This reads like a comment from 2025. LLMs are solving novel math problems and they successfully emulated a PS5, this week lol. You really need to update your priors. It's amazing how many people refuse to do this and make a fool of themselves in public
On the other hand, when I use LLMs, I regularly see them confidently claim information confidently that is false. So, I'd say productivity gains are true but there's also a cost of wrong information being used to make decisions due to AI.
I dont care if you used AI to produce something. If you share it with me, I assume you are responsible for it. You own it, whether you agree or not.
> it's causing people to be confident in information which is false, it's enabling people to treat each other like jerks in even more ways than before
What is the direction of causality here? Does AI make people jerks, or they were already or prone to be jerks?
The same applies to innovations or breakthroughs made by people who use AI in their work.
> LLMs are solving novel math problems and successfully emulated a PS5
Did the AI independently accomplish those things, or did highly competent people accomplish them using AI as a tool?
What would happen if you give someone with no mathematical or programming background the same frontier models that OpenAI used for their mathematical proofs [1]. Would they produce the same discoveries?
I think of humans as constants. They can bring positive, or negative value to the table. The AI is the multiplier.
Take software engineering. If you let someone who doesnt care about code quality (their constant is negative since any code they write will introduce a tech debt) push code to your repo using AI, it will destroy your project. But let an engineer with strong fundamentals and high standards use AI to push code to your repo, they can improve the project much faster than they could without AI.
In a lot of big companies, you had a small majority of people who were incompetent but mostly harmless because their lack of competence prevented them from causing too many issues and another group of people who were competent and productive. Nowadays, with AI those incompetent people can be maximally effective at causing issues due to their incompetence.
The same happens with opensource projects who are now flooded with mostly useless PRs and bug reports from incompetent people directing LLMs to do so. etc...
So yes individually AI makes people productive but I'm not sure if that is true at the scale of a company nor do I think it's necessarily true with opensource projects.
Unfortunately the things we can control regarding other people are limited... So I think least we can do is to either surround ourselves with competent people or if not possible, ignore the AI and hold people accountable based on their final output. I often see people try to defend themselves by just blaming AI when their final output is bad but when its good its their work. But its not an AI problem, its the people problem sadly. I wish the problem was AI btw, it would be much easier to fix.
When talk to people, I regularly see them confidently claim information that is false as well.
This is not the point where LLMs become useless. LLMs become useless when we understand that we are social creatures and that many times the road is more important than the destination. Also, when you provide instant-travel to all the destinations, the destinations lose their value as well.
As any seasoned gamer can confirm. Sometimes there's something lost in that convenience
I wouldn’t say that Dans recent work in maths was necessarily that of an expert. More of a fan of Lean than anything and he did a breakthrough in a relatively short time period.
https://overreacted.io/how-i-vibed-a-proof-of-conways-conjec...
It seems to me it’s about enough money, knowledge, and effort to want to achieve a thing. Which is good, but can and is being weaponized in terrible ways against us.
It's curious seeing "math" and "productivity" in the same sentence when the market assigns much less economic value to math than to other endeavors. The entire annual US federal budget for mathematics a mere ~$100M on average.
I'm not sure how that's a relevant benchmark, considering that the federal budget represents the spending of a single specific political institution whose primary focus isn't related to math per se.
Given the extent to which the market at large is bidding up the prices of devices whose entire function is to do math very rapidly, I don't think the overall premise holds.
How do you think mathematicians came up with those open problems and conjectures - trying stuff until they came up with one that passed their "interesting conjecture" check? What was that check? Can you code it in Lean? Can ChatGPT? Have you seen an LLM come up with one?
> Its a matter of time...
No it isn't, and by saying that I've given as much reason to think I'm right as you have.