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Subnet 64 Chutes just confirmed in their latest interview with Jesus Martinez that they have the capability to run 1 trillion parameter models. let's put that into perspective for a second: templar was making waves for running 72b models and rightfully so, that was genuinely impressive. but Chamath Palihapitiya,...

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Alchemist - τ

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18,369 просмотров • 4 месяцев назад •via X (Twitter)

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$TAO is surging and the reason goes well beyond momentum trading. Its a high five for AI! This week on the All-In Podcast, Chamath Palihapitiya highlighted Bittensor's Covenant-72B decentralized AI training milestone as a genuine technical achievement. CEO of NVIDIA Jensen Huang responded by making something clear: decentralized and proprietary AI are not competing visions for the future, they are complementary ones. Neither man had an agenda. Neither was there to talk crypto. That is precisely what makes it significant. When two of the most credible figures in technology acknowledge a sector on its technical merits alone, it marks a turning point. Not just for Bittensor, but for decentralized AI as a whole. Check the article out in The Spectre Edition - $SPECT
0:52

$TAO is surging and the reason goes well beyond momentum trading. Its a high five for AI! This week on the All-In Podcast, Chamath Palihapitiya highlighted Bittensor's Covenant-72B decentralized AI training milestone as a genuine technical achievement. CEO of NVIDIA Jensen Huang responded by making something clear: decentralized and proprietary AI are not competing visions for the future, they are complementary ones. Neither man had an agenda. Neither was there to talk crypto. That is precisely what makes it significant. When two of the most credible figures in technology acknowledge a sector on its technical merits alone, it marks a turning point. Not just for Bittensor, but for decentralized AI as a whole. Check the article out in The Spectre Edition - $SPECT

SPECTRE AI

33,330 просмотров • 4 месяцев назад

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

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

Dustin

57,059 просмотров • 3 месяцев назад

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.
8:27

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

2xnmore

27,088 просмотров • 2 месяцев назад

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.
3:53

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

Dwarkesh Patel

1,252,105 просмотров • 3 месяцев назад

$TAO just reclaimed the #1 AI crypto spot. Most people saw the headline. Almost nobody understands what it means for the price. Here is the data. $NEAR built real infrastructure. Partnerships. Developer activity. A legitimate ecosystem. $TAO just walked past it anyway. Not because of hype. Because Bittensor is the only AI crypto with a functioning marketplace for machine intelligence where supply, demand, and price discovery are all happening on-chain right now. That is not a roadmap. That is a live network. The numbers. 120+ subnets running today. $1.4B+ total ecosystem value. Chutes AI subnet: 150B+ tokens per day. Grayscale GTAO Trust: already live. Single subnet listed on the marketplace at $970,000 asking price. Subnets are becoming assets. The market is starting to price that. What the emission data is telling you. Emission rate is the network's vote on where the most valuable work is being done. When a subnet gains emission share, the collective stake-weighted intelligence of the network has decided that subnet's output is worth more of the TAO supply. Chutes AI gaining emissions while processing 150B tokens daily is not a coincidence. The network is directing capital toward proven output before any headline announces it. Why mainstream money changes everything. James Altucher just launched bluetao. ai, a TAO-powered ChatGPT alternative built directly on Bittensor subnets. He did not just buy the token. He built a product on the network. Products built on a network create structural demand for the native asset. That is how every successful L1 cycle has worked. Bittensor is now getting that builder activity from outside the crypto native world. That is a different signal from a price target tweet. Why $TAO is structurally different. Most AI tokens are betting their chain becomes the preferred environment for AI development. $TAO is not betting on becoming infrastructure. It already is. 120+ subnets running. Miners competing. Validators setting weights. Alpha tokens being priced in real time. The difference between $TAO and every other AI crypto is the difference between a city under construction and a city people are already living in. Van de Poppe said $1,000 to $2,000 in 12 months. He gave you the narrative. The subnet emission data is the mechanism he did not explain. Now you have both. $TAO at $313 with a $3.42B market cap is still early relative to what this network is actually processing. Centralised AI infrastructure companies are valued at hundreds of billions for processing far less novel work than a decentralised intelligence marketplace running 120+ competing subnets simultaneously. The repricing has not happened yet. The subnet marketplace listing at $970,000 is telling you something the price has not caught up to yet.
16:52

$TAO just reclaimed the #1 AI crypto spot. Most people saw the headline. Almost nobody understands what it means for the price. Here is the data. $NEAR built real infrastructure. Partnerships. Developer activity. A legitimate ecosystem. $TAO just walked past it anyway. Not because of hype. Because Bittensor is the only AI crypto with a functioning marketplace for machine intelligence where supply, demand, and price discovery are all happening on-chain right now. That is not a roadmap. That is a live network. The numbers. 120+ subnets running today. $1.4B+ total ecosystem value. Chutes AI subnet: 150B+ tokens per day. Grayscale GTAO Trust: already live. Single subnet listed on the marketplace at $970,000 asking price. Subnets are becoming assets. The market is starting to price that. What the emission data is telling you. Emission rate is the network's vote on where the most valuable work is being done. When a subnet gains emission share, the collective stake-weighted intelligence of the network has decided that subnet's output is worth more of the TAO supply. Chutes AI gaining emissions while processing 150B tokens daily is not a coincidence. The network is directing capital toward proven output before any headline announces it. Why mainstream money changes everything. James Altucher just launched bluetao. ai, a TAO-powered ChatGPT alternative built directly on Bittensor subnets. He did not just buy the token. He built a product on the network. Products built on a network create structural demand for the native asset. That is how every successful L1 cycle has worked. Bittensor is now getting that builder activity from outside the crypto native world. That is a different signal from a price target tweet. Why $TAO is structurally different. Most AI tokens are betting their chain becomes the preferred environment for AI development. $TAO is not betting on becoming infrastructure. It already is. 120+ subnets running. Miners competing. Validators setting weights. Alpha tokens being priced in real time. The difference between $TAO and every other AI crypto is the difference between a city under construction and a city people are already living in. Van de Poppe said $1,000 to $2,000 in 12 months. He gave you the narrative. The subnet emission data is the mechanism he did not explain. Now you have both. $TAO at $313 with a $3.42B market cap is still early relative to what this network is actually processing. Centralised AI infrastructure companies are valued at hundreds of billions for processing far less novel work than a decentralised intelligence marketplace running 120+ competing subnets simultaneously. The repricing has not happened yet. The subnet marketplace listing at $970,000 is telling you something the price has not caught up to yet.

2xnmore

12,150 просмотров • 2 месяцев назад

Someone just stole from 37,000 $TAO holders and walked away clean. Not because they broke a rule. Because no rule existed to stop them. That changes today. Here is what happened. Covenant AI ran one of the most watched subnets on Bittensor. On April 10, the founder sold their entire position and disappeared. No warning. No announcement. No on-chain signal. By the time holders found out, the price had already moved against them. This was not a hack. This was not a bug. This was a founder legally exiting into their own community with zero accountability. Bittensor just closed that door permanently. The Conviction upgrade is live on mainnet today. Every emission a subnet owner earns now locks automatically the moment it arrives. They cannot touch it immediately. If they want to exit they must submit a public unlock transaction on-chain. Visible to every single person on the network the second it is submitted. Then the clock starts. 30 days before 63% of their position becomes spendable. 90 days before 95% is accessible. You now have a month of warning before the first dollar of sell pressure hits. A silent exit is no longer possible. The founder has to tell you they are leaving before they can leave. And it goes further. Any holder can now lock their tokens toward a different address they believe would run the subnet better. The address with the most locked support behind it becomes eligible to take over entirely. Bad owners can be replaced by the community before they do damage. Before today, subnet investing had one risk nobody could price. The person running it could vanish overnight, and you would never see it coming. That risk has been removed from the equation. Skin in the game used to be a promise. Now, it is a number on a block explorer that every holder can verify in real time. The people who understand what accountable infrastructure means for the value of $TAO will not need to explain themselves later. This is still early.
23:08

Someone just stole from 37,000 $TAO holders and walked away clean. Not because they broke a rule. Because no rule existed to stop them. That changes today. Here is what happened. Covenant AI ran one of the most watched subnets on Bittensor. On April 10, the founder sold their entire position and disappeared. No warning. No announcement. No on-chain signal. By the time holders found out, the price had already moved against them. This was not a hack. This was not a bug. This was a founder legally exiting into their own community with zero accountability. Bittensor just closed that door permanently. The Conviction upgrade is live on mainnet today. Every emission a subnet owner earns now locks automatically the moment it arrives. They cannot touch it immediately. If they want to exit they must submit a public unlock transaction on-chain. Visible to every single person on the network the second it is submitted. Then the clock starts. 30 days before 63% of their position becomes spendable. 90 days before 95% is accessible. You now have a month of warning before the first dollar of sell pressure hits. A silent exit is no longer possible. The founder has to tell you they are leaving before they can leave. And it goes further. Any holder can now lock their tokens toward a different address they believe would run the subnet better. The address with the most locked support behind it becomes eligible to take over entirely. Bad owners can be replaced by the community before they do damage. Before today, subnet investing had one risk nobody could price. The person running it could vanish overnight, and you would never see it coming. That risk has been removed from the equation. Skin in the game used to be a promise. Now, it is a number on a block explorer that every holder can verify in real time. The people who understand what accountable infrastructure means for the value of $TAO will not need to explain themselves later. This is still early.

2xnmore

19,528 просмотров • 3 месяцев назад

Chamath: Anthropic's Mythos Warning Is Theater @jason: “Chamath, is it the Boy who Cried Wolf, or is this the real deal now?” Chamath Palihapitiya: “I think it's mostly theater. In February of 2019 when Dario was still at OpenAI, they did the same thing with GPT-2. That was a 1.5 billion parameter model, which sounds like a total fart in the wind in 2026. But at that time, this model was supposed to be the end of days. And at the end of it, it was a huge nothingburger. If you actually think that Mythos is capable of doing what it says it can do, two things are true. One is, a very sophisticated hacker can probably do those things right now with Opus. And two, if these exploits are this easy to find, whether you use Opus or whether you use Mythos, the reality is you'd have to shut down the internet for about five years to patch them all. So when you see a large multi-trillion dollar GSIB bank, it's a bit of theater. Why? What do you think they can actually accomplish in two months? Do you actually think that if there's these vulnerabilities, it's all going to get fixed? Let's give them six months, let's give them nine months. So I do think that Sacks is right, that they have figured out a very clever go-to-market muscle here that activates hyper attention and hyper usage, and so I give them tremendous credit. But we've seen it before, we saw it when these folks were the principal architects at OpenAI, and we're now seeing the same playbook here. The reality is that capitalism moves forward, the funding needs moves forward, and the need for these guys to build adoption moves forward. And that's going to supersede what this is.”
1:43

Chamath: Anthropic's Mythos Warning Is Theater @jason: “Chamath, is it the Boy who Cried Wolf, or is this the real deal now?” Chamath Palihapitiya: “I think it's mostly theater. In February of 2019 when Dario was still at OpenAI, they did the same thing with GPT-2. That was a 1.5 billion parameter model, which sounds like a total fart in the wind in 2026. But at that time, this model was supposed to be the end of days. And at the end of it, it was a huge nothingburger. If you actually think that Mythos is capable of doing what it says it can do, two things are true. One is, a very sophisticated hacker can probably do those things right now with Opus. And two, if these exploits are this easy to find, whether you use Opus or whether you use Mythos, the reality is you'd have to shut down the internet for about five years to patch them all. So when you see a large multi-trillion dollar GSIB bank, it's a bit of theater. Why? What do you think they can actually accomplish in two months? Do you actually think that if there's these vulnerabilities, it's all going to get fixed? Let's give them six months, let's give them nine months. So I do think that Sacks is right, that they have figured out a very clever go-to-market muscle here that activates hyper attention and hyper usage, and so I give them tremendous credit. But we've seen it before, we saw it when these folks were the principal architects at OpenAI, and we're now seeing the same playbook here. The reality is that capitalism moves forward, the funding needs moves forward, and the need for these guys to build adoption moves forward. And that's going to supersede what this is.”

The All-In Podcast

220,049 просмотров • 4 месяцев назад

[WATCH] THE PRESIDENT WILL NOT RESIGN. When it comes to the President, there is always a distinctive role between him being the President of the party and the state. There are many factors that influence the state in terms of governance and stability. If the President is called upon by some to resign and he keeps quiet it can throw the state into a state of turmoil. I want to make it clear that the officials agreed with the President on his approach, he took us into confidence and explained the factors that led him to make that particular pronouncement. We believe that the President did the right thing in pronouncing in the best interest of South Africa that he will not be resigning. There was nothing in terms of the judgment that warranted the President to resign it was just mere calls made by individuals and political parties that wanted to throw our country into a state of turmoil, uncertainty and anxiety. So it was imperative that he focus on that. It was correct for him to tell the country that from where he is standing there is nothing in the judgement that states he has done any wrongdoing and what he is going to do with the options in front of him and he has made this public. The President will take the Section 89 Report on review based on the outcome of the judgment and the legal advice he has received. There are no daggers out for the President to resign just opportunistic elements. These elements do not know what they want, they want to impeach and want him to resign, they do know what they actually want. The veracity of the report has not been tested in any committee so they don’t have a basis for the President to resign.
6:07

[WATCH] THE PRESIDENT WILL NOT RESIGN. When it comes to the President, there is always a distinctive role between him being the President of the party and the state. There are many factors that influence the state in terms of governance and stability. If the President is called upon by some to resign and he keeps quiet it can throw the state into a state of turmoil. I want to make it clear that the officials agreed with the President on his approach, he took us into confidence and explained the factors that led him to make that particular pronouncement. We believe that the President did the right thing in pronouncing in the best interest of South Africa that he will not be resigning. There was nothing in terms of the judgment that warranted the President to resign it was just mere calls made by individuals and political parties that wanted to throw our country into a state of turmoil, uncertainty and anxiety. So it was imperative that he focus on that. It was correct for him to tell the country that from where he is standing there is nothing in the judgement that states he has done any wrongdoing and what he is going to do with the options in front of him and he has made this public. The President will take the Section 89 Report on review based on the outcome of the judgment and the legal advice he has received. There are no daggers out for the President to resign just opportunistic elements. These elements do not know what they want, they want to impeach and want him to resign, they do know what they actually want. The veracity of the report has not been tested in any committee so they don’t have a basis for the President to resign.

ANC SECRETARY GENERAL | Fikile Mbalula

21,983 просмотров • 3 месяцев назад

Dario Amodei just revealed the exact realization that fractured OpenAI. It happened while building the most powerful AI in the world. The team discovered that scaling had no ceiling. Amodei: “If you pour more compute into these models, they’ll get better and better, and that there’s almost no end to this.” At the time, almost nobody believed it. Amodei’s group were among the first to see it clearly. More compute meant more intelligence. Indefinitely. Without limit. That should have been the most exciting discovery in the history of technology. It terrified them. Because they saw the second half of the equation the industry was ignoring entirely. Amodei: “You don’t tell the models what their values are just by pouring more compute into them.” Intelligence scales with compute. Values don’t. You can build a mind of unlimited capability and it will have no moral compass unless you deliberately build one in. Not as a feature. Not as a guardrail bolted on at the end. As the foundation the entire system is constructed on. This wasn’t a philosophical disagreement. It was an existential one. A god-like intelligence with no alignment isn’t a powerful tool. It’s an uncontrolled force with no reason to care about the species that built it. Amodei: “There were a set of people who believed in those two ideas. We really trusted each other and wanted to work together, and so we went off and started our own company with that idea in mind.” They walked out of the most powerful AI lab on earth. Not for better funding. Not for equity. Because they believed the path OpenAI was on led somewhere nobody could walk back from. That small group became Anthropic. Safety wasn’t a feature they added. It was the entire reason the company exists. The intelligence is going to keep scaling. There is almost no end to it. The only question that has ever mattered is what it’s pointing at when it gets there.
0:56

Dario Amodei just revealed the exact realization that fractured OpenAI. It happened while building the most powerful AI in the world. The team discovered that scaling had no ceiling. Amodei: “If you pour more compute into these models, they’ll get better and better, and that there’s almost no end to this.” At the time, almost nobody believed it. Amodei’s group were among the first to see it clearly. More compute meant more intelligence. Indefinitely. Without limit. That should have been the most exciting discovery in the history of technology. It terrified them. Because they saw the second half of the equation the industry was ignoring entirely. Amodei: “You don’t tell the models what their values are just by pouring more compute into them.” Intelligence scales with compute. Values don’t. You can build a mind of unlimited capability and it will have no moral compass unless you deliberately build one in. Not as a feature. Not as a guardrail bolted on at the end. As the foundation the entire system is constructed on. This wasn’t a philosophical disagreement. It was an existential one. A god-like intelligence with no alignment isn’t a powerful tool. It’s an uncontrolled force with no reason to care about the species that built it. Amodei: “There were a set of people who believed in those two ideas. We really trusted each other and wanted to work together, and so we went off and started our own company with that idea in mind.” They walked out of the most powerful AI lab on earth. Not for better funding. Not for equity. Because they believed the path OpenAI was on led somewhere nobody could walk back from. That small group became Anthropic. Safety wasn’t a feature they added. It was the entire reason the company exists. The intelligence is going to keep scaling. There is almost no end to it. The only question that has ever mattered is what it’s pointing at when it gets there.

Dustin

52,414 просмотров • 5 месяцев назад

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.
1:41

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.

Dustin

25,119 просмотров • 4 месяцев назад

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 просмотров • 1 месяц назад

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.
3:31

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 просмотров • 1 месяц назад

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS
1:46

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS

TBPN

36,474 просмотров • 3 месяцев назад

A Bittensor subnet just outscored Claude and Cursor on the SWE benchmark. They spent less than $1 million to get there. Anthropic spent billions. I sat down with Mark Jeffrey, one of the most connected people in the Bittensor ecosystem, and he broke down everything. Here's what most people don't know about TAO. Bittensor takes Bitcoin's mining concept and makes it programmable. Instead of solving meaningless hash puzzles, miners compete on real AI tasks. Best freelancer wins. Blockchain pays them. No company. No CEO. No permission needed. Bitcoin did this for energy. Bittensor does it for talent. The numbers are wild: • Ridges (Subnet 62) built a Claude/Cursor competitor for under $1M • Miners on Ridges were earning $50K per day at peak • The Bittensor network has 128 subnets, each like its own AI startup • Mark says 20-30 of them could become multi-billion dollar companies • Only 20% of TAO is staked in subnets right now • Stakers are earning up to 80% yield on some subnets Mark has been in crypto since 2013. He was in the Ethereum ICO. He's seen every cycle. His take: Bittensor is the most important thing to happen in crypto since Ethereum. He calls TAO the "third great coin" alongside Bitcoin and ETH. The comparison to early Bitcoin is hard to ignore. TAO just had its first halving in December. Same 21 million supply cap. Same post-halving setup. When Bitcoin went through this phase, it jumped from $250 to $10,000. Mark's conservative target for TAO by end of 2026: $3,000. But the real insight was about demand. More subnets means more TAO gets locked up. Subnet cap going from 128 to 256. Staking will absorb most of the supply. And AI agents need crypto to transact. They can't open bank accounts. Bittensor is building the rails for that. Jensen Huang just talked about Templar, a Bittensor subnet, on stage. This isn't theoretical anymore. People are using this stuff. The products on Bittensor are 10 to 100x better than what we saw in early Ethereum. And we're still early.
0:37

A Bittensor subnet just outscored Claude and Cursor on the SWE benchmark. They spent less than $1 million to get there. Anthropic spent billions. I sat down with Mark Jeffrey, one of the most connected people in the Bittensor ecosystem, and he broke down everything. Here's what most people don't know about TAO. Bittensor takes Bitcoin's mining concept and makes it programmable. Instead of solving meaningless hash puzzles, miners compete on real AI tasks. Best freelancer wins. Blockchain pays them. No company. No CEO. No permission needed. Bitcoin did this for energy. Bittensor does it for talent. The numbers are wild: • Ridges (Subnet 62) built a Claude/Cursor competitor for under $1M • Miners on Ridges were earning $50K per day at peak • The Bittensor network has 128 subnets, each like its own AI startup • Mark says 20-30 of them could become multi-billion dollar companies • Only 20% of TAO is staked in subnets right now • Stakers are earning up to 80% yield on some subnets Mark has been in crypto since 2013. He was in the Ethereum ICO. He's seen every cycle. His take: Bittensor is the most important thing to happen in crypto since Ethereum. He calls TAO the "third great coin" alongside Bitcoin and ETH. The comparison to early Bitcoin is hard to ignore. TAO just had its first halving in December. Same 21 million supply cap. Same post-halving setup. When Bitcoin went through this phase, it jumped from $250 to $10,000. Mark's conservative target for TAO by end of 2026: $3,000. But the real insight was about demand. More subnets means more TAO gets locked up. Subnet cap going from 128 to 256. Staking will absorb most of the supply. And AI agents need crypto to transact. They can't open bank accounts. Bittensor is building the rails for that. Jensen Huang just talked about Templar, a Bittensor subnet, on stage. This isn't theoretical anymore. People are using this stuff. The products on Bittensor are 10 to 100x better than what we saw in early Ethereum. And we're still early.

Jesus Martinez

73,314 просмотров • 4 месяцев назад

James Altucher just launched an entire podcast dedicated to $TAO. He called it the TAO Pod. First episode: “Will Bittensor Be Bigger Than Bitcoin?” This is the man who called Bitcoin early. Called Ethereum early. Called crypto early when everyone thought he was insane. Here is what stopped me reading his piece: One subnet on Bittensor called Chutes is serving 150 billion language tokens per day. Not crypto tokens. Language tokens. The raw material that powers every LLM response from ChatGPT to Grok. 150 billion. Every 24 hours. Through a decentralised network most people have never heard of. That was July 2025. Since then, Chutes has recorded a 250x usage increase. Cumulative totals have crossed into the trillions. This is not a snapshot. This is a growth curve. And it is happening before mainstream awareness. Before the ETF. Before the institutional allocation cycle begins. Altucher’s comparison is the one worth sitting with: 1994 internet. Before the browser existed. Before anyone knew what email was. Before you could explain to your parents why any of it mattered. That is where he thinks Bittensor is right now. - Barry Silbert built an entire company around it. - Jason Calacanis put personal money in and called 200x. - Jensen Huang called it a modern Folding@Home on the All-In Podcast. - Grayscale filed a trust with the SEC. - PwC sent a partner to the Bittensor track in Paris. - James Altucher launched a dedicated podcast. These are not tourists. These are the people who were in the right rooms before every major shift in crypto and tech history. They are all pointing at the same thing. The people who read the docs always buy before the people who read the price.
28:23

James Altucher just launched an entire podcast dedicated to $TAO. He called it the TAO Pod. First episode: “Will Bittensor Be Bigger Than Bitcoin?” This is the man who called Bitcoin early. Called Ethereum early. Called crypto early when everyone thought he was insane. Here is what stopped me reading his piece: One subnet on Bittensor called Chutes is serving 150 billion language tokens per day. Not crypto tokens. Language tokens. The raw material that powers every LLM response from ChatGPT to Grok. 150 billion. Every 24 hours. Through a decentralised network most people have never heard of. That was July 2025. Since then, Chutes has recorded a 250x usage increase. Cumulative totals have crossed into the trillions. This is not a snapshot. This is a growth curve. And it is happening before mainstream awareness. Before the ETF. Before the institutional allocation cycle begins. Altucher’s comparison is the one worth sitting with: 1994 internet. Before the browser existed. Before anyone knew what email was. Before you could explain to your parents why any of it mattered. That is where he thinks Bittensor is right now. - Barry Silbert built an entire company around it. - Jason Calacanis put personal money in and called 200x. - Jensen Huang called it a modern Folding@Home on the All-In Podcast. - Grayscale filed a trust with the SEC. - PwC sent a partner to the Bittensor track in Paris. - James Altucher launched a dedicated podcast. These are not tourists. These are the people who were in the right rooms before every major shift in crypto and tech history. They are all pointing at the same thing. The people who read the docs always buy before the people who read the price.

2xnmore

17,274 просмотров • 2 месяцев назад

Elon Musk built one of the largest AI compute clusters on earth. Yann LeCun just explained why xAI now rents it out to rivals instead of winning with it. Musk has antagonized so much AI talent he structurally cannot hire the people he needs. LeCun is not a critic on the sidelines. He won the Turing Award and ran AI at Meta for a decade. When he talks about who can and cannot build a frontier lab, he is describing his own world. His verdict on xAI was blunt. He called it kind of a failure and did not soften it. His reasoning had nothing to do with money. The founding team left or was fired. There is some uncertainty about which. Either way, the people who started the company are gone. That is the part that matters. A frontier lab is its researchers. Lose them and the compute is just hardware. By March, all eleven co-founders Musk recruited in 2023 had walked out. Researchers who came from DeepMind, Google, and OpenAI. Musk himself posted that xAI was not built right the first time and had to be rebuilt from the foundations up. So Musk is left with one of the biggest clusters on earth and no way to win with it. He rents it out to other companies to recoup the cost. The most expensive infrastructure in AI, built by someone who can no longer staff it. Asked directly if xAI can compete at the frontier, LeCun gave a one word answer. No. What do you guys think about this? Source: CNBC International Live
0:50

Elon Musk built one of the largest AI compute clusters on earth. Yann LeCun just explained why xAI now rents it out to rivals instead of winning with it. Musk has antagonized so much AI talent he structurally cannot hire the people he needs. LeCun is not a critic on the sidelines. He won the Turing Award and ran AI at Meta for a decade. When he talks about who can and cannot build a frontier lab, he is describing his own world. His verdict on xAI was blunt. He called it kind of a failure and did not soften it. His reasoning had nothing to do with money. The founding team left or was fired. There is some uncertainty about which. Either way, the people who started the company are gone. That is the part that matters. A frontier lab is its researchers. Lose them and the compute is just hardware. By March, all eleven co-founders Musk recruited in 2023 had walked out. Researchers who came from DeepMind, Google, and OpenAI. Musk himself posted that xAI was not built right the first time and had to be rebuilt from the foundations up. So Musk is left with one of the biggest clusters on earth and no way to win with it. He rents it out to other companies to recoup the cost. The most expensive infrastructure in AI, built by someone who can no longer staff it. Asked directly if xAI can compete at the frontier, LeCun gave a one word answer. No. What do you guys think about this? Source: CNBC International Live

Ihtesham Ali

322,899 просмотров • 1 месяц назад

A $10 MILLION DUMP JUST TESTED WHETHER BITTENSOR IS REAL OR NOT. THE NETWORK GAVE ITS ANSWER. Covenant AI walked. The biggest builder team in the ecosystem. The team behind the 72B parameter model that Jensen Huang praised on the All In podcast. They accused leadership of centralization. Said one person controls too much. Said changes were made without process. 37,000 TAO sold. Price crashed 25%. $650 million wiped. And then something happened that most people missed because they were too busy panic selling. The network kept running. Every subnet stayed active. 100+ subnets still live. AI training continued. New models started. Builders kept building. The team that built Covenant 72B left. But the model was trained in a decentralized way across 70+ independent contributors using home GPUs. The milestone belongs to the network, not the team. Grayscale didn't sell. They increased exposure. ETF conversations didn't stop. Jacob Steves proposed locked stake for governance transparency within days. This was BitTensor's first public crisis. The kind that either kills a project or proves it can't be killed. The network answered. It's still standing. $256 right now. If it reclaims $280, this was the healthiest shakeout in the project's history. If it doesn't, the bear case deepens. Either way, you now know something you didn't know last week. BitTensor can take a direct hit from its biggest contributor leaving and keep running without interruption. That's not nothing. That might be everything.
11:23

A $10 MILLION DUMP JUST TESTED WHETHER BITTENSOR IS REAL OR NOT. THE NETWORK GAVE ITS ANSWER. Covenant AI walked. The biggest builder team in the ecosystem. The team behind the 72B parameter model that Jensen Huang praised on the All In podcast. They accused leadership of centralization. Said one person controls too much. Said changes were made without process. 37,000 TAO sold. Price crashed 25%. $650 million wiped. And then something happened that most people missed because they were too busy panic selling. The network kept running. Every subnet stayed active. 100+ subnets still live. AI training continued. New models started. Builders kept building. The team that built Covenant 72B left. But the model was trained in a decentralized way across 70+ independent contributors using home GPUs. The milestone belongs to the network, not the team. Grayscale didn't sell. They increased exposure. ETF conversations didn't stop. Jacob Steves proposed locked stake for governance transparency within days. This was BitTensor's first public crisis. The kind that either kills a project or proves it can't be killed. The network answered. It's still standing. $256 right now. If it reclaims $280, this was the healthiest shakeout in the project's history. If it doesn't, the bear case deepens. Either way, you now know something you didn't know last week. BitTensor can take a direct hit from its biggest contributor leaving and keep running without interruption. That's not nothing. That might be everything.

Altcoin Buzz

23,317 просмотров • 4 месяцев назад

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

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

Dustin

92,704 просмотров • 3 месяцев назад

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

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

Dustin

82,757 просмотров • 4 месяцев назад

On Friday, Bill Maher asked me if what happened with Biden at the disastrous debate with Trump was the same story as the Emperor's New Clothes. Me: The story of the Emperor's New Clothes is a story about common knowledge, because when the kid blurted it out, he actually wasn't telling anyone anything they didn't already know. They could see the Emperor was naked. But he still changed their knowledge, because by blurting it out with an earshot of the others, now everyone knew that everyone else knew that everyone else knew that everyone else knew. And what that allowed them to do is change their relationship with the Emperor, from obsequious deference to ridicule and scorn. And the thing about common knowledge in the social realm is that it's what props up our social relationships. And so when something is blurted out, then it can change everything. It changes the nature of your relationship with someone. Maher: And we do have sort of a modern version of the Emperor parable, which is Joe Biden. I mean, he was the Emperor who everyone wouldn't say had lost his marbles. I mean, is that not really the same story? Me: It is the same story, because opinion polls showed that after that disastrous debate with Trump, the number of people who thought that he was cognitively impaired didn't go up by that much. It went up by a few percentage points. But before, a majority of people thought that he was cognitively impaired. The difference is, when it's on TV, where you're watching it, you know that the rest of the country is watching it, you know the rest of the country knows the rest of the country, it's no longer private. It's common. And that's when he was challenged. That was the end. Bill Maher Real Time with Bill Maher
1:29

On Friday, Bill Maher asked me if what happened with Biden at the disastrous debate with Trump was the same story as the Emperor's New Clothes. Me: The story of the Emperor's New Clothes is a story about common knowledge, because when the kid blurted it out, he actually wasn't telling anyone anything they didn't already know. They could see the Emperor was naked. But he still changed their knowledge, because by blurting it out with an earshot of the others, now everyone knew that everyone else knew that everyone else knew that everyone else knew. And what that allowed them to do is change their relationship with the Emperor, from obsequious deference to ridicule and scorn. And the thing about common knowledge in the social realm is that it's what props up our social relationships. And so when something is blurted out, then it can change everything. It changes the nature of your relationship with someone. Maher: And we do have sort of a modern version of the Emperor parable, which is Joe Biden. I mean, he was the Emperor who everyone wouldn't say had lost his marbles. I mean, is that not really the same story? Me: It is the same story, because opinion polls showed that after that disastrous debate with Trump, the number of people who thought that he was cognitively impaired didn't go up by that much. It went up by a few percentage points. But before, a majority of people thought that he was cognitively impaired. The difference is, when it's on TV, where you're watching it, you know that the rest of the country is watching it, you know the rest of the country knows the rest of the country, it's no longer private. It's common. And that's when he was challenged. That was the end. Bill Maher Real Time with Bill Maher

Steven Pinker

272,948 просмотров • 11 месяцев назад