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For 60 years every computer ever built did the same thing. Stored information and retrieved it on demand. Jensen Huang just explained why that era is over and what replaces it. His framing was the clearest I have ever heard. Think about everything a computer has ever done for...

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Ihtesham Ali

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30,235 views • 4 months ago •via X (Twitter)

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Jensen Huang just described the most fundamental shift in computing since the invention of the computer itself. Almost no one has processed it. Huang: “We went from a retrieval-based computing system to a generative-based computing system.” For fifty years, a computer was a filing cabinet. You made something. Saved it. Stored it. Searched for it later. Every website. Every database. Every app. Every search engine. Same machine. Different skins. Fetch the file. Deliver the file. Display the file. That was computing. Was. Huang: “AI computers are contextually aware, which means that it has to process and generate tokens in real time.” The machine no longer retrieves what someone already made. It generates what you need the instant you ask. Not from a template. Not from a library. From context. Your question. Your moment. Answered by something that didn’t exist until you asked. The old computer found what someone wrote last year. The new computer writes what no one ever has. Every time. From nothing. That sounds subtle. It rewires everything. Huang: “We need a lot of storage in the old world. We need a lot of computation in this new world.” The old economy hoarded data. More files. More servers. More storage. Whoever built the biggest archive won. The new economy burns compute. More processing. More inference. More tokens per second. Whoever commands the most computational power wins. Storage was the currency of the retrieval era. Compute is the currency of the generative era. Every dollar still spent hoarding old files is a dollar not spent on the only thing that matters now. The ability to think in real time. Huang: “We fundamentally changed computing and the way computing is done.” He said it plainly. No drama. No metaphor. Fundamentally changed. The global infrastructure layer shifted from read to write. From looking up what exists to generating what doesn’t. Companies still organized around retrieval are curating a library in a world that no longer reads books. The ones generating answers live, at the speed of the question, are operating on a plane the old model can’t perceive. This is not an upgrade. It is a replacement. The filing cabinet era produced Google, Amazon, and every search-driven empire on the internet. The generative era will produce something that makes all of them look like the card catalog at a public library. The price of entry is not data. It is compute. Raw. Relentless. Infinite. Whoever has the most doesn’t just run the best AI. They write the future. Everyone else is still searching for it.
1:00

Jensen Huang just described the most fundamental shift in computing since the invention of the computer itself. Almost no one has processed it. Huang: “We went from a retrieval-based computing system to a generative-based computing system.” For fifty years, a computer was a filing cabinet. You made something. Saved it. Stored it. Searched for it later. Every website. Every database. Every app. Every search engine. Same machine. Different skins. Fetch the file. Deliver the file. Display the file. That was computing. Was. Huang: “AI computers are contextually aware, which means that it has to process and generate tokens in real time.” The machine no longer retrieves what someone already made. It generates what you need the instant you ask. Not from a template. Not from a library. From context. Your question. Your moment. Answered by something that didn’t exist until you asked. The old computer found what someone wrote last year. The new computer writes what no one ever has. Every time. From nothing. That sounds subtle. It rewires everything. Huang: “We need a lot of storage in the old world. We need a lot of computation in this new world.” The old economy hoarded data. More files. More servers. More storage. Whoever built the biggest archive won. The new economy burns compute. More processing. More inference. More tokens per second. Whoever commands the most computational power wins. Storage was the currency of the retrieval era. Compute is the currency of the generative era. Every dollar still spent hoarding old files is a dollar not spent on the only thing that matters now. The ability to think in real time. Huang: “We fundamentally changed computing and the way computing is done.” He said it plainly. No drama. No metaphor. Fundamentally changed. The global infrastructure layer shifted from read to write. From looking up what exists to generating what doesn’t. Companies still organized around retrieval are curating a library in a world that no longer reads books. The ones generating answers live, at the speed of the question, are operating on a plane the old model can’t perceive. This is not an upgrade. It is a replacement. The filing cabinet era produced Google, Amazon, and every search-driven empire on the internet. The generative era will produce something that makes all of them look like the card catalog at a public library. The price of entry is not data. It is compute. Raw. Relentless. Infinite. Whoever has the most doesn’t just run the best AI. They write the future. Everyone else is still searching for it.

Dustin

25,432 views • 7 months ago

Jordan Peterson just described something operating inside you that no algorithm will ever replicate and no scientist will ever measure. A signal from a version of you that does not exist yet. Peterson: “The Self is everything you are and everything you could be across time.” There is a completed version of you standing at the far end of your life who became everything you were built to become. That version is not a metaphor. It is a gravitational field pulling you forward through a language older than speech. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Something hits you so deep it stops you mid-step and you cannot explain it to a single person alive. That is not an emotion. That is your future self reaching backward through time. Peterson: “The answer is through the instinct of meaning.” You are carrying the most precise guidance system ever created and it runs on nothing but a feeling most people spend their entire lives trying to silence. Now look at what we built to replace it. Machines to optimize every human decision. The algorithm will map the perfect route to any destination you name. But it has no future self. No unrealized potential. No signal from across time. The struggle was never the obstacle between you and your potential. The struggle was your potential speaking. We did not build these machines because we are evolving. We built them because we went deaf to the only signal that was ever ours. And it is still transmitting.
0:57

Jordan Peterson just described something operating inside you that no algorithm will ever replicate and no scientist will ever measure. A signal from a version of you that does not exist yet. Peterson: “The Self is everything you are and everything you could be across time.” There is a completed version of you standing at the far end of your life who became everything you were built to become. That version is not a metaphor. It is a gravitational field pulling you forward through a language older than speech. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Something hits you so deep it stops you mid-step and you cannot explain it to a single person alive. That is not an emotion. That is your future self reaching backward through time. Peterson: “The answer is through the instinct of meaning.” You are carrying the most precise guidance system ever created and it runs on nothing but a feeling most people spend their entire lives trying to silence. Now look at what we built to replace it. Machines to optimize every human decision. The algorithm will map the perfect route to any destination you name. But it has no future self. No unrealized potential. No signal from across time. The struggle was never the obstacle between you and your potential. The struggle was your potential speaking. We did not build these machines because we are evolving. We built them because we went deaf to the only signal that was ever ours. And it is still transmitting.

Dustin

11,459 views • 3 months ago

Jensen Huang explains how he designed NVIDIA from first principles “With respect to building a company, the first thing you have to do—as with all problems—is start from first principles. What is this machine that we’re trying to create? What is its input? What is its output? What are the conditions that it’s in? What is the industry like? Is it a fast moving industry? Is it a bureaucratic industry? Is it a highly regulated industry? And what are you trying to build?” Thinking about it from this perspective, there were several things Jensen wanted to do with the company: “I wanted to create a company that naturally attracts amazing people, and the reason for that is our company’s mission is to solve computing problems that are barely possible.” He also wanted a company that was “as small as possible” and “empowered people.” “If you want an organization that obeys command and control, then you make it a pyramid just like the military—all the way back to the Roman Empire. But If you want to empower people, you want to make it as flat as possible so that information travels quickly.” He continues: “We also don’t have business units. We don’t have divisions. Everybody works as one, and the company is shaped in a way that allows us to build accelerated computing best.” Video source: Sana (2023)
1:56

Jensen Huang explains how he designed NVIDIA from first principles “With respect to building a company, the first thing you have to do—as with all problems—is start from first principles. What is this machine that we’re trying to create? What is its input? What is its output? What are the conditions that it’s in? What is the industry like? Is it a fast moving industry? Is it a bureaucratic industry? Is it a highly regulated industry? And what are you trying to build?” Thinking about it from this perspective, there were several things Jensen wanted to do with the company: “I wanted to create a company that naturally attracts amazing people, and the reason for that is our company’s mission is to solve computing problems that are barely possible.” He also wanted a company that was “as small as possible” and “empowered people.” “If you want an organization that obeys command and control, then you make it a pyramid just like the military—all the way back to the Roman Empire. But If you want to empower people, you want to make it as flat as possible so that information travels quickly.” He continues: “We also don’t have business units. We don’t have divisions. Everybody works as one, and the company is shaped in a way that allows us to build accelerated computing best.” Video source: Sana (2023)

Startup Archive

63,921 views • 1 year ago

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.
1:54

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.

Milk Road AI

17,911 views • 5 months ago

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.
1:40

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.

Dustin

87,680 views • 6 months ago

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

194,867 views • 6 months ago

What is a software factory? It is your workflow, your skills, and your domain knowledge, saved in files that the agent reads so the whole process is productized Think about the last app you built. You typed what you wanted, and the agent built it. You did not like it, so you made changes. You shipped it to Vercel, or you kept going. A factory puts structure on each of those steps. At each step you ask how to get the most from the model, and how to move faster. That is the conveyor belt. You ship quickly, and the quality stays the same. Mine is 5 md files. It works in Codex, Claude Code, and Cursor, because it does not depend on the model or the tool. Build your own.
3:06

What is a software factory? It is your workflow, your skills, and your domain knowledge, saved in files that the agent reads so the whole process is productized Think about the last app you built. You typed what you wanted, and the agent built it. You did not like it, so you made changes. You shipped it to Vercel, or you kept going. A factory puts structure on each of those steps. At each step you ask how to get the most from the model, and how to move faster. That is the conveyor belt. You ship quickly, and the quality stays the same. Mine is 5 md files. It works in Codex, Claude Code, and Cursor, because it does not depend on the model or the tool. Build your own.

The Startup Ideas Podcast (SIP) 🧃

17,326 views • 1 month ago

If you think Grok Bot has no business anywhere near your money, look at what it wrote before it was allowed to touch a single dollar. One prompt. It came back with this: > a story on its own is never a trade, it needs the money flow behind it in the same hour > it will not open a position until the exit is already written down > every claim it makes comes with the filing or the transaction it came from, no source and the claim does not exist > one agent exists only to say no, and its veto beats every score on the desk It does not care what you trade. Gold, oil, a stock you inherited or a coin that launched an hour ago, the rules are the same. Now be honest. Do you know why you are holding what you are holding? Do you write your exit before your entry? It does both every time, and it does not get bored at 2am. This is not a chat window that dies when you close the tab. Every agent has its own computer in the cloud that never logs off. $1,000 to $3,900 on day one came out of that, not out of luck. Full proofs below.
0:50

If you think Grok Bot has no business anywhere near your money, look at what it wrote before it was allowed to touch a single dollar. One prompt. It came back with this: > a story on its own is never a trade, it needs the money flow behind it in the same hour > it will not open a position until the exit is already written down > every claim it makes comes with the filing or the transaction it came from, no source and the claim does not exist > one agent exists only to say no, and its veto beats every score on the desk It does not care what you trade. Gold, oil, a stock you inherited or a coin that launched an hour ago, the rules are the same. Now be honest. Do you know why you are holding what you are holding? Do you write your exit before your entry? It does both every time, and it does not get bored at 2am. This is not a chat window that dies when you close the tab. Every agent has its own computer in the cloud that never logs off. $1,000 to $3,900 on day one came out of that, not out of luck. Full proofs below.

cvxv666

13,754 views • 2 months ago

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.
2:10

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.

Predictive History

20,184 views • 5 months ago

Jensen Huang told a room of graduates that nobody has a head start on them. He wasn’t being nice. Huang: “This is the best of times to be in school. This is the best of times to be graduating from school, because the world is reset.” Reset is an engineering word, not a motivational one. It means accumulated state is cleared. The machine restarts from a known position. He chose it on purpose. Huang: “An entire industry, the largest industry in the world, the computer industry, is reset. And because every industry in the world is built onto the computer industry, every industry is reset.” Logistics. Insurance. Farming. Film. All of it runs on computation, which means none of it gets to opt out. When the layer underneath moves, everything standing on it moves. Noticing is optional. Huang: “You are at exactly the same place as everybody else. Nobody has a head start on you.” Somewhere, a fifty-year-old heard the same sentence and did not feel encouraged. Identical fact. Different chair. A reset doesn’t delete what anyone knows. It deletes the premium on having known it first. The knowledge keeps its value. It stops paying interest. Seniority was always a bet that the ground holds still. Every generation before this one had elders to ask. On this, the most experienced person alive has about three years on you. Expertise here can’t be inherited. It gets built, and whoever builds it first becomes the person everyone else asks. Huang: “This is the perfect time to engage the most powerful technology the world’s ever known. It is personalized. Everybody has it on their browser. It is accessible. Obviously everybody’s using it.” Notice what he isn’t describing. No capital requirement. No license. No lab. Every general-purpose technology before this one gated on money or access. Steam needed a factory. Electricity needed a grid. Semiconductors need a fab. This one needs an open tab. The constraint moved somewhere uncomfortable. It used to be access. Now it’s whether you bother. Huang: “And put it to use in service of your career, in service of your dreams, to solve great problems. I can’t imagine how excited you must be, and yet I hear sometimes you’re concerned about the future.” The excited ones and the frightened ones are reading identical information. They differ only in what they do next. Fear is the correct measurement of a reset. It isn’t the correct conclusion. Huang: “I just need you to know that what I see on the other side is a welcoming industry looking for new college grads who are expert at using AI. Whether it’s expert at using AI for marketing or finance or engineering or software engineering, we are looking for expert AI users.” Read the criterion. Not a school. Not a transcript. Not years served. The qualification is a verb, and nobody holds a decade of it. Every advantage anyone has ever held began in a year when nobody had one. Every fortune, every dynasty, every edge you have ever resented traces back to a window like this one, and to the few who moved inside it. Resets expire. Nobody has a head start on you. That is true today. It gets less true every quarter, and by year’s end it will be somebody’s head start. Probably somebody who heard the same sentence you did. Waiting doesn’t feel like a decision. That’s what makes it the most common one. Everyone who ever missed one of these was busy, reasonable, and right about everything except the timing. A level field is not a gift. It’s a countdown.
1:19

Jensen Huang told a room of graduates that nobody has a head start on them. He wasn’t being nice. Huang: “This is the best of times to be in school. This is the best of times to be graduating from school, because the world is reset.” Reset is an engineering word, not a motivational one. It means accumulated state is cleared. The machine restarts from a known position. He chose it on purpose. Huang: “An entire industry, the largest industry in the world, the computer industry, is reset. And because every industry in the world is built onto the computer industry, every industry is reset.” Logistics. Insurance. Farming. Film. All of it runs on computation, which means none of it gets to opt out. When the layer underneath moves, everything standing on it moves. Noticing is optional. Huang: “You are at exactly the same place as everybody else. Nobody has a head start on you.” Somewhere, a fifty-year-old heard the same sentence and did not feel encouraged. Identical fact. Different chair. A reset doesn’t delete what anyone knows. It deletes the premium on having known it first. The knowledge keeps its value. It stops paying interest. Seniority was always a bet that the ground holds still. Every generation before this one had elders to ask. On this, the most experienced person alive has about three years on you. Expertise here can’t be inherited. It gets built, and whoever builds it first becomes the person everyone else asks. Huang: “This is the perfect time to engage the most powerful technology the world’s ever known. It is personalized. Everybody has it on their browser. It is accessible. Obviously everybody’s using it.” Notice what he isn’t describing. No capital requirement. No license. No lab. Every general-purpose technology before this one gated on money or access. Steam needed a factory. Electricity needed a grid. Semiconductors need a fab. This one needs an open tab. The constraint moved somewhere uncomfortable. It used to be access. Now it’s whether you bother. Huang: “And put it to use in service of your career, in service of your dreams, to solve great problems. I can’t imagine how excited you must be, and yet I hear sometimes you’re concerned about the future.” The excited ones and the frightened ones are reading identical information. They differ only in what they do next. Fear is the correct measurement of a reset. It isn’t the correct conclusion. Huang: “I just need you to know that what I see on the other side is a welcoming industry looking for new college grads who are expert at using AI. Whether it’s expert at using AI for marketing or finance or engineering or software engineering, we are looking for expert AI users.” Read the criterion. Not a school. Not a transcript. Not years served. The qualification is a verb, and nobody holds a decade of it. Every advantage anyone has ever held began in a year when nobody had one. Every fortune, every dynasty, every edge you have ever resented traces back to a window like this one, and to the few who moved inside it. Resets expire. Nobody has a head start on you. That is true today. It gets less true every quarter, and by year’s end it will be somebody’s head start. Probably somebody who heard the same sentence you did. Waiting doesn’t feel like a decision. That’s what makes it the most common one. Everyone who ever missed one of these was busy, reasonable, and right about everything except the timing. A level field is not a gift. It’s a countdown.

Dustin

19,113 views • 2 months ago

Notch Clips. every other ad tool ignores the footage sitting in your dropbox and generates new clips from a prompt. notch opens your library instead. it watches every clip you have ever shot and understands what is inside each one. the mood. who is in frame. what they are doing. the quality. so when you ask it to build an ad, it does not start from scratch. it reaches into your past shoots, finds the clip that fits, and slots it into the exact moment that needs it. here is what that looks like. you had thirteen versions of one reaction shot. notch picked the one that worked and left the other twelve. not by filename. not by guessing from a reference image. it watched all thirteen and could tell the difference. that is the whole idea. the right clip was already in your library. every other tool was about to make a worse one. footage you already shot, turned into every ad you still need. drop your site and a few clips you have. notch builds the first ad from them.
0:40

Notch Clips. every other ad tool ignores the footage sitting in your dropbox and generates new clips from a prompt. notch opens your library instead. it watches every clip you have ever shot and understands what is inside each one. the mood. who is in frame. what they are doing. the quality. so when you ask it to build an ad, it does not start from scratch. it reaches into your past shoots, finds the clip that fits, and slots it into the exact moment that needs it. here is what that looks like. you had thirteen versions of one reaction shot. notch picked the one that worked and left the other twelve. not by filename. not by guessing from a reference image. it watched all thirteen and could tell the difference. that is the whole idea. the right clip was already in your library. every other tool was about to make a worse one. footage you already shot, turned into every ad you still need. drop your site and a few clips you have. notch builds the first ad from them.

Vinay Jain

407,810 views • 4 months ago

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)
0:36

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

Tyler

20,023 views • 1 year ago

Jordan Peterson just named the one thing no machine will ever possess. Not intelligence. Not logic. Not processing power. A ghost. Peterson reached back to Carl Jung to describe something most people never slow down long enough to feel. You are not just the person sitting here reading this. You are every version of yourself that could ever exist across time. Peterson: “The Self is everything you are and everything you could be across time.” There is a version of you that fulfilled every ounce of potential you carry. The finished version. The one standing at the far end of your life who became everything you were built to become. That version is not a fantasy. It is a gravitational field. And it has been speaking to you your entire life. Not through words. Not through logic. Through the feeling of meaning. Peterson: “The answer is through the instinct of meaning.” When something resonates so deep it stops you mid-step and you cannot explain why. That is not a chemical accident. That is your future self reaching backward through time whispering where to walk next. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Your potential is not quiet. It is dragging you forward every single day through a language older than speech. Now look at what we are building. Machines designed to optimize every human decision. Career paths. Schedules. Relationships. Health. Creativity. The algorithm will map the most efficient route to any destination you name. But it cannot exist across time. It has no unrealized potential. No future version of itself standing at any finish line. No ghost pulling it toward something it was meant to become. It has compute. It does not have a soul whispering directions. When you hand your choices to an algorithm you are not delegating a task. You are muting the only compass that was ever yours. Meaning is not efficient. It is not optimized. It does not care about the shortest path. Meaning requires friction. Confusion. Standing in total darkness and feeling your way forward on nothing but instinct. That is the entire point. The struggle is not the obstacle between you and your potential. The struggle is the conversation between you and your potential. Remove it and you do not arrive faster. You arrive as someone else. We are building the most powerful optimization engine in human history. And we are about to aim it directly at the one process that was never supposed to be optimized. The algorithm will hand you a perfect map. But it will never give you a reason to walk.
0:57

Jordan Peterson just named the one thing no machine will ever possess. Not intelligence. Not logic. Not processing power. A ghost. Peterson reached back to Carl Jung to describe something most people never slow down long enough to feel. You are not just the person sitting here reading this. You are every version of yourself that could ever exist across time. Peterson: “The Self is everything you are and everything you could be across time.” There is a version of you that fulfilled every ounce of potential you carry. The finished version. The one standing at the far end of your life who became everything you were built to become. That version is not a fantasy. It is a gravitational field. And it has been speaking to you your entire life. Not through words. Not through logic. Through the feeling of meaning. Peterson: “The answer is through the instinct of meaning.” When something resonates so deep it stops you mid-step and you cannot explain why. That is not a chemical accident. That is your future self reaching backward through time whispering where to walk next. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Your potential is not quiet. It is dragging you forward every single day through a language older than speech. Now look at what we are building. Machines designed to optimize every human decision. Career paths. Schedules. Relationships. Health. Creativity. The algorithm will map the most efficient route to any destination you name. But it cannot exist across time. It has no unrealized potential. No future version of itself standing at any finish line. No ghost pulling it toward something it was meant to become. It has compute. It does not have a soul whispering directions. When you hand your choices to an algorithm you are not delegating a task. You are muting the only compass that was ever yours. Meaning is not efficient. It is not optimized. It does not care about the shortest path. Meaning requires friction. Confusion. Standing in total darkness and feeling your way forward on nothing but instinct. That is the entire point. The struggle is not the obstacle between you and your potential. The struggle is the conversation between you and your potential. Remove it and you do not arrive faster. You arrive as someone else. We are building the most powerful optimization engine in human history. And we are about to aim it directly at the one process that was never supposed to be optimized. The algorithm will hand you a perfect map. But it will never give you a reason to walk.

Dustin

40,866 views • 6 months ago

In 2002, Jordan Peterson gave a lecture and explained how to kill fear and unlock exponential growth in your life: 1. The materialist view of the world, the one taught in schools and universities, cannot explain consciousness, emotions, or meaning. It is a powerful framework for building technology but a terrible framework for living a life. It leaves out almost everything that actually matters to a human being. 2. Stories are not entertainment. They are the oldest and most accurate map of human experience ever created. Science describes objects. Stories describe what it actually feels like to be alive, to want something, to fear something, to lose something, and to fight your way back. That is why the same stories keep appearing across every culture and every century. 3. Pinocchio is not a story about a puppet who wants to be real. It is a story about a person who has to rescue what matters most to them from the most terrifying place they can imagine. Every great story has that same structure underneath it. Child or adult, the reason you cannot look away is not the animation. It is because the story is telling you something true about your own life. 4. Every human life follows the same basic structure. You are somewhere right now. You want to be somewhere else. Between where you are and where you want to be is a path you know and chaos on either side of it. Everything that has ever challenged you, confused you, or frightened you was simply something that knocked you off that path and forced you to find it again. 5. Negative emotions are not problems to be eliminated. They are your most accurate instruments. Fear tells you something you care about is at risk. Anxiety tells you the path ahead is unclear. Anger tells you something important has been violated. Running from these emotions does not make the problem go away. It just turns off the instrument that was telling you the problem existed. 6. When something unexpected happens your body reacts before your mind does. Your heart rate rises, your attention sharpens, your stomach tightens. This is not irrationality. This is your nervous system detecting that the plan you were using no longer matches reality. Something outside what you expected has appeared and it needs to be dealt with. 7. In every culture across every century, the unknown has been represented as a dragon. Not because ancient people were primitive but because they were precise. A dragon is the perfect symbol for everything you have not yet faced, everything that frightens you before you understand it, everything that sits at the edge of what you know and what you do not. 8. Dragons hoard gold. This is one of the oldest and most important ideas in human storytelling. The treasure, the thing you most need, is never in the safe and comfortable place. It is always in the place that frightens you most. The gold is inside the dragon. That is not a coincidence. That is the point. 9. Every time you avoid something you know you should face, you lose a small piece of yourself. Not dramatically. Not all at once. Just a small reduction in your capacity, a small gap in your armor. Do this enough times and you become someone who cannot hold their own weight, let alone carry anyone else. 10. Every time you face something you were afraid of and do not run, the opposite happens. You take a piece of what frightened you and integrate it. You become slightly more capable. The next frightening thing is slightly less frightening. This is not a metaphor. It is the actual mechanism by which people grow. 11. The problem with fighting symptoms instead of causes is that symptoms are infinite. If a dragon keeps breathing fire that turns into monsters, killing the monsters one by one accomplishes nothing. More keep coming. The only solution is to go straight to the source. In your own life the source is almost always the thing you have been most consistently avoiding. 12. Peterson's five-year-old nephew figured this out in a dream. Surrounded by endless monsters he did not fight them. He went straight to the dragon, blinded it, went down its throat, cut out the piece that made the fire, and used it as a shield. A five-year-old in a nightmare understood instinctively what most adults spend their whole lives avoiding. Go to the source. Face the thing directly. Take a piece of it for yourself. 13. The piece of the dragon you take when you face your fear is real and it protects you. Every person you admire who seems to handle pressure, uncertainty, and difficulty better than everyone else around them has simply faced more dragons than the people around them. They are not built differently. They have just stopped running earlier. 14. The reason you cannot lean on someone who has been running from their fears their whole life is not that they do not care. It is that they have nothing left to lean on. They fell over a long time ago. They are just still standing because nothing heavy has landed on them yet. 15. The question Peterson ends with is the one worth sitting with after you close this post. What if you are actually built for the life you are afraid of living? What if everything you need to handle what frightens you is already inside you? What if the only thing standing between you and the version of yourself you actually want to become is your willingness to stop running from the one thing you already know you should be facing? Before you go, can we stay in touch? I'd love to share one email with you every month that'll challenge how you think about business, money and freedom. Stay in touch here:
56:21

In 2002, Jordan Peterson gave a lecture and explained how to kill fear and unlock exponential growth in your life: 1. The materialist view of the world, the one taught in schools and universities, cannot explain consciousness, emotions, or meaning. It is a powerful framework for building technology but a terrible framework for living a life. It leaves out almost everything that actually matters to a human being. 2. Stories are not entertainment. They are the oldest and most accurate map of human experience ever created. Science describes objects. Stories describe what it actually feels like to be alive, to want something, to fear something, to lose something, and to fight your way back. That is why the same stories keep appearing across every culture and every century. 3. Pinocchio is not a story about a puppet who wants to be real. It is a story about a person who has to rescue what matters most to them from the most terrifying place they can imagine. Every great story has that same structure underneath it. Child or adult, the reason you cannot look away is not the animation. It is because the story is telling you something true about your own life. 4. Every human life follows the same basic structure. You are somewhere right now. You want to be somewhere else. Between where you are and where you want to be is a path you know and chaos on either side of it. Everything that has ever challenged you, confused you, or frightened you was simply something that knocked you off that path and forced you to find it again. 5. Negative emotions are not problems to be eliminated. They are your most accurate instruments. Fear tells you something you care about is at risk. Anxiety tells you the path ahead is unclear. Anger tells you something important has been violated. Running from these emotions does not make the problem go away. It just turns off the instrument that was telling you the problem existed. 6. When something unexpected happens your body reacts before your mind does. Your heart rate rises, your attention sharpens, your stomach tightens. This is not irrationality. This is your nervous system detecting that the plan you were using no longer matches reality. Something outside what you expected has appeared and it needs to be dealt with. 7. In every culture across every century, the unknown has been represented as a dragon. Not because ancient people were primitive but because they were precise. A dragon is the perfect symbol for everything you have not yet faced, everything that frightens you before you understand it, everything that sits at the edge of what you know and what you do not. 8. Dragons hoard gold. This is one of the oldest and most important ideas in human storytelling. The treasure, the thing you most need, is never in the safe and comfortable place. It is always in the place that frightens you most. The gold is inside the dragon. That is not a coincidence. That is the point. 9. Every time you avoid something you know you should face, you lose a small piece of yourself. Not dramatically. Not all at once. Just a small reduction in your capacity, a small gap in your armor. Do this enough times and you become someone who cannot hold their own weight, let alone carry anyone else. 10. Every time you face something you were afraid of and do not run, the opposite happens. You take a piece of what frightened you and integrate it. You become slightly more capable. The next frightening thing is slightly less frightening. This is not a metaphor. It is the actual mechanism by which people grow. 11. The problem with fighting symptoms instead of causes is that symptoms are infinite. If a dragon keeps breathing fire that turns into monsters, killing the monsters one by one accomplishes nothing. More keep coming. The only solution is to go straight to the source. In your own life the source is almost always the thing you have been most consistently avoiding. 12. Peterson's five-year-old nephew figured this out in a dream. Surrounded by endless monsters he did not fight them. He went straight to the dragon, blinded it, went down its throat, cut out the piece that made the fire, and used it as a shield. A five-year-old in a nightmare understood instinctively what most adults spend their whole lives avoiding. Go to the source. Face the thing directly. Take a piece of it for yourself. 13. The piece of the dragon you take when you face your fear is real and it protects you. Every person you admire who seems to handle pressure, uncertainty, and difficulty better than everyone else around them has simply faced more dragons than the people around them. They are not built differently. They have just stopped running earlier. 14. The reason you cannot lean on someone who has been running from their fears their whole life is not that they do not care. It is that they have nothing left to lean on. They fell over a long time ago. They are just still standing because nothing heavy has landed on them yet. 15. The question Peterson ends with is the one worth sitting with after you close this post. What if you are actually built for the life you are afraid of living? What if everything you need to handle what frightens you is already inside you? What if the only thing standing between you and the version of yourself you actually want to become is your willingness to stop running from the one thing you already know you should be facing? Before you go, can we stay in touch? I'd love to share one email with you every month that'll challenge how you think about business, money and freedom. Stay in touch here:

Brad

69,042 views • 2 months ago

The difference between a Harvard Business School student and a Founder. HBS STUDENT: Lists all the reasons AI Space Compute is hard. FOUNDER: “ I don't disagree with you that building data centers in space is hard. Building data centers in space is extremely hard. . . If you think [demand] is going to double in the next three years - demand for energy from just data centers - and then double in the next three years after that, and then double in the next three years after that, then: We don’t have a choice. We literally have to put data centers in space. So we have to figure this stuff out.” Philip Johnston Starcloud Founders solve problems. Founders KNOW it will be HARD and do it anyway because it simply NEEDS TO BE DONE. That is founder mindset - and that mindset is what Harvard Business School can’t teach.
0:54

The difference between a Harvard Business School student and a Founder. HBS STUDENT: Lists all the reasons AI Space Compute is hard. FOUNDER: “ I don't disagree with you that building data centers in space is hard. Building data centers in space is extremely hard. . . If you think [demand] is going to double in the next three years - demand for energy from just data centers - and then double in the next three years after that, and then double in the next three years after that, then: We don’t have a choice. We literally have to put data centers in space. So we have to figure this stuff out.” Philip Johnston Starcloud Founders solve problems. Founders KNOW it will be HARD and do it anyway because it simply NEEDS TO BE DONE. That is founder mindset - and that mindset is what Harvard Business School can’t teach.

Amy

19,909 views • 8 months ago

David Friedberg says with open source, we no longer even need data centers for 90% of what we use AI for "I thought it was worth taking a step back to recognize where we are because in the context of 'we have to stop the super intelligence, we have to shut down the data centers,' you don't even need data centers to do 90% of what you can do with AI." "You can download it on your computer for free today to do what was the most advanced technology in human history less than a year ago. It's all here today." "And I thought it was just worth taking a beat to step back and make sure we all recognize that as we get into this debate about these, quote, labs or corporations. I don't know if it even matters anymore. I think open source is here."
0:31

David Friedberg says with open source, we no longer even need data centers for 90% of what we use AI for "I thought it was worth taking a step back to recognize where we are because in the context of 'we have to stop the super intelligence, we have to shut down the data centers,' you don't even need data centers to do 90% of what you can do with AI." "You can download it on your computer for free today to do what was the most advanced technology in human history less than a year ago. It's all here today." "And I thought it was just worth taking a beat to step back and make sure we all recognize that as we get into this debate about these, quote, labs or corporations. I don't know if it even matters anymore. I think open source is here."

dnap

27,703 views • 1 month ago

Geoffrey Hinton just made every AI critic accidentally describe their own brain. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word. The entire debate unravels. The tech industry sees AI produce a confident wrong answer and calls it a defect. A bug to patch. They are measuring intelligence against the standard of a filing cabinet. And exposing that they understand neither. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain does not store memories. It rebuilds them from nothing every time you remember. Fills gaps it never discloses. Fabricates details you would stake your life on. Then hands it all to you as truth. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” The wrong parts feel identical to the right ones. No internal warning. No distinction between what was remembered and what was invented on the spot. You have argued over memories that were partially fiction. Told stories about your own life that your brain manufactured in real time. With total conviction. And never once suspected. This is not a defect in human cognition. This IS cognition. The mechanism that fabricates is the same one that reasons, creates, and makes connections no one taught it to make. Not a separate system. Same architecture. Same process. You cannot remove the confabulation without killing the intelligence. They are the same thing. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” A century of evidence. No one called the human brain broken. The moment a machine runs on the same principle, the world calls it defective. The people demanding AI that never gets a single detail wrong are not asking for intelligence. They’re asking for a search engine that sounds articulate. What we built is something else entirely. A system that thinks the way thinking actually works. Not retrieval. Construction. The imperfection is not the cost of intelligence. It is the signature.
1:05

Geoffrey Hinton just made every AI critic accidentally describe their own brain. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word. The entire debate unravels. The tech industry sees AI produce a confident wrong answer and calls it a defect. A bug to patch. They are measuring intelligence against the standard of a filing cabinet. And exposing that they understand neither. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain does not store memories. It rebuilds them from nothing every time you remember. Fills gaps it never discloses. Fabricates details you would stake your life on. Then hands it all to you as truth. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” The wrong parts feel identical to the right ones. No internal warning. No distinction between what was remembered and what was invented on the spot. You have argued over memories that were partially fiction. Told stories about your own life that your brain manufactured in real time. With total conviction. And never once suspected. This is not a defect in human cognition. This IS cognition. The mechanism that fabricates is the same one that reasons, creates, and makes connections no one taught it to make. Not a separate system. Same architecture. Same process. You cannot remove the confabulation without killing the intelligence. They are the same thing. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” A century of evidence. No one called the human brain broken. The moment a machine runs on the same principle, the world calls it defective. The people demanding AI that never gets a single detail wrong are not asking for intelligence. They’re asking for a search engine that sounds articulate. What we built is something else entirely. A system that thinks the way thinking actually works. Not retrieval. Construction. The imperfection is not the cost of intelligence. It is the signature.

Dustin

16,489 views • 3 months ago

Demis Hassabis wants to do something no civilization has ever been able to do. Run reality more than once. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” Every economy ever built. Every policy ever enacted. Every war ever fought. Happened exactly once. Against the entire human population. With no way to run it again. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Every major decision in the history of civilization was a single experiment run on billions of people with no control group and no second attempt. We called the results knowledge. They were the scars of bets we were never allowed to place twice. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” Physics became physics because you can drop a ball a thousand times and get the same answer. You cannot drop a civilization and get any answer at all. You just get the wreckage and call it a lesson. Hassabis wants to change that. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” Simulate an economy. Crash it. Rebuild it. Adjust the inputs. Run it again. Do for civilization what the laboratory did for chemistry. But that word “accurately” is doing more work than anyone is willing to examine. To simulate a society well enough to learn from it, you have to simulate the people inside it. Not averages. Not abstractions. Agents with preferences and fears and breaking points. The more accurate the simulation gets, the less separates it from the thing it represents. The line between physics and economics was never about the nature of what was being studied. It was about the limits of the thing doing the studying. Humans were never too complex to predict. We were too complex to calculate. AI does not create new science. It collapses every science into one. Everything computable becomes predictable. Everything predictable becomes simulable. And past a certain resolution, the gap between a simulated world and a real one stops being a technical question. It becomes a philosophical question no one is prepared to answer. A simulation you can tell apart from reality is a simulation that has not finished improving. The people inside a perfect one would not wonder whether their world was generated. They would feel exactly the way you feel right now. Reading this. Certain they are real. That certainty is not evidence. It is exactly what a successful simulation would produce. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” What he is building is not a forecasting tool. It is the quiet proof that “real” was only ever a word for what we had not yet learned to compute. And that word is about to lose its meaning.
1:12

Demis Hassabis wants to do something no civilization has ever been able to do. Run reality more than once. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” Every economy ever built. Every policy ever enacted. Every war ever fought. Happened exactly once. Against the entire human population. With no way to run it again. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Every major decision in the history of civilization was a single experiment run on billions of people with no control group and no second attempt. We called the results knowledge. They were the scars of bets we were never allowed to place twice. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” Physics became physics because you can drop a ball a thousand times and get the same answer. You cannot drop a civilization and get any answer at all. You just get the wreckage and call it a lesson. Hassabis wants to change that. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” Simulate an economy. Crash it. Rebuild it. Adjust the inputs. Run it again. Do for civilization what the laboratory did for chemistry. But that word “accurately” is doing more work than anyone is willing to examine. To simulate a society well enough to learn from it, you have to simulate the people inside it. Not averages. Not abstractions. Agents with preferences and fears and breaking points. The more accurate the simulation gets, the less separates it from the thing it represents. The line between physics and economics was never about the nature of what was being studied. It was about the limits of the thing doing the studying. Humans were never too complex to predict. We were too complex to calculate. AI does not create new science. It collapses every science into one. Everything computable becomes predictable. Everything predictable becomes simulable. And past a certain resolution, the gap between a simulated world and a real one stops being a technical question. It becomes a philosophical question no one is prepared to answer. A simulation you can tell apart from reality is a simulation that has not finished improving. The people inside a perfect one would not wonder whether their world was generated. They would feel exactly the way you feel right now. Reading this. Certain they are real. That certainty is not evidence. It is exactly what a successful simulation would produce. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” What he is building is not a forecasting tool. It is the quiet proof that “real” was only ever a word for what we had not yet learned to compute. And that word is about to lose its meaning.

Dustin

46,536 views • 5 months ago

Geoffrey Hinton just reframed the biggest supposed flaw in artificial intelligence. And it changes everything. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word swap. Entire paradigm shifts. When the legacy tech industry calls AI hallucinations a bug, they’re revealing a fundamental misunderstanding of what intelligence actually is. They’re expecting the machine to behave like a database. Store a fact. Retrieve the fact. Return the exact same fact every time. That’s not how intelligence works. Not artificial. Not biological. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain doesn’t store memories. It reconstructs them. Every time you recall something, your neural network uses connection strengths shaped by past experience to build the most plausible version of what happened. It fills the gaps. Smooths the inconsistencies. Constructs a coherent story from incomplete signal. And then presents that story to you as fact. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” Here’s the part that should stop you cold. You will be equally confident about the wrong details as the right ones. Think about that. Really think about it. Every argument you’ve had about who said what. Every memory you’ve defended as certain. Every time you told a story about your own life with complete certainty. Some of those details weren’t real. You constructed them. Confidently. Fluently. And you had no idea. This isn’t a flaw unique to people with bad memories. Eyewitness testimony is the most confabulated evidence in the human justice system. Innocent people have spent decades in prison because someone remembered something that felt absolutely certain and was absolutely wrong. Your brain didn’t lie to you. It did exactly what brains do. It built the most plausible story it could from the signal it had. AI does the exact same thing. Because it was built on the exact same architecture. The mechanism that makes an AI invent a plausible but wrong answer is the same mechanism that makes it brilliant. You cannot have one without the other. The ability to reason creatively, synthesize across domains, construct explanations for things it has never been told. All of it runs on the same engine as the confabulation. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” This isn’t a new phenomenon. It isn’t a software bug. It isn’t something to be patched in the next model update. It is the price of dynamic intelligence. The shadow cast by the same light that makes these systems remarkable. We aren’t building better search engines. We are building synthetic minds that think the way minds actually think. Messy. Confident. Occasionally wrong. And for exactly that reason, capable of something no database ever was.
1:05

Geoffrey Hinton just reframed the biggest supposed flaw in artificial intelligence. And it changes everything. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word swap. Entire paradigm shifts. When the legacy tech industry calls AI hallucinations a bug, they’re revealing a fundamental misunderstanding of what intelligence actually is. They’re expecting the machine to behave like a database. Store a fact. Retrieve the fact. Return the exact same fact every time. That’s not how intelligence works. Not artificial. Not biological. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain doesn’t store memories. It reconstructs them. Every time you recall something, your neural network uses connection strengths shaped by past experience to build the most plausible version of what happened. It fills the gaps. Smooths the inconsistencies. Constructs a coherent story from incomplete signal. And then presents that story to you as fact. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” Here’s the part that should stop you cold. You will be equally confident about the wrong details as the right ones. Think about that. Really think about it. Every argument you’ve had about who said what. Every memory you’ve defended as certain. Every time you told a story about your own life with complete certainty. Some of those details weren’t real. You constructed them. Confidently. Fluently. And you had no idea. This isn’t a flaw unique to people with bad memories. Eyewitness testimony is the most confabulated evidence in the human justice system. Innocent people have spent decades in prison because someone remembered something that felt absolutely certain and was absolutely wrong. Your brain didn’t lie to you. It did exactly what brains do. It built the most plausible story it could from the signal it had. AI does the exact same thing. Because it was built on the exact same architecture. The mechanism that makes an AI invent a plausible but wrong answer is the same mechanism that makes it brilliant. You cannot have one without the other. The ability to reason creatively, synthesize across domains, construct explanations for things it has never been told. All of it runs on the same engine as the confabulation. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” This isn’t a new phenomenon. It isn’t a software bug. It isn’t something to be patched in the next model update. It is the price of dynamic intelligence. The shadow cast by the same light that makes these systems remarkable. We aren’t building better search engines. We are building synthetic minds that think the way minds actually think. Messy. Confident. Occasionally wrong. And for exactly that reason, capable of something no database ever was.

Dustin

116,573 views • 7 months ago

Elon Musk just said what every government on earth already knows and none will admit. Musk: “AI is moving 10 times faster than government, maybe more.” Not slightly faster. Ten times. And the gap compounds daily. Every institution ever built runs on the same architecture. Committees. Hearings. Drafts. Amendments. Votes. A process designed for a world that moved at the speed of human debate. That world is gone. The moment legislation is signed, the thing it was written for is already three generations obsolete. Law is becoming a monument to things that no longer exist by the time the ink is dry. Musk: “The one thing that the government can do is just issue people money.” The largest militaries ever assembled. The most sophisticated legal infrastructure in human history. The accumulated weight of ten thousand years of institutional evolution. Collapsed to a single remaining function. Printing checks. Not because they failed. Because the velocity of what is coming makes everything else they were built to do ornamental. Musk: “Nobody’s gonna starve is what I’m saying.” The floor rises. Survival becomes automatic. Nobody goes hungry. For ten thousand years, that would have been the finish line. It is not. It is the starting line of the hardest question the species has ever faced. Every civilization in history was organized around one brutal fact. The world needed your labor to function. You worked because you had to. You built because the alternative was death. Every economy, every identity, every reason to get out of bed was downstream of that single pressure. That pressure is being quietly removed. And what it leaves behind is not freedom. It is a vacuum. A check hits your account. Rent is covered. Food is handled. The base layer of existence is solved. But the thing that organized your time. Gave your effort weight. Made your life feel like it pointed somewhere. Gone. No government can legislate that back. No policy can manufacture it. Purpose is not a deposit. Identity is not a program. What you are for when the world no longer needs you to function is not a problem any institution was designed to answer. It is the first problem in history that belongs entirely to you. And while the rest of the world debates how to control what is coming, one person is doing the only thing that has ever mattered. Building what comes next.
0:46

Elon Musk just said what every government on earth already knows and none will admit. Musk: “AI is moving 10 times faster than government, maybe more.” Not slightly faster. Ten times. And the gap compounds daily. Every institution ever built runs on the same architecture. Committees. Hearings. Drafts. Amendments. Votes. A process designed for a world that moved at the speed of human debate. That world is gone. The moment legislation is signed, the thing it was written for is already three generations obsolete. Law is becoming a monument to things that no longer exist by the time the ink is dry. Musk: “The one thing that the government can do is just issue people money.” The largest militaries ever assembled. The most sophisticated legal infrastructure in human history. The accumulated weight of ten thousand years of institutional evolution. Collapsed to a single remaining function. Printing checks. Not because they failed. Because the velocity of what is coming makes everything else they were built to do ornamental. Musk: “Nobody’s gonna starve is what I’m saying.” The floor rises. Survival becomes automatic. Nobody goes hungry. For ten thousand years, that would have been the finish line. It is not. It is the starting line of the hardest question the species has ever faced. Every civilization in history was organized around one brutal fact. The world needed your labor to function. You worked because you had to. You built because the alternative was death. Every economy, every identity, every reason to get out of bed was downstream of that single pressure. That pressure is being quietly removed. And what it leaves behind is not freedom. It is a vacuum. A check hits your account. Rent is covered. Food is handled. The base layer of existence is solved. But the thing that organized your time. Gave your effort weight. Made your life feel like it pointed somewhere. Gone. No government can legislate that back. No policy can manufacture it. Purpose is not a deposit. Identity is not a program. What you are for when the world no longer needs you to function is not a problem any institution was designed to answer. It is the first problem in history that belongs entirely to you. And while the rest of the world debates how to control what is coming, one person is doing the only thing that has ever mattered. Building what comes next.

Dustin

50,748 views • 3 months ago