Why Are We Even Doing All This? Rikab Gambhir's AI Math Satire on Terry Tao's Blog
Rikab Gambhir's guest essay "Why Are We Even Doing All This?" on Terry Tao's blog is a satirical attack on AI-driven "vibe research" in mathematics and physics. Published on October 15, 2026, the essay argues that AI systems are being used to generate large volumes of potentially true but unreadable results while the work of verifying and understanding them is outsourced to human researchers. The Hacker News discussion that followed (156 points, 124 comments) largely agreed with the diagnosis while disputing the essay's cynicism, its Library of Babel metaphor, and its implications for how society should fund pure research.
What the essay says
The essay's central claim is that AI has turned mathematical and physical research into a race to produce potentially true factoids rather than understanding. It was written by Rikab Gambhir, a theoretical particle physicist, and published as a guest post on Terry Tao's blog. The essay was initially written in a different file format and converted using AI, according to a note from Tao.
The essay opens with a parody of Martin Niemöller's "First they came..." quote:
First, they came for the artists,
and I did not speak out, because I was not an artist.
Then, they came for the mathematicians,
and I did not speak out, because I was not a mathematician.
Then, they came for the physicists,
and it was actually really cool and they gave me free tokens and promised to solve my entire field for me and now I can finally devote myself to the important work of publishing papers instead of doing physics
It mocks the idea that an "unfeeling corporate robot" will burn a rainforest to compile 700 obscure facts as part of a marketing gimmick for an IPO. The essay references OpenAI's 722 mathematics manuscripts and the physics community's 2500 write-ups. It says the bottleneck of understanding solutions has been removed, so there is no theoretical limit to the amount of scientific factoids we can fail to understand. It links to Goodhart's law and closes with "And honestly? That's rare."
The Library of Babel metaphor and its limits
The essay uses Borges's Library of Babel to argue that AI-generated research risks producing an infinite archive of mostly wrong or unreadable results. Borges imagined a library containing every possible book. The essay says that by 2026 research standards, that library would be the most successful research institution in human history, containing an inconceivably large number of potentially correct mathematical and physical results, dwarfing OpenAI's 722 write-ups and the physics community's 2500 write-ups. It satirically says Borges was a "visionless fool" for not commercializing the library.
Hacker News commenter @A_D_E_P_T argues the metaphor fails because the actual Library of Babel is pure noise, while AI math results have high signal-to-noise:
The actual library Borges describes has 25^1312000 books, the very vast majority of which are utter nonsense. (As the simulator makes clear.) Yet with AI, we're getting an abundance of very credible -- and, as Tao himself notes, frequently correct -- mathematical proofs. In these new AI math results, the ratio of signal to noise is high, which makes our current situation the exact opposite of the library, which is pure noise where signal emerges only by random necessity.
Another commenter, @its_ethan, linked to the Library of Babel simulator at libraryofbabel.info, where random pages are always gibberish.
The trust problem: verification is not enough
The most substantive Hacker News counterpoint is that AI-generated proofs create a trust problem even if they are formally verified. @bryan0 writes:
The problem isn't that people don't care and resources should not be spent to explain AI results to human minds, the problem is that AI will soon be better at explaining itself than humans. The puzzle then changes from can AI formally verify and explain a proof to: do human minds trust the proof. Do we believe that the proof is correct and not an elaborate AI hallucination using tricks that are beyond our understanding. In order to trust it a formal verification is not enough unless it is human understandable. In fact human understandability is the only thing that matters. That is something that AI can never do on its own.
This reframes the essay's complaint: the issue is not just inefficiency but epistemic legitimacy. A proof that no human can understand may be formally correct, but it cannot function as shared knowledge in the way mathematical research traditionally has.
The funding question: why fund pure mathematics?
Hacker News commenter @lacker argues the essay asks the right question but misses the institutional one: why does society fund pure mathematics at all?
To me it seems that we fund pure mathematics because we believe that some pure mathematics will end up being useful. Turing was doing "pure mathematics" when he invented the Turing machine, because we didn't have another word for it, yet. So, which is going to be more useful - the sort of pure mathematics that AI does in 2026, or the sort of pure mathematics that humans do in 2026? That is the debate I would like to see.
This shifts the discussion from academic sentiment to comparative utility. If AI can generate useful pure mathematics faster or more reliably than humans, the funding rationale may change regardless of whether mathematicians enjoy the work.
The status question: what happens when insight is automated?
Several commenters argue the essay is really about status, not epistemology. @derektank writes:
The problem is that, for a long time, humans have attributed meaning, value, and social status to great cognitive insights. Taking physics as an example, it has almost always been the theorists who develop new ideas, not the experimentalists that test and validate them, who have won praise and acclaim. The name Arthur Eddington doesn't have the same cachet as that of Albert Einstein. But now that the creation of those insights can be automated, there is a great wrenching as esteem shifts away from the former.
He adds that this may be good:
Maybe it is good and right that we begin valuing your ability to exist in the real world, rather than play math puzzles on a sheet of paper.
@kenjackson notes that software engineers always built for a specific use, while math and science may now have to shift because "no one will be impressed by their intellectual achievements any longer." He asks how this impacts "our perception of who/what is smart." @zemo adds: "well yeah the purpose of STEM education is to be a factory to produce the correct worker for another factory."
The tool question: is AI just a better CAS?
Some commenters reject the essay's framing as a strawman. @sobellian writes:
If one truly philo's sophos, then wouldn't one at least be curious what the computer says about a difficult problem? Like, if you've been wrestling with an especially troublesome integral for years then I feel like that somewhere along the line you should have plugged it into a CAS - even if you want to clean up the derivation afterwards it would be foolish to ignore every tool at your disposal and even rail at the Mathematica authors for the temerity to write a symbolic integrator.
He says the article is "so preoccupied with clever sarcasm that it doesn't really engage with nuanced views and is instead happy to just spend a few KB of text tearing down vibe research strawmen." @bluepeter similarly says the "first they came for..." template is tired and that programmers got a productivity boost: "I can do what would take me years in an afternoon." @mwillis says tools allow deeper exploration: "The library Borges describes is infinite. I won't mind getting to read a few books I wouldn't have otherwise."
The cultural critique: sarcasm, cynicism, and the real question
The Hacker News thread is divided on the essay's tone. Supporters call it "well written, deep, funny, and sad" (@bryan0) and "great satire" (@ahsgtq). Critics say the cynicism undermines the argument. @cbdumas writes: "I sympathize with some feelings the author is trying to communicate, but the cynical and sarcastic style really detract from the authors points in my view." @its_ethan says: "Not a huge fan of the sentiment in this article, and the very cynical way it was communicated."
@eadwu argues the essay's premise that math results are normally understandable is already false:
Modern mathematics is so far removed from the base average knowledge... To the average person, asking a mathematician to explain something is the same as asking the AI/LLM. Both are asking an oracle to dumb it down to their level of abstraction.
@deepsun notes the Collatz conjecture remains unsolved, so "it's too early to say that all our problems are solved." @throwawaytea compares the feeling to construction project management: "Why does anyone want this fancy reception desk?? This desk is not worth the human aggravation that it's causing." @throwaway624352 writes: "We must secure the future of humanity and preserve dignity, freedom, purpose and agency." @Gigachad responds to the essay's list of future activities with: "Grim but this is exactly where society is heading."
What the essay ultimately argues
The essay's final argument is that AI-driven research culture is not just a math problem but a redefinition of what it means to be human. It says:
By outsourcing the burdens of curiosity, creativity, and understanding, we can finally focus on what matters most: generating value, maximizing productivity, and accelerating progress towards the ultimate goal of science: a complete repo of potentially true facts in a Library of Babel that nobody will bother reading.
The essay's footnotes clarify that the "robot" is a giant corporation's robot made in part by stealing from researchers, the IPO enriches someone else, and "humanity" is metaphorical and does not refer to any actual person understanding anything. The Hacker News discussion suggests the essay's value is less as a policy proposal than as a provocation: it forces the question of whether research should be judged by the volume of potentially true outputs or by human understanding, trust, and purpose.