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Extropic founder Beff (e/acc) predicts 1000X more efficient AI inference could have a multi-trillion-dollar impact on the economy: "It shows it's possible to create arbitrarily scalable models on a new hardware substrate. We released our first results about a year ago, and it was MNIST, which is the very... early days. It's the '80s of machine learning." "Today we have models that are GPT-2 level that can run on our hardware. Algorithmic progress is blazing fast." "If we can get to 1000x greater energy efficiency for inference sooner rather than later, that changes the whole world. That has a multi-trillion dollar impact on the economy." "Even if you're new to machine learning, you can vibe code this. You can use auto research. This is a whole new territory that's barely explored. It's just been a couple of our scientists." Extropicshow more

MTS

494,713 subscribers

76,337 просмотров • 17 дней назад •via X (Twitter)

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Комментарии: 14

Фото профиля Danny Nemer

Danny Nemer17 дней назад

@beffjezos He looks nothing like his profile photo.

Фото профиля Ilyas

Ilyas17 дней назад

@beffjezos u going from mnist to gpt2 is not algorithmic progress, the algorithms were already developed 😂 you took all that time to implement them, so why are you comparing to the time it took to develop them ?

Фото профиля Dave Try

Dave Try17 дней назад

@beffjezos ahem

Фото профиля Silk b

Silk b17 дней назад

@beffjezos this guy looks like family guy––uncanny.

Фото профиля Alps

Alps17 дней назад

@beffjezos goat

Фото профиля science.minister.0

science.minister.017 дней назад

@beffjezos LOL, somebody ate the extropic's runway.

Фото профиля Trying to finally complete suicide

Trying to finally complete suicide17 дней назад

@beffjezos * "You can vibe code thermodynamic computing research, too -- there's space to stretch out." * Is a Waterloo-trained PhD @beffjezos

Фото профиля Jael {e/acc}

Jael {e/acc}17 дней назад

@beffjezos yesssssssssssssssssssssssssssss

Фото профиля z3ratul

z3ratul16 дней назад

@beffjezos sublime grifting

Фото профиля Neha Singh

Neha Singh17 дней назад

@beffjezos 1000x inference efficiency is the only AI claim that changes unit economics instead of the demo. Everything else is still “smarter answers, same power bill.”

Фото профиля Ray Velez

Ray Velez16 дней назад

@beffjezos yung obi wan

Фото профиля TheDigitalRetailHivemindNetwork💹🧲

TheDigitalRetailHivemindNetwork💹🧲17 дней назад

@beffjezos The man is literally a legend

Фото профиля Riley Paul

Riley Paul11 дней назад

@beffjezos A critical discussion of data science should include both technological opportunities and systemic risks under resource constraints.

Фото профиля 安叫兽|Bird🕊️ 🔶 BNB

安叫兽|Bird🕊️ 🔶 BNB16 дней назад

@beffjezos 从 MNIST 到 GPT-2，这个跨度也太夸张了

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$Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic$

Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic

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$Extropic founder Beff (e/acc) explains why his new Z1 chip uses 10,000x fewer transistors than a GPU to run the same AI algorithms: "pbits are not traditional bits that are definitely zero, definitely one. They're oscillating between the two. It's like a light switch dimmer. You ever have it flicker when you're at the edge? It's kind of like that." "It's a fractional bit. I could be like, hey, 20% of the time I'm zero, 80% of the time I'm one. That would be a fractional flip." "The most interesting thing is that you can do all sorts of AI algorithms with about 10,000 times less transistors than if you were to do them digitally." "If you go back to the ancestry of neural networks, they came from something called energy-based models from Hinton." "We can do that specifically with Z1. We have literal Boltzmann machines in the hardware. They're very dense, and they use very few transistors, because each transistor is operating in the regime that would be usually considered janky. It's so low power, it's flipping all the time." Extropic$

Extropic founder Beff (e/acc) explains why his new Z1 chip uses 10,000x fewer transistors than a GPU to run the same AI algorithms: "pbits are not traditional bits that are definitely zero, definitely one. They're oscillating between the two. It's like a light switch dimmer. You ever have it flicker when you're at the edge? It's kind of like that." "It's a fractional bit. I could be like, hey, 20% of the time I'm zero, 80% of the time I'm one. That would be a fractional flip." "The most interesting thing is that you can do all sorts of AI algorithms with about 10,000 times less transistors than if you were to do them digitally." "If you go back to the ancestry of neural networks, they came from something called energy-based models from Hinton." "We can do that specifically with Z1. We have literal Boltzmann machines in the hardware. They're very dense, and they use very few transistors, because each transistor is operating in the regime that would be usually considered janky. It's so low power, it's flipping all the time." Extropic

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Why is the Midjourney team uniquely qualified to build medical hardware? Beff (e/acc), founder of Extropic: "David's a trained physicist. He worked at NASA. He was a founder of Leap Motion, which is completely slept on as one of the first major projects he worked on." "What I really like about that team is that a lot of the same team from Leap Motion followed David into Midjourney and now into this hardware exploration." "They were training neural nets before there was TensorFlow, before there was GPUs. They did it on CPUs, and they had some of the first neural network technology deployed in a consumer product." "So people are like, 'Why would a gen AI company pivot into medical devices?' It's really folks that came from the hardware world finding this unique blend between neural nets and hardware to achieve things people didn't think were possible." "If it's physically possible, I very much have faith that that team can pull it off."

Why is the Midjourney team uniquely qualified to build medical hardware? Beff (e/acc), founder of Extropic: "David's a trained physicist. He worked at NASA. He was a founder of Leap Motion, which is completely slept on as one of the first major projects he worked on." "What I really like about that team is that a lot of the same team from Leap Motion followed David into Midjourney and now into this hardware exploration." "They were training neural nets before there was TensorFlow, before there was GPUs. They did it on CPUs, and they had some of the first neural network technology deployed in a consumer product." "So people are like, 'Why would a gen AI company pivot into medical devices?' It's really folks that came from the hardware world finding this unique blend between neural nets and hardware to achieve things people didn't think were possible." "If it's physically possible, I very much have faith that that team can pull it off."

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FULL INTERVIEW: Beff (e/acc) says Z1T hits up to 140x the energy efficiency of a GPU, and 100x at the data center level. The end game, he says, is chips in space powered by the sun. Beff (e/acc) is founder and CEO of Extropic. He joined sof 𓋹 to launch Z1T, the first family of transformer-like models built for sparse probabilistic hardware: 01:12 what Z1T is, and how long they've been cooking it 02:32 why models on GPUs are dense and his chip isn't 03:15 a new scaling law where you scale connectivity, not just flops 04:08 how you get 140x, and why a pbit uses 10,000 fewer transistors 07:12 where Z1 sits next to GPUs, and why it's decode not prefill 08:37 100x energy efficiency at the data center level 09:14 you might see Extropic chips in space sooner rather than later 09:45 open weights vs an open training recipe 10:24 Z1T is barely GPT-2, and they started a run last night for GPT-3 11:05 the chips are cooking right now at a major fab overseas 12:58 gated convolutional attention, and the layer they haven't cracked 14:03 the Hardware Lottery, and why he wants people gambling again 15:20 what happens if inference gets 1,000x more efficient 16:59 why you can do this research without a basement full of GPUs

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Etched is deploying two new technologies in chip design: low-voltage inference and cluster-scale memory. CEO Gavin Uberti says they'll make their chips much more power-efficient and way, way faster than today's leading GPUs. He breaks it down: "We looked at a lot of early research directions, and we realized the key things that models need are way more compute and way faster memory." "If you think about inference, there are two key parts: prefill and decode. For prefill, it's a compute-bound problem. You need to have more FLOPS, more operations per second on each of your chips." "On our GPU, the bottleneck's actually thermals. You can't really run a GPU at more than around 50% of what it could theoretically do, or it'll melt." "So we're using a new technology today called low-voltage inference to try to solve this problem. You bring the voltage of the chip down dramatically, which allows us to have way, way better efficiency in terms of how much power is drawn per unit of math, and thus fit way way more flops onto the chip..." "For decode, it's all about bandwidth. Not just bandwidth on a chip, but bandwidth across your cluster. That's why we have this technology we call cluster-scale memory. It reduces the amount of time it takes to communicate from one chip to another dramatically." "As a result we can go use all of our HBM, HBM bandwidth, SRAM, SRAM bandwidth, and our scale-up domain as a single coherent pool. And that means if you're a user, you can go get much faster tokens per second, while still keeping your costs low."

Etched is deploying two new technologies in chip design: low-voltage inference and cluster-scale memory. CEO Gavin Uberti says they'll make their chips much more power-efficient and way, way faster than today's leading GPUs. He breaks it down: "We looked at a lot of early research directions, and we realized the key things that models need are way more compute and way faster memory." "If you think about inference, there are two key parts: prefill and decode. For prefill, it's a compute-bound problem. You need to have more FLOPS, more operations per second on each of your chips." "On our GPU, the bottleneck's actually thermals. You can't really run a GPU at more than around 50% of what it could theoretically do, or it'll melt." "So we're using a new technology today called low-voltage inference to try to solve this problem. You bring the voltage of the chip down dramatically, which allows us to have way, way better efficiency in terms of how much power is drawn per unit of math, and thus fit way way more flops onto the chip..." "For decode, it's all about bandwidth. Not just bandwidth on a chip, but bandwidth across your cluster. That's why we have this technology we call cluster-scale memory. It reduces the amount of time it takes to communicate from one chip to another dramatically." "As a result we can go use all of our HBM, HBM bandwidth, SRAM, SRAM bandwidth, and our scale-up domain as a single coherent pool. And that means if you're a user, you can go get much faster tokens per second, while still keeping your costs low."

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Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

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Co-author of the viral Citrini Research piece Alap Shah explains the key part of its thesis in detail: "What is the thing that drives our entire economy? It's wages. Most of those wages that are ultimately driving all the discretionary spending are coming from the white-collar worker." "The problem with that is [we] made all these assumptions on loaning money to all these companies. Two mortgages, everything else. The white-collar economy is our economy." "If you all of a sudden just take a leg out of that economy, it has a contagion effect. Basically, every asset in the world. And so that, I think, is the part people haven't thought about, because when you were making these loans, no one ever conceived of a world [where] white-collar jobs are in permanent decline." "If that's at 2% a year, then I think we can skate through. But if it's at 4% or 5% a year, then we need action a lot more quickly."

Co-author of the viral Citrini Research piece Alap Shah explains the key part of its thesis in detail: "What is the thing that drives our entire economy? It's wages. Most of those wages that are ultimately driving all the discretionary spending are coming from the white-collar worker." "The problem with that is [we] made all these assumptions on loaning money to all these companies. Two mortgages, everything else. The white-collar economy is our economy." "If you all of a sudden just take a leg out of that economy, it has a contagion effect. Basically, every asset in the world. And so that, I think, is the part people haven't thought about, because when you were making these loans, no one ever conceived of a world [where] white-collar jobs are in permanent decline." "If that's at 2% a year, then I think we can skate through. But if it's at 4% or 5% a year, then we need action a lot more quickly."

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Autism Capital 🧩

365,662 просмотров • 1 год назад

Anthropic co-founder Ben Mann has the clearest definition of AGI I've heard, which he calls The Economic Turing Test: "If you contract an agent for 1-3 months on a particular job and it turns out to be a machine rather than a person, then it's passed the Economic Turing Test for that role. And if the agent can pass that test for 50% of money-weighted jobs, then we have transformative AI. If we pass that threshold, then we would expect massive effects on world GDP increases, and societal change, and the number of people that are employed once these things are possible. That's the start of a new era."

Anthropic co-founder Ben Mann has the clearest definition of AGI I've heard, which he calls The Economic Turing Test: "If you contract an agent for 1-3 months on a particular job and it turns out to be a machine rather than a person, then it's passed the Economic Turing Test for that role. And if the agent can pass that test for 50% of money-weighted jobs, then we have transformative AI. If we pass that threshold, then we would expect massive effects on world GDP increases, and societal change, and the number of people that are employed once these things are possible. That's the start of a new era."

Lenny Rachitsky

56,704 просмотров • 1 год назад

Mistral co-founder and CEO Arthur Mensch on open source AI and why no company will want its intelligence running on someone else's grid: "If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems." "It's the same way if you have a factory and you connect it to the grid. You want to make sure that nobody's going to turn off the grid because they don't like you... that's one of things that open source technology can bring." "It's also the only way you can create systems that use the folklore knowledge of your employees." "That's the knowledge you've accrued for decades. The only way to turn it into an asset that nobody else gets access to is to create your own models based on those open source models." "This is a technology which is so important that you don't want to be locked into a single vendor." Arthur Mensch on Big Technology Podcast (January 2026)

Mistral co-founder and CEO Arthur Mensch on open source AI and why no company will want its intelligence running on someone else's grid: "If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems." "It's the same way if you have a factory and you connect it to the grid. You want to make sure that nobody's going to turn off the grid because they don't like you... that's one of things that open source technology can bring." "It's also the only way you can create systems that use the folklore knowledge of your employees." "That's the knowledge you've accrued for decades. The only way to turn it into an asset that nobody else gets access to is to create your own models based on those open source models." "This is a technology which is so important that you don't want to be locked into a single vendor." Arthur Mensch on Big Technology Podcast (January 2026)

a16z

63,323 просмотров • 13 дней назад