2026-06-28 22:56:56
One of the most popular posts in this blog’s history was Common Knowledge and Aumann’s Agreement Theorem, based on a lecture that I gave to high-school students 11 years ago. One of the impacts of that post, I’m proud to say, is that (according to Steven Pinker) it helped to inspire Steve’s excellent recent popular book, which you should read, entitled What Everyone Knows That Everyone Knows…: Common Knowledge and the Mysteries of Money, Power, and Everyday Life.
Two weeks ago, I was privileged to attend a workshop in Paris on “50 Years of Agreeing to Disagree,” where (among other things) I got to meet the 96-year-old Economics Nobel Laureate Robert Aumann for the first time.
I got to catch up there with Steven Pinker as well, who gave a phenomenal talk on the psychology of common knowledge. My own talk was entitled The Complexity of Agreement, with New Directions and Applications (link goes to my PowerPoint slides).
Aran Nayebi has graciously posted on YouTube some partial video from the meeting, including his talk, brief snippets from my talk, and Aumann’s own remarks:
Meanwhile, here were the Aumannian insights that I remembered to write down:
AUDIENCE QUESTION: What questions did people ask you after you published your famous agreement theorem in 1976?
AUMANN: I don’t remember what happened yesterday, let alone 1976.
Also:
ME: I thought you might enjoy knowing that I just came here from a meeting of rationalists…
AUMANN: A meeting of who?
ME: Rationalists, they call themselves, at a beautiful venue called Lighthaven in Berkeley, and that they named the main building there “Aumann Hall” in your honor.
AUMANN: OK, so I’ve made it then.
One reccent result announced at the workshop, for those who care, is that the proof of Aumann’s Theorem has now been formalized in Lean, by Scott Kominers at Harvard and a group from the startup company Axiom Math.
Thanks so much to Christina Katt-Pawlowitsch, Ziv Hellman, and others for organizing the workshop and for including me in it.
Happy to field questions in the comments, although if someone wants to call me an idiot like usual, we’ll just need to agree to disagree!
2026-06-23 22:13:49
I’ve been getting emails from journalists asking me to comment on the new White House executive order on quantum computing. Alas, I don’t have time for a long response or interviews since I’m at a beautiful science camp in the California mountains, and heading soon to STOC’2026 in Salt Lake City. But I gave anyone who asked me the following statement, which I thought might be of interest to readers of this blog as well.
“I hope that at least some of the new funds made available from this Executive Order will go to basic, curiosity-driven academic research — the kind that led to the idea of quantum computing in the first place, and to the main quantum algorithms and other advances that everything builds on today — and not only to large organizations that have gotten good at capturing federal funds by repeating the requisite buzzwords.”
2026-06-23 01:27:42
Since I’m a good mood today—at a beautiful science camp with my kids, high in the mountains near Big Bear Lake in California—I thought I’d blog about something positive. Last week, five authors (Chatterjee, Ghosh, Gurjar, Raj, and Thierauf) posted a major paper to the Electronic Colloquium on Computational Complexity, which shows (or anyway, credibly claims to show) that the Bipartite Matching problem is in the complexity class NC. Assuming this stands, it resolves a central problem in parallel algorithms and derandomization that’s been open since the 1980s.
In Bipartite Matching, you’re given a list of n men and n women, you’re told who’s willing to date whom, and your goal is to
One of the great early discoveries of combinatorial algorithms, taught in every introductory algorithms course, is that this problem is solvable in time polynomial in n, even though the naïve, brute-force approach would require examining n! possibilities.
(Note that in the bipartite version, we assume that the men and women are all straight. If the men and women can be LGBT, we get the problem of matching in general graphs, which again turns out to be solvable in polynomial time, but now the algorithm is much more sophisticated, and was a major discovery of Edmonds in the 1960s.)
Anyway, the question is whether we can do even better than polynomial time: in particular, can we solve the problem in time polynomial in log(n), given polynomially many parallel processors?
Back in the 1980s, first Karp, Upfal, and Wigderson, and then (via a very different method) Mulmuley, my former PhD adviser Umesh Vazirani, and Umesh’s brother Vijay Vazirani managed to show that the answer is yes, but only if the parallel processors additionally get access to random bits, and only need to succeed with high probability.
The new achievement is to derandomize the Mulmuley-Vazirani-Vazirani algorithm, and show that problems 1 and 2 above are both solvable in deterministic polylogarithmic time with parallel processing (in other words, in the complexity class NC).
No, I don’t understand how it works yet. If anyone does, feel free to explain in the comments! Or ask your favorite AI to generate a summary. If I run out of options, at some point I might actually try reading the paper.
(Note: Thanks to Gil Kalai for some corrections to an earlier version of this post.)
One other announcement: Today is the day of primary elections in NYC! Virtually all of my smartest friends who work on AI governance and safety are extremely excited about the Congressional campaign of Alex Bores—indeed, it would be little exaggeration to say that they consider him the last best hope of humankind. Bores has been a national leader on trying to regulate AI, to the extent that Marc Andreessen’s “Leading the Future” anti-AI-regulation PAC has spent millions of dollars trying to sink his candidacy. Outside of AI, Bores seems like a sane, conventional Democrat, i.e. the kind I like, and much more moderate than his base on Israel (note that his main opponent is also such). Without commenting on Bores’ views on every possible issue, I’ll simply say: if you live in New York’s 12th Congressional District (comprising a huge chunk of central Manhattan), and you care about AI safety, please consider a vote for Bores while there’s still time.
2026-06-20 09:06:51
In 2024, at the same time as I was being called a genocide apologist, Zionist baby killer, etc. etc., I was also being hounded by my right-wing, pro-Israel readers, who demanded of me: knowing what you know, understanding what you understand, how could you possibly vote for Kamala Harris? How could you donate to Kamala’s campaign, urge your readers to vote for her, when she (like most Democrats) is obviously beholden to young left-wing activists, and young left-wing activists’ central unifying cause has become the death of “Zionists” like yourself and your children? Can’t you see that, even if Trump is a raging, lying, bullying, incoherent monster, at least he’s our monster?
This view, I confess, gave me more pause than “just accept that the liberation of the world’s oppressed requires Israel’s annihilation and hence the death of much of your family,” which my far-left critics considered their most persuasive argument. And yet I also rejected, with extreme prejudice, the right-wingers’ constant invitations to join their MAGA brigade. I replied: I’ll simply continue being a moderate Enlightenment liberal, transported here in a block of ice from the 1990s, even if I’m the last such on earth, even if I’m condemned by everyone on either side for it.
Why? Because, I said at the time, while I’m honest enough to admit when a rampaging T-Rex happens to do something that aligns with my interests—when, e.g., it chases away some velociraptors ready to slice me open, or cracks down on antisemitic fanatics trying to dominate the universities where I spend my life—still, I’ll never delude myself into imagining the T-Rex my ally. I understand that it would just as soon devour me. If the monster goes after my enemies not because of liberal principles or recognizable moral emotions, but just raw self-interest and ego gratification, then what happens the instant its perceived self-interest changes?
It gives me no pleasure that this week Trump proved my instincts here 3,000% correct, by fully capitulating to the Islamic Republic of Iran, far more so than Barack Obama ever did, or than President Kamala Harris ever would have. Trump has now abandoned both the Iranian and the Israeli peoples to suffer and die at the hands of the IRGC, as he abandoned the Ukrainians and the Venezuelans before them, as Neville Chamberlain abandoned Czechoslovakia. All because of the price of gas and the midterm elections.
On the most charitable reading, Trump gambled that taking out Ayatollah Khamenei would lead to a cowed and compliant puppet regime, as basically happened in Venezuela, with no need for a ground invasion or arming the Iranian resistance. His gamble predictably failed, because (alas) the Iranian government actually believes in something—horrifying and medieval though it is. Trump was unable to process that fact, because he’s never believed in anything beyond himself, and he wrongly assumes that everyone else is the same.
With the $300 billion and control over the Strait of Hormuz, I expect that Iran will rebuild its military and proxies to stronger than they were before Trump’s idiotically mismanaged war. I expect that Iran will then launch attacks against Israel that make October 7 look like the Little League—and that, when it does so, a large fraction of the Western world will ecstatically cheer, as it did on October 7 itself. Netanyahu is a fool for expecting otherwise from Trump, a man who’s betrayed everyone who’s ever trusted him outside possibly his immediate family.
I salute those Israelis who will choose to stay and fight even after their abandonment and betrayal, in the spirit of the Bar-Kochba revolt and other desperate battles of Jewish history. Despite the existential danger that Israel will soon be in, facing a victorious and emboldened Iran essentially alone, I also see it as possible that Western countries will rapidly become even more dangerous for Jews than Israel. If that happens, I’ll be grateful that Israel still exists, that it considers itself unbound by America’s surrender, and that I’ll be able to seek refuge there, as was the idea when Israel was founded.
At the same time, I wouldn’t begrudge any Israelis who moved to the US, or Switzerland, or whatever other country will take them. As in the 1930s and at countless other points in Jewish history, the priority now is physical survival, wherever that turns out to be possible.
With hindsight, I spent the first half of my life in a strange interregnum, wherein Jewish history seemed to have finally ended. Now, fueled by fading memory of the Holocaust and by the greatest lie-amplification technologies the world has ever seen, the history I learned as a child has come roaring back. Jews, as they have for millennia, look out on a world of murderous enemies and fickle friends. It’s just the restoration of a norm.
Incredibly, abandoning both the Iranian and Israeli peoples to the Revolutionary Guard might not have been the most shortsighted or catastrophic thing Trump did in the last couple weeks. Another candidate would have to be Trump’s attempt to destroy Anthropic (and as collateral damage, American AI development more generally), transparently to punish Anthropic for the crime of having any principles that it was willing to put ahead of obedience to Trump and Pete Hegseth. Specifically, the White House forced Anthropic to pull Fable, its new flagship model (and the “safe” version of Mythos), off the market just a few days after customers had started using it, by subjecting it to export controls that it knew were impossible to enforce. Even the many foreign nationals who work at Anthropic are no longer allowed to use their own model (!). A plausible consequence is that those foreign nationals will stop working at American AI companies altogether, and will move to China or whichever other country rolls out the red carpet for them.
For AI accelerationists, you’d think that this would be a worst-case scenario: a direct government crackdown on AI that goes beyond anything the AI safetyists had proposed, and that indeed would’ve sounded like fantasy even a year ago. And yet many of the accelerationists are gleeful. Why? Because Anthropic, in supporting reasonable AI regulations, had made itself the accelerationists’ enemy. So, the accelerationists’ attitude now is quintessentially Trumpian: “Haha, Anthropic, you say you like regulation? Then take that!” Never mind that whatever dangerous behavior can be elicited from Fable, can almost certainly be elicited as well from GPT 5.5 Pro, and yet there’s no talk of any similar crackdown against OpenAI. Sam Altman, after all, donated $1 million to Trump’s inaugural fund. No one finds it remarkable anymore, in Trump’s destroyed and recreated United States, that your rights depend entirely on your standing with the Don.
And so, just like the question of whether Trump would side with the isolationists or with the hawks who wanted to liberate Iran, was resolved by his worst-of-all-worlds choice to surrender to Iran, so too the question of whether Trump would hit the brakes on the race to dangerous AI, or accelerate in order to beat China, has been resolved by his worst-of-all-worlds choice to lose the race to China. I.e., we’re still in full race mode, but we’re also going to do whatever we can to lose the race—by, for example, letting NVIDIA sell its chips to China, and now, scaring away our top researchers and punishing our AI firms with capricious and arbitrary crackdowns.
It’s disconcerting to reflect that, while the prognosis of the world is arguably the worst it’s ever been in my lifetime, my own life is pleasant. Intellectually I know that the Titanic has already hit the iceberg, but the band is still playing and I’m still being served delicious food.
Last week I visited Paris and the French countryside with my wife and kids. In addition to sightseeing, I spoke at a workshop celebrating 50 years of Aumann’s Agreement Theorem (where I got to meet the 96-year-old Aumann), and gave a quantum computing talk at the Sorbonne. Next week I’m going with my family to a science camp in California, then to STOC in Salt Lake City, where I’ll accept the Trevisan Prize and give an after-dinner speech, then to Epsilon Camp where I’ll again teach theoretical computer science to 11- and 12-year-olds.
Like I said, life is good here on the Titanic, if you ignore the rapidly rising seawater at your feet.
2026-06-03 02:49:47
The comments on my previous post, on recent AI breakthroughs in solving Erdös problems and beyond, must’ve set some sort of record for the number of separate reasons commenters offered me to despair about the future of humanity. All this in a post that I saw as relatively nerdy and anodyne, goring few oxen, when I clicked “Publish”!
According to some persistent commenters, the only reason why I wrote about recent AI-enabled math breakthroughs is that I’m a shameless shill for the AI companies, my loud public criticisms of those companies being nothing more than a cynical smokescreen. Except that I’m also a laughable dupe of the AI companies.
See, AI, despite all appearances to the contrary, has not solved the Erdös unit distance problem or any other important math problems at all. It’s merely produced vast amounts of garbage via brute-force search, and then human mathematicians, sifting through the digital garbage pile, found some things they could call “proofs.” Except also, those human mathematicians aren’t even real mathematicians! They’re merely Hungarian combinatorialists, the kind obsessed with trivial, uninteresting Erdös problems, which it stands to reason that AI can now solve. AI will never touch the truly deep, creative parts of math, epitomized by Grothendieck-style algebraic geometry.
(When I relayed this to a world-leading algebraic geometer of my acquaintance, he laughed and said that everyone has to tell themselves whatever it takes to cope with the situation. He himself has been using LLMs in his research, and while they can’t yet write his papers for him, he expects them to improve very rapidly.)
When pressed, my commenters made it explicit: Timothy Gowers, the Fields Medalist who told his fellow mathematicians that he hopes they’re sitting down before he broke the news about the Erdös distance problem, is not a real mathematician, just a combinatorial puzzle-solver. Paul Erdös himself was not a real mathematician either.
Oh, and also, I’m a genocidal Judeofascist Zionist. That entered the comments as well, with the pretext being that I had shared a GPT-written story about ancient Israelites.
(Note: For every comment that I allowed to appear in the thread, assume as usual that there are many worse ones that I didn’t.)
Does anyone wonder why I blog so much less than I used to? Seeing what humanity has to offer, as reflected in my comment section, I feel like maybe we should take our chances with our future AI overlords. Except that some of my comments are—ironically, given their content—likely to be AI-generated as well.
These days friend after friend of mine, colleague after colleague, acquaintance after acquaintance is becoming a multimillionaire or even a billionaire from startup equity. Meanwhile, I’ve scrupulously abstained from all of that.
Why? Well, probably the single biggest reason has been Shtetl-Optimized. I’ve zealously sought to protect my “neutrality” and “objectivity” as a commentator, on this free (and even ad-free) public forum—the one where I try every week to reason with anonymous trolls with “proton.me” email addresses who show up to call me a hack and a shill and a baby-killer and a dunce. Ironically, the actual billionaires who I know hardly ever get called those sorts of names, mostly because they don’t offer the world a huge attack surface like I do. Or if they occasionally do get called them, they don’t care.
On reflection, all the commenters calling me a dunce have a point. When one looks at how I chose to spend my life, versus how all these billionaires did, I am kind of a moron.
And yet I titled this post “On Hope.” In a situation like the present, one needs to find hope wherever I can. And right now, I’m choosing to find it in this open letter, which has been signed by over 1,250 professors at the University of California. Let me quote the beginning of it:
To the UC Regents, UCOP, Academic Senate leadership, and the people of California:
We write as University of California mathematics faculty, joined by faculty from other STEM disciplines. UC has long served students from every background and has been a powerful engine of social mobility for the people of California. That public trust must be protected for future generations. Today, UC’s mission is at risk. To preserve that mission:
We call for the reinstatement of the SAT/ACT mathematics requirement for applicants to STEM majors beginning with the 2027 admissions cycle, alongside STEM faculty oversight of readiness standards and admissions practices affecting those majors.
Over the past five years, we have seen a widening divergence in mathematical preparation levels within the same classroom. This trend indicates that current admissions practices do not provide a sufficiently reliable check on mathematical readiness for STEM majors. The UC San Diego Senate–Administration Workgroup on Admissions report documents this crisis in stark terms: in the last five years, the number of students whose mathematics skills fall below high school level increased nearly thirtyfold; moreover, 70% of those students fall below middle school levels, reaching roughly one in twelve members of the entering cohort. These findings are corroborated by data across our campuses…
Everywhere one looks right now, and on every part of the political spectrum, doofuses and blankfaces strut across the earth triumphantly. Yet there remain pockets of sanity. What reading this open letter told me is that the University of California STEM faculty is one of them.
With enough such pockets, I could live a perfectly reasonable rest of my life, from now until my natural death (or until AI changes all our lives beyond recognition), regardless of who shows up in 3 … 2 … 1 … with a “proton.me” email address to confidently tell me otherwise.
2026-05-28 12:36:07
As most readers have presumably heard by now, Paul Erdös’s Unit Distance Problem from 1946—one of the central open problems from the field of discrete geometry—has been solved by an internal OpenAI model. Erdös had conjectured that, given n points in the plane, at most n1+o(1) pairs of them could be unit distance apart. Using high-powered results from algebraic number theory, GPT refuted this, constructing a set with n1+ε unit-distance pairs, for ε ~ 10-38. Shortly afterward, Will Sawin, a human (!), improved GPT’s construction to get ~n1.014 pairs. There’s since been a claim to improve this further, to ~n1.034. Meanwhile, the best known upper bound remains n4/3, improving Erdös’s n3/2.
The entire process seems have been one-shot: my former student Lijie Chen simply gave GPT the problem, then GPT thought for a while and output a several-page argument that, on analysis by human experts, turned out to be correct. Of course there’s selection bias here; we’re not hearing as much about the hundreds of other problems GPT was given that it didn’t solve (isn’t that the case with humans too?). Clearly, too, GPT was helped by the facts that human mathematicians had wasted most of their time trying to prove Erdös right rather than looking for a counterexample, and that, even if they did look for a counterexample, they’d need to be experts in algebraic number theory to find this one, which hardly any discrete geometers are. So, maybe that suggests that AI, right now, is “merely” picking various medium-hanging fruits that human mathematicians missed for contingent reasons? With emphasis on the “right now.”
In a companion paper, OpenAI helpfully included commentary from Timothy Gowers, Noga Alon, Will Sawin, Daniel Litt, and many other experts, reflecting on the breakthrough, the path that GPT took to get to it (which can actually be seen by examining its chain-of-thought), and what this might mean for the future of mathematical research.
I heard the news maybe an hour after it broke, when some UT grad students came to my office to tell me. For what it’s worth: these students were morose, musing about how everything might soon be over for young scientists and mathematicians like themselves. I don’t know whether they’re right, but I feel like I should tell the truth about what their reaction was.
[Update: News has been coming faster than I can write about it, but today we learned that another important conjecture of Erdös has been refuted. Erdös and Szemerédi’s strong sumset conjecture over R, from the 1970s, had said that, if A is a finite set of real numbers, then either |A+A| or |A×A| must be at least |A|2-o(1). In this case humans, including the aforementioned Sawin, did almost all of the work of constructing the counterexample, but they were directly inspired by GPT’s earlier refutation of the unit distance conjecture. It remains open whether such a counterexample exists where A is a set of integers.]
Then, a few days later, a team at DeepMind, including my UT Austin colleague Swarat Chaudhuri, announced that they were able to use a system called AlphaProof Nexus to settle nine more (!) Erdös problems, many of them in additive combinatorics, along with miscellaneous other open math problems. Notably, in this case the AI also fully formalized its proofs in Lean.
And then, just today, Jelani Nelson alerted me to a new CS theory paper, which solves a longstanding open problem about electrical flows on graphs using a proof from GPT5.5Pro.
It seems to me that we’re now over the top of this particular rollercoaster, and it will keep accelerating until we reach the bottom, wherever that might be. I don’t know whether to hope or dread that solutions to P versus NP and all our other great problems will be included in the ride—that our role, as human mathematicians, will be reduced to (at most) deciding which questions we find interesting and then understanding AI models’ answers to those questions.
But maybe that won’t happen. Maybe the new AI mathematicians will soon hit a wall, because they lack the uncomputable quantum gravity microtubules of Penrose and Hameroff, or some other magic human ingredient. The fantastical thing is that, one way or the other, we’re going to find out empirically before very long.
Readers may have also seen the news that multiple prizewinning entries in a short fiction contest called the Commonwealth Prize, give overwhelming indications of having been written by AIs. As Kelsey Piper puts it:
There are, let’s say, also some noticeable similarities in the prose style between the winning stories that were flagged for AI use. AI chatbots love metaphors and similes, and they often spit out ones that sound vaguely pleasing but are logically incoherent or ascribe properties to things that don’t make sense.
“The Serpent in the Grove” gave us, “The girl smiled like sunrise over a sink.” “The Bastion’s Shadow” says, “She carried it now in her bag, heavy as a charm.” “Mehendi Nights” describes something as “swaying against plaster like a warning bell.”
The Commonwealth Foundation, whose judges chose these stories, hasn’t exactly covered itself in glory—saying, on the one hand, that it strictly forbids AI use but on the other, that it will continue taking authors at their word that they didn’t use AI, no matter the immensity of evidence to the contrary. As many others have pointed out, judges more versed in AI would’ve ironically been better placed to notice the signs of its use.
If only there were some sort of automated way to detect AI-generated text. Someone should really get on that problem, don’t you think?
But maybe we should just throw in the towel—as some of my colleagues have already done in the context of undergraduate projects? Maybe we should simply say that a good story is a good story, regardless of what manner of entity produced it?
As it happens, just last week I read my very first AI-written story that affected me as a story, to the extent that I wanted to read it more than once. This happened when I gave GPT5.5Pro the following simple prompt:
Write me a story about the most ancient Israelites that’s riveting like the stories of the Bible but that’s also consistent with all of the archeological evidence.
You can read the result here. One of my Facebook friends called it “disturbingly good,” and whatever the problems with the piece, I share that feeling. Of course, I’m well aware that GPT could easily generate a thousand stories like it—sampled from the same probability distribution—and then I could even do statistics on which tropes were the most common. This makes it feel silly to overindex on the first story that happened to be output, and yet somehow I did.
I feel like at this point, both the prophets of AI utopia like Ray Kurzweil, and of AI doom like Eliezer Yudkowsky, could be forgiven for asking: dude, will you listen to us YET? Do you still find it prudent to call this new form of terrestrial intelligence a stochastic parrot, a laughable fraud, or a fad that’s about to go away? Fear it all you want, hate it even, but at least respect it!
Which brings me to the other big AI news from the past week, namely that Pope Leo released his first encyclical, which is entitled “Safeguarding the Human Person in the Time of Artificial Intelligence.” I read it and … well, I certainly agreed with the theme that such a world-changing technology needs to be developed for the common good (as the Pope would have it, like the walls of Jerusalem), rather than for the profit or vanity of any one individual or company (in his analogy, like the Tower of Babel). I had quibbles with some of the other parts. Zvi Mowshowitz, as he often does, had a superb paragraph-by-paragraph analysis. Amusingly, there are indications that parts of the encyclical were written by AI.
To me, though, maybe the most notable part was that Chris Olah, who leads Anthropic’s interpretability team, was standing next to the Pope at the ceremony, and delivered his own remarks. I felt like Chris, who I met even before Anthropic existed, was a non-obvious yet inspired choice here, one of the rare figures in frontier AI whose technical and moral authority are both completely unimpeachable by anyone.
And so, at this momentous era for the human project, and on no less of an authority than that of the Vicar of Christ himself, the Supreme Pontiff and the Successor of Peter, I hereby throw myself on the wisdom and mercy of … uhh, I guess, Chris Olah and his team at Anthropic.
Chris, if I am soon to share the earth with entities that can prove the Riemann Hypothesis and solve quantum gravity after 30 seconds of thought, then may you understand those entities well enough to cause them to be nice.
Endnote: I should have foreseen, but didn’t, that the comments on this post would be dominated by people looking for ways to minimize whichever specific AI accomplishments I blogged about. Thus, it turns out, the ability of AI to solve Erdös problems just demonstrates that Erdös’s problems were never “serious” math in the first place—nothing like algebraic geometry or Grothendieck-style theory-building, which remain untouched. Likewise, the story I shared was obvious AI slop.
I had taken it as obvious that, when assessing AI’s impact on the world, one needs to look at least somewhat into the future: to remember where things were four years ago, compared to where they are today, and at least try to draw a straight line through the data, if not the exponential that seems to fit better.
Does anyone seriously doubt at this point that major open problems in algebraic geometry and other “Grothendieck-friendly” areas of math will fall to future AI models? Or that AI-written stories will improve, not only to win literary awards from AI-naïve judges, but to avoid the features that commenters here are complaining about? And that, whenever that happens, there will be new confident reasons not to care immediately offered up in comment sections like mine?
Apparently people do still doubt—hence the throwaway remark in my post about Penrose and Hameroff and microtubules. If not that or something like it, what exactly do they think the ceiling will be, and why?
Recently (I should have mentioned this before), I came across what I consider one of the greatest social experiments of all time, one that illuminates people’s reactions to every AI advance. A Twitter/X user named SHL0MS displayed the following AI-generated fake “Monet painting,” and asked people to explain what made it worse than real Monet paintings:
If you haven’t seen this yet, I recommend that you try the exercise yourself before reading further.
As it was, numerous art aficionados responded at length, savaging the flat, lifeless, uncreative AI slop, the emotionless composition, the missing spark, the lack of tranquility, the harshness, the lack of depth and symbiosis, and on and on and on.
Only after they had all said their piece did SHL0MS reveal that this is an actual Monet painting.