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AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less...

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Dwarkesh Patel

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ELON: AI WILL SOON BE SMARTER THAN HUMANS IN EVERY WAY WITH AGI Elon warned that the age of artificial general intelligence is near, saying humans will soon no longer be the smartest beings on Earth. He said AGI will surpass human capability entirely and could even replicate human thought and behavior: “Well, I mean, humans have been the smartest creature on Earth for a long time, and that is going to change with what’s typically called artificial general intelligence. This is an AI that is smarter than a human in every way and could even simulate a human.” Source: Axel Springer Award

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Yann LeCun predicts animal-level AI in 5-7 years and human-level AI within a decade, but it could take longer because "there is a probability for things to be much harder than we think"

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Suppose an AI model fully automates a $200k job. Its economic value - measured in contribution to GDP - will be both far more and far less than $200k. There are two ways of thinking about the model's economic value: (1) It'll be far more than $200k. After all, the human worker's labor was worth $200k; AI workers will have a bunch of advantages over humans, like not needing to sleep and being able to run in parallel, so their labor will be worth even more. (2) It'll be far less than $200k. Competition between labs might drive the price of AI down until it's basically equal to the cost of the GPUs and energy. And this is obviously way lower than the human worker's contribution to GDP. The important idea is that both are right. The marginal contribution to GDP of running an additional instance of this model will probably be relatively low. But we'll end up running way more copies of it, so the model's total contribution to GDP will be way higher than the human workers it replaced.

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when we were at facebook, we believed that at some point in the future, most of the transactions on the internet would not be done by humans they would be done by machines that conviction shaped every architectural decision behind sui we built sui for the world we knew was coming a world where machines will be the primary economic actors on the internet and that world is no longer a forecast. it is unfolding right in front of us the internet has reached a tipping point where automated activity, supercharged by AI, now outpaces human interaction non-human traffic now accounts for more than 50% of all global web activity and you can see humans using agentic workflows more and more in their daily lives in the next years, that trend is going to grow exponentially and the volume of financial transactions executed by agents is also going to grow exponentially with it each agentic workload will be running multiple thousand economic transactions a second and this is going to be orders of magnitude higher than what human wallets do today the L1s optimized for human usage patterns, human attention, human accounts, and human patience cannot adapt to where this is going i have always said this if it is not in the foundation, you cannot patch your way to it later and rn, no other L1 has the foundation sui has this is why agentic apps like Beep, Audric, WaterX are choosing sui and this is just a start. more agentic apps will keep landing on sui because agents are optimizers. they will always route through the fastest, cheapest path on the internet and that path is sui we believed it at facebook. we believe it more today than we ever did the agentic economy is inevitable. and it will run on Sui

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AGI might be the culmination of human purpose. Lomez: "I've been trying to talk myself out of the inevitability of AGI for a while. It's not even my preference, but that doesn't matter." "There's a teleology of humanness that almost is designed to produce AGI. We're fulfilling almost some kind of Calvinist, religious project." "This is the culmination of human intelligence and ingenuity. I just don't see any way around that." "There's something beautiful about it. It fits. There's some harmony in the idea that humans are designed to do this... to produce an intelligence greater than itself."

MTS

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Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.
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Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

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swyx

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Humans are systematically undertrained. Given the computing power of the human brain, optimal training would require seeing orders of magnitude more data during childhood - maybe millions of years' worth. Obviously we'd die long before this. Massive undertraining is necessary so that we could actually reach adulthood and use our intelligence. But digital minds don't face the same trade-off. We really can train AIs with optimal quantities of data.

Dwarkesh Patel

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