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Microsoft invented the knowledge worker to sell more office software. That's not a conspiracy theory. That's what happened. Bill Gates vision was simple: put a PC on every desk. Not to make computing beautiful (that was Steve Jobs). Just to sell more software. Word. Excel. Email. People went to... tutorials to learn these tools. Then companies made them the price of admission for employment. The entire concept of an office career was a sales strategy. Now AI is replacing the very work Microsoft created. And maybe that's not a bad thing. Because sitting in a cubicle using Excel was never how humans were supposed to find purpose. The most curious people have the most fulfilling lives. Not the richest. Not the smartest. The whole knowledge economy was built to sell software. What happens when the software sells itself?show more

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12,976 просмотров • 28 дней назад •via X (Twitter)

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New: Joe Rogan is shocked when Perplexity AI CEO Aravind Srinivas explains how Bill Gates tricked all of America into becoming office workers: SRINIVAS: “Microsoft built this concept of the office worker because they wanted to sell office software.” ROGAN: “Wow. This type of lifestyle is a very recent thing that we’ve just now accepted as normal.” SRINIVAS: “It was the mission of Bill Gates to put a PC on every desk and get people glued to them.” ROGAN: “What a freaking wizard. Boy did they nail that one.” SRINIVAS: “It wasn’t about making computing beautiful like the vision Steve Jobs had. All they cared about was selling computers so they could sell software. When you sell more software that’s when you become rich.” ROGAN: “They’re so sneaky man.” SRINIVAS: “That’s why we all got trained into using Microsoft Word and Excel. Once you learned that then you could work for a company that has a PC on their desk who’s paying Microsoft money to use software.”

New: Joe Rogan is shocked when Perplexity AI CEO Aravind Srinivas explains how Bill Gates tricked all of America into becoming office workers: SRINIVAS: “Microsoft built this concept of the office worker because they wanted to sell office software.” ROGAN: “Wow. This type of lifestyle is a very recent thing that we’ve just now accepted as normal.” SRINIVAS: “It was the mission of Bill Gates to put a PC on every desk and get people glued to them.” ROGAN: “What a freaking wizard. Boy did they nail that one.” SRINIVAS: “It wasn’t about making computing beautiful like the vision Steve Jobs had. All they cared about was selling computers so they could sell software. When you sell more software that’s when you become rich.” ROGAN: “They’re so sneaky man.” SRINIVAS: “That’s why we all got trained into using Microsoft Word and Excel. Once you learned that then you could work for a company that has a PC on their desk who’s paying Microsoft money to use software.”

Fan Commentary: Joe Rogan Recaps

2,195,675 просмотров • 1 месяц назад

Perplexity CEO told Joe Rogan that Microsoft created office work. The dates say otherwise. To be fair to him, half of it is true. Gates really did want a PC on every desk, and Microsoft did make Word and Excel the default tools that almost every office job trains you on. That part is real strategy, and it worked. The history is where it falls apart. Office work didn't start with Microsoft. Purpose-built offices go back to the 1700s, and office jobs boomed in the late 1800s. In the US, clerical work climbed for most of the 20th century and peaked in 1980. Word shipped in 1983. Excel was shipped in 1985. So the lifestyle hit its high point before the software everyone blames for it existed. Microsoft didn't build the wave. It sold the surfboards once the wave was already a century deep. When a founder narrates history, and their own product turns out to be the hero, check the dates. The story flatters the product by design.

Perplexity CEO told Joe Rogan that Microsoft created office work. The dates say otherwise. To be fair to him, half of it is true. Gates really did want a PC on every desk, and Microsoft did make Word and Excel the default tools that almost every office job trains you on. That part is real strategy, and it worked. The history is where it falls apart. Office work didn't start with Microsoft. Purpose-built offices go back to the 1700s, and office jobs boomed in the late 1800s. In the US, clerical work climbed for most of the 20th century and peaked in 1980. Word shipped in 1983. Excel was shipped in 1985. So the lifestyle hit its high point before the software everyone blames for it existed. Microsoft didn't build the wave. It sold the surfboards once the wave was already a century deep. When a founder narrates history, and their own product turns out to be the hero, check the dates. The story flatters the product by design.

Alex Prompter

31,366 просмотров • 26 дней назад

Microsoft makes software for humans doing work. Office and 365 generate ~$100B in revenue/year. I asked Satya Nadella, what will happen to Microsoft as we move towards a world where AIs, not humans, are doing all the knowledge work? An interesting back and forth.

Microsoft makes software for humans doing work. Office and 365 generate ~$100B in revenue/year. I asked Satya Nadella, what will happen to Microsoft as we move towards a world where AIs, not humans, are doing all the knowledge work? An interesting back and forth.

Dwarkesh Patel

241,573 просмотров • 8 месяцев назад

For the past few years, the product we sold was software. Beautiful software that helped you get your work done. In the years ahead, the product we’ll sell is actual knowledge work: repetitive tasks you can fully hand off to Notion Agents. Here’s a quick demo:

For the past few years, the product we sold was software. Beautiful software that helped you get your work done. In the years ahead, the product we’ll sell is actual knowledge work: repetitive tasks you can fully hand off to Notion Agents. Here’s a quick demo:

Akshay Kothari

42,070 просмотров • 8 месяцев назад

CLAUDE CODE CREATOR: "BUILDING SOFTWARE IS GOING TO BE A SKILL LIKE KNOWING HOW TO SEND A TEXT MESSAGE." Boris Cherny, the creator of Claude Code, just made the most important prediction about software in years. His parallel: the printing press. Before the printing press, 10% of Europeans were literate. Their entire job was reading and writing for people who couldn't. In the 50 years after it was invented, more literature was published than in the thousand years before. Book costs dropped 100x. Literacy went from 10% to 70%. He believes the same thing is about to happen to software. Faster than 50 years. The best person to write accounting software isn't an engineer. It's a great accountant. Because knowing the domain is the hard part. Coding is just the tool. AI just made that tool available to everyone. Doctors will build medical software. Teachers will build education tools. Accountants will build accounting systems. Without a single engineer involved. We're not watching software get easier. We're watching it get democratized the way literacy did 600 years ago. Bookmark this post. The full interview is in the video below.

CLAUDE CODE CREATOR: "BUILDING SOFTWARE IS GOING TO BE A SKILL LIKE KNOWING HOW TO SEND A TEXT MESSAGE." Boris Cherny, the creator of Claude Code, just made the most important prediction about software in years. His parallel: the printing press. Before the printing press, 10% of Europeans were literate. Their entire job was reading and writing for people who couldn't. In the 50 years after it was invented, more literature was published than in the thousand years before. Book costs dropped 100x. Literacy went from 10% to 70%. He believes the same thing is about to happen to software. Faster than 50 years. The best person to write accounting software isn't an engineer. It's a great accountant. Because knowing the domain is the hard part. Coding is just the tool. AI just made that tool available to everyone. Doctors will build medical software. Teachers will build education tools. Accountants will build accounting systems. Without a single engineer involved. We're not watching software get easier. We're watching it get democratized the way literacy did 600 years ago. Bookmark this post. The full interview is in the video below.

SCOTTY BEAM

10,786 просмотров • 1 месяц назад

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.

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

554,159 просмотров • 4 месяцев назад

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.

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 просмотров • 1 месяц назад

.Opendoor CEO Kaz Nejatian on how investors misunderstand the company: "Opendoor is a market maker, not a prop desk." The business is to make it easier to buy and sell houses, not to profit from owning them. "The underlying assumptions about the business are fundamentally wrong. Those assumptions were held by some people inside the building for a while. People in New York were wrong. People in San Francisco were also wrong. Saying Opendoor is an asset manager that happens to have software is like saying Amazon in its early days was a warehouser of books that just happened to have software. Did Amazon warehouse books? Sure. But was it a warehouser of books? No. That's not where the leverage comes from. There's a very real difference between a prop desk and a market maker. If you're a prop desk and you hold assets for profit, you do one set of things. If you're a market maker and your fundamental job is to not hold assets for profit, you do a different set of things. Opendoor is a market maker, not a prop desk. Do we have to be very good at underwriting? Damn right we do. But there's a real difference about where that underwriting engine is pointed. Citadel gathers a crap ton of data. The end matters as much as the means."

.Opendoor CEO Kaz Nejatian on how investors misunderstand the company: "Opendoor is a market maker, not a prop desk." The business is to make it easier to buy and sell houses, not to profit from owning them. "The underlying assumptions about the business are fundamentally wrong. Those assumptions were held by some people inside the building for a while. People in New York were wrong. People in San Francisco were also wrong. Saying Opendoor is an asset manager that happens to have software is like saying Amazon in its early days was a warehouser of books that just happened to have software. Did Amazon warehouse books? Sure. But was it a warehouser of books? No. That's not where the leverage comes from. There's a very real difference between a prop desk and a market maker. If you're a prop desk and you hold assets for profit, you do one set of things. If you're a market maker and your fundamental job is to not hold assets for profit, you do a different set of things. Opendoor is a market maker, not a prop desk. Do we have to be very good at underwriting? Damn right we do. But there's a real difference about where that underwriting engine is pointed. Citadel gathers a crap ton of data. The end matters as much as the means."

Business Breakdowns

43,691 просмотров • 2 месяцев назад

"SaaS was Software as a Service. I believe it's going to be service as software." General Catalyst's Madhu Namburi on the AI roll-up thesis. Services is a $20 trillion market.... multiple times the size of software. not the PE playbook of debt and cost-cutting. Venture is buying legacy companies and using AI to drive growth

"SaaS was Software as a Service. I believe it's going to be service as software." General Catalyst's Madhu Namburi on the AI roll-up thesis. Services is a $20 trillion market.... multiple times the size of software. not the PE playbook of debt and cost-cutting. Venture is buying legacy companies and using AI to drive growth

Deirdre Bosa

86,713 просмотров • 2 месяцев назад

Marc Andreessen (Marc Andreessen 🇺🇸): Airbnb could have been boutique booking software. Uber could have been taxi dispatch software. Tesla could have been self-driving software. They decided to take over the entire industry instead: "Silicon Valley between 1950 and 2010 was primarily just in the tools business. You'd build a tool like an operating system or a disk drive, sell it to people, and they'd figure out what to do with it. Then something changed. Alternate universe Airbnb is just boutique booking software. A tiny little business building spreadsheet software. But Brian Chesky decided: we're going to go into the hospitality business and compete with hotels directly. Uber and Lyft in the old world were just taxi dispatch software. In the new world, they're full transportation providers. Tesla in the old world would have just been software for self-driving cars. In the new world, it builds the entire car. Facebook, same thing. Prior to Facebook, if you built online ad software, you were selling it to media companies. Mark said: no. We're just going to beat the media company. We're going to build the entire thing. That was the pivot point when the Valley's ambitions went from just building tools to going directly into incumbent industries. And then AI makes that crystal clear. The winning AI companies are raising billions, tens of billions, in some cases hundreds of billions of dollars. The old world of $10,000,000 or $50,000,000 — where VCs tap out — is just not relevant anymore."

Marc Andreessen (Marc Andreessen 🇺🇸): Airbnb could have been boutique booking software. Uber could have been taxi dispatch software. Tesla could have been self-driving software. They decided to take over the entire industry instead: "Silicon Valley between 1950 and 2010 was primarily just in the tools business. You'd build a tool like an operating system or a disk drive, sell it to people, and they'd figure out what to do with it. Then something changed. Alternate universe Airbnb is just boutique booking software. A tiny little business building spreadsheet software. But Brian Chesky decided: we're going to go into the hospitality business and compete with hotels directly. Uber and Lyft in the old world were just taxi dispatch software. In the new world, they're full transportation providers. Tesla in the old world would have just been software for self-driving cars. In the new world, it builds the entire car. Facebook, same thing. Prior to Facebook, if you built online ad software, you were selling it to media companies. Mark said: no. We're just going to beat the media company. We're going to build the entire thing. That was the pivot point when the Valley's ambitions went from just building tools to going directly into incumbent industries. And then AI makes that crystal clear. The winning AI companies are raising billions, tens of billions, in some cases hundreds of billions of dollars. The old world of $10,000,000 or $50,000,000 — where VCs tap out — is just not relevant anymore."

David Senra

132,916 просмотров • 26 дней назад

Marc Andreessen (Marc Andreessen 🇺🇸): Airbnb could have been boutique booking software. Uber could have been taxi dispatch software. Tesla could have been self-driving software. They decided to take over the entire industry instead: "Silicon Valley between 1950 and 2010 was primarily just in the tools business. You'd build a tool like an operating system or a disk drive, sell it to people, and they'd figure out what to do with it. Then something changed. Alternate universe Airbnb is just boutique booking software. A tiny little business building spreadsheet software. But Brian Chesky decided: we're going to go into the hospitality business and compete with hotels directly. Uber and Lyft in the old world were just taxi dispatch software. In the new world, they're full transportation providers. Tesla in the old world would have just been software for self-driving cars. In the new world, it builds the entire car. Facebook, same thing. Prior to Facebook, if you built online ad software, you were selling it to media companies. Mark said: no. We're just going to beat the media company. We're going to build the entire thing. That was the pivot point when the Valley's ambitions went from just building tools to going directly into incumbent industries. And then AI makes that crystal clear. The winning AI companies are raising billions, tens of billions, in some cases hundreds of billions of dollars. The old world of $10,000,000 or $50,000,000 — where VCs tap out — is just not relevant anymore."

Marc Andreessen (Marc Andreessen 🇺🇸): Airbnb could have been boutique booking software. Uber could have been taxi dispatch software. Tesla could have been self-driving software. They decided to take over the entire industry instead: "Silicon Valley between 1950 and 2010 was primarily just in the tools business. You'd build a tool like an operating system or a disk drive, sell it to people, and they'd figure out what to do with it. Then something changed. Alternate universe Airbnb is just boutique booking software. A tiny little business building spreadsheet software. But Brian Chesky decided: we're going to go into the hospitality business and compete with hotels directly. Uber and Lyft in the old world were just taxi dispatch software. In the new world, they're full transportation providers. Tesla in the old world would have just been software for self-driving cars. In the new world, it builds the entire car. Facebook, same thing. Prior to Facebook, if you built online ad software, you were selling it to media companies. Mark said: no. We're just going to beat the media company. We're going to build the entire thing. That was the pivot point when the Valley's ambitions went from just building tools to going directly into incumbent industries. And then AI makes that crystal clear. The winning AI companies are raising billions, tens of billions, in some cases hundreds of billions of dollars. The old world of $10,000,000 or $50,000,000 — where VCs tap out — is just not relevant anymore."

David Senra

259,016 просмотров • 4 месяцев назад

Wix at 1x ARR is not a SaaS bloodbath. It is a reckoning for businesses that were always just sales and marketing operations dressed up as software. 1. The client-server to SaaS shift was more disruptive than anything AI is doing to software right now. This is incremental. The market is finally pricing it that way. 2. SaaS was never that hard to spin up. When the moat is a sales team and a logo, the multiple compresses. That is not a surprise, that is gravity. 3. The companies that survive this are the ones with complex customer relationships and data sets that cannot be recreated. Wix does not have that. Base44 might not either. 4. Vibe coding tools doing $170M ARR getting priced at $2.1BN is the same mistake in reverse. Getting real workflow change adopted at scale takes longer than the ARR curve suggests. 5. The best software companies right now are not pure software companies. The ones with physical world integration and proprietary data loops are harder to replicate and harder to compress. 6. Everyone who raised at 20x ARR on a sales-led motion took on leverage they did not call leverage. It cuts just as hard on the way down. The bloodbath is not random. It is selection pressure doing exactly what it is supposed to do. Will Quist

Slow Ventures

10,068 просмотров • 21 дней назад

Anthropic CEO Dario Amodei: "Software is going to become cheap. Maybe essentially free." In a 32-minute Davos interview, he says the thing software companies don't want to hear. When the tool costs nothing, the money goes to whoever knows how to wield it. That skill, not the software, is what the first solo fortunes are being built on. Watch the interview, then see the skill in the article below. Save this.

Anthropic CEO Dario Amodei: "Software is going to become cheap. Maybe essentially free." In a 32-minute Davos interview, he says the thing software companies don't want to hear. When the tool costs nothing, the money goes to whoever knows how to wield it. That skill, not the software, is what the first solo fortunes are being built on. Watch the interview, then see the skill in the article below. Save this.

Zephyr

74,105 просмотров • 1 месяц назад

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 просмотров • 2 месяцев назад

Palantir CTO Shyam Sankar on the software stock sell-off: "Modestly— we're the only company that has the knowledge to turn AI into economic value." "It speaks to the amount of fear that exists." "People are looking around at the software they've bought historically and are saying, 'maybe it's not actually that valuable.'" "If you look at Palantir— we've had the 10th quarter of accelerating growth on a very substantial basis."

Palantir CTO Shyam Sankar on the software stock sell-off: "Modestly— we're the only company that has the knowledge to turn AI into economic value." "It speaks to the amount of fear that exists." "People are looking around at the software they've bought historically and are saying, 'maybe it's not actually that valuable.'" "If you look at Palantir— we've had the 10th quarter of accelerating growth on a very substantial basis."

Jawwwn

93,306 просмотров • 6 месяцев назад

Jensen Huang says we’ve passed the point of no return. “Software is not just a tool. Now software uses tools.” For the first time, intelligence is generated in real time as tokens. Now that it’s profitable, the only option is to scale. “We just have to make more of it.”

Jensen Huang says we’ve passed the point of no return. “Software is not just a tool. Now software uses tools.” For the first time, intelligence is generated in real time as tokens. Now that it’s profitable, the only option is to scale. “We just have to make more of it.”

vitrupo

28,345 просмотров • 5 месяцев назад

Satya Nadella was asked directly: in two years, will Microsoft have more engineers or fewer ? He didn't answer with a headcount. He answered with a job description that doesn't exist yet. In the 1980s, if someone had predicted 3.5 billion people would spend their days typing, the world would have laughed. Nobody needs 3.5 billion typists. Except that's exactly what happened and every one of them had a wage, a title, and a career built around it. Now here's where it gets interesting. The software developer of the future isn't writing code. They're managing 100 agents, 1,000 agents and doing something Nadella's team just named for the first time. "One of the new things that we are learning is what I'll call cognitive coverage." His point: when your entire codebase is written by agents, the human job becomes comprehending what was built. Auditing it. Understanding the decisions the agent made and why. That is not a task AI can replace because the AI is the thing being understood. So do the math on what that means. The workflow changed. The artifact changed. The input output format of software development changed. And the job changed with it not away, but upward. "That's the job of a software developer. In order to do that you've got to go to school. You've got to learn computer science and have cognitive coverage." Nadella is not saying jobs are safe. He's saying the jobs that survive are the ones AI cannot verify. And the unverifiable part of human work the meeting observations, the judgment calls, the things that leave no trace is exactly what no model can be trained on. I wonder why nobody in San Francisco is talking about that.

Satya Nadella was asked directly: in two years, will Microsoft have more engineers or fewer ? He didn't answer with a headcount. He answered with a job description that doesn't exist yet. In the 1980s, if someone had predicted 3.5 billion people would spend their days typing, the world would have laughed. Nobody needs 3.5 billion typists. Except that's exactly what happened and every one of them had a wage, a title, and a career built around it. Now here's where it gets interesting. The software developer of the future isn't writing code. They're managing 100 agents, 1,000 agents and doing something Nadella's team just named for the first time. "One of the new things that we are learning is what I'll call cognitive coverage." His point: when your entire codebase is written by agents, the human job becomes comprehending what was built. Auditing it. Understanding the decisions the agent made and why. That is not a task AI can replace because the AI is the thing being understood. So do the math on what that means. The workflow changed. The artifact changed. The input output format of software development changed. And the job changed with it not away, but upward. "That's the job of a software developer. In order to do that you've got to go to school. You've got to learn computer science and have cognitive coverage." Nadella is not saying jobs are safe. He's saying the jobs that survive are the ones AI cannot verify. And the unverifiable part of human work the meeting observations, the judgment calls, the things that leave no trace is exactly what no model can be trained on. I wonder why nobody in San Francisco is talking about that.

Vikram M

96,897 просмотров • 1 месяц назад

🚨 HOLY SMOKES. The software firm that New Jersey Gov. Mikie Sherrill (D) blamed for registering thousands of noncitizens to vote just CONFIRMED there WAS no "software glitch..." ...NJ KNOWINGLY registered them to vote, and it happened in the Division of Elections! JOHN SOLOMON: "The governor said 'not our fault. It was a software glitch'...we reached out to the software company that runs New Jersey's system. And you know what they said? There's no software glitch AT ALL." "The state of New Jersey KNEW they were putting non-citizens, i.e. foreigners, on to the voter rolls." "It is the state's responsibility to determine who is a eligible vote and who's not. The software had it marked RIGHT." 🤯 "That is a major contradiction. And just a little while ago, the Assistant Attorney General for Civil Rights in the US Justice Department, Harmeet K. Dhillon, announced that there is an investigation of New Jersey now into whether they knowingly were allowing non-citizens to vote and to get on the voter rolls." John Solomon

🚨 HOLY SMOKES. The software firm that New Jersey Gov. Mikie Sherrill (D) blamed for registering thousands of noncitizens to vote just CONFIRMED there WAS no "software glitch..." ...NJ KNOWINGLY registered them to vote, and it happened in the Division of Elections! JOHN SOLOMON: "The governor said 'not our fault. It was a software glitch'...we reached out to the software company that runs New Jersey's system. And you know what they said? There's no software glitch AT ALL." "The state of New Jersey KNEW they were putting non-citizens, i.e. foreigners, on to the voter rolls." "It is the state's responsibility to determine who is a eligible vote and who's not. The software had it marked RIGHT." 🤯 "That is a major contradiction. And just a little while ago, the Assistant Attorney General for Civil Rights in the US Justice Department, Harmeet K. Dhillon, announced that there is an investigation of New Jersey now into whether they knowingly were allowing non-citizens to vote and to get on the voter rolls." John Solomon

Eric Daugherty

97,944 просмотров • 12 дней назад

Sam Altman said something recently that most people skimmed and immediately forgot. That was a mistake. Altman: “If it’s me and X hundred GPUs, we can do the work of a whole software team.” Not twice as fast. Not three times as efficient. One person. One team’s output. Zero team. The entire architecture of the modern company was built to solve a single problem. Coordinating human labor. Org charts. Management layers. Hiring pipelines. Performance reviews. HR departments. Slack channels with forty people and no decisions. Every piece of corporate infrastructure exists because humans are slow, expensive, and need to be organized. Remove the coordination problem and the infrastructure collapses on contact. We spent two years debating whether AI makes workers more productive. That was the wrong debate entirely. 2x productivity is a raise. One person replacing an entire team is a structural extinction event. Altman: “You start to hear people say I’m able to do the work of a whole team with these tools.” This is not a productivity story. This is a headcount story. Every company on Earth is currently valued on the assumption that scaling output requires scaling people. That assumption is wrong. It was always wrong. We just never had the tool to prove it. The company that figures this out first does not get a competitive advantage. It gets an irreversible one. One operator. Hundreds of GPUs. The output of a team that no longer needs to exist. No salaries. No standups. No friction. Most people will read Altman’s words, nod, and scroll past. A small number will understand that something structural just shifted beneath them. They will build accordingly. The companies that define the next decade will not be the ones with the most people. They will be the ones with the fewest people who understood what compute replaced. For a hundred years, you needed more people to do more work. That was never a strategy. It was a constraint we mistook for a law. The org chart was a monument to human limitation. The limitation just vanished. And everything built on top of it is now standing on nothing.

Sam Altman said something recently that most people skimmed and immediately forgot. That was a mistake. Altman: “If it’s me and X hundred GPUs, we can do the work of a whole software team.” Not twice as fast. Not three times as efficient. One person. One team’s output. Zero team. The entire architecture of the modern company was built to solve a single problem. Coordinating human labor. Org charts. Management layers. Hiring pipelines. Performance reviews. HR departments. Slack channels with forty people and no decisions. Every piece of corporate infrastructure exists because humans are slow, expensive, and need to be organized. Remove the coordination problem and the infrastructure collapses on contact. We spent two years debating whether AI makes workers more productive. That was the wrong debate entirely. 2x productivity is a raise. One person replacing an entire team is a structural extinction event. Altman: “You start to hear people say I’m able to do the work of a whole team with these tools.” This is not a productivity story. This is a headcount story. Every company on Earth is currently valued on the assumption that scaling output requires scaling people. That assumption is wrong. It was always wrong. We just never had the tool to prove it. The company that figures this out first does not get a competitive advantage. It gets an irreversible one. One operator. Hundreds of GPUs. The output of a team that no longer needs to exist. No salaries. No standups. No friction. Most people will read Altman’s words, nod, and scroll past. A small number will understand that something structural just shifted beneath them. They will build accordingly. The companies that define the next decade will not be the ones with the most people. They will be the ones with the fewest people who understood what compute replaced. For a hundred years, you needed more people to do more work. That was never a strategy. It was a constraint we mistook for a law. The org chart was a monument to human limitation. The limitation just vanished. And everything built on top of it is now standing on nothing.

Dustin

26,721 просмотров • 3 месяцев назад

Dario Amodei, CEO of Anthropic, just shortened your career timeline. His own engineers have stopped writing code. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most advanced AI on Earth are already being replaced by what they built. Not in theory. Not in a forecast. Inside the building. Right now. Amodei: “I think we might be 6 to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” Six to twelve months. Not from automating busywork. From replacing the full scope of what a software engineer does. Architecture. Logic. Debugging. Deployment. The entire chain. Software engineering is not some fading trade. It is the highest-paid, highest-demand, most protected skill the modern economy ever produced. And the man running a frontier lab just gave it a six-month shelf life. If the most technically sophisticated job in the economy falls first, nothing beneath it is safe. That is the inversion no one saw coming. The assumption was always that AI would eat from the bottom. Routine work. Data entry. Simple automation. It started at the top. Engineers first. Then analysts. Then strategists. Then the managers overseeing work that no longer needs them. The displacement doesn’t crawl upward. It cascades downward. Starting with the people closest to the technology itself. Amodei: “If I had to guess, I would guess that this goes faster than people imagine, and that that key element of code, and increasingly research, going faster than we imagine.” Not just code. Research. Hypotheses. Experiments. Interpretation. Discovery itself. If AI closes that loop, it doesn’t just write software. It improves itself. Every iteration compresses the timeline further. Amodei: “It’s very hard for me to see how it could take longer than a few years.” He is not selling optimism. He is setting a ceiling. A few years. Maximum. For AI to absorb the two most important intellectual functions in the economy. The window to position yourself is not a decade. It is already closing.

Dario Amodei, CEO of Anthropic, just shortened your career timeline. His own engineers have stopped writing code. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most advanced AI on Earth are already being replaced by what they built. Not in theory. Not in a forecast. Inside the building. Right now. Amodei: “I think we might be 6 to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” Six to twelve months. Not from automating busywork. From replacing the full scope of what a software engineer does. Architecture. Logic. Debugging. Deployment. The entire chain. Software engineering is not some fading trade. It is the highest-paid, highest-demand, most protected skill the modern economy ever produced. And the man running a frontier lab just gave it a six-month shelf life. If the most technically sophisticated job in the economy falls first, nothing beneath it is safe. That is the inversion no one saw coming. The assumption was always that AI would eat from the bottom. Routine work. Data entry. Simple automation. It started at the top. Engineers first. Then analysts. Then strategists. Then the managers overseeing work that no longer needs them. The displacement doesn’t crawl upward. It cascades downward. Starting with the people closest to the technology itself. Amodei: “If I had to guess, I would guess that this goes faster than people imagine, and that that key element of code, and increasingly research, going faster than we imagine.” Not just code. Research. Hypotheses. Experiments. Interpretation. Discovery itself. If AI closes that loop, it doesn’t just write software. It improves itself. Every iteration compresses the timeline further. Amodei: “It’s very hard for me to see how it could take longer than a few years.” He is not selling optimism. He is setting a ceiling. A few years. Maximum. For AI to absorb the two most important intellectual functions in the economy. The window to position yourself is not a decade. It is already closing.

Dustin

16,132 просмотров • 3 месяцев назад