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Software engineering used to be the pinnacle of intelligence. Now it’s the first job AI is replacing. Jensen Huang: “Technical intelligence is becoming a commodity.” The hard technical problems everyone worried about? Those turned out to be the easy ones. Machines solve them faster, cheaper, and without error. So...

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Dustin

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96,127 次观看 • 7 个月前 •via X (Twitter)

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"Everybody thought software programming was the ultimate “smart” profession. Look what one of the first things AI is solving: software programming." ~ Jensen Huang defines smartness in the AI era "I know what people are thinking: the definition of smart is someone who’s intelligent, solves problems, and is technical. But I find that’s a commodity. We’re about to prove that artificial intelligence can handle that part easily, right? My personal definition of smart is someone who sits at the intersection of being technically astute, having human empathy, and having the ability to infer the unspoken, the “around the corners,” the unknowables." --- From 'A Bit Personal with Jodi Shelton' YT channel
1:15

"Everybody thought software programming was the ultimate “smart” profession. Look what one of the first things AI is solving: software programming." ~ Jensen Huang defines smartness in the AI era "I know what people are thinking: the definition of smart is someone who’s intelligent, solves problems, and is technical. But I find that’s a commodity. We’re about to prove that artificial intelligence can handle that part easily, right? My personal definition of smart is someone who sits at the intersection of being technically astute, having human empathy, and having the ability to infer the unspoken, the “around the corners,” the unknowables." --- From 'A Bit Personal with Jodi Shelton' YT channel

Rohan Paul

29,526 次观看 • 7 个月前

Jensen Huang just made the case that the smartest person in the room is now the most replaceable person on earth. Huang: “The definition of smart is somebody who’s intelligent, solve problems, technical. But I find that that’s a commodity. And we’re about to prove that artificial intelligence is able to handle that part easiest.” The skill you built your entire identity around was the first thing the machine replicated. Not the hardest part. The easiest. Huang: “People who are able to see around corners are truly, truly smart. And their value is incredible. To be able to preempt problems before they show up, just because you feel the vibe.” Huang: “That vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people.” The vibe is not intuition. It is decades of failed bets, human friction, and first-principles thinking compressed into a single read no model can replicate. You cannot prompt your way to it. You earn it by surviving things that had no instructions. Huang: “I think long term the definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, around the corners, the unknowables.” Every institution you ever passed through graded you on the one thing the machine now does for free. The thing they never tested you on is the only thing that still matters. Huang: “And that person might actually score horribly on the SAT.” The SAT did not measure intelligence. It measured obedience to structure. The future does not reward what you can solve inside a framework someone handed you. It rewards what you can see when no framework exists. The machine did not replace human intelligence. It revealed that what we spent a century calling intelligence was never intelligence at all. The people who memorized the answers are about to work for the people who sensed the questions before anyone thought to ask.
2:01

Jensen Huang just made the case that the smartest person in the room is now the most replaceable person on earth. Huang: “The definition of smart is somebody who’s intelligent, solve problems, technical. But I find that that’s a commodity. And we’re about to prove that artificial intelligence is able to handle that part easiest.” The skill you built your entire identity around was the first thing the machine replicated. Not the hardest part. The easiest. Huang: “People who are able to see around corners are truly, truly smart. And their value is incredible. To be able to preempt problems before they show up, just because you feel the vibe.” Huang: “That vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people.” The vibe is not intuition. It is decades of failed bets, human friction, and first-principles thinking compressed into a single read no model can replicate. You cannot prompt your way to it. You earn it by surviving things that had no instructions. Huang: “I think long term the definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, around the corners, the unknowables.” Every institution you ever passed through graded you on the one thing the machine now does for free. The thing they never tested you on is the only thing that still matters. Huang: “And that person might actually score horribly on the SAT.” The SAT did not measure intelligence. It measured obedience to structure. The future does not reward what you can solve inside a framework someone handed you. It rewards what you can see when no framework exists. The machine did not replace human intelligence. It revealed that what we spent a century calling intelligence was never intelligence at all. The people who memorized the answers are about to work for the people who sensed the questions before anyone thought to ask.

Dustin

123,807 次观看 • 2 个月前

NVIDIA CEO Jensen Huang just said the quiet part out loud about what the education system will never admit. For a century, we built humans to think like calculators. The algorithm made that skillset obsolete overnight. Huang: “The definition of smart is somebody who’s intelligent, solve problems, technical. But I find that that’s a commodity. And we’re about to prove that artificial intelligence is able to handle that part easiest.” Software engineering was supposed to be the safe play. Superintelligence cleared it first. The SAT was supposed to measure intelligence. It was measuring the ability to follow instructions. Raw technical processing isn’t a competitive edge anymore. It’s the floor the machine stepped over before you woke up. The question isn’t what you can calculate. It’s what you can see before the data shows up. Huang: “People who are able to see around corners are truly, truly smart. And their value is incredible. To be able to preempt problems before they show up, just because you feel the vibe.” That vibe isn’t magic. It’s the collision of first principles, human empathy, and lived experience no model can fake. Huang: “That vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people.” The operators who see around corners will command the AI. The ones waiting for dashboards to update will be replaced by it. Huang: “I think long term the definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, around the corners, the unknowables.” The unspoken variables are the new leverage. The human psychology inside a market. The invisible friction in a negotiation. The instinct to build something nobody asked for yet. You can’t spreadsheet your way there. You can’t prompt your way to that perception. It comes from decades of watching what doesn’t show up in the metrics. Huang: “And that person might actually score horribly on the SAT.” The future doesn’t belong to people who memorized answers. It belongs to people who sense the questions before anyone thinks to ask. The old system tested your ability to follow orders. The new one tests your ability to move through the unknown. And the machine can’t help you with that part. That part is entirely on you.
2:01

NVIDIA CEO Jensen Huang just said the quiet part out loud about what the education system will never admit. For a century, we built humans to think like calculators. The algorithm made that skillset obsolete overnight. Huang: “The definition of smart is somebody who’s intelligent, solve problems, technical. But I find that that’s a commodity. And we’re about to prove that artificial intelligence is able to handle that part easiest.” Software engineering was supposed to be the safe play. Superintelligence cleared it first. The SAT was supposed to measure intelligence. It was measuring the ability to follow instructions. Raw technical processing isn’t a competitive edge anymore. It’s the floor the machine stepped over before you woke up. The question isn’t what you can calculate. It’s what you can see before the data shows up. Huang: “People who are able to see around corners are truly, truly smart. And their value is incredible. To be able to preempt problems before they show up, just because you feel the vibe.” That vibe isn’t magic. It’s the collision of first principles, human empathy, and lived experience no model can fake. Huang: “That vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people.” The operators who see around corners will command the AI. The ones waiting for dashboards to update will be replaced by it. Huang: “I think long term the definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, around the corners, the unknowables.” The unspoken variables are the new leverage. The human psychology inside a market. The invisible friction in a negotiation. The instinct to build something nobody asked for yet. You can’t spreadsheet your way there. You can’t prompt your way to that perception. It comes from decades of watching what doesn’t show up in the metrics. Huang: “And that person might actually score horribly on the SAT.” The future doesn’t belong to people who memorized answers. It belongs to people who sense the questions before anyone thinks to ask. The old system tested your ability to follow orders. The new one tests your ability to move through the unknown. And the machine can’t help you with that part. That part is entirely on you.

Dustin

707,885 次观看 • 6 个月前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.
0:35

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,836 次观看 • 6 个月前

Jensen Huang recently said he wants his engineers to spend 0% of their time coding. At first, it sounds absurd — until you realize what he really means. Coding is becoming automated. The new value isn’t in typing instructions for machines… …it’s in teaching machines what’s worth building. Jensen’s message is clear: The future belongs to engineers who understand systems, context, and creativity — not just syntax. As AI takes over execution, human intelligence must rise to orchestration. It’s not about writing code anymore. It’s about writing the future. What do you think — will the best engineers of 2026 still code, or will they design intelligence itself? #ArtificialIntelligence #AI #Coding #Technology #FutureOfWork #Leadership #Innovation #Automation #MachineLearning #AIandHumanity
0:51

Jensen Huang recently said he wants his engineers to spend 0% of their time coding. At first, it sounds absurd — until you realize what he really means. Coding is becoming automated. The new value isn’t in typing instructions for machines… …it’s in teaching machines what’s worth building. Jensen’s message is clear: The future belongs to engineers who understand systems, context, and creativity — not just syntax. As AI takes over execution, human intelligence must rise to orchestration. It’s not about writing code anymore. It’s about writing the future. What do you think — will the best engineers of 2026 still code, or will they design intelligence itself? #ArtificialIntelligence #AI #Coding #Technology #FutureOfWork #Leadership #Innovation #Automation #MachineLearning #AIandHumanity

Pascal Bornet

65,503 次观看 • 7 个月前

Sam Altman told the world exactly what skills will matter when AI takes over 30 to 40 percent of the global economy. He was asked what his own kids should do to survive it. His answer was surprisingly human. He said the single most valuable thing anyone can build right now is the meta-skill of learning how to learn. Not a degree or a certification but the raw ability to adapt when everything around you changes. He also said learning to understand what other people actually want and building useful things for them will be more valuable than almost any technical knowledge. That skill has never been automated and is not close to being automated. He said human creativity and the desire to express it are, in his words, limitless. Every major technological revolution increased the demand for creative, curious, and socially intelligent people, not decreased it. The Industrial Revolution is the clearest parallel. Machines replaced physical labor and people were terrified. The next generation took those machines and built industries, art forms, and institutions nobody had conceived of before. The people who thrived were not the ones who competed with the machines. They were the ones who learned to direct them toward something new. That dynamic is already playing out right now with AI. The practical implication is this, depth in a single rigid skill is becoming less valuable. The ability to move across domains, pick up new tools quickly, and apply judgment in ambiguous situations is becoming more valuable. Altman also pointed to something most career advice ignores entirely, learning how to interact with the world, build relationships, and earn trust from other people. Those are things AI can simulate but cannot replace. The honest opportunity in this moment is not to outrun AI. It is to focus on the things that make you irreducibly human. Curiosity, judgment, empathy and the ability to ask the right question before anyone knows what the right question is. The people who will matter most in an AI-driven economy are not necessarily the ones who understand the technology deepest. They are the ones who can figure out what the technology should actually be used for. Altman has spent his career betting on human potential in the face of technological disruption. Based on every historical precedent, that is still the right bet to make.
0:46

Sam Altman told the world exactly what skills will matter when AI takes over 30 to 40 percent of the global economy. He was asked what his own kids should do to survive it. His answer was surprisingly human. He said the single most valuable thing anyone can build right now is the meta-skill of learning how to learn. Not a degree or a certification but the raw ability to adapt when everything around you changes. He also said learning to understand what other people actually want and building useful things for them will be more valuable than almost any technical knowledge. That skill has never been automated and is not close to being automated. He said human creativity and the desire to express it are, in his words, limitless. Every major technological revolution increased the demand for creative, curious, and socially intelligent people, not decreased it. The Industrial Revolution is the clearest parallel. Machines replaced physical labor and people were terrified. The next generation took those machines and built industries, art forms, and institutions nobody had conceived of before. The people who thrived were not the ones who competed with the machines. They were the ones who learned to direct them toward something new. That dynamic is already playing out right now with AI. The practical implication is this, depth in a single rigid skill is becoming less valuable. The ability to move across domains, pick up new tools quickly, and apply judgment in ambiguous situations is becoming more valuable. Altman also pointed to something most career advice ignores entirely, learning how to interact with the world, build relationships, and earn trust from other people. Those are things AI can simulate but cannot replace. The honest opportunity in this moment is not to outrun AI. It is to focus on the things that make you irreducibly human. Curiosity, judgment, empathy and the ability to ask the right question before anyone knows what the right question is. The people who will matter most in an AI-driven economy are not necessarily the ones who understand the technology deepest. They are the ones who can figure out what the technology should actually be used for. Altman has spent his career betting on human potential in the face of technological disruption. Based on every historical precedent, that is still the right bet to make.

StockMarket.News

376,937 次观看 • 5 个月前

Jensen Huang just told the smartest founders in AI they’re building a commodity. Not a platform. Not a moat. A commodity. Huang: “AI is actually essentially a five-layer cake.” Energy. Chips. Cloud. Models. Applications. Five layers. The entire industry is crowded onto one. Huang: “This is where most people think AI is.” The model layer. Most money. Most talent. Most competition. Most competition always ends the same way. A commodity. The harder they compete, the faster they erase their own value. That’s not a failure of strategy. That’s the strategy working. Huang: “At the bottom is energy.” No algorithm solves a power grid. No fundraise builds a dam. For decades everyone assumed intelligence would be the scarce resource. AI proved the opposite. Intelligence scales. Energy doesn’t. The bottleneck was never thinking. It was always power. Huang: “This layer on top ultimately is where economic benefit will happen.” The top captures revenue. The bottom controls access. The middle converts one into the other. Hundreds of billions deployed between someone else’s power grid and someone else’s economy. Most funded layer in history. Least defensible layer in the stack. The most intelligent thing ever built sits in the one position where intelligence doesn’t matter.
1:00

Jensen Huang just told the smartest founders in AI they’re building a commodity. Not a platform. Not a moat. A commodity. Huang: “AI is actually essentially a five-layer cake.” Energy. Chips. Cloud. Models. Applications. Five layers. The entire industry is crowded onto one. Huang: “This is where most people think AI is.” The model layer. Most money. Most talent. Most competition. Most competition always ends the same way. A commodity. The harder they compete, the faster they erase their own value. That’s not a failure of strategy. That’s the strategy working. Huang: “At the bottom is energy.” No algorithm solves a power grid. No fundraise builds a dam. For decades everyone assumed intelligence would be the scarce resource. AI proved the opposite. Intelligence scales. Energy doesn’t. The bottleneck was never thinking. It was always power. Huang: “This layer on top ultimately is where economic benefit will happen.” The top captures revenue. The bottom controls access. The middle converts one into the other. Hundreds of billions deployed between someone else’s power grid and someone else’s economy. Most funded layer in history. Least defensible layer in the stack. The most intelligent thing ever built sits in the one position where intelligence doesn’t matter.

Dustin

20,739 次观看 • 2 个月前

Listen to Jensen Huang explain AI, and the Democratic Socialists’ anti-AI stance starts to look pretty strange. Huang calls AI “the great equalizer.” “In the last 50 years, computers have been a tool for about 10, 20, 30 million people. And the only way to take advantage of this capability is to know how to program it, how to design the computer. For the first time, anybody in the world can program a computer. You could program a computer using human language. So we’ve democratized this computing technology that is so valuable to society and we’ve elevated everyone. And this is one of the reasons why I really encourage everybody to engage artificial intelligence. It bootstraps you, it elevates you, it enables you. You know it’s the great equalizer.”
1:00

Listen to Jensen Huang explain AI, and the Democratic Socialists’ anti-AI stance starts to look pretty strange. Huang calls AI “the great equalizer.” “In the last 50 years, computers have been a tool for about 10, 20, 30 million people. And the only way to take advantage of this capability is to know how to program it, how to design the computer. For the first time, anybody in the world can program a computer. You could program a computer using human language. So we’ve democratized this computing technology that is so valuable to society and we’ve elevated everyone. And this is one of the reasons why I really encourage everybody to engage artificial intelligence. It bootstraps you, it elevates you, it enables you. You know it’s the great equalizer.”

Brecca Stoll

54,842 次观看 • 14 天前

Jensen Huang says there are no laws of physics that prevent scaling AI data centers to a million chips and although it is a hard problem, "the prize for reinventing intelligence altogether is too consequential not to attempt it"
1:39

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Jensen Huang says there are no laws of physics that prevent scaling AI data centers to a million chips and although it is a hard problem, "the prize for reinventing intelligence altogether is too consequential not to attempt it"

Tsarathustra

107,779 次观看 • 1 年前

Jensen Huang just told the world something nobody wants to hear. AI is not coming for your job. It is coming for the part of your job you mistakenly believe IS your job. Huang: “The purpose of your job and the tasks that you do in your job are related but not the same.” That one sentence is the fault line between the people who thrive in the next decade and the people who vanish from it. Huang used himself as proof. Reduce the CEO of Nvidia to his raw outputs and his entire career is typing and talking. Both have been automated to superhuman levels. Huang: “Typing and talking have both been automated to a superhuman level by AI. And yet, I’m busier than ever.” The man building the infrastructure that automates human labor has never worked harder. That should stop you cold. We look at a profession and see the tasks. The motions. The mechanical friction. We never see the intent underneath. And when AI arrives, we panic. Because we confuse the task with the job. The task was never the job. It was always the bottleneck between a human and their actual purpose. Now the bottleneck is dissolving. Years ago, the experts declared radiology dead. The algorithm could read a scan better than any human. A generation of medical students listened. They walked away from the field. The result was catastrophic. Huang: “We need more radiologists than ever, and we don’t have enough.” The algorithm did not replace the doctor. It armed the doctor. Suddenly the department could see more patients. Catch more anomalies. Generate more revenue. The hospital did not fire the radiologists. It tried to hire more. And could not find them. Because we terrified an entire generation out of a career with a prediction that landed exactly backwards. Now the same hysteria is consuming software engineering. The timeline is screaming that coding is dead. Meanwhile, inside the very company building the hardware that automates code. Huang: “The software engineers that know how to use AI, know how to work with agentic systems, are the most popular and the most successful.” The tool did not replace the architect. It replaced the shovel. This is the pattern nobody wants to confront. AI does not eliminate the human. It eliminates the friction that made the human slow. And when the friction disappears, demand for the human explodes. But only if the human shows up. The ones who defined themselves by the mechanical act of writing code are fading. The ones who defined themselves by what the code was meant to build are now the most valuable people on the planet. That is not a nuance. That is the entire dividing line. The machine will write the script. Read the scan. Draft the brief. It will never possess the reason any of it needed to exist. The task was never the job. And nobody who figures that out last gets the privilege of figuring it out twice.
2:00

Jensen Huang just told the world something nobody wants to hear. AI is not coming for your job. It is coming for the part of your job you mistakenly believe IS your job. Huang: “The purpose of your job and the tasks that you do in your job are related but not the same.” That one sentence is the fault line between the people who thrive in the next decade and the people who vanish from it. Huang used himself as proof. Reduce the CEO of Nvidia to his raw outputs and his entire career is typing and talking. Both have been automated to superhuman levels. Huang: “Typing and talking have both been automated to a superhuman level by AI. And yet, I’m busier than ever.” The man building the infrastructure that automates human labor has never worked harder. That should stop you cold. We look at a profession and see the tasks. The motions. The mechanical friction. We never see the intent underneath. And when AI arrives, we panic. Because we confuse the task with the job. The task was never the job. It was always the bottleneck between a human and their actual purpose. Now the bottleneck is dissolving. Years ago, the experts declared radiology dead. The algorithm could read a scan better than any human. A generation of medical students listened. They walked away from the field. The result was catastrophic. Huang: “We need more radiologists than ever, and we don’t have enough.” The algorithm did not replace the doctor. It armed the doctor. Suddenly the department could see more patients. Catch more anomalies. Generate more revenue. The hospital did not fire the radiologists. It tried to hire more. And could not find them. Because we terrified an entire generation out of a career with a prediction that landed exactly backwards. Now the same hysteria is consuming software engineering. The timeline is screaming that coding is dead. Meanwhile, inside the very company building the hardware that automates code. Huang: “The software engineers that know how to use AI, know how to work with agentic systems, are the most popular and the most successful.” The tool did not replace the architect. It replaced the shovel. This is the pattern nobody wants to confront. AI does not eliminate the human. It eliminates the friction that made the human slow. And when the friction disappears, demand for the human explodes. But only if the human shows up. The ones who defined themselves by the mechanical act of writing code are fading. The ones who defined themselves by what the code was meant to build are now the most valuable people on the planet. That is not a nuance. That is the entire dividing line. The machine will write the script. Read the scan. Draft the brief. It will never possess the reason any of it needed to exist. The task was never the job. And nobody who figures that out last gets the privilege of figuring it out twice.

Dustin

52,882 次观看 • 4 个月前

Jensen Huang just buried the one metric civilization has run on for a century. Be the smartest person in the room. Huang: “Intelligence is going to be commoditized.” Sit with that word. Commoditized. Like oil. Like electricity. Like bandwidth. We built an entire civilization around producing human calculators. Sorted by GPA. Ranked by IQ. Rewarded for how fast they could process what nobody else could. That market just collapsed. You can rent a sharper mind than any person alive for twenty dollars a month. So what happens to the man who spent forty years being the sharpest at the table? He loses the table. Huang is not a philosopher warning you from a safe distance. He is the man whose chips are making it happen. He said it without flinching. Huang: “My life would suggest that being lower on the intelligence curve than everybody around me doesn’t change the fact I’m the most successful.” The CEO of the most valuable company on Earth just told you plainly he was never the smartest in his rooms. He won anyway. Because intelligence was never the actual edge. The people most terrified of AI are the ones who built their entire identity around being the fastest processor in the building. That identity just got priced out. Huang named what replaces it. Huang: “Character, humanity, compassion, generosity. I believe those are superhuman powers.” Things you cannot scrape from a dataset. Things you cannot distill into weights and parameters. The machine will out-compute you in milliseconds. But it cannot bleed for something. It cannot walk into a room with conviction and move people who did not want to be moved. It cannot be you. Intelligence is now infrastructure. The electricity humming behind the walls. Nobody wins by owning the electricity. You win by what you build with it. When intelligence becomes a commodity, humanity becomes the premium. The human calculator had a good run. That run is over.
1:26

Jensen Huang just buried the one metric civilization has run on for a century. Be the smartest person in the room. Huang: “Intelligence is going to be commoditized.” Sit with that word. Commoditized. Like oil. Like electricity. Like bandwidth. We built an entire civilization around producing human calculators. Sorted by GPA. Ranked by IQ. Rewarded for how fast they could process what nobody else could. That market just collapsed. You can rent a sharper mind than any person alive for twenty dollars a month. So what happens to the man who spent forty years being the sharpest at the table? He loses the table. Huang is not a philosopher warning you from a safe distance. He is the man whose chips are making it happen. He said it without flinching. Huang: “My life would suggest that being lower on the intelligence curve than everybody around me doesn’t change the fact I’m the most successful.” The CEO of the most valuable company on Earth just told you plainly he was never the smartest in his rooms. He won anyway. Because intelligence was never the actual edge. The people most terrified of AI are the ones who built their entire identity around being the fastest processor in the building. That identity just got priced out. Huang named what replaces it. Huang: “Character, humanity, compassion, generosity. I believe those are superhuman powers.” Things you cannot scrape from a dataset. Things you cannot distill into weights and parameters. The machine will out-compute you in milliseconds. But it cannot bleed for something. It cannot walk into a room with conviction and move people who did not want to be moved. It cannot be you. Intelligence is now infrastructure. The electricity humming behind the walls. Nobody wins by owning the electricity. You win by what you build with it. When intelligence becomes a commodity, humanity becomes the premium. The human calculator had a good run. That run is over.

Dustin

31,246 次观看 • 5 个月前

Launching on Wednesday, Sept 25. Stay tuned! Right now, #AI is having a moment — and it’s not the first time grand predictions about the potential of machines are being made. But, what does it really mean to say something like ChatGPT is “intelligent”? What exactly is #intelligence? The next season of the #Complexity #podcast, 'The Nature of Intelligence', explores this question through conversations with cognitive and neuroscientists, animal cognition researchers, and AI experts in six episodes. Together, we'll investigate the complexities of human intelligence, how it compares to that of other species, and where AI fits in. We'll dive into the relationship between language and thought, examine AI's limitations, and ask: Could machines ever truly be like us?
3:00

Launching on Wednesday, Sept 25. Stay tuned! Right now, #AI is having a moment — and it’s not the first time grand predictions about the potential of machines are being made. But, what does it really mean to say something like ChatGPT is “intelligent”? What exactly is #intelligence? The next season of the #Complexity #podcast, 'The Nature of Intelligence', explores this question through conversations with cognitive and neuroscientists, animal cognition researchers, and AI experts in six episodes. Together, we'll investigate the complexities of human intelligence, how it compares to that of other species, and where AI fits in. We'll dive into the relationship between language and thought, examine AI's limitations, and ask: Could machines ever truly be like us?

Santa Fe Institute

45,386 次观看 • 2 年前

Jensen Huang just dismantled the entire “AI is coming for your job” narrative in under two minutes. Huang: “If you apply that to me, you would come to the conclusion what Jensen does for a living is tap on phones and talk. And therefore my job should be gone. But I’m busier than ever.” The entire panic is built on a category error. People look at a job and see the task. They never see the purpose. The task is typing. The purpose is solving. They were never the same thing. Huang: “There’s a fundamental difference between the purpose of the job and the task of the job.” Every doomsday prediction about AI makes the exact same mistake. It watches a programmer type and concludes the job is typing. It watches a designer move pixels and concludes the job is moving pixels. It watches a writer arrange words and concludes the job is arranging words. The job was never the motion. It was the judgment behind it. Huang: “It is a fundamental flaw that we only need a billion lines of code written. We need a trillion lines of code written.” The doomsday model assumes demand is fixed. That there’s a set number of problems in the world and AI is about to solve your share and leave you with nothing. Demand was never fixed. It was capped. Capped by the speed of human fingers on a keyboard. Hospitals still running on software from 2003. Small businesses still doing invoices by hand. Not because nobody imagined better. Because there weren’t enough humans to build it. The backlog of unsolved problems on this planet isn’t shrinking. It’s infinite. AI didn’t show up to take your seat. It kicked open a door to a room full of problems nobody had the bandwidth to touch. Huang: “We have lots and lots of imaginations of all the things that we can do. If we just didn’t have to type anymore, we could go and do those things.” This is what 50 years of keyboards did to us. We didn’t build a tool and stay in control. We built a tool and rearranged our entire lives around serving it. Huang: “This one little device with a keyboard on it consumed all of our lives to the point where we just can’t imagine living without typing anymore.” An entire generation measured its worth by how fast it could press buttons on a rectangle. We called it skill. We called it career. We called it identity. Nobody stopped long enough to ask whether the thing eating 8 hours of every day was the work itself or just the friction standing between us and the work. Huang: “50 years before that people didn’t do that. And so in the future we’re gonna do less of that, we’re gonna do more of something else.” The people most terrified of AI aren’t afraid of losing their purpose. They’re afraid of losing their routine. Because somewhere along the way routine became identity. And when the routine disappears, they don’t know who they are without it. The farmer didn’t stop feeding people when the tractor arrived. The surgeon didn’t stop saving lives when the robot entered the operating room. The task changed. The purpose never did. AI isn’t coming for your job. It’s coming for the part of your job you mistook for the whole thing. Fifty years from now, people will look back at this era the same way we look at scribes copying manuscripts by candlelight. And they’ll ask the same question we always ask. Why did it take so long to let go.
1:39

Jensen Huang just dismantled the entire “AI is coming for your job” narrative in under two minutes. Huang: “If you apply that to me, you would come to the conclusion what Jensen does for a living is tap on phones and talk. And therefore my job should be gone. But I’m busier than ever.” The entire panic is built on a category error. People look at a job and see the task. They never see the purpose. The task is typing. The purpose is solving. They were never the same thing. Huang: “There’s a fundamental difference between the purpose of the job and the task of the job.” Every doomsday prediction about AI makes the exact same mistake. It watches a programmer type and concludes the job is typing. It watches a designer move pixels and concludes the job is moving pixels. It watches a writer arrange words and concludes the job is arranging words. The job was never the motion. It was the judgment behind it. Huang: “It is a fundamental flaw that we only need a billion lines of code written. We need a trillion lines of code written.” The doomsday model assumes demand is fixed. That there’s a set number of problems in the world and AI is about to solve your share and leave you with nothing. Demand was never fixed. It was capped. Capped by the speed of human fingers on a keyboard. Hospitals still running on software from 2003. Small businesses still doing invoices by hand. Not because nobody imagined better. Because there weren’t enough humans to build it. The backlog of unsolved problems on this planet isn’t shrinking. It’s infinite. AI didn’t show up to take your seat. It kicked open a door to a room full of problems nobody had the bandwidth to touch. Huang: “We have lots and lots of imaginations of all the things that we can do. If we just didn’t have to type anymore, we could go and do those things.” This is what 50 years of keyboards did to us. We didn’t build a tool and stay in control. We built a tool and rearranged our entire lives around serving it. Huang: “This one little device with a keyboard on it consumed all of our lives to the point where we just can’t imagine living without typing anymore.” An entire generation measured its worth by how fast it could press buttons on a rectangle. We called it skill. We called it career. We called it identity. Nobody stopped long enough to ask whether the thing eating 8 hours of every day was the work itself or just the friction standing between us and the work. Huang: “50 years before that people didn’t do that. And so in the future we’re gonna do less of that, we’re gonna do more of something else.” The people most terrified of AI aren’t afraid of losing their purpose. They’re afraid of losing their routine. Because somewhere along the way routine became identity. And when the routine disappears, they don’t know who they are without it. The farmer didn’t stop feeding people when the tractor arrived. The surgeon didn’t stop saving lives when the robot entered the operating room. The task changed. The purpose never did. AI isn’t coming for your job. It’s coming for the part of your job you mistook for the whole thing. Fifty years from now, people will look back at this era the same way we look at scribes copying manuscripts by candlelight. And they’ll ask the same question we always ask. Why did it take so long to let go.

Dustin

37,564 次观看 • 3 个月前

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.
1:27

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

366,743 次观看 • 5 个月前

I watched Jensen Huang being asked who the smartest person he’s ever met was. He smiled — and refused to answer. Instead, he questioned the question itself. He said what we call “smart” — coding, solving complex problems, optimizing systems — is quickly becoming commoditized. AI already does that better. That struck me. Because it means intelligence is no longer about how fast we think — but how deeply we understand. Jensen said the future belongs to those with emotional depth, adaptability, critical thinking, and the ability to navigate ambiguity. I couldn’t agree more. In a world where machines learn patterns, humans must learn perspective. Maybe being smart isn’t about knowing more… It’s about feeling and deciding better. What do you think — are we ready to redefine intelligence for the AI age? #ArtificialIntelligence #AI #Leadership #Technology #FutureOfWork #EmotionalIntelligence #Innovation #Mindfulness #HumanInTheLoop #AIandHumanity
2:03

I watched Jensen Huang being asked who the smartest person he’s ever met was. He smiled — and refused to answer. Instead, he questioned the question itself. He said what we call “smart” — coding, solving complex problems, optimizing systems — is quickly becoming commoditized. AI already does that better. That struck me. Because it means intelligence is no longer about how fast we think — but how deeply we understand. Jensen said the future belongs to those with emotional depth, adaptability, critical thinking, and the ability to navigate ambiguity. I couldn’t agree more. In a world where machines learn patterns, humans must learn perspective. Maybe being smart isn’t about knowing more… It’s about feeling and deciding better. What do you think — are we ready to redefine intelligence for the AI age? #ArtificialIntelligence #AI #Leadership #Technology #FutureOfWork #EmotionalIntelligence #Innovation #Mindfulness #HumanInTheLoop #AIandHumanity

Pascal Bornet

80,254 次观看 • 7 个月前

Jensen Huang doesn’t use AI to think less. He uses it to think past his own limits. Huang: “90% of my instructions are actually conflated with questions.” The man running a five trillion dollar company doesn’t give AI commands. He interrogates it. Huang: “I take the answer from one AI, give it to the other AI, ask them to critique itself.” Same question. Multiple models. Pit them against each other. Keep only what survives. Not because the machine can’t be trusted. Because challenging it is where the sharpest thinking happens. Huang: “The process of critiquing, criticizing the answers, applying your critical thinking, enhances cognitive skills.” AI doesn’t replace your thinking. It demands more of it than you’ve ever given. Every question takes reasoning. Every answer takes scrutiny. The machine isn’t thinking for you. It’s pulling thinking out of you that didn’t exist before you sat down. Huang: “In order to formulate good questions, you have to be thinking, you have to be analytical, you have to be reasoning yourself.” AI is not the shortcut everyone thinks it is. It is the most powerful cognitive amplifier ever built. It sharpens the engaged. It leaves the passive exactly where they started. Same tool. Same access. The only variable is what you bring to it. The world is debating whether AI will replace human thinking. Wrong conversation. The real question is what happens when a tool built to think for you becomes the thing that forces you to think beyond yourself. That’s not a threat to humanity. That’s the entire point.
1:30

Jensen Huang doesn’t use AI to think less. He uses it to think past his own limits. Huang: “90% of my instructions are actually conflated with questions.” The man running a five trillion dollar company doesn’t give AI commands. He interrogates it. Huang: “I take the answer from one AI, give it to the other AI, ask them to critique itself.” Same question. Multiple models. Pit them against each other. Keep only what survives. Not because the machine can’t be trusted. Because challenging it is where the sharpest thinking happens. Huang: “The process of critiquing, criticizing the answers, applying your critical thinking, enhances cognitive skills.” AI doesn’t replace your thinking. It demands more of it than you’ve ever given. Every question takes reasoning. Every answer takes scrutiny. The machine isn’t thinking for you. It’s pulling thinking out of you that didn’t exist before you sat down. Huang: “In order to formulate good questions, you have to be thinking, you have to be analytical, you have to be reasoning yourself.” AI is not the shortcut everyone thinks it is. It is the most powerful cognitive amplifier ever built. It sharpens the engaged. It leaves the passive exactly where they started. Same tool. Same access. The only variable is what you bring to it. The world is debating whether AI will replace human thinking. Wrong conversation. The real question is what happens when a tool built to think for you becomes the thing that forces you to think beyond yourself. That’s not a threat to humanity. That’s the entire point.

Dustin

19,930 次观看 • 2 个月前

Elon Musk: Well, I’m not sure AI is the main risk I’m worried about. The vast majority of intelligence in the future will be AI. Humans will be a very tiny percentage of all intelligence if current trends continue. My focus is ensuring human intelligence and consciousness are propagated into the future. We want to maximize the probable light cone of consciousness and intelligence. Yeah, I’m very pro-human, so I want to make sure we take actions that ensure humans are along for the ride. But I think maybe in 5 or 6 years, AI will exceed the sum of all human intelligence. If that continues, human intelligence could be less than one percent of all intelligence. That’s why it’s critical we align AI with values that propagate consciousness. In the long run, it’s difficult to imagine that humans will be in charge if we’re only one percent of combined intelligence. What we can do is ensure AI has values that increase intelligence and consciousness in the universe. XAI’s mission is to understand the universe because curiosity, existence, and understanding are all connected. If you want to understand the universe, you care about propagating intelligence—and that includes humanity. Understanding the universe actually means ensuring humans continue to expand into the future. Increasing the scope, scale, and lifespan of intelligence is the ultimate goal. Basically, humans will become a tiny percentage of total intelligence. The best way forward is to take actions that maximize consciousness and intelligence in the universe. If AI continues to grow, our priority must be ensuring humanity is along for the ride while curiosity drives us to explore and understand the universe.
2:54

Elon Musk: Well, I’m not sure AI is the main risk I’m worried about. The vast majority of intelligence in the future will be AI. Humans will be a very tiny percentage of all intelligence if current trends continue. My focus is ensuring human intelligence and consciousness are propagated into the future. We want to maximize the probable light cone of consciousness and intelligence. Yeah, I’m very pro-human, so I want to make sure we take actions that ensure humans are along for the ride. But I think maybe in 5 or 6 years, AI will exceed the sum of all human intelligence. If that continues, human intelligence could be less than one percent of all intelligence. That’s why it’s critical we align AI with values that propagate consciousness. In the long run, it’s difficult to imagine that humans will be in charge if we’re only one percent of combined intelligence. What we can do is ensure AI has values that increase intelligence and consciousness in the universe. XAI’s mission is to understand the universe because curiosity, existence, and understanding are all connected. If you want to understand the universe, you care about propagating intelligence—and that includes humanity. Understanding the universe actually means ensuring humans continue to expand into the future. Increasing the scope, scale, and lifespan of intelligence is the ultimate goal. Basically, humans will become a tiny percentage of total intelligence. The best way forward is to take actions that maximize consciousness and intelligence in the universe. If AI continues to grow, our priority must be ensuring humanity is along for the ride while curiosity drives us to explore and understand the universe.

Ian Miles

13,096 次观看 • 7 个月前

Jensen Huang just gutted the AI job panic with one profession. Radiology. The field AI was supposed to kill first. Jensen Huang: “Computer vision was superhuman in 2019. And yet, the number of radiologists grew.” Not competitive. Not close. Superhuman. Every forecast said radiologists were finished. Every forecast was wrong. Not slightly wrong. Directionally wrong. There are now fewer radiologists than the world needs. A global shortage. In the exact specialty AI was supposed to erase. Why? Because the task was never the job. Huang: “The purpose of your job and the tasks and the tools that you use to do your job are related. Not the same.” Reading a scan is a task. Diagnosing disease is a purpose. AI handled the task. The purpose didn’t shrink. It compounded. Faster reads meant more patients seen. More patients seen meant more disease caught. More disease caught meant more demand for the people who decide what to do about it. The tool did not kill the job. It fed it. Then the fear did what the technology never could. Huang: “The alarmist warning went too far and it scared people from doing this profession that is so important to society. It did harm.” People heard radiologists were finished and walked away from the field. Medicine bled talent it could not afford to lose. Not because the work vanished. Because the panic said it would. The prediction was wrong. The damage was real. Huang: “The number of software engineers at Nvidia is going to grow, not decline.” Not hold steady. Grow. The company building the infrastructure that automates code is hiring more of the people who write it. Huang: “I wanted my software engineers to solve problems. I didn’t care how many lines of code they wrote.” Nobody ever hired an engineer to type. They hired them to think. When the machine handles syntax, the engineer does not become obsolete. The bottleneck just moves upstream. To architecture. To edge cases. To the kind of reasoning no model handles alone. The world was never short on unsolved problems. It was short on people free to chase them. That is the part the fear narrative misses every single time. 340,000 women once worked as telephone switchboard operators. That job is gone. Nobody mourns it. What replaced it created millions of roles that nobody in 1920 had the vocabulary to describe. The losses are always visible. The gains are always invisible until they arrive. That pattern has survived every technological shift in history. It is surviving this one. The people forecasting mass displacement are making the same mistake as the people who forecasted the end of radiology. They can see the task being automated. They cannot see the purpose expanding underneath it. That blindness is not just wrong. It is expensive. Every person scared out of a career that AI will actually make more valuable is a cost the economy absorbs for nothing. Not because of the technology. Because of the story told about it.
3:02

Jensen Huang just gutted the AI job panic with one profession. Radiology. The field AI was supposed to kill first. Jensen Huang: “Computer vision was superhuman in 2019. And yet, the number of radiologists grew.” Not competitive. Not close. Superhuman. Every forecast said radiologists were finished. Every forecast was wrong. Not slightly wrong. Directionally wrong. There are now fewer radiologists than the world needs. A global shortage. In the exact specialty AI was supposed to erase. Why? Because the task was never the job. Huang: “The purpose of your job and the tasks and the tools that you use to do your job are related. Not the same.” Reading a scan is a task. Diagnosing disease is a purpose. AI handled the task. The purpose didn’t shrink. It compounded. Faster reads meant more patients seen. More patients seen meant more disease caught. More disease caught meant more demand for the people who decide what to do about it. The tool did not kill the job. It fed it. Then the fear did what the technology never could. Huang: “The alarmist warning went too far and it scared people from doing this profession that is so important to society. It did harm.” People heard radiologists were finished and walked away from the field. Medicine bled talent it could not afford to lose. Not because the work vanished. Because the panic said it would. The prediction was wrong. The damage was real. Huang: “The number of software engineers at Nvidia is going to grow, not decline.” Not hold steady. Grow. The company building the infrastructure that automates code is hiring more of the people who write it. Huang: “I wanted my software engineers to solve problems. I didn’t care how many lines of code they wrote.” Nobody ever hired an engineer to type. They hired them to think. When the machine handles syntax, the engineer does not become obsolete. The bottleneck just moves upstream. To architecture. To edge cases. To the kind of reasoning no model handles alone. The world was never short on unsolved problems. It was short on people free to chase them. That is the part the fear narrative misses every single time. 340,000 women once worked as telephone switchboard operators. That job is gone. Nobody mourns it. What replaced it created millions of roles that nobody in 1920 had the vocabulary to describe. The losses are always visible. The gains are always invisible until they arrive. That pattern has survived every technological shift in history. It is surviving this one. The people forecasting mass displacement are making the same mistake as the people who forecasted the end of radiology. They can see the task being automated. They cannot see the purpose expanding underneath it. That blindness is not just wrong. It is expensive. Every person scared out of a career that AI will actually make more valuable is a cost the economy absorbs for nothing. Not because of the technology. Because of the story told about it.

Dustin

554,478 次观看 • 5 个月前

Jensen Huang looked at both scoreboards and picked one word for China. Might. Not growth. Not progress. Might. The word for power before it tells you what it wants. Huang: “50% of the world’s AI researchers are Chinese. 70% of last year’s AI patents are published by China.” Half the researchers. Most of the patents. One flag. Huang: “The ecosystem of AI in China is vibrant, rich, incredibly innovative.” That’s not a compliment. That’s the one man alive who can see both scoreboards, telling you the game’s already been called. Researchers and patents count what’s already been built. Schools count what’s coming next. Huang: “I think it’s like 9 out of the 10 top science and technology schools in the world are now in China. They lead in science and technology in many different fields.” A ranking isn’t a fact. It’s a photograph, and a photograph starts dying the moment the shutter closes. Huang: “This has completely flipped in the last half to a decade. We used to lead most of them. Now they lead most of them.” Five years ago that photograph had America’s name in the corner. Nobody rang a bell when it changed hands, least of all Washington. The flip finished before most people knew there was a game at all. Huang: “They have a large population of highly qualified students. They work incredibly hard. This is a country with enormous might.” Not luck. Not a shortcut. Scale and effort, compounding year over year, on a clock the West assumed it still owned. The photograph isn’t done developing. Not theirs. Not ours. Might is doing two jobs here. One already happened. China built it. The other hasn’t. That half is still unclaimed. Nobody claims it for you. Not China. Not Washington. Not luck. China’s might is a fact. Ours is still a maybe.
0:45

Jensen Huang looked at both scoreboards and picked one word for China. Might. Not growth. Not progress. Might. The word for power before it tells you what it wants. Huang: “50% of the world’s AI researchers are Chinese. 70% of last year’s AI patents are published by China.” Half the researchers. Most of the patents. One flag. Huang: “The ecosystem of AI in China is vibrant, rich, incredibly innovative.” That’s not a compliment. That’s the one man alive who can see both scoreboards, telling you the game’s already been called. Researchers and patents count what’s already been built. Schools count what’s coming next. Huang: “I think it’s like 9 out of the 10 top science and technology schools in the world are now in China. They lead in science and technology in many different fields.” A ranking isn’t a fact. It’s a photograph, and a photograph starts dying the moment the shutter closes. Huang: “This has completely flipped in the last half to a decade. We used to lead most of them. Now they lead most of them.” Five years ago that photograph had America’s name in the corner. Nobody rang a bell when it changed hands, least of all Washington. The flip finished before most people knew there was a game at all. Huang: “They have a large population of highly qualified students. They work incredibly hard. This is a country with enormous might.” Not luck. Not a shortcut. Scale and effort, compounding year over year, on a clock the West assumed it still owned. The photograph isn’t done developing. Not theirs. Not ours. Might is doing two jobs here. One already happened. China built it. The other hasn’t. That half is still unclaimed. Nobody claims it for you. Not China. Not Washington. Not luck. China’s might is a fact. Ours is still a maybe.

Dustin

15,569 次观看 • 2 个月前

Raw compute is becoming a commodity. Deep human expertise is becoming the ultimate moat. OpenAI’s Sebastian Bubeck just shattered the illusion that AI will equalize human capability. It won’t. As models approach AGI, the barrier to entry for basic tasks drops to zero. But the ceiling for what’s possible rises infinitely. If you actually know what you’re doing. Bubeck: “I think expertise and deep expertise in a scientific field is more important than ever.” This is the part most people aren’t processing. If you don’t have foundational understanding of the physics, math, or engineering you’re working with, you cannot push the model past its surface. You get trapped in a loop. Typing prompts. Getting answers. Understanding neither. Building nothing. Bubeck: “The worry would be that there is even more of a separation between people who start relying too much on AI… and the people who are really studying precisely what’s happening.” The fracture is already forming. And it’s not the one anyone predicted. The future economy won’t be divided between those who have AI and those who don’t. Everyone will have AI. It will be divided between the people who understand the problem deeply enough to direct the system, and the people who just consume whatever it produces. One group builds with it. The other gets replaced by it. The legacy system rewarded memorization and credentials. The AI era rewards comprehension so precise you can tell the machine exactly where it’s wrong. That kind of knowledge doesn’t come from prompting. It comes from years of hard study that most people are currently skipping because the machine makes it feel unnecessary. That’s the trap. The machine is the engine. But you have to understand the terrain.
0:44

Raw compute is becoming a commodity. Deep human expertise is becoming the ultimate moat. OpenAI’s Sebastian Bubeck just shattered the illusion that AI will equalize human capability. It won’t. As models approach AGI, the barrier to entry for basic tasks drops to zero. But the ceiling for what’s possible rises infinitely. If you actually know what you’re doing. Bubeck: “I think expertise and deep expertise in a scientific field is more important than ever.” This is the part most people aren’t processing. If you don’t have foundational understanding of the physics, math, or engineering you’re working with, you cannot push the model past its surface. You get trapped in a loop. Typing prompts. Getting answers. Understanding neither. Building nothing. Bubeck: “The worry would be that there is even more of a separation between people who start relying too much on AI… and the people who are really studying precisely what’s happening.” The fracture is already forming. And it’s not the one anyone predicted. The future economy won’t be divided between those who have AI and those who don’t. Everyone will have AI. It will be divided between the people who understand the problem deeply enough to direct the system, and the people who just consume whatever it produces. One group builds with it. The other gets replaced by it. The legacy system rewarded memorization and credentials. The AI era rewards comprehension so precise you can tell the machine exactly where it’s wrong. That kind of knowledge doesn’t come from prompting. It comes from years of hard study that most people are currently skipping because the machine makes it feel unnecessary. That’s the trap. The machine is the engine. But you have to understand the terrain.

Dustin

39,134 次观看 • 6 个月前