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I made a comment recently speculating that Terence Tao may be a paid promoter of AI. After some off-site discussion elsewhere, our resident shaman on whom the mods cast a long-duration silence posted my comment as a circus exhibit on Twitter to demonstrate a dangerous form of psychosis. Fortunately, OpenAI deemed this a worthy time to intervene and publish an ad featuring Terence Tao, who has taken time out of his busy schedule to assist this struggling non-profit with their promotional.
I want to dissect what I think is really going on.
For starters, every time I sit down to watch WoW on YouTube, I'm greeted with my favourite streamer telling me about the super fun game Raid Shadow Legends, which I should definitely download and play because it's super fun. Is the OpenAI-Terence Tao relationship like this? Not really. Terence Tao does appear to actually spend some time playing around with LLMs. Further, he's not exactly saying a bunch of empty marketing blather, either. In fact, probably to the annoyance of some readers here, I want to take a couple paragraphs for a technical aside, because there is actually subtlety here. I'll bound this in horizontal bars so non-technical readers can skip it:
The main talking point is that automated theorem proving is a perfect fit for LLMs precisely because it's not vulnerable to their main catastrophic failure mode: hallucination. The model can hallucinate whatever it wants, but the text still goes into the theorem prover, and if it's bullshit, well, the prover just rejects it and you query the LLM again. Do this in a loop, burn whatever unholy amount of compute you want, and if the loop stops, you've got yourself a proof! (Well, or a bug in the theorem prover. Or a "You've run out of tokens on your budget" error message. But I digress). This story is largely true. There's a giant asterisk of "Uh, so how much compute we talkin' about?", and the answer is "As much as you need or can afford, whichever comes first!" Which is, of course, the business model.
I will point out one additional technical nit-pick that annoys me because Terence Tao is working in Lean 4, which is a dependently-typed theorem prover. In classical mathematics, one is concerned solely with whether a theorem is true or false, and the structure of the proof is basically irrelevant as long as it's valid. Lean 4 is not based on this model. Rather, it's based on a more computationally-motivated model of mathematics pioneered by Brouwer in the early 1900s called "constructivism." In this world, the question isn't the boolean notion of "Is this theorem true or false?" but rather the related but distinct notion of "Which proof do you have?" To ground this in practical terms, consider the following example: I can prove that True|False and Yes|No are isomorphic, but I can do so in multiple ways: I can map True to Yes and False to No, or I can map True to No and False to Yes (and then show that there are respective inverses which preserve identity, obviously). It is in this sense that one can meaningfully say "Which proof of isomorphism?" when I say I have a proof of isomorphism. Perhaps this all sounds like technobabble, but to connect it to the preceding paragraph: you can immediately see how this does reveal some cracks into the narrative being sold there. It does actually matter which proof is produced, not merely in a social sense of "can any human understand this wall of text the LLM spit out", but in a technical, computationally-relevant sense. For pure mathematics, this distinction is often not considered important -- in fact, many classical mathematicians aren't even aware of the difference, and will be confused if you try to explain it to them and think this is all a bit silly. However, it's not a silly or minor distinction for the following reason: one of the motivations of this computational model for "theorem provers" (it's really a programming language + compiler, rebranded for mathematicians) like Lean is so that formal methods can be applied not just to classical mathematics, but to software in general. And as soon as you enter software formalisation, this distinction is no mere intellectual curiosity, but of paramount relevance. The classical-style logic in the preceding paragraph does not apply to constructivist logic used for software formalisation! I'm sure this distinction is not lost on Terence Tao. But that doesn't concern OpenAI. OpenAI is more concerned with whether the distinction will be lost on the MBAs listening to Terence Tao, and the answer is "absolutely."
Ok, no more technical details like that, I promise. Back to the social level:
So, I mentioned Raid Shadow Legends is a poor metaphor for the OpenAI-Tao relationship. Let me propose some better ones: Michael Phelps and Wheaties (with the added benefit that Terence Tao never smokes weed. See, this is why mathematicians are better than athletes), or better yet, attending Harvard University. This may seem like a strange juxtaposition, but I've done so intentionally, because the marketing is obvious in one but subtle in the other, but it's actually the same trick: the goal is to misattribute performance. With Wheaties, the goal is to sell the notion that Michael Phelps is a great swimmer because he has a healthy diet of stuff like Wheaties, and if you eat Wheaties, maybe you'll perform well, too! Of course, in reality, he was eating sugar-coated french toast and chocolate chip pancakes because he needed 10k Calories/day just to break even on energy, and the reason he's such an awesome swimmer is in large part genetics. Wheaties, or anything similar to it, has virtually no relevance to Michael Phelps at all. But what about Harvard?
Well, Harvard sells the image so well that most people outside this forum outright believe the illusion. The illusion is, of course, that attending Harvard makes you smart and likely to succeed, rather than Harvard accepting only people who were smart and likely to succeed in the first place and thus redirecting credit for these future achievements to Harvard. Mark Zuckerberg may see Harvard as a pointless waste of time, but the world sees Harvard as "The university that made Mark Zuckerberg happen!"
I like the Harvard analogy because this is surely the intent with Terence Tao. There's a high chance sooner or later Terence Tao will prove something cool "using" ChatGPT, and if he does, it would be really awesome if we could make it sound like the secret ingredient in the ChatGPT-Terence Tao alliance was ChatGPT, when obviously the actual secret ingredient is Terence Tao. The analogy I always use for this is stone soup, a European folktale where starving travelers dupe gullible townsfolk into helping them make soup from stones by requesting "extra" ingredients bit by bit until they've just made actual soup, thus astonishing the gullible townsfolk.
There are a lot of other things I could say on this, especially on the technical side, as there are a lot of clever tricks you can pull to make it look like a model is doing more than it really is, but I'll stop for now and conclude with this:
Just be cognizant that OpenAI, and all the other LLM vendors, do marketing. They have an enormous budget that dwarfs anything you have ever seen before. Remember the reality distortion field of Black Lives Matter? Or trans people? Imagine that, but like... two orders of magnitude larger. That is the level of persuasive pressure we're dealing with here.
Just take it all with a grain of salt.
I’m confident Terrace Tao is pro-AI both because it funds him and he finds it interesting and potentially useful. That’s academia (usually).
OpenAI’s model did solve a long-standing Erdos problem (not in Lean, hand-checked by mathematicians, but still)
I think crypto is a good analogy. There is actual tech there, and many do indeed believe in it (me being among them). But the hype and noise was wildly disproportionate to what was realistic and done.
At this point, if you put your bet on anything other than "Slightly improved Bitcoin with privacy that was obviously intended originally but not known how to do at the time," you’re probably down 70+%, if not entirely liquidated. Entire narratives about "business on the blockchain!" were complete nonsense.
Fun fact: Sam Altman himself launched a crypto coin back in the heyday. It's down 96%.
I think crypto isn't such a good analogy. I never saw anyone get value out of crypto qua crypto. As an asset and an investment, yes, and occasionally as a way of paying for mildly shady or super-techy things, but in general the value proposition just never seems to have manifested to me.
Whereas I get massive value out of AI. For writing, for my hobby projects. My startup would be facing much larger headwinds without AI for coding and research. I think the hype is still kind of overdone, but only because the hype is so strong that only the immanent eschaton could live up to it and because it's not clear how much of a directly-related ecosystem there will be for third parties.
I have mixed feelings about AI; I have concerns about it being used to automate military decisions that should require human moral judgment (the traditional Terminator-style concern over computer command and control), and also the potential for deepfaking and manufacturing false content to mislead or manipulate. The latter has already been used in new and more sophisticated scams, and I worry about what a nation-state-level actor could do with that kind of power. Economic disruption is there as a genuine possibility, and that's difficult, but I'd prefer if people expressed that possibility directly as a livelihood threat rather than trying to launder the (genuinely sympathetic) concern into environmentalism or moral grandstanding about human creativity or interpretations of IP law in which AI training is assumed-illegal.
I've rarely actually heard someone say, "I don't like AI because doing my job without it gives me satisfaction and a good-paying job, and the introduction of AI into the workplace makes me feel like I'm losing the livelihood I prefer." Instead, I typically hear things like "AI was developed by stealing the intellectual property of hardworking people in order to enrich the billionaires and ELON MUSK and DONALD TRUMP," part of the large egregore of "all my enemies are evil rich fascists."
People would rather be angry than admit vulnerability. Our discussions over issues of social importance would be strikingly improved if people were willing to admit when their principles are self-serving -- which there's nothing wrong with, everyone deserves to advocate for themselves -- instead of trying to convert everything into an argument in which justice, law, the hand of God, and the long arc of history all militate against whoever you think is opposing your interests.
I don't agree with the environmental or land-use concerns for the most part, and it strikes me as degrowth corporate-hate and NIMBYism rather than principled objections. Energy use is not automatically immoral. I'm disappointed in the ways in which AI's demand for silicon is draining the consumer market of computer components and I worry about the impact on individual people's ability to control the means of technological production, but at least so far, this is offset to me by the increase in the ability to interface with computers using natural language.
The kind of generalized AI hate I see out there, online, occasionally in person, is hard for me to wrap my head around. I'm in the 10% of Americans who are more excited than concerned about AI. Generative AI has been great for me, in ways similar to what it's been for you. I enjoy using it. I get value out of it. I think AI slop memes are funny sometimes. I don't like when it's used to write personal messages or fill out marketing boilerplate copy, but I don't hate AI text as a general principle, especially if it's used to bolster and not replace human effort and creativity. And I dislike the invective and contempt that valid uses of AI generate in critics far, far more than I dislike the silliness or laziness of uses of AI that are in poor taste. That's the self-interested vulnerability of my own: I don't want a tool that has expanded my capability to become socially radioactive.
I don't know enough about AI to comment with any level of expertise on the research frontier. But I do have a skeptical prior towards the idea that this generation of AI will produce genuinely generalized AI that can meaningfully, affordably, and trustworthily replace human oversight. But we've gone farther with agentic AI use than I would have expected, so I might be wrong about that.
I agree with pretty much all of this.
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