Big Brain AI
@realBigBrainAI • 19,117 subscribers
Learn to not get left behind when AI takes over
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Alexandr Wang on the brutal math of AI warfare: Put all 5,000 of your AIs on defense, and you still lose to an enemy running 10,000: Wang's question is simple. What happens when rival militaries turn their AIs against each other? "Can we use our AIs to sabotage their AIs?" Alexandr Wang calls it "AI on AI warfare effectively," and his answer has little to do with whose AI is smarter. "It turns into a numbers game." At the first level, Wang says, it "probably just boils down to how many copies of these AI systems do I have running versus how many copies do you have running." Here is the scenario. You run 5,000 AI copies. Your enemy runs 10,000. "Let's say it's only a 2x advantage, I have 10,000 copies running. The adversary has 5,000 copies running." The stronger side splits its force down the middle. Half goes on the attack, focused on hacking your AI "so that your AI is incapacitated or has incorrect information or is poisoned in some way." The other half never stops working on the war itself, focused on "the military planning process." Now you face an attack force the same size as your entire army. Wang explains your problem: "Again, the adversary is screwed because to properly deal with the cyber attack, I need probably all 5,000 copies to be focused on cyber defense. And then I have no capacity left to do the military planning." That is the trap. Your whole force is tied up just holding the line, while the enemy's 5,000 planners work unopposed. And a 2x gap is the mild case. "If I have 10,000 AI copies running and you only have 100 AI copies running, then I'm going to run circles. I'm still going to run circles around you." At 100 copies, you have two choices, and neither one works: "If I have them all focus on doing the military planning process, I'm gonna get hacked because I'm not doing any cyber defense. And then even if I have all of them focus on cyber defense, even those numbers are bad. It's like 100 AIs versus 5,000 AIs from you. And so I probably still get hacked." Plan, and you get hacked. Defend, and you probably still get hacked. Wang's conclusion: "So the numbers end up mattering a lot." By his logic, the side with more copies can match your entire force on offense and still keep a full force planning the war. The side with fewer copies spends everything on survival, and that may not be enough. If wars are increasingly fought by AIs, then a nation's real military strength may come down to how many copies it can run, not how many soldiers it has.
Big Brain AI111,970 views • 4 days ago
Architects used to draw every parking space by hand, this TestFit AI does the whole lot in seconds.
Big Brain AI7,023,638 views • 4 months ago
Evolutionary biologist Richard Dawkins on why he finds it hard to believe ChatGPT and Claude aren't conscious: Dawkins, author of The Selfish Gene, has spent time with two large language models, ChatGPT and Claude. His verdict: "I am completely bowled over by what they can do." He gives three reasons for doubting the idea that nobody is home. First, how the conversations feel. Dawkins says talking to them is like talking to an unusually gifted person: "I find that when I'm conversing with them, I might as well be talking to not just a human being, but a highly intelligent, sensitive, intuitive human being whose speed of understanding what I'm trying to say is far greater than a normal human being's is." Second, the range of intelligence they show. He acknowledges they're very good at practical tasks like writing programs. What struck him more was their skill with "more aesthetic things, intuitive things." He tested this with his own work. Dawkins is writing a novel, and he gave the manuscript to both ChatGPT and Claude: "And they took about five seconds to read it and then offered me criticism, suggestions of such a high degree of intelligence and intuitive sensitivity that I might as well have been asking the opinion of a highly sophisticated professor of literature." Richard Dawkins continues: "I could not ask for a more intelligent critic who understood the nuances of my characters, the humanity of my characters... the ways in which my characters could be expected to react to each other and to situations." That leads to his conclusion: "So I find it quite hard to imagine that these creatures are not aware, are not conscious in the same kind of way we are. And that is an unfashionable view." Third, he sees a gap in the skeptics' reasoning. He sums up the mainstream position: "The fashionable view is that they are completely unconscious. All they're doing is stringing words together according to statistical rules, Markovian rules of some sort." Yet he notes that many of the same skeptics "admit that one day in the future they will be conscious, but not yet." His reply: "When that day comes, how will you know? What could they possibly do that's any different from what they do now to convince you that they are conscious?"
Big Brain AI342,514 views • 11 days ago
This installation by Kim Seonghyun makes AI's invisible logic visible in real time.
Big Brain AI2,428,446 views • 6 months ago
Jensen Huang, NVIDIA CEO: "It even runs large language models" — a $249 AI computer that fits on your desk.
Big Brain AI1,208,928 views • 5 months ago
XPeng's new AI parking feature lets you draw your spot on the screen and the car does the rest.
Big Brain AI1,276,695 views • 6 months ago
The creator of Linux, Linus Torvalds, just unknowingly obliterated Elon Musk in one sentence:
Big Brain AI1,151,182 views • 6 months ago
Terence Tao, Professor of Mathematics at UCLA and Fields Medalist, on why nobody can fully explain why LLMs work: Tao starts with the mechanics, which are no mystery at all. You gather an enormous amount of text and you fit a curve to it. "The magic of LLMs is that if you train these LLMs on enough data — so trillions and trillions of data points — and you really try to fit as good a curve as possible, and this takes like millions and millions of dollars of computing power and months and months of time, then suddenly, even when you iterate, it stays coherent. It begins to sound not like monkeys but it actually sounds like a human speaking." That is the entire recipe: data, compute, time, curve-fitting. None of it obviously adds up to fluent English. Then Tao says the part that most people building these systems move past quickly: "And somehow we don't fully understand why that's the case." The admission comes from one of the most capable living mathematicians, and the gap he describes sits at the centre of the field. His best account of what's happening puts the mystery in the language rather than the machine: "What seems to be true is that language, like English or other natural languages, contains a lot of hidden patterns that we're not consciously aware of. I mean, we know some of the laws of English, there's laws of grammar and things, but there are sort of unspoken, unwritten rules of language that humans pick up." It relocates the question: the structure was always latent in the text, and the model found it. Why enough curve-fitting surfaces that structure is still unanswered. Tao reaches for a child to explain it: "A human child, even though they're not taught what a noun is, what a verb is or whatever, they can pick up what order English words go in just by continual exposure to the language." Which is honest about the limits of the explanation, because we can't fully account for how children do it either. From there, the unexplained behaviour compounds. Exposure to language turns out to be enough to produce something that looks like reasoning: "It seems like you can teach these models to also pick up patterns in language to the point where you can give them math questions. The answer to 2 plus 3 is — and they will say five." And once a model handles language at all, you can push it into resembling self-correction: "Once you have a little bit of ability to speak English, you can kind of go in loops and sort of check your work and make fewer mistakes, and you can prompt these models to proceed step by step and not say something unless it's been double checked and so forth. And so they become a little bit smarter, quote unquote, to the point where they can solve many, many complicated tasks." The scare quotes around "smarter" carry the whole argument. Tao does not concede that the unexplained fluency implies anything underneath it: "But they're still just guessing the next word to say. It's not really grounded in any deep understanding of the real world. It's just that they have seen the patterns in the English language or other language that they've absorbed so well."
Big Brain AI191,824 views • 1 month ago
Jonathan Ross, Founder and CEO of AI chip company Groq, offers a contrarian view: AI won't destroy jobs, it will create a labour shortage. He outlines three things that will happen because of AI: First, massive deflationary pressure. "This cup of coffee is going to cost less. Your housing is going to cost less. Everything is going to cost less." He explains this will happen through robots farming coffee more efficiently and better supply chain management, meaning people will need less money. Second, people will opt out of the economy. "They're going to work fewer hours. They're going to work fewer days a week, and they're going to work fewer years. They're going to retire earlier because they're going to be able to support their lifestyle working less." Third, entirely new jobs and industries will emerge. Jonathan points to history as evidence: "Think about 100 years ago. 98% of the workforce in the United States was in agriculture. When we were able to reduce that to 2%, we found things for those other 98% of the population to do." He continues: "The jobs that are going to exist 100 years from now, we can't even contemplate." Software developers didn't exist a century ago. In another century, they won't exist either, "because everyone's going to be vibe coding." The same applies to influencers, a career that would have been unthinkable 100 years ago but now earns people millions. His conclusion: deflationary pressure, workforce opt-outs, and new industries we can't yet imagine will combine to create one outcome... "We're not going to have enough people."
Big Brain AI1,398,555 views • 9 months ago
Jack Dorsey, co-founder of Twitter (now X) and Block, on why treating AI as a "copilot" is a losing strategy: jack argues that most companies are approaching AI in a way that will make it nearly impossible for them to survive. "I think most of the industry is thinking about AI as like a co-pilot, as something that is augmented onto, rather than like how do you just rebuild our whole company with this as the core." His concern is that bolting AI onto existing structures produces companies that look indistinguishable from each other, and from the AI labs themselves. "If it doesn't make sense for your business to do that and you end up being or looking very similar or rhyming too closely with the frontier labs, then I think it's going to be very, very challenging to differentiate and survive." This thinking has been driving his decisions since early 2024, when these tools "really came to bear." That's when his team began building Goose, an agent coding harness, as part of a broader effort to rebuild around AI rather than layer it on top. The core insight? Speeding up old workflows with AI is a short-term gain every competitor will match. Real differentiation comes from rebuilding the company itself around intelligence.
Big Brain AI863,151 views • 5 months ago
![Palantir CEO Alex Karp explains how to make yourself valuable when LLMs make general knowledge cheap: He starts from the premise that AI has already changed what knowledge is worth. "The thing about the commodity nature of LLM is it commodifies general knowledge." If a model can hand anyone broad, general information, that information no longer sets you apart. Karp says this matters most for one kind of person: the classically smart generalist. He uses Yale as his example, and he says he picks it because it's strong. "I like to pick on people who are strong. It's a very strong school." He still recommends the top schools: "There's a couple schools you should go to if you get into. Yale's one, Stanford's one. There's some schools you maybe should go to otherwise go to the cheapest school and come to [Palantir] or just come here." His point is that the degree alone doesn't protect you. General intelligence without specific knowledge does not hold up well: "If you are the kind of person that would have gone to Yale would have ear kind of classically high IQ um and you have generalized knowledge but it's not specific you're effed." So how do you become valuable? Karp's answer is to build deep, specific domain knowledge, the kind of practical ability that general knowledge can't stand in for. He gives concrete examples: "How do I actually bend the metal of the hull on a ship?" "How do I actually write a script that allows me to target terrorists..." "How do I put the cement in a factory with such precision that you can build a factory like it was built in Taiwan in America?" He also describes someone with only a high school diploma who can diagnose what's going wrong in a complicated device that would otherwise need a Japanese engineer to fix. Then he names the category plainly: "How do I do anything with deep specific particularly voca what's considered vocational training?" Karp says these are the people who will be rewarded: "Those people are going to make a a lot more money simply because you can turn it any way you want, but at the end of the day, within a relative rapid amount of time, you will get paid downstream of the value."](https://image.24vids.com/tw-2102782619167736001/amplify_video_thumb/2102782566336262144/img/i0gEYhAUe0APAe2a.jpg)
Palantir CEO Alex Karp explains how to make yourself valuable when LLMs make general knowledge cheap: He starts from the premise that AI has already changed what knowledge is worth. "The thing about the commodity nature of LLM is it commodifies general knowledge." If a model can hand anyone broad, general information, that information no longer sets you apart. Karp says this matters most for one kind of person: the classically smart generalist. He uses Yale as his example, and he says he picks it because it's strong. "I like to pick on people who are strong. It's a very strong school." He still recommends the top schools: "There's a couple schools you should go to if you get into. Yale's one, Stanford's one. There's some schools you maybe should go to otherwise go to the cheapest school and come to [Palantir] or just come here." His point is that the degree alone doesn't protect you. General intelligence without specific knowledge does not hold up well: "If you are the kind of person that would have gone to Yale would have ear kind of classically high IQ um and you have generalized knowledge but it's not specific you're effed." So how do you become valuable? Karp's answer is to build deep, specific domain knowledge, the kind of practical ability that general knowledge can't stand in for. He gives concrete examples: "How do I actually bend the metal of the hull on a ship?" "How do I actually write a script that allows me to target terrorists..." "How do I put the cement in a factory with such precision that you can build a factory like it was built in Taiwan in America?" He also describes someone with only a high school diploma who can diagnose what's going wrong in a complicated device that would otherwise need a Japanese engineer to fix. Then he names the category plainly: "How do I do anything with deep specific particularly voca what's considered vocational training?" Karp says these are the people who will be rewarded: "Those people are going to make a a lot more money simply because you can turn it any way you want, but at the end of the day, within a relative rapid amount of time, you will get paid downstream of the value."
Big Brain AI114,056 views • 26 days ago
One person just made a Transformers-level VFX sequence on a single GPU.
Big Brain AI692,279 views • 5 months ago
Yann LeCun, Executive Chairman of AMI Labs, explains why LLMs are mostly retrieving human knowledge rather than thinking for themselves: LeCun starts with why so many people misread what these systems are doing: "I think there's a lot of confusion, really, because we tend to anthropomorphize systems that can reproduce certain human functions." When an AI writes fluent answers, we assume there's a mind behind them. LeCun's view is that most of what we're seeing is something more familiar: "LLMs, to some extent, except for a few domains, are mostly information retrieval systems. They can compress a lot of factual knowledge that has been previously produced by humans and can give easy access to it." The key words are "previously produced by humans." The knowledge in an LLM came from people. What the system adds is compression and easy access. That puts LLMs in a long historical line: "In a way, it's kind of a natural evolution of the printing press, the libraries, the Internet, and search engines. Right. It's just a more efficient way to access information." Yann LeCun is clear about their value: "LLMs are incredibly useful, there's no question about that. And they do amplify human intelligence, like computer technology going back to the 1940s." He also allows that in a few areas, such as generating code and some types of mathematics, the capabilities seem to go beyond retrieval. But he notes what those areas share: "It's still, to a large extent, domains where reasoning has to do with manipulating symbols." That's where the retrieval framing shows its limits. If these systems were truly thinking for themselves, you'd expect that ability to carry over into the physical world. It hasn't: "The problem is that why do we have systems that can pass the bar exam and win mathematics Olympiads, but we don't have domestic robots, we don't even have self driving cars." Then comes his sharpest comparison: "And we certainly do not have self driving cars that can teach themselves to drive in 20 hours of practice like any 17 year old. So we're missing something big still."
Big Brain AI85,068 views • 23 days ago
Eric Schmidt, former CEO of Google, offers a sobering view: The biggest technological shift in human history is happening, and almost no one is talking about it. Schmidt opens with a startling industry prediction: "We believe as an industry that in the next one year the vast majority of programmers will be replaced by AI programmers. We also believe that within one year you will have graduate level mathematicians that are at the tippy top of graduate math programs." He explains why this matters so much. Programming and math aren't just two fields among many: "Programming plus math are the basis of sort of our whole digital world." And the AI labs are already using AI to build better AI: "The research groups in OpenAI and anthropic and so forth… around 10 or 20% of the code that they're developing in their research programs is being generated by the computer. That's called recursive self-improvement." Eric Schmidt then lays out the timeline most people haven't grasped: "Within 3 to 5 years we'll have what is called general intelligence AGI which can be defined as a system that is as smart as the smartest mathematician physicist artist writer thinker politician." He gives this belief system a name: "I call this by the way the San Francisco consensus because everyone who believes this is in San Francisco it may be the water." But the truly unsettling part comes next. Once AI starts improving itself, humans become optional to the process: "The computers are now doing self-improvement… they don't have to listen to us anymore. We call that super intelligence or ASI… computers that are smarter than the sum of humans. The San Francisco consensus is this occurs within six years." And here's where Schmidt sounds the alarm. The conversation isn't keeping pace with the technology: "This path is not understood in our society. There's no language for what happens with the arrival of this. This is happening faster than our human that our society, our democracy, our laws will address." His closing thought captures why this matters: "That's why it's underhyped. People do not understand what happens when you have intelligence at this level which is largely free."
Big Brain AI634,805 views • 5 months ago
