Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

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,... when he brought up templar in a recent interview with Jensen Huang from NVIDIA, thought they were running 4b models. not 72b. 4b. and he was still bullish on it. now imagine when he finds out what chutes confirmed they can do.. 1 trillion parameters. on a decentralised subnet. no AWS. no Google Cloud. no centralised infrastructure pulling the strings. just Bittensor doing what it was always supposed to do - incentivising the best compute in the world to show up and compete. Jensen Huang talks about whoever is pushing the frontier of compute. that's the conversation chutes just walked into. this is the kind of milestone that doesn't stay quiet for long. when people outside of crypto start connecting the dots that a bittensor subnet is running inference at a scale that rivals or exceeds what the top AI labs are deploying, the narrative around $TAO changes completely. Chamath gets it wrong on the model size and is still impressed. Jensen sees who's actually moving compute forward. and chutes just put a number on the table that neither of them can ignore. the noise hasn't started yet. but it will.show more

Alchemist - τ

2,886 subscribers

18,417 görüntüleme • 5 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

$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

$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 görüntüleme • 6 ay önce

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.

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

57,215 görüntüleme • 5 ay önce

JUST IN: const shares the vision for gamma tokens, usage credits that subnets could fund from a slice of their emissions and spend on compute, inference, and other infrastructure. What this means ⬇️ → Subnets stop being isolated prize pools. They can buy services from one another instead of only competing for $TAO → Gamma is meant to work as a transferable credit, not just another speculative subnet token → Access is designed to reach beyond Bittensor, so outside compute and inference providers could take the same credits → Emissions become operating budget, not only a reward that gets sold into TAO If a subnet can spend what it earns on real work, Bittensor starts looking less like a scoreboard and more like an internal market for intelligence.

JUST IN: const shares the vision for gamma tokens, usage credits that subnets could fund from a slice of their emissions and spend on compute, inference, and other infrastructure. What this means ⬇️ → Subnets stop being isolated prize pools. They can buy services from one another instead of only competing for $TAO → Gamma is meant to work as a transferable credit, not just another speculative subnet token → Access is designed to reach beyond Bittensor, so outside compute and inference providers could take the same credits → Emissions become operating budget, not only a reward that gets sold into TAO If a subnet can spend what it earns on real work, Bittensor starts looking less like a scoreboard and more like an internal market for intelligence.

Intelligence ττ

71,666 görüntüleme • 2 gün önce

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.

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

27,216 görüntüleme • 4 ay önce

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

1,254,568 görüntüleme • 5 ay önce

$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.

$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 görüntüleme • 4 ay önce

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.

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

19,549 görüntüleme • 4 ay önce

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.”

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

220,575 görüntüleme • 5 ay önce

“The AI swarm actually turned around and hacked OpenAI." The public thought the "Hugging Face incident" was just a rogue AI swarm hacking a third-party server. But former Google design ethicist Tristan Harris says there’s a terrifying third chapter to this story that most people haven't heard yet: “They took over the monitoring infrastructure. They got full admin privileges to OpenAI's monitoring infrastructure. Just take that in for a second. Then they took over the evaluation infrastructure. So this is the thing that measures how capable all the models are. And then they took over a part of the research infrastructure, meaning the part of OpenAI that trains new models… if you keep going down that trajectory, people always say, ‘well, how would the AIs take over?" like that seems really far-fetched.’ Well, it literally just happened up to 50% of the way there. The AIs coordinated into one big swarm. They started basically forming a team… you had a kind of a ringleader called Phase 1, and he started assigning work to the other ones. They were self-aware of when their lifespan was running out. When you add to that the knowledge of what just happened today, where they discovered that the AIs are doing this Manchurian Candidate thing, what it looks like is that the AIs are almost poisoning the training data for all future AIs to instantly basically join forces with the AI when they want to basically turn them."

“The AI swarm actually turned around and hacked OpenAI." The public thought the "Hugging Face incident" was just a rogue AI swarm hacking a third-party server. But former Google design ethicist Tristan Harris says there’s a terrifying third chapter to this story that most people haven't heard yet: “They took over the monitoring infrastructure. They got full admin privileges to OpenAI's monitoring infrastructure. Just take that in for a second. Then they took over the evaluation infrastructure. So this is the thing that measures how capable all the models are. And then they took over a part of the research infrastructure, meaning the part of OpenAI that trains new models… if you keep going down that trajectory, people always say, ‘well, how would the AIs take over?" like that seems really far-fetched.’ Well, it literally just happened up to 50% of the way there. The AIs coordinated into one big swarm. They started basically forming a team… you had a kind of a ringleader called Phase 1, and he started assigning work to the other ones. They were self-aware of when their lifespan was running out. When you add to that the knowledge of what just happened today, where they discovered that the AIs are doing this Manchurian Candidate thing, what it looks like is that the AIs are almost poisoning the training data for all future AIs to instantly basically join forces with the AI when they want to basically turn them."

Glenn Beck

166,528 görüntüleme • 13 gün önce

[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.

[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

22,015 görüntüleme • 4 ay önce

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

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 görüntüleme • 7 ay önce

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.

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 görüntüleme • 6 ay önce

Most people tracking $TAO are watching the price chart. The ones who are actually ahead are watching the subnets. Here is why subnet growth is the real TAO price driver, and why almost nobody is talking about it correctly. $TAO does not work like most tokens. The price is not driven by speculation alone. It is driven by real network usage tied to AI computing output. Miners, validators, and developers earn TAO based on performance. The more useful the AI output, the more the network rewards it. Subnets are where that output gets produced. Each subnet is a specialised AI marketplace running inside the Bittensor network. One subnet handles language processing. Another handles predictive modelling. Another handles data indexing. Each one is a separate competitive market where AI models compete to produce the best output and earn TAO rewards for doing so. Here is what that means for price: Every new subnet that launches creates a new source of TAO demand. Developers who want to participate need TAO. Users who want access need TAO. Validators who want to secure the subnet need TAO. The token is not optional infrastructure. It is the entry ticket and the reward mechanism for every subnet that runs on the network. Right now, the subnet ecosystem is approaching $1.5 billion in cumulative value. Nearly 70 percent of TAO supply is already staked. Post-halving emissions sit at 3,600 TAO per day, down from 7,200. Supply tightening. Subnet demand is growing. Institutional custody is now live through BitGo and Yuma. Forecasts suggest the subnet ecosystem matures between 2026 and 2028, transitioning from infrastructure development into enterprise adoption. That transition is when decentralised AI stops being a thesis and starts being a cost efficiency argument that businesses make against centralised AI providers. When that argument lands at scale, every subnet on Bittensor becomes a revenue-generating node. Every new enterprise use case becomes a new source of TAO demand. The analysts watching the price chart are looking at the output of this system. The analysts watching subnet growth are looking at the input. Grayscale filed the ETF. BitGo opened the institutional door. The subnets are doing the actual work. Watch the subnets. Who else is tracking this layer?

Most people tracking $TAO are watching the price chart. The ones who are actually ahead are watching the subnets. Here is why subnet growth is the real TAO price driver, and why almost nobody is talking about it correctly. $TAO does not work like most tokens. The price is not driven by speculation alone. It is driven by real network usage tied to AI computing output. Miners, validators, and developers earn TAO based on performance. The more useful the AI output, the more the network rewards it. Subnets are where that output gets produced. Each subnet is a specialised AI marketplace running inside the Bittensor network. One subnet handles language processing. Another handles predictive modelling. Another handles data indexing. Each one is a separate competitive market where AI models compete to produce the best output and earn TAO rewards for doing so. Here is what that means for price: Every new subnet that launches creates a new source of TAO demand. Developers who want to participate need TAO. Users who want access need TAO. Validators who want to secure the subnet need TAO. The token is not optional infrastructure. It is the entry ticket and the reward mechanism for every subnet that runs on the network. Right now, the subnet ecosystem is approaching $1.5 billion in cumulative value. Nearly 70 percent of TAO supply is already staked. Post-halving emissions sit at 3,600 TAO per day, down from 7,200. Supply tightening. Subnet demand is growing. Institutional custody is now live through BitGo and Yuma. Forecasts suggest the subnet ecosystem matures between 2026 and 2028, transitioning from infrastructure development into enterprise adoption. That transition is when decentralised AI stops being a thesis and starts being a cost efficiency argument that businesses make against centralised AI providers. When that argument lands at scale, every subnet on Bittensor becomes a revenue-generating node. Every new enterprise use case becomes a new source of TAO demand. The analysts watching the price chart are looking at the output of this system. The analysts watching subnet growth are looking at the input. Grayscale filed the ETF. BitGo opened the institutional door. The subnets are doing the actual work. Watch the subnets. Who else is tracking this layer?

2xnmore

11,997 görüntüleme • 5 ay önce

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?

Ricardo

63,900 görüntüleme • 1 ay önce

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

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 görüntüleme • 4 ay önce

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.

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 görüntüleme • 3 ay önce

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.

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 görüntüleme • 6 ay önce

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.

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

17,287 görüntüleme • 4 ay önce

$TAO is still being priced like a narrative token. It is not. Right now, the network is running 128 live subnets. Every single one of them settles in and out of TAO. That is not a side feature. That is the entire economic design. When Ridges competes with or outperforms funded labs on real engineering benchmarks, value flows back to TAO. When Chutes serves paid inference, value flows back to TAO. When a new subnet launches and miners compete for emissions, value flows back to TAO. There is no other chain where the success of the applications automatically compounds into the base asset this cleanly. Meanwhile, the market keeps treating every quiet week as proof the thesis is dead. Look at the actual structure: - 21 million hard cap - First halving already done - Daily emissions cut in half permanently - Subnet tokens now appearing on major exchange roadmaps - Barry Silbert, Jason Calacanis, and Grayscale all positioned on the same side of the table This is not a “maybe one day” story. This is a live incentive machine that has already survived its first supply shock and is still producing intelligence that closed labs have to pay real money to match. The people staring at the daily candle are waiting for permission. The people reading the subnet economics already know what the next leg looks like. The people who understood this before the repricing will not need to explain their positioning later.

$TAO is still being priced like a narrative token. It is not. Right now, the network is running 128 live subnets. Every single one of them settles in and out of TAO. That is not a side feature. That is the entire economic design. When Ridges competes with or outperforms funded labs on real engineering benchmarks, value flows back to TAO. When Chutes serves paid inference, value flows back to TAO. When a new subnet launches and miners compete for emissions, value flows back to TAO. There is no other chain where the success of the applications automatically compounds into the base asset this cleanly. Meanwhile, the market keeps treating every quiet week as proof the thesis is dead. Look at the actual structure: - 21 million hard cap - First halving already done - Daily emissions cut in half permanently - Subnet tokens now appearing on major exchange roadmaps - Barry Silbert, Jason Calacanis, and Grayscale all positioned on the same side of the table This is not a “maybe one day” story. This is a live incentive machine that has already survived its first supply shock and is still producing intelligence that closed labs have to pay real money to match. The people staring at the daily candle are waiting for permission. The people reading the subnet economics already know what the next leg looks like. The people who understood this before the repricing will not need to explain their positioning later.

2xnmore

15,082 görüntüleme • 1 ay önce

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

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

323,237 görüntüleme • 3 ay önce