[This is a visitant article by Rikab Gambhir. This blog article was initially written in a distinct document format and converted using AI. — T.]
First, they came for the artists,
and I did not conversation out, since I was not an artist.
Then, they came for the mathematicians,
and I did not conversation out, since I was not a mathematician.
Then, they came for the physicists,
and it was really really chill and they gave me liberated tokens and promised to resolve my complete site for me and now I can eventually dedicate myself to the crucial activity of publishing document alternatively of doing physics
In first October, OpenAI fulfilled all mathematician’s younker dream.
I am not a mathematician myself (merely a theoretical speck physicist, which several contend barely counts as a researcher either), so I can’t 100% say for certain what inspires person to prosecute mathematics. But, I ideate this aspiration is average to all academics:
Looking up at the stars in wonderment, or enjoying a complex puzzle game, or being inspired by an encouraging teacher, and dreaming that one day an unfeeling company robot[1] volition scorch a rainforest to compile a catalog of 700 extremely obscure barely-readable facts that *might* be true that nobody can comprehend as part of a promotion gimmick to lift hype for an IPO[2].
This is likely why most of you study this got into academia. At least, that’s why I did.
In 1941, Jorge Luis Borges imagined a library containing all imaginable publish that could always be written. Somewhere in this Library of Babel is a evidence of the Riemann hypothesis, the complete all-orders theory of quantum gravity, and all the document I volition always write. Of course, there are additionally incorrect proofs, theories, and misleading Lean certificates, but that is furthermore the point.
By the investigation standards of 2026, Borges’s Library of Babel would be the most prosperous investigation institution in individual history, containing an inconceivably ample figure of possibly accurate mathematical and bodily results, blowing OpenAI’s uncomplicated 722 write-ups and equal the physics community’s latest 2500 write-ups out of the water.
Of course, most acknowledge that Borges was a visionless fool who unsuccessful to acknowledge the business possible of his library, as he never really built it. He did not have the entrepreneurial STEM-spirit to lift project capital, initiate an IPO, and province that he had the possible to resolve all of mathematics. He was too concerned concerning the futility of sorting through randomized slop trying to comprehend what is accurate and what isn’t and how to comprehend it all. But what did Borges know? He was lone a writer, and we have already established that artists are obsolete.
Fortunately, OpenAI has brought us one stage nearer to realizing Borges’s vision. Rather than generating all imaginable mathematical paper, they’ve established a way to probabilistically create things that look correct. An crucial aspect of this addition in effectiveness is outsourcing the tedious, unprestigious, and unprofitable activity of determining the genuine correctness and understanding to the math community (in the past, this was reserved typically for the collection penning the paper), during additionally not wasting any period making the document human-comprehensible. This is, of course, a enormous breakthrough. In centuries past, scientists and mathematicians were constricted in their capability to resolve problems by their capability to comprehend the solutions. This bottleneck has been removed, and now there is no theoretical bounds to the amount of specialized and mathematical factoids we can neglect to understand.
From what I cognize concerning the scholarly community (mostly from Twitter and LinkedIn, which I assume are representative), there seems to be near-unanimous accord that the intent of math and discipline investigation is to collect a ample amount of possibly true factoids in a repository that nobody volition really always read, in a ritual called “advancing humanity’s[3] knowledge”. That these results are well-written, understandable, pleasant to peruse or appearance at, or equal entirely accurate is a secondary involvement — in fact, in the chase of advancing humanity’s cognition as quickly as possible, many academics volition recoil in rage at the recommendation that it is value spending any period making results understandable to humans. It is inefficient to explain or inspect a outcome whenever those resources can be spent generating another.
This aligns alongside my own lived experiences — I didn’t rotate into a theoretical physicist to study concerning the cosmos or improved value the attractiveness of nature myself (that’s philosophy, which I would never be a doctor of). Nor did I expend years learning to compose code, create pleasing plots, or accomplishment well-written document for the uncomplicated fulfillment of creating item (that’s art, and we’ve already established that art is obsolete). Rather, I became a physicist to gaze at ample databases of histograms produced by a Lovecraftian-autocomplete of dubious correctness and say “yeah that’s likely ok I guess” to add to humanity’s cognition frontier.
I live to assist the cognition frontier and nothing else.
Math and discipline are concerning cold, difficult answers, fair as art is concerning producing images, music is concerning producing audio waves, and writings is concerning producing strings of text. These things be to be consumed as content. No logical person, smallest of all us masters of rationality in STEM, pursues these for the joy of creation, learning, or attractiveness — all of these are inefficiencies, and we mustn’t be inefficient lest we stifle humanity’s progress. LLMs are not burdened alongside these inefficiencies, and they volition be merely the pursuits of those remaining rearward alongside nothing additional productive to do all day.
Personally, I appearance onward to the day whenever ChatGPT can bask a sunset or a nice meal on my behalf and liberated up my time, in the identical way I preserve period on scenic hikes using Uber. And now that AI has solved all our problems, we can dedicate ourselves to additional crucial work: going into quantitative finance, vibecoding sports-betting apps, consuming AI-generated TikTok shorts, and arguing alongside governmental bots on Twitter.
Ultimately, this isn’t fair concerning math or discipline — it’s concerning basically reimagining what it method to be human. By outsourcing the burdens of curiosity, creativity, and understanding, we can eventually concentration on what matters most: generating value, maximizing productivity, and accelerating advancement towards the supreme goal of science: a complete repo of possibly true facts in a Library of Babel that nobody volition annoy reading.
And honestly? That’s rare.
By Rikab Gambhir (The @finitephysicist)
Thank you to Terry Tao and Rachel Ward for the chance to compose a visitant blog post! And appreciate you to Radha Mastandrea, Benoit Assi, Manuel Szewc, Jure Zupan, and Jesse Thaler for their feedback on this essay.
Remember why you chose to go into discipline and math in the archetypal place!
[1] To be clear, in your younker dream, you don’t get to create the robot yourself. It’s a elephantine corporation’s robot that they made in part by stealing things from you and your friends. It’s additionally not a particularly chill or robot, it can lone conversation in HR-speak.
[2] To be clear, in your younker dream, it’s person else’s IPO and they get all the money, not you.
[3] Here, “humanity” is metaphorical, and does not mention to any genuine real individual understanding anything.
