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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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Minor point, this seems to be the wrong way round. The asset aspect is the boring (if hyped) part, and the proposal to do financial transactions without a trusted party (which can easily coerced to block some transactions by the feds) was the innovation. Of course, this freedom to do transactions has mostly been used in darknet marketplaces and for ransomware, but that's humans for you.
I will grant you that the anarcho-libertarian utopia promised by the blockchain has not happened, though. 'crypto' is 99% get-rich-quick scams, and the 1% are probably mostly ransomware and the like, with 0.01% being nerds buying acid or donating to wikileaks. Legal crypto exchanges are very much centralized, and banking laws in the US are probably broad enough that the feds can jail you for decades if you put substantial amounts of your money through a mix or otherwise annoy them.
And gen-AI is definitely the bigger deal, sure. It might take six or eight orders of magnitudes more money to train a LLM than it takes to train an individual human, but my feeling is that if we assume that the tech will keep the current intelligence level and and simply improve on the execution, that is already enough to make the mean white collar worker obsolete. Heck, I have a PhD-level education and consider a future where I am reduced to wearing AR goggles and connecting cables to where some AI decides they should go while it takes care of the software tasks far more efficiently than I ever could distinctly possible.
This is not to say that the AI bubble bursting is not also possible. I mean, investors in the late 90s were not wrong about everything -- the internet did have an enormous effect on commerce. It was more the specifics which they were wrong about, like if pets.com would ever become profitable.
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For complex tasks though, or for cutting down the massive overhead? I just think for most of the use cases I keep coming across, AI is nothing more than a fancy lawnmower that saves you time to cut the grass, but doesn’t do anything revolutionary for you.
That line gets fuzzy.
I can't talk work examples, but a hobbyist thing I've been crunching for the last two weeks is building a couple small educational robots.
That's not revolutionary, in the sense of completely breaking the field. It's something I've even done before at smaller scales: the first DIY educational robot kit I provided for a summer camp is almost a decade old now. But it's the sort of thing that's a massive time investment, especially when you're looking at a new microcontroller architecture or building something far from the standard line-follower or simple ESP32 websocket racer. Figuring out chip documentation, finding actual sane BoM materials instead of the wacky versions people go with at unit size 10k, managing errata, sanity-checking EMI, it's a nontrivial effort at even the smallest scale. On that side, AI's probably dropped it from a month of nights-and-weekends to a week or two, and probably made it better or surfaced information I would have missed otherwise.
(though even there, being able to actually find and translate information has encouraged me to go a lot broader than I did in 2018: there's been a few chips and targets I can genuinely evaluate five or ten options now, where before it'd just be a matter of finding anything not-EOL.)
The harder part is where I want to sell these things. People did that, pre-AI, don't get me wrong. But it was insurmountable to me, and probably insurmountable at my expected business scale. The actual build and development costs are trivial compared to the compliance costs, just figuring out the order of magnitude of the compliance costs meant hiring an expert, and worst of all, there's a lot of landmines I knew about even then which could invalidate a lot of your past efforts all at once, and others I didn't.
That's not, pointedly, revolutionary to the world. But it's revolutionary for my use case.
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As Napoleon once said, quantity has a quality all its own.
My private project is a graphics thing for ricing. To get what I wanted, I would have had to become proficient in desktop compositing, OpenGL, wayland, and several disciplines around graphics and rendering. Then I would have had to write several thousand lines of fairly finicky boilerplate, including several false starts and bad assumptions.
If I were retired and had the time and the energy, I could do that. In practice, though, switching from 5% ideas 95% grind to 60% ideas 30% reading 10% grind means that it’s fun and I’m a good chunk of the way there after maybe three good evenings of work. Without AI that just wouldn’t have happened and it would go into the bin of ‘someday’.
For my startup, again, AI is not a superintelligence but it sirfaces good papers, explains the maths when I get stuck, implements diagnostics in minutes that would take me hours. It’s not like having a Nobel winner in my pocket, it’s like having a textbook that can talk to me and a bunch of PhD students on Speed. Very senior people in very serious organisations are using it for proof of concepts and your projects.
TLDR: no individual thing it does is truly revolutionary except maybe the maths from my perspective, but I find the ease and quality and speed with which it does it is revolutionary in aggregate.
That just proves the point though. That also holds true for most things in the industrial world. The gap for me stems not from it providing no value, but how it differentiates itself from everything else that achieves the same thing in its respective domain. There’s one of two categories the tech falls into:
AI making existing technologies easier to use and increase productivity.
AI inventing new tools, technologies, methods, routines and research.
The problem I have with so many people who love to talk up the AI ladder is they use 1 as a way to argue for 2. 1 has been the whole long read of technological and economic progress since humanity has existed. There’s nothing “new” about that. I’m glad in your case it’s lowered the barrier to entry for you, but I don’t see that as a strongly given “new inroad” for the tech itself.
I think that's a broadly artificial separation. In my opinion the vast majority of new tools / technologies / methods / routines / research come from some combination of:
I have observed AI doing (2) and it makes (1) and (3) considerably easier.
If my project works it will be an entirely new way of doing desktops, and I guess it was my idea not the AI's, which is maybe what you mean? But I got a lot of the techniques from another area and 90% of the design is the AI's suggestion and uses techniques I'd never heard of, so it's still more complicated. I'm quite happy for the top-level what to stay my job and leave the how to the machine, of course.
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