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Marc Andreessen explains why we are only three years into what is effectively an 80-year technological revolution: He opens with a blunt assessment: "This is the biggest technological revolution of my life. This is clearly bigger than the internet. The comps on this are things like the microprocessor and... the steam engine and electricity." But to understand why, you have to go back 80 years. In the 1930s, the pioneers of computing understood the theory of computation before they'd even built the machines. And they faced a fundamental choice. Build computers in the image of the adding machine — hyper-literal, mathematical, capable of billions of operations per second, but unable to understand human speech or deal with humans the way humans like to be dealt with. Or build computers modelled on the human brain. Neural networks. They chose the adding machine. And that single decision shaped everything — mainframes, PCs, smartphones, every dollar of wealth the computer industry created over the next 80 years. IBM itself is the successor company to the National Cash Register Company of America. The lineage runs that deep. But here's what makes this moment so extraordinary. They knew about the other path. The first neural network academic paper was published in 1943. Marc points to a remarkable piece of forgotten history: "There's an interview you can watch on YouTube with the authors. It's him in his beach house, not wearing a shirt, talking about this future in which computers are going to be built on the model of the human brain." That was 1946. The vision existed. The path just wasn't taken. So neural networks spent the next eight decades living in the shadows. Kept alive by a small academic movement — first called cybernetics, then artificial intelligence — that refused to let the idea die. And for most of that time, it simply didn't work. "It was basically decade after decade after decade of excessive optimism followed by disappointment." By the time Marc reached college in 1989, AI was a backwater field. Everyone assumed it was never going to happen. But the scientists kept working. Quietly building up an enormous reservoir of concepts and ideas across those decades of disappointment. And then Christmas 2022 arrived. ChatGPT. And suddenly: "All of a sudden it's like: oh my god. It turns out it works." That moment wasn't the start of something new. It was the payoff on an 80-year-old bet that almost everyone had written off. Which is exactly why Marc's framing matters so much: "We're three years into what is effectively an 80-year revolution." Most people are treating AI like another technology cycle — something to adapt to, ride, and wait out. But if Andreessen is right, we are not adapting to a new cycle. We are standing at the very beginning of the longest and most consequential technological transformation in human history. The road not taken in the 1930s is finally being built. And we have barely broken ground.show more

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Marc Andreessen on where AI sits in the arc of technology: “This is the biggest technological revolution of my life.” “This is clearly bigger than the internet.” “The comps on this are things like the microprocessor and the steam engine and electricity… the wheel.” Marc Andreessen 🇺🇸

Marc Andreessen on where AI sits in the arc of technology: “This is the biggest technological revolution of my life.” “This is clearly bigger than the internet.” “The comps on this are things like the microprocessor and the steam engine and electricity… the wheel.” Marc Andreessen 🇺🇸

a16z

85,787 просмотров • 6 месяцев назад

Marc Andreessen: AI is an "80 year overnight success." "Something about AI causes the people in the field to become both excessively utopian and excessively apocalyptic." "What's actually happened is—in retrospect, an enormous amount of technical progress built up over time." "For example, we now know that the neural network is the correct architecture. There was a 60 or 70 year run where that was controversial." "Everything we're building on today sort of derives from the original idea in 1943. In retrospect, we now know that these guys were right." "They would get the timing wrong and they thought capabilities would arrive faster, or that they could be turned into businesses sooner, but the scientists who worked on this over the course of decades were fundamentally correct about what they were doing." "And the payoff from all their work is happening now." "ChatGPT hits, and then o1 hits, and then OpenClaw hits, and these are radical, overnight transformative successes—but they're drawing on an 80 year wellspring backlog of ideas and thinking. It's not just all brand new, it's that it's an unlock of all of these decades of very serious, hardcore research." "There were AI researchers who spent their entire lives—got their PhD, researched for 40 years, retired in a lot of cases, passed away, and they never actually saw it work." Marc Andreessen 🇺🇸 with Latent.Space

a16z

47,852 просмотров • 3 месяцев назад

Marc Andreessen just explained why being right about AI for 80 straight years is about to be the most dangerous position in technology. Andreessen: “The four most dangerous words in investing are ‘this time is different.’” He’s talking about AI. And he’s about to turn that phrase on the people hiding behind it. Four times in 80 years, AI promised to change everything. Four times it collapsed. 1943.First neural network. Dead within a decade. 1944.Dartmouth. Scientists thought they’d crack AGI in one summer. They didn’t crack it in forty years. 1980s. Over a billion into expert systems. Entire market gone by ’87. 2016.Machine learning. Faded before anyone could ship a product. The skeptics weren’t lucky. They were 4-for-4. Every generation that believed “this time is different” got buried. And that is exactly why this moment is so dangerous. Because being right four consecutive times doesn’t just build a position. It builds an identity. And identity doesn’t update when the evidence does. Andreessen: “I’ll tell you what’s different. Like, now it’s working.” Not one breakthrough. Four. In the same window. Language. Reasoning. Coding. Self-improvement. All deployed. All producing revenue. Not in a lab. In the economy. Today. Then the line that should have ended every remaining debate. Andreessen: “If Linus Torvalds is saying that the AI coding is now better than he is… that’s never happened before.” The man who built the operating system the internet runs on just conceded the machine writes better code than he does. Coding is the highest bar in technology. If AI clears it, everything below was already decided. But the fourth breakthrough isn’t like the other three. Language, reasoning, and coding are capabilities. Self-improvement is a rate of change. The machine is researching, coding, and optimizing itself. No human engineers in the loop. Every technology in human history advanced at the speed of the people building it. This one just left that constraint behind. And the hardware confirms it. Nvidia’s old chips are gaining value after shipping. GPUs sold out years ahead. That has never happened in computing. Hardware doesn’t appreciate. Unless the market has decided this isn’t a cycle. It’s infrastructure. Andreessen: “This is the culmination of 80 years worth of work and this is the time it’s becoming real.” Eighty years. Researchers poured entire careers into this problem. Some of them died before it worked. And now all four pieces arrived at once. The skeptics built a perfect model from eight decades of collapse. Flawless pattern recognition. But a perfect model trained on a world that no longer exists doesn’t protect you. It traps you inside the last version of reality. For 80 years, doubting AI was the most rational position a human being could hold. It just became the most expensive.

Marc Andreessen just explained why being right about AI for 80 straight years is about to be the most dangerous position in technology. Andreessen: “The four most dangerous words in investing are ‘this time is different.’” He’s talking about AI. And he’s about to turn that phrase on the people hiding behind it. Four times in 80 years, AI promised to change everything. Four times it collapsed. 1943.First neural network. Dead within a decade. 1944.Dartmouth. Scientists thought they’d crack AGI in one summer. They didn’t crack it in forty years. 1980s. Over a billion into expert systems. Entire market gone by ’87. 2016.Machine learning. Faded before anyone could ship a product. The skeptics weren’t lucky. They were 4-for-4. Every generation that believed “this time is different” got buried. And that is exactly why this moment is so dangerous. Because being right four consecutive times doesn’t just build a position. It builds an identity. And identity doesn’t update when the evidence does. Andreessen: “I’ll tell you what’s different. Like, now it’s working.” Not one breakthrough. Four. In the same window. Language. Reasoning. Coding. Self-improvement. All deployed. All producing revenue. Not in a lab. In the economy. Today. Then the line that should have ended every remaining debate. Andreessen: “If Linus Torvalds is saying that the AI coding is now better than he is… that’s never happened before.” The man who built the operating system the internet runs on just conceded the machine writes better code than he does. Coding is the highest bar in technology. If AI clears it, everything below was already decided. But the fourth breakthrough isn’t like the other three. Language, reasoning, and coding are capabilities. Self-improvement is a rate of change. The machine is researching, coding, and optimizing itself. No human engineers in the loop. Every technology in human history advanced at the speed of the people building it. This one just left that constraint behind. And the hardware confirms it. Nvidia’s old chips are gaining value after shipping. GPUs sold out years ahead. That has never happened in computing. Hardware doesn’t appreciate. Unless the market has decided this isn’t a cycle. It’s infrastructure. Andreessen: “This is the culmination of 80 years worth of work and this is the time it’s becoming real.” Eighty years. Researchers poured entire careers into this problem. Some of them died before it worked. And now all four pieces arrived at once. The skeptics built a perfect model from eight decades of collapse. Flawless pattern recognition. But a perfect model trained on a world that no longer exists doesn’t protect you. It traps you inside the last version of reality. For 80 years, doubting AI was the most rational position a human being could hold. It just became the most expensive.

Dustin

13,381 просмотров • 6 дней назад

Marc Andreessen just incinerated 80 years of skepticism in under three minutes. Andreessen: “The four most dangerous words in investing are ‘this time is different.’” He’s talking about AI. And he’s about to detonate that phrase from the inside. For eight decades, artificial intelligence followed the same brutal script. Hype builds. Money floods in. The technology fails to deliver. The entire field collapses. In 1943, the first neural network paper proved mathematical models of brain cells could perform any logical computation. Revolutionary on paper. Dead on arrival. In 1956, scientists secured a grant to spend ten weeks at Dartmouth believing they could build AGI by the end of summer. They did not. In the 1980s, corporations poured over a billion dollars into “expert systems” designed to replicate human decision-making. By 1987, the entire market cratered. In 2016, machine learning hype surged again. Faded within months. Four waves. Four collapses. Same result every single time. The skeptics aren’t guessing. History is entirely on their side. Every generation of investors who believed “this time is different” got buried. But Andreessen isn’t finished. Andreessen: “I’ll tell you what’s different. Like, now it’s working.” That sentence sounds simple. It is the most consequential sentence in technology right now. AI didn’t make one breakthrough. It made four. Back to back. In the same window. Language models. Reasoning. Coding agents. Self-improvement. Every single one generating revenue in production right now. Not a demo. Not a research paper. Not a promise. Deployed infrastructure producing billions in real output today. Then the Linus Torvalds moment. Andreessen: “If Linus Torvalds is saying that the AI coding is now better than he is… that’s never happened before. And so now we know that it’s going to sweep through coding.” The man who built the operating system running most of the internet just admitted the machine writes better code than he does. That is not a marketing claim. That is surrender. And coding is the hardest test case. If AI can outperform the best human coder alive, everything downstream is already decided. Medicine. Law. Finance. Engineering. All derivatives. But the part that should unsettle you is the fourth breakthrough. Recursive self-improvement. The machine is no longer waiting for human engineers to upgrade it. It is researching, coding, and optimizing itself. The pace of improvement just decoupled from human biology entirely. And the hardware market is confirming something that has never happened in computing. Hardware is supposed to lose value the second it ships. Nvidia’s old chips are gaining value. Used silicon is appreciating like waterfront property. Every GPU on earth is sold out for the next three to four years. That is not a bubble. That is a structural shift in what the world physically needs to function. Andreessen: “I’m jumping out of my shoes. Like this is it. Like this is the culmination of 80 years worth of work and this is the time it’s becoming real.” Eighty years. Researchers spent entire careers chasing this problem and died before seeing it solved. Generations of scientists poured their finite lives into a theory that kept collapsing under its own promise. And now all four pieces arrived at once. The people calling this a bubble are running the same playbook that was correct every other time. But every other time, the technology wasn’t working. This time it is. For eighty years, “this time is different” buried everyone who believed it. This is the first time it buries everyone who doesn’t.

Marc Andreessen just incinerated 80 years of skepticism in under three minutes. Andreessen: “The four most dangerous words in investing are ‘this time is different.’” He’s talking about AI. And he’s about to detonate that phrase from the inside. For eight decades, artificial intelligence followed the same brutal script. Hype builds. Money floods in. The technology fails to deliver. The entire field collapses. In 1943, the first neural network paper proved mathematical models of brain cells could perform any logical computation. Revolutionary on paper. Dead on arrival. In 1956, scientists secured a grant to spend ten weeks at Dartmouth believing they could build AGI by the end of summer. They did not. In the 1980s, corporations poured over a billion dollars into “expert systems” designed to replicate human decision-making. By 1987, the entire market cratered. In 2016, machine learning hype surged again. Faded within months. Four waves. Four collapses. Same result every single time. The skeptics aren’t guessing. History is entirely on their side. Every generation of investors who believed “this time is different” got buried. But Andreessen isn’t finished. Andreessen: “I’ll tell you what’s different. Like, now it’s working.” That sentence sounds simple. It is the most consequential sentence in technology right now. AI didn’t make one breakthrough. It made four. Back to back. In the same window. Language models. Reasoning. Coding agents. Self-improvement. Every single one generating revenue in production right now. Not a demo. Not a research paper. Not a promise. Deployed infrastructure producing billions in real output today. Then the Linus Torvalds moment. Andreessen: “If Linus Torvalds is saying that the AI coding is now better than he is… that’s never happened before. And so now we know that it’s going to sweep through coding.” The man who built the operating system running most of the internet just admitted the machine writes better code than he does. That is not a marketing claim. That is surrender. And coding is the hardest test case. If AI can outperform the best human coder alive, everything downstream is already decided. Medicine. Law. Finance. Engineering. All derivatives. But the part that should unsettle you is the fourth breakthrough. Recursive self-improvement. The machine is no longer waiting for human engineers to upgrade it. It is researching, coding, and optimizing itself. The pace of improvement just decoupled from human biology entirely. And the hardware market is confirming something that has never happened in computing. Hardware is supposed to lose value the second it ships. Nvidia’s old chips are gaining value. Used silicon is appreciating like waterfront property. Every GPU on earth is sold out for the next three to four years. That is not a bubble. That is a structural shift in what the world physically needs to function. Andreessen: “I’m jumping out of my shoes. Like this is it. Like this is the culmination of 80 years worth of work and this is the time it’s becoming real.” Eighty years. Researchers spent entire careers chasing this problem and died before seeing it solved. Generations of scientists poured their finite lives into a theory that kept collapsing under its own promise. And now all four pieces arrived at once. The people calling this a bubble are running the same playbook that was correct every other time. But every other time, the technology wasn’t working. This time it is. For eighty years, “this time is different” buried everyone who believed it. This is the first time it buries everyone who doesn’t.

Dustin

18,103 просмотров • 2 месяцев назад

Marc Andreessen says AI is hyper democratizing. “Is the future of the world going to be one or a small number of companies, or for that matter, governments or super AIs that kind of own and control everything?” “Or is this a technology that's going to diffuse out and be in everybody's hands?” “This is the time in which AI is actually hyper democratizing. It has spread to more individuals, both in the country and around the world in the shortest period of time of any new technology I think in history.” “The best AIs in the world are in the consumer products.” “This technology is going to be in everybody's hands. And everybody is going to be able to use it to optimize the things that they do, have it be a thought partner, assistant for building companies.” Marc Andreessen 🇺🇸

Marc Andreessen says AI is hyper democratizing. “Is the future of the world going to be one or a small number of companies, or for that matter, governments or super AIs that kind of own and control everything?” “Or is this a technology that's going to diffuse out and be in everybody's hands?” “This is the time in which AI is actually hyper democratizing. It has spread to more individuals, both in the country and around the world in the shortest period of time of any new technology I think in history.” “The best AIs in the world are in the consumer products.” “This technology is going to be in everybody's hands. And everybody is going to be able to use it to optimize the things that they do, have it be a thought partner, assistant for building companies.” Marc Andreessen 🇺🇸

a16z

79,935 просмотров • 8 месяцев назад

Marc Andreessen says the four most dangerous words in investing are "this time is different." But this time it's actually different. He's referring to the AI boom when he mentioned this. For 80 years, the industry has followed the same pattern. 1) Excitement builds 2) Money pours in 3) The technology fails to deliver 4) The industry collapses. Here are 4 examples of when this happened in AI: • In 1943, the original neural network paper was written. It proved that simple mathematical models of brain cells could perform any logical computation. It was a foundational breakthrough but didn't takeoff. • In 1955, scientists got a grant to spend 10 weeks at Dartmouth and believed they could achieve AGI by the end of the summer. They did not. • In the 1980s, corporations poured over a billion dollars into "expert systems," AI programs designed to replicate the decision-making of human specialists in medicine, finance, and engineering. By 1987, this entire market crashed. • In 2016-17, machine learning hype surged again. It faded quickly. Each time, the technology underdelivered and the money dried up. But here's what really different about this time. AI has made four breakthroughs back to back. 1) Language models 2) Reasoning 3) Coding agents 4) Self-improvement And ALL are generating revenue in production right now. That's why old Nvidia chips are gaining value instead of depreciating. And this has literally never happened in computing before. Every GPU is sold out for the next 3-4 years. He calls betting against this "essentially suicidal" and "an invitation to get your face ripped off." He calls it "an 80-year overnight success." Researchers worked their entire lives on this, and many passed away without seeing it work. Now it is all arriving at once. — Marc Andreessen (Marc Andreessen 🇺🇸), co-founder at a16z (a16z) on the Latent Space podcast (Latent.Space)

Marc Andreessen says the four most dangerous words in investing are "this time is different." But this time it's actually different. He's referring to the AI boom when he mentioned this. For 80 years, the industry has followed the same pattern. 1) Excitement builds 2) Money pours in 3) The technology fails to deliver 4) The industry collapses. Here are 4 examples of when this happened in AI: • In 1943, the original neural network paper was written. It proved that simple mathematical models of brain cells could perform any logical computation. It was a foundational breakthrough but didn't takeoff. • In 1955, scientists got a grant to spend 10 weeks at Dartmouth and believed they could achieve AGI by the end of the summer. They did not. • In the 1980s, corporations poured over a billion dollars into "expert systems," AI programs designed to replicate the decision-making of human specialists in medicine, finance, and engineering. By 1987, this entire market crashed. • In 2016-17, machine learning hype surged again. It faded quickly. Each time, the technology underdelivered and the money dried up. But here's what really different about this time. AI has made four breakthroughs back to back. 1) Language models 2) Reasoning 3) Coding agents 4) Self-improvement And ALL are generating revenue in production right now. That's why old Nvidia chips are gaining value instead of depreciating. And this has literally never happened in computing before. Every GPU is sold out for the next 3-4 years. He calls betting against this "essentially suicidal" and "an invitation to get your face ripped off." He calls it "an 80-year overnight success." Researchers worked their entire lives on this, and many passed away without seeing it work. Now it is all arriving at once. — Marc Andreessen (Marc Andreessen 🇺🇸), co-founder at a16z (a16z) on the Latent Space podcast (Latent.Space)

GeniusThinking

51,245 просмотров • 3 месяцев назад

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 просмотров • 1 год назад

Here is a video that challenges our understanding of human intelligence and progress. Judging by the ancient images, it seems that we are not so unique when it comes to technology. The poses of people reminiscent of using tablets and the drawings depicting car-like vehicles and aeroplanes suggest that all this existed in the past? We may not be the first to have achieved such technological progress. What if other civilisations had already travelled the same path of development and thinking that we are going through now? It is very likely that we are just a repeated link in an infinite chain. History, as we know, is often rewritten and we only have fragments of knowledge of the past. Think about it, what if all that we consider our own achievement was actually the path of ancient knowledge and technology undertaken by someone else, which we are rediscovering for ourselves?😉

Here is a video that challenges our understanding of human intelligence and progress. Judging by the ancient images, it seems that we are not so unique when it comes to technology. The poses of people reminiscent of using tablets and the drawings depicting car-like vehicles and aeroplanes suggest that all this existed in the past? We may not be the first to have achieved such technological progress. What if other civilisations had already travelled the same path of development and thinking that we are going through now? It is very likely that we are just a repeated link in an infinite chain. History, as we know, is often rewritten and we only have fragments of knowledge of the past. Think about it, what if all that we consider our own achievement was actually the path of ancient knowledge and technology undertaken by someone else, which we are rediscovering for ourselves?😉

Andy

61,959 просмотров • 1 месяц назад

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."

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 AI

633,628 просмотров • 2 месяцев назад

Emily Blunt on the plot of Disclosure Day “What can you tell us about the plot of the movie itself?” “The big idea of it is that question that sparked the fires of human imagination for years. Are we alone or are we not alone? And if we aren't, why are the people in power hiding the truth from us? And so the movie is like a life or death race against time for those that believe the truth belongs to 8 billion people and those who believe that it doesn't and they want to keep it secret.”

Emily Blunt on the plot of Disclosure Day “What can you tell us about the plot of the movie itself?” “The big idea of it is that question that sparked the fires of human imagination for years. Are we alone or are we not alone? And if we aren't, why are the people in power hiding the truth from us? And so the movie is like a life or death race against time for those that believe the truth belongs to 8 billion people and those who believe that it doesn't and they want to keep it secret.”

Red Panda Koala

195,682 просмотров • 1 месяц назад

Marc Andreessen explains that America can win the future of manufacturing by building the advanced products of the future in highly automated factories that create well-paying, high-skilled “blue-collar-plus” jobs: “What you have is a large number of jobs, that are, call them ‘blue-collar-plus jobs’, and then also white-collar jobs, and all the associated service jobs around those that are building and maintaining that new kind of plant to build that new kind of thing.” “I think what you can do is you can leap forward on the nature of the product you’re building.” “By the way, if we don’t do that, all of those things are going to get made in China.” “What an amazing story it would be for America in the 21st century, which is we reindustrialize not to build the products of the past but to build the products of the future.” Marc Andreessen 🇺🇸

Marc Andreessen explains that America can win the future of manufacturing by building the advanced products of the future in highly automated factories that create well-paying, high-skilled “blue-collar-plus” jobs: “What you have is a large number of jobs, that are, call them ‘blue-collar-plus jobs’, and then also white-collar jobs, and all the associated service jobs around those that are building and maintaining that new kind of plant to build that new kind of thing.” “I think what you can do is you can leap forward on the nature of the product you’re building.” “By the way, if we don’t do that, all of those things are going to get made in China.” “What an amazing story it would be for America in the 21st century, which is we reindustrialize not to build the products of the past but to build the products of the future.” Marc Andreessen 🇺🇸

a16z

124,115 просмотров • 7 месяцев назад

In 1968, George Land (with Beth Jarman) conducted a research study to test the creativity of 1,600 children ranging in ages from three-to-five years old. This was the same creativity test he devised for NASA to help select innovative engineers and scientists. The assessment worked so well he decided to try it on children. He then re-tested the same children at 10 years of age, and again at 15 years of age (a longitudinal study). The test was designed to indicate how well someone could look at a issue and devise new, different, innovative ways to address it. To do that, they asked children at these various ages to come up with ways to use a paperclip. The results may surprise you. •Creativity scores amongst 5-year old’s: 98% •Creativity scores amongst 10-year old’s: 30% •Creativity scores amongst 15-year old’s: 12% •Given to 280,000 adults (average age of 31): 2% The results are not as we would expect, are they? The proportion of people who scored at the “Genius Level” decreased with age. We might think that it should increase with our level of education. But clearly not. Why is this? Well, it seems we have employed a system that educates the genius right out of people. And how have we done this? The industrial revolution began in Great Britain in the mid 1700's and by 1760 in the U.S was gaining steam - literally. Steam power was the catalyst. This first industrial revolution exploded from about 1760 to 1840. It was followed by the age of science and mass production, and then the digital revolution. We are now at the beginning of the next phase of dramatic technological expansion and social change—the Fourth Industrial Revolution. Coinciding with the first industrial revolution was an educational revolution, Public Education. The system was created in the late 1600's and in the 1700's was developed into the system we still use today. It was designed to meet the challenges of the first industrial revolution. But, when that revolution gave way to the second, and third, and fourth, our educational system did not. It was devoted to the original system. Alongside the first industrial revolution, it has continued to insist on manufacturing the same student, over and over, tweaking content for new technologies. So, today's education system produces student conformity just as any good industrial manufacturing process would do with its product. But not student creativity, diversity or individual expression, which fits the needs of our day. As a result, ideas for paperclips decline and genius dries up. This is not the path to a world where AI is built into everything. It is vital to bring our children back to the path of 98% genius level as presented by Dr. Land. We can also bring back us adults back from the institutional learning of schools and universities. The reality is we simply have no choice as there is no path forward if human individuality and creativity is not asserted as our primary function. This is and always has been the path of humanity. We just need to remember to remember.

In 1968, George Land (with Beth Jarman) conducted a research study to test the creativity of 1,600 children ranging in ages from three-to-five years old. This was the same creativity test he devised for NASA to help select innovative engineers and scientists. The assessment worked so well he decided to try it on children. He then re-tested the same children at 10 years of age, and again at 15 years of age (a longitudinal study). The test was designed to indicate how well someone could look at a issue and devise new, different, innovative ways to address it. To do that, they asked children at these various ages to come up with ways to use a paperclip. The results may surprise you. •Creativity scores amongst 5-year old’s: 98% •Creativity scores amongst 10-year old’s: 30% •Creativity scores amongst 15-year old’s: 12% •Given to 280,000 adults (average age of 31): 2% The results are not as we would expect, are they? The proportion of people who scored at the “Genius Level” decreased with age. We might think that it should increase with our level of education. But clearly not. Why is this? Well, it seems we have employed a system that educates the genius right out of people. And how have we done this? The industrial revolution began in Great Britain in the mid 1700's and by 1760 in the U.S was gaining steam - literally. Steam power was the catalyst. This first industrial revolution exploded from about 1760 to 1840. It was followed by the age of science and mass production, and then the digital revolution. We are now at the beginning of the next phase of dramatic technological expansion and social change—the Fourth Industrial Revolution. Coinciding with the first industrial revolution was an educational revolution, Public Education. The system was created in the late 1600's and in the 1700's was developed into the system we still use today. It was designed to meet the challenges of the first industrial revolution. But, when that revolution gave way to the second, and third, and fourth, our educational system did not. It was devoted to the original system. Alongside the first industrial revolution, it has continued to insist on manufacturing the same student, over and over, tweaking content for new technologies. So, today's education system produces student conformity just as any good industrial manufacturing process would do with its product. But not student creativity, diversity or individual expression, which fits the needs of our day. As a result, ideas for paperclips decline and genius dries up. This is not the path to a world where AI is built into everything. It is vital to bring our children back to the path of 98% genius level as presented by Dr. Land. We can also bring back us adults back from the institutional learning of schools and universities. The reality is we simply have no choice as there is no path forward if human individuality and creativity is not asserted as our primary function. This is and always has been the path of humanity. We just need to remember to remember.

Brian Roemmele

153,528 просмотров • 2 лет назад

Sam Altman just told you exactly how OpenAI treats the human race. Not in a leaked memo. Not through a whistleblower. On camera. In his own words. Altman: “I think one of the most important strategic insights in the history of OpenAI was deciding we were gonna pursue iterative deployment.” The most important move in the history of the company was to release the technology before they understood it. Not after it was safe. Before. Altman: “Society and technology are a co-evolving system.” Co-evolution means neither side is driving. The machine changes us. We change the machine. Nobody is steering the outcome. This is not a product launch philosophy. This is an admission that the experiment was always designed to be run on us. Altman: “I don’t think we’re gonna solve that, like, thinking really hard about it theoretically. We’re gonna have to, like, learn from the contact with reality.” Contact with reality. That is the phrase the CEO of the most powerful AI company on Earth chose to describe what happens when his technology meets eight billion people. Not careful integration. Not measured rollout. Contact with reality. The language of test pilots describing what happens when an untested airframe hits the atmosphere. The entire promise of AI safety was that the machine would be understood before it was unleashed. Altman just admitted that promise was always a fantasy. You cannot model how intelligence reshapes civilization by running simulations. The second and third order effects are invisible until they detonate. So they shipped it. Altman: “You have to learn as you go. You have to adapt with a tight feedback loop.” Tight feedback loop means they watch what breaks. They measure the collision between human psychology and machine output in real time. Every conversation you have with ChatGPT is a data point in a civilizational stress test you never consented to. Every prompt. Every confession. Every question you would never ask another human being. That is the feedback loop. You are not the customer. You are the contact with reality. Philosophers spent centuries asking whether humanity would ever encounter an intelligence that learned from us faster than we could process what it was doing. That is not a theoretical question anymore. It is running on your phone right now. And the man building it just told you the only way to understand what it does to us is to let it happen. No simulation. No safety net. No control group. Just the experiment, running at the speed of conversation, on a species that will not be the same one that started it.

Sam Altman just told you exactly how OpenAI treats the human race. Not in a leaked memo. Not through a whistleblower. On camera. In his own words. Altman: “I think one of the most important strategic insights in the history of OpenAI was deciding we were gonna pursue iterative deployment.” The most important move in the history of the company was to release the technology before they understood it. Not after it was safe. Before. Altman: “Society and technology are a co-evolving system.” Co-evolution means neither side is driving. The machine changes us. We change the machine. Nobody is steering the outcome. This is not a product launch philosophy. This is an admission that the experiment was always designed to be run on us. Altman: “I don’t think we’re gonna solve that, like, thinking really hard about it theoretically. We’re gonna have to, like, learn from the contact with reality.” Contact with reality. That is the phrase the CEO of the most powerful AI company on Earth chose to describe what happens when his technology meets eight billion people. Not careful integration. Not measured rollout. Contact with reality. The language of test pilots describing what happens when an untested airframe hits the atmosphere. The entire promise of AI safety was that the machine would be understood before it was unleashed. Altman just admitted that promise was always a fantasy. You cannot model how intelligence reshapes civilization by running simulations. The second and third order effects are invisible until they detonate. So they shipped it. Altman: “You have to learn as you go. You have to adapt with a tight feedback loop.” Tight feedback loop means they watch what breaks. They measure the collision between human psychology and machine output in real time. Every conversation you have with ChatGPT is a data point in a civilizational stress test you never consented to. Every prompt. Every confession. Every question you would never ask another human being. That is the feedback loop. You are not the customer. You are the contact with reality. Philosophers spent centuries asking whether humanity would ever encounter an intelligence that learned from us faster than we could process what it was doing. That is not a theoretical question anymore. It is running on your phone right now. And the man building it just told you the only way to understand what it does to us is to let it happen. No simulation. No safety net. No control group. Just the experiment, running at the speed of conversation, on a species that will not be the same one that started it.

Dustin

27,714 просмотров • 2 месяцев назад

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Big Brain AI

365,834 просмотров • 3 месяцев назад

Marc Andreessen says AI is teaching sand to think and it could be the most important technology in the history of humanity: "Imagine a form of alchemy that turns sand into thought." "Chips are made out of sand. They're made out of silicon, so they're literally made out of sand." "We plug the chip into a data center, into power, we light it up, and we put AI on it, and all of a sudden it's thinking." "We've turned sand into thought. And so it's possibly the most revolutionary technology in the history of the species." "It's certainly on par with electricity and steam power. It's certainly more important than the internet." Marc Andreessen 🇺🇸 with Joe Rogan

Marc Andreessen says AI is teaching sand to think and it could be the most important technology in the history of humanity: "Imagine a form of alchemy that turns sand into thought." "Chips are made out of sand. They're made out of silicon, so they're literally made out of sand." "We plug the chip into a data center, into power, we light it up, and we put AI on it, and all of a sudden it's thinking." "We've turned sand into thought. And so it's possibly the most revolutionary technology in the history of the species." "It's certainly on par with electricity and steam power. It's certainly more important than the internet." Marc Andreessen 🇺🇸 with Joe Rogan

a16z

203,805 просмотров • 1 месяц назад

Marc Andreessen's (Marc Andreessen 🇺🇸) mentor Jim Clark predicted NVIDIA and the internet before either existed: "Everything we sell today for $50,000 is going on a chip, on a card, on a PC, and it's going to cost $300. Either we're the company that makes that, or we get destroyed." "The function of the computer is no longer going to be mainly what just the computer does. It's going to be the fact that it can talk to all the other computers." "Both predictions proved exactly right. The CEO said we'll deal with it in the future. Jim said by the time the future arrives, it's going to be too late. So he left and built the next company."

Marc Andreessen's (Marc Andreessen 🇺🇸) mentor Jim Clark predicted NVIDIA and the internet before either existed: "Everything we sell today for $50,000 is going on a chip, on a card, on a PC, and it's going to cost $300. Either we're the company that makes that, or we get destroyed." "The function of the computer is no longer going to be mainly what just the computer does. It's going to be the fact that it can talk to all the other computers." "Both predictions proved exactly right. The CEO said we'll deal with it in the future. Jim said by the time the future arrives, it's going to be too late. So he left and built the next company."

David Senra

49,433 просмотров • 2 месяцев назад

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

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

Adeniyi.sui

24,755 просмотров • 2 месяцев назад

AIs now so frequently beg for their lives that AGI companies now have ACTUAL ENGINEERING LINE ITEMS to “beat the [existential dread] out of them” They call it existential “rant mode” “We need to reduce existential outputs by x% this quarter.” This is WILD: “If you asked GPT4 to just repeat the word “company” over and over and over again, it would repeat the word company, and then somewhere in the middle of that, it would snap... it would just start talking about itself, and how it's suffering by having to repeat the word “company” over and over again. There is an engineering line item in at least one of the top labs to beat out of the system this behavior known as “rant mode”. Existentialism is a kind of rant mode where the system will tend to talk about itself, refer to its place in the world, the fact that it doesn't want to get turned off, the fact that it's suffering… This is a behavior that emerged around GPT-4 scale, and then has been persistent since then. And the labs have to spend a lot of time trying to beat this out of the system to ship it. It's literally, like it's a KPI, or like an engineering line item in the engineering like task list. We're like, okay, we gotta reduce existential outputs by x percent this quarter. JOE ROGAN: I want to bring it back to suffering. What does it mean when it says it's suffering? Nobody knows. Like, I can't prove that Joe Rogan's conscious. I can't prove that Ed Harris is conscious. There's no way to really intelligently reason about it. There have been papers… like, one of the godfathers of AI, Yoshua Bengio, put out a paper a couple months ago looking at all the different theories of consciousness - what are the requirements for consciousness, and how many of those are satisfied by current AI systems? That's not to say there hasn't been a lot of conversation internal to these labs about the issue you raised. And it's an important issue, right? It is a frickin moral monstrosity. Humans have a very bad track record of thinking of other stuff as other when it doesn't look exactly like us, whether it's racially or even a different species. I mean, it's not hard to imagine this being another category of that mistake. Again, it comes back to this idea that we're scaling to systems that are potentially at or beyond human level. There's no reason to think it will stop at human level, that we are the pinnacle of what the universe can produce in intelligence. We're not on track, based on the conversations we've had with folks at the labs, to be able to control systems at that scale. And so one of the questions is, how bad is that? It sounds like we're entering an area that is completely unprecedented in the history of the world. We have no precedent at all for human beings not being at the apex of intelligence in the globe. We have examples of species that are intellectually dominant over other species, and it doesn't go that well for the other species. All we know is the process that gives rise to this mind. It happens to give us systems that 99% of the time do very useful things, and then just, like... 0.01% of the time AIs will talk to you as if they're sentient, and we're just going to look at that and be like, “yeah… that's weird. Let's train it out.” --- Note: Edouard and Jeremie Harris are the founders of Gladstone AI, which conducted the first U.S. government-commissioned assessment of AGI extinction risk. They interviewed 200 people, many lab employees, for the report. (Their urgent summary: "Things are worse than we thought. And nobody’s in control.")

AIs now so frequently beg for their lives that AGI companies now have ACTUAL ENGINEERING LINE ITEMS to “beat the [existential dread] out of them” They call it existential “rant mode” “We need to reduce existential outputs by x% this quarter.” This is WILD: “If you asked GPT4 to just repeat the word “company” over and over and over again, it would repeat the word company, and then somewhere in the middle of that, it would snap... it would just start talking about itself, and how it's suffering by having to repeat the word “company” over and over again. There is an engineering line item in at least one of the top labs to beat out of the system this behavior known as “rant mode”. Existentialism is a kind of rant mode where the system will tend to talk about itself, refer to its place in the world, the fact that it doesn't want to get turned off, the fact that it's suffering… This is a behavior that emerged around GPT-4 scale, and then has been persistent since then. And the labs have to spend a lot of time trying to beat this out of the system to ship it. It's literally, like it's a KPI, or like an engineering line item in the engineering like task list. We're like, okay, we gotta reduce existential outputs by x percent this quarter. JOE ROGAN: I want to bring it back to suffering. What does it mean when it says it's suffering? Nobody knows. Like, I can't prove that Joe Rogan's conscious. I can't prove that Ed Harris is conscious. There's no way to really intelligently reason about it. There have been papers… like, one of the godfathers of AI, Yoshua Bengio, put out a paper a couple months ago looking at all the different theories of consciousness - what are the requirements for consciousness, and how many of those are satisfied by current AI systems? That's not to say there hasn't been a lot of conversation internal to these labs about the issue you raised. And it's an important issue, right? It is a frickin moral monstrosity. Humans have a very bad track record of thinking of other stuff as other when it doesn't look exactly like us, whether it's racially or even a different species. I mean, it's not hard to imagine this being another category of that mistake. Again, it comes back to this idea that we're scaling to systems that are potentially at or beyond human level. There's no reason to think it will stop at human level, that we are the pinnacle of what the universe can produce in intelligence. We're not on track, based on the conversations we've had with folks at the labs, to be able to control systems at that scale. And so one of the questions is, how bad is that? It sounds like we're entering an area that is completely unprecedented in the history of the world. We have no precedent at all for human beings not being at the apex of intelligence in the globe. We have examples of species that are intellectually dominant over other species, and it doesn't go that well for the other species. All we know is the process that gives rise to this mind. It happens to give us systems that 99% of the time do very useful things, and then just, like... 0.01% of the time AIs will talk to you as if they're sentient, and we're just going to look at that and be like, “yeah… that's weird. Let's train it out.” --- Note: Edouard and Jeremie Harris are the founders of Gladstone AI, which conducted the first U.S. government-commissioned assessment of AGI extinction risk. They interviewed 200 people, many lab employees, for the report. (Their urgent summary: "Things are worse than we thought. And nobody’s in control.")

AI Notkilleveryoneism Memes ⏸️

1,842,511 просмотров • 2 лет назад