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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...

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Dustin

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82,757 次观看 • 4 个月前 •via X (Twitter)

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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

554,271 次观看 • 4 个月前

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 • 3 个月前

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.
1:23

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.

Dustin

12,200 次观看 • 3 个月前

Jensen Huang just described something that should keep every worker in America awake tonight. Not because AI is coming for their job. Because most of them never understood what their job actually was. Huang: “The task of our job and the purpose of our job are related, not the same.” Most people think their job is the thing they do with their hands for eight hours a day. Write code. Fill spreadsheets. Draft emails. Push pixels. That was never the job. That was the task. The job was always the thinking underneath it. 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 tapping on phones and talking, AI has done that just fine. And therefore my job should be gone. But I’m busier than ever.” This is the part nobody wants to sit with. The people panicking about AI aren’t afraid of losing their work. They’re afraid of finding out they never had any. They had a routine. A repetitive motion. A series of keystrokes that felt like purpose. Now a machine does it in four seconds. Huang: “AI has created more than half a million jobs in the last couple of years.” The data says one thing. The fear says another. Because the fear was never about employment numbers. It was about identity. We spent fifty years hunched over keyboards, convinced the hunching was the work. Huang: “The idea that being human means to hunch over on this little thing, typing all the time… 50 years before that, people didn’t do that.” Fifty years. That’s all it took to build an entire identity around a posture. We don’t type for a living. We think for a living. We imagine for a living. The keyboard was always just the delivery mechanism. Never the product. 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 demand was always infinite. The bottleneck was always our fingers. AI doesn’t shrink the workforce. It removes the cap on what the workforce can actually build. Huang: “Companies that use AI have demonstrated the ability to grow faster. When they grow faster, they hire more people.” Growth doesn’t eliminate people. It pulls them in. Every industrial revolution triggered the same panic. Same headlines. Same wrong conclusion. And every single time, the economy didn’t contract. It expanded into territory that didn’t exist before. The real question was never whether AI takes your job. It was whether you were ever anything more than the motions you repeated. Because somewhere in the last fifty years, we stopped asking what the work was for. We just kept typing. And now the typing is done. And millions of people are about to meet themselves for the first time. With nothing to hide behind. Some of them won’t survive what they find.
2:55

Jensen Huang just described something that should keep every worker in America awake tonight. Not because AI is coming for their job. Because most of them never understood what their job actually was. Huang: “The task of our job and the purpose of our job are related, not the same.” Most people think their job is the thing they do with their hands for eight hours a day. Write code. Fill spreadsheets. Draft emails. Push pixels. That was never the job. That was the task. The job was always the thinking underneath it. 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 tapping on phones and talking, AI has done that just fine. And therefore my job should be gone. But I’m busier than ever.” This is the part nobody wants to sit with. The people panicking about AI aren’t afraid of losing their work. They’re afraid of finding out they never had any. They had a routine. A repetitive motion. A series of keystrokes that felt like purpose. Now a machine does it in four seconds. Huang: “AI has created more than half a million jobs in the last couple of years.” The data says one thing. The fear says another. Because the fear was never about employment numbers. It was about identity. We spent fifty years hunched over keyboards, convinced the hunching was the work. Huang: “The idea that being human means to hunch over on this little thing, typing all the time… 50 years before that, people didn’t do that.” Fifty years. That’s all it took to build an entire identity around a posture. We don’t type for a living. We think for a living. We imagine for a living. The keyboard was always just the delivery mechanism. Never the product. 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 demand was always infinite. The bottleneck was always our fingers. AI doesn’t shrink the workforce. It removes the cap on what the workforce can actually build. Huang: “Companies that use AI have demonstrated the ability to grow faster. When they grow faster, they hire more people.” Growth doesn’t eliminate people. It pulls them in. Every industrial revolution triggered the same panic. Same headlines. Same wrong conclusion. And every single time, the economy didn’t contract. It expanded into territory that didn’t exist before. The real question was never whether AI takes your job. It was whether you were ever anything more than the motions you repeated. Because somewhere in the last fifty years, we stopped asking what the work was for. We just kept typing. And now the typing is done. And millions of people are about to meet themselves for the first time. With nothing to hide behind. Some of them won’t survive what they find.

Dustin

92,704 次观看 • 3 个月前

Jensen Huang just settled the AI jobs debate in one answer. Huang: “All the engineers at NVIDIA today use AI. AI basically does most of our coding. And yet we’re hiring more engineers than ever.” The largest AI chip company on Earth. Every engineer uses AI. AI handles most of the coding. And headcount is going up. Not down. Up. For three years, the consensus was simple. AI automates, humans exit. NVIDIA just broke that thesis in one sentence. Huang: “Our ambitions are greater than ever. Our expectations are greater than ever.” This is the part nobody sits with long enough. AI didn’t reduce what NVIDIA could do. It detonated what they were willing to attempt. The workload didn’t shrink. The vision outgrew it. When ambition scales faster than automation, you don’t cut people. You add them. Because you’re no longer solving last year’s problems. You’re chasing things that weren’t conceivable twelve months ago. That’s not a job killer. That’s a job multiplier. But only inside the organizations that actually moved. Huang: “How is that a bad condition for our country?” It’s not. The bad condition is what’s forming between the movers and everyone else. Between the companies that retooled and the ones still running pilots. Between the countries that committed and the ones that formed committees. Between the workers who adapted six months ago and the ones still waiting for permission. Huang: “In just the last six months it went from incredible but not useful to useful and incredible.” Six months. Not six years. Not a carefully managed transition. Six months between novelty and necessity. That’s not a timeline. That’s a verdict. And when that gap compounds quarterly, “we’ll get to it next year” stops being patience. It’s a resignation letter you haven’t signed yet. Huang: “We’re all in on it. Supercharged by it. Exhilarated by it.” Not cautious. Not hedging. Not “exploring potential use cases.” All in. Supercharged. Exhilarated. The man building the infrastructure for the entire era isn’t theorizing about what AI might become. He’s documenting what it already is. The shift didn’t send a warning. It landed. Quiet. Six months ago. And while half the world debated readiness, the other half became unreachable. The question was never whether AI replaces you. It’s whether AI moves the standard your entire industry measures itself against. NVIDIA just told you it already did. Not because anyone got worse. Because the people who moved first quietly redefined what good looks like. And that new standard doesn’t wait. Doesn’t negotiate. It just becomes the baseline. Whether you moved with it or not.
1:23

Jensen Huang just settled the AI jobs debate in one answer. Huang: “All the engineers at NVIDIA today use AI. AI basically does most of our coding. And yet we’re hiring more engineers than ever.” The largest AI chip company on Earth. Every engineer uses AI. AI handles most of the coding. And headcount is going up. Not down. Up. For three years, the consensus was simple. AI automates, humans exit. NVIDIA just broke that thesis in one sentence. Huang: “Our ambitions are greater than ever. Our expectations are greater than ever.” This is the part nobody sits with long enough. AI didn’t reduce what NVIDIA could do. It detonated what they were willing to attempt. The workload didn’t shrink. The vision outgrew it. When ambition scales faster than automation, you don’t cut people. You add them. Because you’re no longer solving last year’s problems. You’re chasing things that weren’t conceivable twelve months ago. That’s not a job killer. That’s a job multiplier. But only inside the organizations that actually moved. Huang: “How is that a bad condition for our country?” It’s not. The bad condition is what’s forming between the movers and everyone else. Between the companies that retooled and the ones still running pilots. Between the countries that committed and the ones that formed committees. Between the workers who adapted six months ago and the ones still waiting for permission. Huang: “In just the last six months it went from incredible but not useful to useful and incredible.” Six months. Not six years. Not a carefully managed transition. Six months between novelty and necessity. That’s not a timeline. That’s a verdict. And when that gap compounds quarterly, “we’ll get to it next year” stops being patience. It’s a resignation letter you haven’t signed yet. Huang: “We’re all in on it. Supercharged by it. Exhilarated by it.” Not cautious. Not hedging. Not “exploring potential use cases.” All in. Supercharged. Exhilarated. The man building the infrastructure for the entire era isn’t theorizing about what AI might become. He’s documenting what it already is. The shift didn’t send a warning. It landed. Quiet. Six months ago. And while half the world debated readiness, the other half became unreachable. The question was never whether AI replaces you. It’s whether AI moves the standard your entire industry measures itself against. NVIDIA just told you it already did. Not because anyone got worse. Because the people who moved first quietly redefined what good looks like. And that new standard doesn’t wait. Doesn’t negotiate. It just becomes the baseline. Whether you moved with it or not.

Dustin

21,436 次观看 • 2 个月前

🚨 Nvidia CEO Jensen Huang: “The narratives of AI destroying jobs is not going to help America — It's false … Somebody said that AI Is going to destroy all of the software engineering jobs … We now have agentic AI inside Nvidia … The software engineers are busier than ever.”
6:22

🚨 Nvidia CEO Jensen Huang: “The narratives of AI destroying jobs is not going to help America — It's false … Somebody said that AI Is going to destroy all of the software engineering jobs … We now have agentic AI inside Nvidia … The software engineers are busier than ever.”

Chief Nerd

712,720 次观看 • 3 个月前

Nvidia CEO Jensen Huang just called the idea of AI replacing software engineers "complete nonsense" "The number of software engineers is actually increasing... if you can hire a software engineer and generate $9 trillion worth of productive work, why wouldn't you want to hire more? If that line were flat, then obviously people would hire fewer software engineers. But because the output is so incredible, people want to hire more This is going to show up in our economy somehow, soon. Useful AI has arrived"
0:42

Nvidia CEO Jensen Huang just called the idea of AI replacing software engineers "complete nonsense" "The number of software engineers is actually increasing... if you can hire a software engineer and generate $9 trillion worth of productive work, why wouldn't you want to hire more? If that line were flat, then obviously people would hire fewer software engineers. But because the output is so incredible, people want to hire more This is going to show up in our economy somehow, soon. Useful AI has arrived"

X Freeze

109,113 次观看 • 2 个月前

Jensen Huang just explained why China is winning the technology race in two sentences. Huang: “Our country’s leaders… they’re mostly lawyers. Most of their leaders are incredible engineers.” One country sends engineers to lead. The other sends lawyers. One builds. The other regulates what was already built. Huang: “They showed up at precisely the time when technology is going through that exponential.” China did not stumble into the AI era. They arrived engineered for it. The education system produces engineers at a scale the West refuses to match. The competition is not tough. It is Darwinian. The culture rewards builders. Not commentators. Not consultants. Builders. Then the accelerant. Open source. When your talent pool runs that deep and that hungry, you do not hoard breakthroughs. You release them. The community multiplies everything. What costs American companies a quarter, Chinese teams finish in weeks. Not because they are smarter. Because the entire system points one direction. Zero friction between idea and execution. No committee. No review board. No eighteen-month compliance process. Then Huang said the part that should terrify Washington. Huang: “Their country was built out of poverty.” Comfort makes nations careful. Poverty makes nations relentless. When you built everything from nothing, you do not slow down to protect it. You accelerate because you still taste what nothing felt like. America built its dominance with engineers. The highways. The moon landing. The semiconductor. The internet. Then it handed the keys to the lawyers. Compliance departments. Regulatory bodies. Oversight committees. Review processes for the review processes. Every layer of protection is a layer of friction. And friction is a luxury you cannot afford when your competitor rides an exponential curve. Fridman: “It’s a builder nation.” Huang: “Yeah, it’s a builder nation.” No pushback. No qualifier. The West is not being outspent. It is being out-structured. Engineers ask how do we build this faster. Lawyers ask how do we build this without getting sued. One of those questions wins the century. The other writes a detailed report about why it lost.
1:29

Jensen Huang just explained why China is winning the technology race in two sentences. Huang: “Our country’s leaders… they’re mostly lawyers. Most of their leaders are incredible engineers.” One country sends engineers to lead. The other sends lawyers. One builds. The other regulates what was already built. Huang: “They showed up at precisely the time when technology is going through that exponential.” China did not stumble into the AI era. They arrived engineered for it. The education system produces engineers at a scale the West refuses to match. The competition is not tough. It is Darwinian. The culture rewards builders. Not commentators. Not consultants. Builders. Then the accelerant. Open source. When your talent pool runs that deep and that hungry, you do not hoard breakthroughs. You release them. The community multiplies everything. What costs American companies a quarter, Chinese teams finish in weeks. Not because they are smarter. Because the entire system points one direction. Zero friction between idea and execution. No committee. No review board. No eighteen-month compliance process. Then Huang said the part that should terrify Washington. Huang: “Their country was built out of poverty.” Comfort makes nations careful. Poverty makes nations relentless. When you built everything from nothing, you do not slow down to protect it. You accelerate because you still taste what nothing felt like. America built its dominance with engineers. The highways. The moon landing. The semiconductor. The internet. Then it handed the keys to the lawyers. Compliance departments. Regulatory bodies. Oversight committees. Review processes for the review processes. Every layer of protection is a layer of friction. And friction is a luxury you cannot afford when your competitor rides an exponential curve. Fridman: “It’s a builder nation.” Huang: “Yeah, it’s a builder nation.” No pushback. No qualifier. The West is not being outspent. It is being out-structured. Engineers ask how do we build this faster. Lawyers ask how do we build this without getting sued. One of those questions wins the century. The other writes a detailed report about why it lost.

Dustin

440,067 次观看 • 4 个月前

Jensen Huang says the AI productivity story is already showing up in software output - and he thinks the "AI will reduce jobs" narrative is wrong. "In the first few months of 2026, it has nearly tripled." "30 million software developers, representing about $3 trillion worth of GDP, are now producing nearly three times as much output." "That $3 trillion worth of salary is now producing nearly $9 trillion worth of output." "This is the potential. This is the promise of AI." "People talk about AI reducing jobs. Complete nonsense. It's causing more software engineers to be hired." His logic is simple: If a software engineer can create 3x more output with AI, companies do not stop hiring engineers. They want more of them. The bottleneck moves from typing code to deciding what to build, orchestrating systems, and turning that output into products customers actually use.
1:25

Jensen Huang says the AI productivity story is already showing up in software output - and he thinks the "AI will reduce jobs" narrative is wrong. "In the first few months of 2026, it has nearly tripled." "30 million software developers, representing about $3 trillion worth of GDP, are now producing nearly three times as much output." "That $3 trillion worth of salary is now producing nearly $9 trillion worth of output." "This is the potential. This is the promise of AI." "People talk about AI reducing jobs. Complete nonsense. It's causing more software engineers to be hired." His logic is simple: If a software engineer can create 3x more output with AI, companies do not stop hiring engineers. They want more of them. The bottleneck moves from typing code to deciding what to build, orchestrating systems, and turning that output into products customers actually use.

Karl Mehta

13,780 次观看 • 1 个月前

Jensen Huang said fears about AI destroying jobs are nonsense driven by people making things up. Then he gave the most concrete proof I have ever heard. He called it the radiology story. 12 years ago one of the world's most respected computer scientists made a prediction. Computer vision had become superhuman at reading medical scans. It never got tired. It never missed a detail. So he said radiology was finished. He advised nobody to enter the field. The job was going to be wiped out. Jensen said he was absolutely right. Computer vision did penetrate every single radiology stack. Every radiologist today is augmented by it. The task of reading scans became automated. Then the opposite of the prediction happened. Radiology demand went up. The number of radiologists in the world went up. The reason is the distinction Jensen kept coming back to the whole talk. Task versus purpose. The task of a radiologist is reading scans. The purpose is working with doctors to diagnose disease. When the task got automated, radiologists got more productive. More productive meant more patients admitted. More patients meant more scans. More scans meant more profit. More profit meant they hired more radiologists. The field the expert said would be wiped out needed more people than before. Jensen's point was not that AI creates no disruption. His point was that people confuse the task with the job and then make predictions that cause real damage. Fewer people trained in radiology because of that speech. Now there is a shortage. Someone recently said 90% of software coding will disappear. Jensen said Nvidia is hiring more software engineers than ever. Coding is not a software engineer's job. Solving problems is. When you automate the task, you free the person to do the actual job. That is not a job loss. That is an upgrade.
1:37

Jensen Huang said fears about AI destroying jobs are nonsense driven by people making things up. Then he gave the most concrete proof I have ever heard. He called it the radiology story. 12 years ago one of the world's most respected computer scientists made a prediction. Computer vision had become superhuman at reading medical scans. It never got tired. It never missed a detail. So he said radiology was finished. He advised nobody to enter the field. The job was going to be wiped out. Jensen said he was absolutely right. Computer vision did penetrate every single radiology stack. Every radiologist today is augmented by it. The task of reading scans became automated. Then the opposite of the prediction happened. Radiology demand went up. The number of radiologists in the world went up. The reason is the distinction Jensen kept coming back to the whole talk. Task versus purpose. The task of a radiologist is reading scans. The purpose is working with doctors to diagnose disease. When the task got automated, radiologists got more productive. More productive meant more patients admitted. More patients meant more scans. More scans meant more profit. More profit meant they hired more radiologists. The field the expert said would be wiped out needed more people than before. Jensen's point was not that AI creates no disruption. His point was that people confuse the task with the job and then make predictions that cause real damage. Fewer people trained in radiology because of that speech. Now there is a shortage. Someone recently said 90% of software coding will disappear. Jensen said Nvidia is hiring more software engineers than ever. Coding is not a software engineer's job. Solving problems is. When you automate the task, you free the person to do the actual job. That is not a job loss. That is an upgrade.

Ihtesham Ali

14,480 次观看 • 1 个月前

Jensen Huang just made the case for American empire. Said it plain. Didn’t flinch. Didn’t walk it back. And almost nobody caught what he actually admitted. Jensen Huang: “The amount of compute in the United States is a hundred times more than anywhere else in the world.” One hundred times. That is not a market lead. That is a monopoly on the future of intelligence. The kind that compounds every six months until no one else can close the distance. Jensen Huang: “We make sure that the US labs are the first to hear about it and the first chance to buy it.” Every chip Nvidia designs. Every architecture they ship. America gets first access. Everyone else gets what is left. That is not a sales strategy. That is arms distribution with a quarterly earnings call. Jensen Huang: “And if they don’t have enough money, we even invest in them.” The company building the weapons is bankrolling the people who fire them. Nvidia is no longer a public company. It is a state instrument with a stock ticker. Jensen Huang: “Why would you want the United States to give up the world?” The CEO of the most valuable hardware company on earth did not hedge that. Did not qualify it. He said it like it was obvious. Because to him, it is. Nations used to be measured by steel output. Then oil reserves. Then warhead count. Now it is how much intelligence they can produce per second. Compute is no longer a commodity. It is a strategic resource. Like uranium in 1944. Except this one doubles faster than anyone can respond. Europe understands none of this. They are drafting AI regulations. Compliance frameworks. Ethics panels. Risk tiers. They are bringing paperwork to a physics war. You cannot govern intelligence you do not have the silicon to produce. China gets it. That is why they are building fabs, not filing comment periods. Nvidia already made sure the gap is not annual. It is generational. Silicon Valley still thinks it is building consumer software. Huang just told them they are building American infrastructure. Every model trained here runs on machines that exist nowhere else. Every company that scales here scales on silicon no rival can touch. The world thinks Nvidia sells chips. Nvidia sells the ability to think. And they only sell it under one flag.
0:42

Jensen Huang just made the case for American empire. Said it plain. Didn’t flinch. Didn’t walk it back. And almost nobody caught what he actually admitted. Jensen Huang: “The amount of compute in the United States is a hundred times more than anywhere else in the world.” One hundred times. That is not a market lead. That is a monopoly on the future of intelligence. The kind that compounds every six months until no one else can close the distance. Jensen Huang: “We make sure that the US labs are the first to hear about it and the first chance to buy it.” Every chip Nvidia designs. Every architecture they ship. America gets first access. Everyone else gets what is left. That is not a sales strategy. That is arms distribution with a quarterly earnings call. Jensen Huang: “And if they don’t have enough money, we even invest in them.” The company building the weapons is bankrolling the people who fire them. Nvidia is no longer a public company. It is a state instrument with a stock ticker. Jensen Huang: “Why would you want the United States to give up the world?” The CEO of the most valuable hardware company on earth did not hedge that. Did not qualify it. He said it like it was obvious. Because to him, it is. Nations used to be measured by steel output. Then oil reserves. Then warhead count. Now it is how much intelligence they can produce per second. Compute is no longer a commodity. It is a strategic resource. Like uranium in 1944. Except this one doubles faster than anyone can respond. Europe understands none of this. They are drafting AI regulations. Compliance frameworks. Ethics panels. Risk tiers. They are bringing paperwork to a physics war. You cannot govern intelligence you do not have the silicon to produce. China gets it. That is why they are building fabs, not filing comment periods. Nvidia already made sure the gap is not annual. It is generational. Silicon Valley still thinks it is building consumer software. Huang just told them they are building American infrastructure. Every model trained here runs on machines that exist nowhere else. Every company that scales here scales on silicon no rival can touch. The world thinks Nvidia sells chips. Nvidia sells the ability to think. And they only sell it under one flag.

Dustin

57,059 次观看 • 3 个月前

Jensen Huang just drew a line through the entire global workforce. One sentence. No ambiguity. Huang: “If your job is the task, then you’re very highly going to be disrupted.” Not might be. Not eventually. Very highly going to be. That single distinction between a job and a task is the most important career diagnosis anyone will hear this decade. If you show up every day to execute a repeatable process, you are the process. And the machine runs processes better than you. Faster. Cheaper. Without breaks. Without errors. Without a salary negotiation. The moment your role can be written as a checklist, the checklist gets automated. And your desk gets cleared. That is not a warning about the future. That is a description of what is already underway. But Huang did not stop at the diagnosis. He handed you the prescription in the same breath. Huang: “If your job’s purpose includes you certain tasks, then it is vital that you go learn how to use AI to automate those tasks.” Your job includes tasks. But your job is not the tasks. Your job is the judgment around them. The decisions. The context. The instinct for why the work matters and what to do when everything breaks. That stays human. Everything else gets handed to the machine. And the person who hands it over first does not lose their job. They become more valuable than everyone still doing it by hand. Because they just converted every hour they used to spend on execution into hours spent thinking. The accountant who automates data entry does not get replaced. They become the strategist who used to be buried in spreadsheets. The marketer who automates reporting does not get fired. They become the creative who used to be trapped building dashboards. The person who refuses to automate anything becomes the most expensive way to do the cheapest work. Huang: “It is the case that the technology will dislocate and will eliminate many tasks. And because it will automate it.” No softening. No hopeful footnote. Dislocation is coming. Tasks will be eliminated. That part is settled. The only open question is which side of that line you are standing on. The side that lost the tasks. Or the side that gave them away on purpose and kept the work that actually matters. One side gets disrupted. The other side gets dangerous. The gap between those two outcomes is not talent. Not credentials. Not experience. It is whether you learned to use the machine before the machine learned to replace you. That window is still open. It is closing faster than most people are willing to believe. And it does not reopen.
0:30

Jensen Huang just drew a line through the entire global workforce. One sentence. No ambiguity. Huang: “If your job is the task, then you’re very highly going to be disrupted.” Not might be. Not eventually. Very highly going to be. That single distinction between a job and a task is the most important career diagnosis anyone will hear this decade. If you show up every day to execute a repeatable process, you are the process. And the machine runs processes better than you. Faster. Cheaper. Without breaks. Without errors. Without a salary negotiation. The moment your role can be written as a checklist, the checklist gets automated. And your desk gets cleared. That is not a warning about the future. That is a description of what is already underway. But Huang did not stop at the diagnosis. He handed you the prescription in the same breath. Huang: “If your job’s purpose includes you certain tasks, then it is vital that you go learn how to use AI to automate those tasks.” Your job includes tasks. But your job is not the tasks. Your job is the judgment around them. The decisions. The context. The instinct for why the work matters and what to do when everything breaks. That stays human. Everything else gets handed to the machine. And the person who hands it over first does not lose their job. They become more valuable than everyone still doing it by hand. Because they just converted every hour they used to spend on execution into hours spent thinking. The accountant who automates data entry does not get replaced. They become the strategist who used to be buried in spreadsheets. The marketer who automates reporting does not get fired. They become the creative who used to be trapped building dashboards. The person who refuses to automate anything becomes the most expensive way to do the cheapest work. Huang: “It is the case that the technology will dislocate and will eliminate many tasks. And because it will automate it.” No softening. No hopeful footnote. Dislocation is coming. Tasks will be eliminated. That part is settled. The only open question is which side of that line you are standing on. The side that lost the tasks. Or the side that gave them away on purpose and kept the work that actually matters. One side gets disrupted. The other side gets dangerous. The gap between those two outcomes is not talent. Not credentials. Not experience. It is whether you learned to use the machine before the machine learned to replace you. That window is still open. It is closing faster than most people are willing to believe. And it does not reopen.

Dustin

142,553 次观看 • 4 个月前

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?
1:33

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,968,345 次观看 • 2 个月前

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA
1:48

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Karl Mehta

14,983 次观看 • 1 个月前

Uber CEO Dara Khosrowshahi just described the exact moment companies stop hiring engineers. It’s closer than anyone wants to admit. Khosrowshahi: “About 90% of our coders are using AI.” But that’s not the number that matters. 30% of those engineers have become power users. And what’s happening to their output has no historical precedent. Khosrowshahi: “They are showing a clear differentiation in the number of diffs.” A diff is a code release. The purest measure of engineering productivity. Khosrowshahi: “It’s changing their productivity in a way that I’ve never, ever seen before.” Right now, the math still favors hiring. If an average engineer becomes 25% more efficient, Uber hires more engineers to go faster. But that equation has an expiration date. Khosrowshahi: “Maybe 5 years from now as the engineers get more and more productive, I may not decide to add engineering headcount.” The tipping point isn’t when AI replaces engineers. It’s when adding an AI agent and buying GPUs produces more output per dollar than hiring a human. Khosrowshahi: “At that point instead of adding an engineer, I should add agents and buy some more GPUs from Nvidia.” When the CEO of a company built entirely on software says that out loud, it’s not a prediction. It’s a planning assumption. Khosrowshahi: “The job of a coder is going to change from actually writing the code to orchestrating agents who are writing the code.” Not writing. Orchestrating. The engineer becomes the conductor. The AI becomes the orchestra. The most valuable asset in a tech company is officially shifting from human capital to pure compute. And once that math flips, it doesn’t flip back.
1:50

Uber CEO Dara Khosrowshahi just described the exact moment companies stop hiring engineers. It’s closer than anyone wants to admit. Khosrowshahi: “About 90% of our coders are using AI.” But that’s not the number that matters. 30% of those engineers have become power users. And what’s happening to their output has no historical precedent. Khosrowshahi: “They are showing a clear differentiation in the number of diffs.” A diff is a code release. The purest measure of engineering productivity. Khosrowshahi: “It’s changing their productivity in a way that I’ve never, ever seen before.” Right now, the math still favors hiring. If an average engineer becomes 25% more efficient, Uber hires more engineers to go faster. But that equation has an expiration date. Khosrowshahi: “Maybe 5 years from now as the engineers get more and more productive, I may not decide to add engineering headcount.” The tipping point isn’t when AI replaces engineers. It’s when adding an AI agent and buying GPUs produces more output per dollar than hiring a human. Khosrowshahi: “At that point instead of adding an engineer, I should add agents and buy some more GPUs from Nvidia.” When the CEO of a company built entirely on software says that out loud, it’s not a prediction. It’s a planning assumption. Khosrowshahi: “The job of a coder is going to change from actually writing the code to orchestrating agents who are writing the code.” Not writing. Orchestrating. The engineer becomes the conductor. The AI becomes the orchestra. The most valuable asset in a tech company is officially shifting from human capital to pure compute. And once that math flips, it doesn’t flip back.

Dustin

420,288 次观看 • 5 个月前

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.
0:31

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.

Dustin

535,455 次观看 • 4 个月前

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)
0:59

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 次观看 • 2 个月前

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)
1:15

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

Ihtesham Ali

97,145 次观看 • 1 个月前

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."
1:12

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

Patrick OShaughnessy

24,818 次观看 • 6 个月前

Jensen Huang just said an empty executive seat is worth more than the wrong person sitting in it. That’s the CEO of one of the largest companies ever built. Huang: “An empty chair is better than a chair filled with the wrong person. And so I’m never in a hurry.” Every company on Earth treats a vacant executive role as an emergency. Huang treats it as protection. Because the wrong person in the right chair doesn’t just underperform. They corrupt everything around them. They make decisions that have to be unwound. They hire in their own image. They build systems someone has to tear down before real work can start. An empty chair does none of that. An empty chair has an error rate of zero. Most of the damage inside a company never comes from the wrong decision. It comes from the urgency to make one. Huang: “Whether it’s a missing CEO or missing VP of anything, the company will keep moving on. You just have to have the confidence.” That confidence isn’t personality. It’s architecture. Nvidia doesn’t collapse around an absence because it was never built to depend on any one person. Most companies are built around people. When the person leaves, the system breaks. Because the person was the system. Nvidia is built around standards. When the person leaves, the standard holds. The chair is empty. The standard isn’t. Huang: “It buys you enormous amounts of time until you find somebody that is a combination of a lot of things, including, you just like them.” Past every competence screen, every technical evaluation, every strategic assessment, the last question is the most human one there is. Do you want this person in the room. The entire corporate hiring machine optimizes for credentials, experience, and pedigree. Huang optimizes for all of that and then asks the one question no résumé can answer. Most companies hire to fill chairs. Nvidia hires to protect the standard. The company at the foundation of the AI revolution didn’t get there by filling every seat fast. It got there by leaving the wrong ones empty until the right person walked in.
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Jensen Huang just said an empty executive seat is worth more than the wrong person sitting in it. That’s the CEO of one of the largest companies ever built. Huang: “An empty chair is better than a chair filled with the wrong person. And so I’m never in a hurry.” Every company on Earth treats a vacant executive role as an emergency. Huang treats it as protection. Because the wrong person in the right chair doesn’t just underperform. They corrupt everything around them. They make decisions that have to be unwound. They hire in their own image. They build systems someone has to tear down before real work can start. An empty chair does none of that. An empty chair has an error rate of zero. Most of the damage inside a company never comes from the wrong decision. It comes from the urgency to make one. Huang: “Whether it’s a missing CEO or missing VP of anything, the company will keep moving on. You just have to have the confidence.” That confidence isn’t personality. It’s architecture. Nvidia doesn’t collapse around an absence because it was never built to depend on any one person. Most companies are built around people. When the person leaves, the system breaks. Because the person was the system. Nvidia is built around standards. When the person leaves, the standard holds. The chair is empty. The standard isn’t. Huang: “It buys you enormous amounts of time until you find somebody that is a combination of a lot of things, including, you just like them.” Past every competence screen, every technical evaluation, every strategic assessment, the last question is the most human one there is. Do you want this person in the room. The entire corporate hiring machine optimizes for credentials, experience, and pedigree. Huang optimizes for all of that and then asks the one question no résumé can answer. Most companies hire to fill chairs. Nvidia hires to protect the standard. The company at the foundation of the AI revolution didn’t get there by filling every seat fast. It got there by leaving the wrong ones empty until the right person walked in.

Dustin

11,719 次观看 • 22 天前