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A few years ago, running AI infrastructure required a warehouse. Today, one guy does it from his backyard. He installed an Nvidia AI supercomputer next to his home and connected it to power and fiber internet. From the outside, it looks like ordinary utility equipment. Inside, GPUs process AI...

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Shelpid.WI3M

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154,415 görüntüleme • 2 ay önce •via X (Twitter)

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This guy runs an Nvidia Spark from his desk and pulls in $8,000 a month The device is the size of a small speaker and sits right on his desk. Inside is a full Nvidia GPU running AI workloads 24/7. He rents out the compute power to companies that need local AI processing. They pay him a flat monthly fee to use it remotely. $8,000 lands in his account every single month. He just plugged it in and that was it. The $4,000 box paid for itself in two weeks. Now it prints $96,000 a year from a device smaller than a coffee machine. Companies are desperate for local compute and regular people are quietly supplying it. Save this before everyone figures out what he already knows.
0:42

This guy runs an Nvidia Spark from his desk and pulls in $8,000 a month The device is the size of a small speaker and sits right on his desk. Inside is a full Nvidia GPU running AI workloads 24/7. He rents out the compute power to companies that need local AI processing. They pay him a flat monthly fee to use it remotely. $8,000 lands in his account every single month. He just plugged it in and that was it. The $4,000 box paid for itself in two weeks. Now it prints $96,000 a year from a device smaller than a coffee machine. Companies are desperate for local compute and regular people are quietly supplying it. Save this before everyone figures out what he already knows.

winkle.

57,931 görüntüleme • 2 ay önce

A guy put an XFRA box outside his house and now gets paid $2,500 a month for doing nothing The box is about the size of an AC unit and mounts right on the wall. Inside it connects your home to a distributed AI compute network. Companies pay to run their AI workloads through it around the clock. He just plugged it in and that was it. Now $2,500 lands in his account every single month. That is $30,000 a year from a box he never touches. It is not Nvidia. It is not a government program. It is XFRA and regular homes are quietly becoming AI infrastructure. Every house a node. Every node earning. The people who move first lock in the best rates. Save this before your neighborhood figures it out before you.
0:33

A guy put an XFRA box outside his house and now gets paid $2,500 a month for doing nothing The box is about the size of an AC unit and mounts right on the wall. Inside it connects your home to a distributed AI compute network. Companies pay to run their AI workloads through it around the clock. He just plugged it in and that was it. Now $2,500 lands in his account every single month. That is $30,000 a year from a box he never touches. It is not Nvidia. It is not a government program. It is XFRA and regular homes are quietly becoming AI infrastructure. Every house a node. Every node earning. The people who move first lock in the best rates. Save this before your neighborhood figures it out before you.

winkle.

16,774 görüntüleme • 2 ay önce

Your home is about to become an AI data center and pay off your mortgage This is not a theory. It is already happening. Nvidia partnered with homebuilders to install AI hardware directly into new homes. A white box on the wall. Internet. Power outlet. It processes AI workloads for companies around the world. You do nothing. It just runs 24/7. Homeowners get paid a fixed amount every single month. Estimates put it at $2,000 to $3,000 straight into your account. That is $36,000 a year just for a box on your wall. These guys broke down exactly why this is massive, who is behind it and how regular homeowners get in. Suburbs are quietly becoming the new AI infrastructure. And the people who got in early are already getting paid. Watch the full video. All the alpha and everything you need to know is in there.
13:21

Your home is about to become an AI data center and pay off your mortgage This is not a theory. It is already happening. Nvidia partnered with homebuilders to install AI hardware directly into new homes. A white box on the wall. Internet. Power outlet. It processes AI workloads for companies around the world. You do nothing. It just runs 24/7. Homeowners get paid a fixed amount every single month. Estimates put it at $2,000 to $3,000 straight into your account. That is $36,000 a year just for a box on your wall. These guys broke down exactly why this is massive, who is behind it and how regular homeowners get in. Suburbs are quietly becoming the new AI infrastructure. And the people who got in early are already getting paid. Watch the full video. All the alpha and everything you need to know is in there.

winkle.

315,269 görüntüleme • 2 ay önce

A guy installed a mini Nvidia AI data center on his house and gets paid monthly The box is the size of a small fridge and bolts right onto the wall. Inside it is packed with Nvidia GPUs running AI workloads 24/7. He hooked it up next to his AC and that was it. Now the company pays him a flat fee for the power and Wi-Fi it uses. He says it lands him around $2,500 a month straight into his account. The unit even helps cool the side of his house, dropping his AC bill by $150. That stacks to over $30,000 a year for doing literally nothing. His mortgage is now fully paid by a box in his yard. The crazy part is regular homes are quietly becoming AI infrastructure. Save this, you are watching the next gold rush hit the suburbs.
0:33

A guy installed a mini Nvidia AI data center on his house and gets paid monthly The box is the size of a small fridge and bolts right onto the wall. Inside it is packed with Nvidia GPUs running AI workloads 24/7. He hooked it up next to his AC and that was it. Now the company pays him a flat fee for the power and Wi-Fi it uses. He says it lands him around $2,500 a month straight into his account. The unit even helps cool the side of his house, dropping his AC bill by $150. That stacks to over $30,000 a year for doing literally nothing. His mortgage is now fully paid by a box in his yard. The crazy part is regular homes are quietly becoming AI infrastructure. Save this, you are watching the next gold rush hit the suburbs.

winkle.

7,179,403 görüntüleme • 2 ay önce

🚨 Google and Anthropic are reportedly paying SpaceX more than $2.1 billion per month for AI compute capacity That's a revenue run rate of roughly $26 billion per year! Jeff Lutz 🔋 says the bigger story isn't the money. It's that some of the world's leading AI companies are choosing SpaceX to power their AI ambitions. A few years ago, SpaceX was known for rockets. Today, it may be becoming one of the most important AI infrastructure companies in the world. 🚀 $SPCX
15:32

🚨 Google and Anthropic are reportedly paying SpaceX more than $2.1 billion per month for AI compute capacity That's a revenue run rate of roughly $26 billion per year! Jeff Lutz 🔋 says the bigger story isn't the money. It's that some of the world's leading AI companies are choosing SpaceX to power their AI ambitions. A few years ago, SpaceX was known for rockets. Today, it may be becoming one of the most important AI infrastructure companies in the world. 🚀 $SPCX

Herbert Ong

33,815 görüntüleme • 2 ay önce

NVIDIA is creating two completely different futures In one they put AI infrastructure on your property In the other they put it on your own desk ///// OPTION 1 A box worth more than most houses gets installed outside You provide the location, power and connectivity In return, you get perks like cheaper electricity, backup power and upgraded internet while AI workloads run around the clock You're not buying the hardware You're hosting it ///// OPTION 2 You spend a few thousand dollars on a DGX Spark Small enough to sit beside your monitor Powerful enough to run serious AI models locally No cloud bill No rented GPUs No sending sensitive data somewhere else The interesting part isn't the hardware It's the shift that's happening For years, AI belonged to giant tech companies and massive data centers Now NVIDIA is pushing a world where regular people can either host the infrastructure or own a piece of it themselves
1:13

NVIDIA is creating two completely different futures In one they put AI infrastructure on your property In the other they put it on your own desk ///// OPTION 1 A box worth more than most houses gets installed outside You provide the location, power and connectivity In return, you get perks like cheaper electricity, backup power and upgraded internet while AI workloads run around the clock You're not buying the hardware You're hosting it ///// OPTION 2 You spend a few thousand dollars on a DGX Spark Small enough to sit beside your monitor Powerful enough to run serious AI models locally No cloud bill No rented GPUs No sending sensitive data somewhere else The interesting part isn't the hardware It's the shift that's happening For years, AI belonged to giant tech companies and massive data centers Now NVIDIA is pushing a world where regular people can either host the infrastructure or own a piece of it themselves

Lummox

10,019 görüntüleme • 2 ay önce

🚨 A GUY BOUGHT A $4,000 NVIDIA BOX AND TURNED IT INTO A CASH MACHINE. It sits on his desk. Smaller than a coffee machine. Most people would walk past it and never think twice. Inside? AI workloads running 24/7. Companies rent the compute. He collects the checks. Reportedly around $8,000 every month. That's the part people still don't understand about the AI boom. The biggest opportunity isn't always building the next AI startup. Sometimes it's owning the infrastructure everyone else depends on. The internet had landlords. AI is creating a new version of them.
0:41

🚨 A GUY BOUGHT A $4,000 NVIDIA BOX AND TURNED IT INTO A CASH MACHINE. It sits on his desk. Smaller than a coffee machine. Most people would walk past it and never think twice. Inside? AI workloads running 24/7. Companies rent the compute. He collects the checks. Reportedly around $8,000 every month. That's the part people still don't understand about the AI boom. The biggest opportunity isn't always building the next AI startup. Sometimes it's owning the infrastructure everyone else depends on. The internet had landlords. AI is creating a new version of them.

Phantom_Defi

15,407 görüntüleme • 2 ay önce

Jensen Huang just called out every CEO using AI as an excuse to fire people. He says layoffs tied to AI “don’t make any sense” because “AI just arrived.” “The narrative that connects AI to job loss… it is just too lazy.” “AI has just arrived. How is it possible they’re already losing jobs?” “How is it possible that AI became productive and useful only 6 months ago, and they were somehow laying people off two years ago because of AI?” “It doesn’t make any sense. It was just a way for them to sound smart. And I REALLY HATE that.” A recent survey of nearly 6,000 executives found that 89% of companies have seen no measurable productivity gains from AI. Here we have the CEO of the most valuable AI company on earth, NVIDIA, telling you that the layoffs you’re seeing are not about AI. They’re about CEOs who found a convenient story and ran with it. And he hates it.
0:42

Jensen Huang just called out every CEO using AI as an excuse to fire people. He says layoffs tied to AI “don’t make any sense” because “AI just arrived.” “The narrative that connects AI to job loss… it is just too lazy.” “AI has just arrived. How is it possible they’re already losing jobs?” “How is it possible that AI became productive and useful only 6 months ago, and they were somehow laying people off two years ago because of AI?” “It doesn’t make any sense. It was just a way for them to sound smart. And I REALLY HATE that.” A recent survey of nearly 6,000 executives found that 89% of companies have seen no measurable productivity gains from AI. Here we have the CEO of the most valuable AI company on earth, NVIDIA, telling you that the layoffs you’re seeing are not about AI. They’re about CEOs who found a convenient story and ran with it. And he hates it.

The Vigilant Fox 🦊

130,932 görüntüleme • 2 ay önce

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.
0:24

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 görüntüleme • 22 gün önce

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.
1:06

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

Six Five Media

7,422,478 görüntüleme • 4 ay önce

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

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

Dustin

12,200 görüntüleme • 3 ay önce

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀
0:49

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

Sky Wee

67,555 görüntüleme • 10 ay önce

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.
1:28

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.

Konstantine Buhler

24,887 görüntüleme • 10 ay önce

This man 100X Club explained the $QUBIC and #MONERO situation very well! Thanks for the great explanation. 👏🏻 Only one note: The ultimate goal of $QUBIC is to sell its computing power to AI companies. With the current hardware we have on Monero, if we sold that computing power to AI companies, it could generate $25M to $40M in monthly revenue. Imagine how much the price of QUBIC could be! Just think about it. 🧠 But even that is not the final goal, the true goal is AGI (Artificial General Intelligence), also known as Aigarth. 🤖 Source: #UPoW #AI #DAI #DePIN #100X #GEM
6:05

This man 100X Club explained the $QUBIC and #MONERO situation very well! Thanks for the great explanation. 👏🏻 Only one note: The ultimate goal of $QUBIC is to sell its computing power to AI companies. With the current hardware we have on Monero, if we sold that computing power to AI companies, it could generate $25M to $40M in monthly revenue. Imagine how much the price of QUBIC could be! Just think about it. 🧠 But even that is not the final goal, the true goal is AGI (Artificial General Intelligence), also known as Aigarth. 🤖 Source: #UPoW #AI #DAI #DePIN #100X #GEM

odbashWizard

13,558 görüntüleme • 1 yıl önce

This is very true. AI integration is both hard and necessary for companies. And AI-native junior employees can drive this adoption. Earlier this year Aaron Levie explained with martin_casado on the a16z podcast how AI fluency is one of the most powerful skills a new grad can have. The real value comes from knowing how to make AI work inside an organization. Most companies, both large and small, are slow to adopt new technology. They need people who can explain what AI can do, show them how to use it, and help them move faster. If you are fluent in AI, you can be that person. You can walk into a company, spot the areas where AI can save time or create new opportunities, and make it happen. That is a wedge into your first job and a way to stand out from everyone else.
0:39

This is very true. AI integration is both hard and necessary for companies. And AI-native junior employees can drive this adoption. Earlier this year Aaron Levie explained with martin_casado on the a16z podcast how AI fluency is one of the most powerful skills a new grad can have. The real value comes from knowing how to make AI work inside an organization. Most companies, both large and small, are slow to adopt new technology. They need people who can explain what AI can do, show them how to use it, and help them move faster. If you are fluent in AI, you can be that person. You can walk into a company, spot the areas where AI can save time or create new opportunities, and make it happen. That is a wedge into your first job and a way to stand out from everyone else.

a16z

366,027 görüntüleme • 11 ay önce

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud
1:31

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,663 görüntüleme • 16 gün önce

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

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

Karl Mehta

14,983 görüntüleme • 1 ay önce

What if the AI boom is not just a technology race, but a capital machine hiding in plain sight? The deeper I look at this ecosystem, the less it feels like a messy market and the more it looks like a closed financial loop. That is what makes this so striking. → Big Tech funds AI labs and infrastructure → AI labs and cloud players buy chips, GPUs, and networking → Model companies license capabilities back to the same giants funding the buildout What looks complicated is, in many ways, brutally simple. A money machine. And right now, that machine is being priced as if demand, revenue, and adoption will keep compounding with very little friction. That is the part I find most fascinating. Because the numbers are not just big. They are staggering. → Microsoft has invested more than $13B into OpenAI since 2019 → Oracle signed a $300B data centre capacity deal tied to OpenAI through 2029 → Meta is racing from roughly 150,000 NVIDIA GPUs in 2023 to around 1.3 million by the end of 2025 → Broadcom’s AI chip revenue is projected to jump from $3.8B in 2023 to $40B by 2026 What really stands out to me is how concentrated this loop has become. NVIDIA gets paid by nearly everyone. Infrastructure providers benefit early. AI companies are still betting on future monetization. Maybe it works. But that is the real question. Are we looking at durable economics, or one of the most elegantly circular bets the tech world has ever built? Do you think this AI capital loop is sustainable, or are we watching a beautifully engineered cycle that still has to prove itself? #AI #ArtificialIntelligence #OpenAI #NVIDIA #Microsoft #Infrastructure #DataCenters #Investing #BusinessStrategy #Innovation
2:32

What if the AI boom is not just a technology race, but a capital machine hiding in plain sight? The deeper I look at this ecosystem, the less it feels like a messy market and the more it looks like a closed financial loop. That is what makes this so striking. → Big Tech funds AI labs and infrastructure → AI labs and cloud players buy chips, GPUs, and networking → Model companies license capabilities back to the same giants funding the buildout What looks complicated is, in many ways, brutally simple. A money machine. And right now, that machine is being priced as if demand, revenue, and adoption will keep compounding with very little friction. That is the part I find most fascinating. Because the numbers are not just big. They are staggering. → Microsoft has invested more than $13B into OpenAI since 2019 → Oracle signed a $300B data centre capacity deal tied to OpenAI through 2029 → Meta is racing from roughly 150,000 NVIDIA GPUs in 2023 to around 1.3 million by the end of 2025 → Broadcom’s AI chip revenue is projected to jump from $3.8B in 2023 to $40B by 2026 What really stands out to me is how concentrated this loop has become. NVIDIA gets paid by nearly everyone. Infrastructure providers benefit early. AI companies are still betting on future monetization. Maybe it works. But that is the real question. Are we looking at durable economics, or one of the most elegantly circular bets the tech world has ever built? Do you think this AI capital loop is sustainable, or are we watching a beautifully engineered cycle that still has to prove itself? #AI #ArtificialIntelligence #OpenAI #NVIDIA #Microsoft #Infrastructure #DataCenters #Investing #BusinessStrategy #Innovation

Pascal Bornet

13,686 görüntüleme • 4 ay önce

Nvidia will pay you to put a $250,000 data center outside your house you don't touch it, you don't manage it, you just collect around $1,000 a month for the electricity and Wi-Fi it uses it looks like an AC unit, it's actually 16 Blackwell GPUs running AI workloads around the clock it's built by Span, a startup Nvidia invested in to make distributed compute a reality the reason Nvidia put money into this is simple they need compute everywhere and building data centers takes years and their solution is to skip the construction entirely, use residential grid capacity and deploy through your neighborhood instead this is much cheaper, faster, and can scale to millions of homes but there is still a question nobody has answered yet, what happens if someone steals it also, technically they could give each homeowner access to a slice of the compute even 2% of 16 Blackwell GPUs is more processing power than most people will ever use bookmark this, it's worth coming back to when you have time
0:57

Nvidia will pay you to put a $250,000 data center outside your house you don't touch it, you don't manage it, you just collect around $1,000 a month for the electricity and Wi-Fi it uses it looks like an AC unit, it's actually 16 Blackwell GPUs running AI workloads around the clock it's built by Span, a startup Nvidia invested in to make distributed compute a reality the reason Nvidia put money into this is simple they need compute everywhere and building data centers takes years and their solution is to skip the construction entirely, use residential grid capacity and deploy through your neighborhood instead this is much cheaper, faster, and can scale to millions of homes but there is still a question nobody has answered yet, what happens if someone steals it also, technically they could give each homeowner access to a slice of the compute even 2% of 16 Blackwell GPUs is more processing power than most people will ever use bookmark this, it's worth coming back to when you have time

Anatoli Kopadze

403,338 görüntüleme • 2 ay önce

AWS made history by turning $20M data centers into a metered drip you never stopped paying for. The challenge was no one really knew what they were paying for at first (by design). In reaction, cloud computing became a monthly expense with a whole new set of tools designed to manage that expense (hello, DevOps). AI is going to follow a similar trajectory. LLM providers want AI to be a monthly cost we all mindlessly pay. Companies know they’ll need to get a handle on AI spend, just like they did with cloud computing, but there's a problem: The infrastructure doesn't exist yet. But companies can’t afford to wait. Simon Taylor and I dig into a company trying to solve this problem on Not Fintech Investment Advice, among other topics and areas! Watch the clip and listen to the full episode here:
1:38

AWS made history by turning $20M data centers into a metered drip you never stopped paying for. The challenge was no one really knew what they were paying for at first (by design). In reaction, cloud computing became a monthly expense with a whole new set of tools designed to manage that expense (hello, DevOps). AI is going to follow a similar trajectory. LLM providers want AI to be a monthly cost we all mindlessly pay. Companies know they’ll need to get a handle on AI spend, just like they did with cloud computing, but there's a problem: The infrastructure doesn't exist yet. But companies can’t afford to wait. Simon Taylor and I dig into a company trying to solve this problem on Not Fintech Investment Advice, among other topics and areas! Watch the clip and listen to the full episode here:

Alex Johnson

22,627 görüntüleme • 11 ay önce