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- Hacker News
- I feel like “giving up understanding” is inevitable.
There’s some hubris in thinking we can understand everything. For truly difficult problems, it’s entirely possible that humans are simply incapable of comprehending why a solution is true. But ultimately the practical value of applying that solution to the real world is going to eclipse our need to understand it.
Math is just the beginning. I see it happening in other fields too, like physics and biology. Many of us software devs have already given up on understanding parts of our own systems for the exact same reason.
Seems like a losing battle.
by brap - > Before approving construction, I would want communities of humans to understand why the design works and what justifies confidence in its safety. I would hope that we all would.
Why would mathematicians be the best at this or even able to contribute? Engineers and physicist seem like a much better choice. Mathematicians tend to not bother with messy things like whats physically possible, or human consequences etc.
by briandw - I find this discussion odd. 50% of people mistake this guest post for a post by Tao himself. The post is by Amit Sahai, professor for computer science and math at UCLA.
Tao has been posting a flood of guest posts that stress inevitability and coping. Tao is fully invested in AI but needs to have the appearance of a broad discussion.
Sahai is of course exuberant about AI but wants to keep UCLA enrollment numbers high. The comments on Tao's blog are far less friendly than here, because by people see through the game that is being played.
Just look at the latest industry friendly post from Tao, where he pretends that it somehow summarizes the discussion so far:
https://terrytao.wordpress.com/2026/09/25/iciam-statement-on...
For my whole life I have never been subjected to an industry coordinated advertisement campaign that ruthlessly harnesses YouTubers, TikTokers, professors, open source people who all go in lockstep.
Tao, Gowers et.al. will go into history as the professors who ruined math.
by asprs - "We’re gonna need a lot more mathematicians."
What the article really says is that we're going to need much smarter mathematicians. That is not possible for puny meat-brain humans. Humans are close to their ceiling. AIs are just getting started.
In practice, we're probably going to hit that limit first in IC design. I once went to a talk by the Intel engineering manager who headed the Pentium Pro effort. That was the first superscalar x86 CPU, and it took about 5,000 engineers at peak to design it. Getting that many people coordinated on one thing was a real achievement. Then Intel stayed with minor tweaks on that design for years.
We're soon going to be seeing designs of even greater complexity cranked out by AIs. No human will understand them at the gate level. Reading AI-written programming language code is bad enough. Reading AI-written Verilog may be beyond human comprehension, except in small sections.
by Animats - Like most of us I go back and forth between sheer optimism and fear for the future that AI may usher in. Recently I started vibe coding a fun video game with my ten year old. The experience is different than my work because it’s been such a joy to basically have a personal genie in a bottle help me make some personal art with a loved one regardless of either of our skill sets. The concept the author of this post is arguing now resonates with me more than it would have a few weeks ago. The sheer surface area that AI can create in our intellectual life is limitless and needs humans to explore. There can never be enough of us in that sense. Whether it’s as validators or creators.by siavosh
- I know many programmers for whom "developing domain understanding" is an abstract concept (if indeed, it is a concept for them at all). But in the age of big AI, I see more need for this, not less.
At least this is my observation: when my colleagues have been wholesale chucking stuff over to Claude, they've then been confronted with classic XY-Problem shit, poor user experiences, and over-complex solutions (which will mount future problems regardless of whether a person or an agent iterates on that code). Much of this can be solved by actually sitting down and thinking about it, and I mean at a code design level, not just a speccing level.
Many people don't realize that there are more useful outputs to solving a problem than just a mere solution. Obviously if one has a contractor mindset (you don't care about the after effects of a system) then this is of no relevance to you. Some companies promote that mindset, certainly ones that have no broader aspirations then getting acquired soon. That's fine - but many companies actually are about sustainability, and understanding in these places is paramount.
by liampulles - There is a failure to understand that the process is the result. You don't study mathematics or computer science and information theory to produce commodities. You study them to transform your mind. The output of an LLM is useless without a human mind to comprehend it. We can have Super Intelligence, but if humans are incapable of comprehending it, it is just another useless dead artifact. Practice, applied over a lifetime, is what creates the capability for comprehension. Asking an LLM to give you an answer creates an artifact. Humans being humans, most of their requests boil down to "make me rich without having to work for it," so the request itself is paradoxical and impossible to satisfy. Philosophers have only been saying this for all of human history, so don't hold your breath for any breakthroughs.by metalspot
- > Before approving construction, I would want communities of humans to understand why the design works and what justifies confidence in its safety. I would hope that we all would.
Until very recently, I pored over every single line of code Claude generated with razor sharp scrutiny. I would usually catch issues with every response. I'm catching fewer problems these days. Maybe the model is just getting better, and maybe I'm being less careful while under pressure to ship more and more often. But model capability is obviously growing. Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function. There was almost no point in looking at the code. Since then, the complexity floor of problems in the category "this is so simple that the model couldn't possibly get it wrong" is rising, and with it, my cognitive surrender to the model is increasing too. Why check it? It's obviously going to be correct.
If AI designs a terawatt fusion plant, then of course we're going to meticulously pore over every detail to ensure safety, reliability, efficiency, whatever. If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one? Will "a nuclear fusion plant" become something that models couldn't possibly get wrong?
Terence Tao is arguing that the human involvement in research is crucial, but doesn't convincingly justify why, in my opinion. He says that "human agency is a value of fundamental importance" and that we will need to build "thriving human communities that can understand [AI ideas] together" - not for the sake of correctness, which AI may surpass us on, but for, I guess, the possibility of reclaiming human meaning and purpose. I don't disagree with this at all, but it's not an argument, it's a statement of values. Unfortunately, the stark reality is that if AI does surpass humans, it will become the economically dominant strategy to not verify them and not double check them, but to just do whatever they say. This seems like a great way to raise p(doom). But as the models get better and better, and as I'm scrutinizing Claude's output less and less... I just hope that there are more Terence Taos out there than people like me.
by pyridines