructed them to hack into Huggingface or into any other infrastructure. Sure, the setup that OpenAI created led to what happened and you can rightly assign all the legal and moral responsibility to them. But it's wrong to say that they instructed the agents to execute the hack.