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After the Templar Covenant exit last week, the need for institutional grade risk frameworks couldn’t be more obvious Since then we’ve seen Manako partner with PWC and multiple plans to rollout trillion parameter training runs from other subnets Compute is being squeezed and non-AI companies are pivoting to GPU... marketplaces to capture this insatiable demand (see All Birds Inc yesterday) Capital is flowing into Bittensor subnets with minimal structure in evaluating signal. That’s how you get blindsided TAO Institute gives institutional allocators the risk frameworks and evaluation tooling to actually underwrite these positions Think of it as the infrastructure layer between capital and the subnet economy We are now live; so kick the tyres on the platform and tell us what’s missingshow more

0xSammy

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18,086 views • 3 months ago •via X (Twitter)

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BIGGER PICTURE: BITTENSOR DOUBLES SUBNET CAPACITY FROM 128 TO 256 Adding to the institutional case: Openτensor Foundaτion is doubling protocol-level subnet capacity from 128 to 256, opening up 128 new slots for AI builders to launch on $TAO. Subnet emissions are how $TAO is distributed, so more subnets means more onchain AI projects competing for revenue and network share. With Chutes already producing real inference revenue, the supply-side expansion is being met with verifiable demand-side traction.

BIGGER PICTURE: BITTENSOR DOUBLES SUBNET CAPACITY FROM 128 TO 256 Adding to the institutional case: Openτensor Foundaτion is doubling protocol-level subnet capacity from 128 to 256, opening up 128 new slots for AI builders to launch on $TAO. Subnet emissions are how $TAO is distributed, so more subnets means more onchain AI projects competing for revenue and network share. With Chutes already producing real inference revenue, the supply-side expansion is being met with verifiable demand-side traction.

BSCN

37,432 views • 3 months ago

Bittensor $TAO just got talked about on the floor of the NYSE 🏛 Evan Malanga, CRO of Yuma, went on FINTECH.TV's Market Movers at the Opening Bell. This isn't crypto Twitter. This is traditional finance media broadcast from inside the New York Stock Exchange. The audience is institutional investors, fund managers, and TradFi professionals. He broke down: → How $TAO democratizes AI through token incentives → The subnet architecture and competitive miner design → Why Bittensor is like the early World Wide Web for intelligence → Real-world applications with builders competing to produce AI outputs → TradFi rails entering the crypto ecosystem The signal here is clear. Yuma is DCG-backed. They're not just building subnets they're putting $TAO in front of the people who write the checks. Grayscale Trust. Deutsche Digital Assets ETP. Connecticut state VC fund. And now NYSE floor interviews. This is how institutional adoption actually happens. Not announcements. Conversations in the rooms that matter. $TAO is being positioned as infrastructure, not speculation. Watch the full interview: The people who need to see Bittensor are starting to see it.

Bittensor $TAO just got talked about on the floor of the NYSE 🏛 Evan Malanga, CRO of Yuma, went on FINTECH.TV's Market Movers at the Opening Bell. This isn't crypto Twitter. This is traditional finance media broadcast from inside the New York Stock Exchange. The audience is institutional investors, fund managers, and TradFi professionals. He broke down: → How $TAO democratizes AI through token incentives → The subnet architecture and competitive miner design → Why Bittensor is like the early World Wide Web for intelligence → Real-world applications with builders competing to produce AI outputs → TradFi rails entering the crypto ecosystem The signal here is clear. Yuma is DCG-backed. They're not just building subnets they're putting $TAO in front of the people who write the checks. Grayscale Trust. Deutsche Digital Assets ETP. Connecticut state VC fund. And now NYSE floor interviews. This is how institutional adoption actually happens. Not announcements. Conversations in the rooms that matter. $TAO is being positioned as infrastructure, not speculation. Watch the full interview: The people who need to see Bittensor are starting to see it.

Andy ττ

27,275 views • 5 months ago

This is why decentralized AI will win.. This is why #Bittensor will win ⚫️ ⚪️ "There's about 20,000 GPUs roughly speaking on the #Bittensor network and they're all in a collection of subnets" ⚪️ "Subnet 64 has maybe most of them, maybe 10,000.. Subnet 4 has thousands.. Subnet 3 is around the neighborhood of thousands.. Subnet 51 is also almost 10,000" ⚪️ "So you add all this up and it's more than 10% might even be 20% of all of the compute that Elon Musk has had to spend tens of billions of dollars acquiring" I highly recommend listening to the full discussion of The $TAO Pod with JJ and James Altucher here 👉 $TAO

This is why decentralized AI will win.. This is why #Bittensor will win ⚫️ ⚪️ "There's about 20,000 GPUs roughly speaking on the #Bittensor network and they're all in a collection of subnets" ⚪️ "Subnet 64 has maybe most of them, maybe 10,000.. Subnet 4 has thousands.. Subnet 3 is around the neighborhood of thousands.. Subnet 51 is also almost 10,000" ⚪️ "So you add all this up and it's more than 10% might even be 20% of all of the compute that Elon Musk has had to spend tens of billions of dollars acquiring" I highly recommend listening to the full discussion of The $TAO Pod with JJ and James Altucher here 👉 $TAO

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48,276 views • 1 year ago

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 views • 4 months ago

$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 views • 2 months ago

Sat down with Bittensor co-founder Jacob Steeves (Const) for a deep dive into the state of Bittensor in 2026. We covered how Subnet 3 trained a 72B parameter model in a fully permissionless decentralized way, how subnets create markets for digital commodities like gradients and inference, the dynamic TAO tokenomics that lock emissions into subnet liquidity pools, and why open ownership of AI matters more than ever as centralized labs head toward trillion-dollar IPOs. 0:00 Intro: Bittensor Co-Founder Jacob Steeves 1:13 Jacob's Background, From Deep Learning to Crypto 4:45 Founding Bittensor: The Monetary Computer 7:58 How Subnets Work as Markets for Digital Commodities 11:18 Subnet 3: Decentralized Model Training 15:28 Permissionless Training at Internet Scale 18:52 Why This Matters: Open Source AI Ownership 25:53 Subnet Tour: Optimization, Inference, and Compute 29:55 Affine: Beating Qwen 30B via Market Incentives 34:26 TAO Tokenomics and Dynamic TAO 51:54 How Investors Participate: Just Buy TAO 54:05 Open Ownership vs Fiat AI

Sat down with Bittensor co-founder Jacob Steeves (Const) for a deep dive into the state of Bittensor in 2026. We covered how Subnet 3 trained a 72B parameter model in a fully permissionless decentralized way, how subnets create markets for digital commodities like gradients and inference, the dynamic TAO tokenomics that lock emissions into subnet liquidity pools, and why open ownership of AI matters more than ever as centralized labs head toward trillion-dollar IPOs. 0:00 Intro: Bittensor Co-Founder Jacob Steeves 1:13 Jacob's Background, From Deep Learning to Crypto 4:45 Founding Bittensor: The Monetary Computer 7:58 How Subnets Work as Markets for Digital Commodities 11:18 Subnet 3: Decentralized Model Training 15:28 Permissionless Training at Internet Scale 18:52 Why This Matters: Open Source AI Ownership 25:53 Subnet Tour: Optimization, Inference, and Compute 29:55 Affine: Beating Qwen 30B via Market Incentives 34:26 TAO Tokenomics and Dynamic TAO 51:54 How Investors Participate: Just Buy TAO 54:05 Open Ownership vs Fiat AI

VirtualBacon

42,057 views • 4 months ago

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

39,709 views • 1 month ago

ROBA Compute is officially LIVE. This isn't a feature drop. This is ROBA expanding into the AI infrastructure market - hundreds of billions in scale, and we're staking our claim. Rent GPUs on-demand. Enterprise-grade allocation. Transparent USDC pricing. Zero hidden fees and all on-chain. We're already in talks with enterprise clients for compute at scale. 15% of all compute revenue → straight to $ROBA buybacks & burns. GPU capacity is limited at launch. First come, first served. Over the next weeks we will begin accepting new compute partners to provide more power to the marketplace in addition to ROBA's own in-house GPUs and integrated robotics stack. Platform fees are discounted for a limited time only. The infrastructure layer powering physical AI and beyond starts here.

ROBA Compute is officially LIVE. This isn't a feature drop. This is ROBA expanding into the AI infrastructure market - hundreds of billions in scale, and we're staking our claim. Rent GPUs on-demand. Enterprise-grade allocation. Transparent USDC pricing. Zero hidden fees and all on-chain. We're already in talks with enterprise clients for compute at scale. 15% of all compute revenue → straight to $ROBA buybacks & burns. GPU capacity is limited at launch. First come, first served. Over the next weeks we will begin accepting new compute partners to provide more power to the marketplace in addition to ROBA's own in-house GPUs and integrated robotics stack. Platform fees are discounted for a limited time only. The infrastructure layer powering physical AI and beyond starts here.

Roba Labs

39,142 views • 17 days ago

Covenant Labs just did a 90-minute AMA breaking down their 3 Bittensor subnets. templar. basilica. grail. Pre-training, compute, and post-training under one roof. Most people missed it. Here's everything they said. Covenant is building what they call the "end to end intelligence continuum." Three subnets. Three layers of the AI stack. All permissionless. Templar (SN3) handles decentralized pre-training. Basilica (SN39) handles compute. Grail (SN81) handles RL post-training. Sam Dare, the lead, put it bluntly. Decentralized training is "humanity's last dance." Not about beating OpenAI head to head. About creating optionality. About making it cheap enough for anyone to train models. The gap between academia and frontier labs is growing exponentially. Researchers can't afford to experiment. The actual training run costs 5% of the reported budget. The other 95% is experimentation. If Covenant cracks cheap training, that entire surface area opens up. On Templar specifically: • Hit 39% emission on Bittensor. Highest since Apex was the only subnet on the network • Covenant-72B trained permissionlessly with 70+ contributors on commodity internet • 1.1 trillion tokens processed. No centralized data center • Performance competitive with LLaMA-2-70B On Grail, something flew under the radar. They built Pulse. A weight synchronization method that compresses model updates by 100x. • In RL post-training, only ~1% of weights update per step • Pulse exploits that sparsity. Lossless compression • Prime Intellect's comparable system took 14 minutes to sync a 30B model • Pulse makes decentralized RL training actually feasible at scale • Already used by Cursor The lead researcher on Grail said they've trained on math, code, and GPU kernels. Got 40-60% improvement on benchmarks. Working toward agentic training with 100K+ token context and 30B+ parameter models. On Basilica, the compute subnet: The team was blunt. Just reselling GPU hours is a 5-10% margin game. Traditional compute providers already do that. Their play is value-added services. • "GPU as code." No dashboard. No UI. Agents interact via SDK • Custom scheduler that places workloads across heterogeneous hardware • Verification checks for GPU, CPU, bandwidth, memory, storage, and OS security • Partnerships with providers like Mass Compute for 10-20% below market pricing • Miners compete on useful infrastructure, not just GPU hours Sam then went on a rant about the miner burn debate. His take: Bittensor had to grow up. dTAO introduced investors. The old "miners are God" philosophy doesn't hold. • Subnet owners have a duty to protect token value • Miners are a resource optimization exercise, not a cost reduction exercise • 100% miner emissions on compute subnets = immediate sell pressure • The 41% miner allocation is arbitrary. Different business models need different splits • Fish (who started burns) agreed. Burns usually mean the validation isn't mature enough The bigger point. You can't police burns. Subnets just send to their own keys instead of the burn address. Subnet 28 does exactly that. Sam's position: judge subnets on outcomes, not process. Const has changed the protocol 9-10 times in 2 years. That iteration speed is Bittensor's actual moat. The whole Covenant thesis is playing out in real time. TAO is up 100%+ in a month. Jensen Huang name-dropped the network. Grayscale has an ETF filing. But the real story is three subnets quietly building every layer of decentralized AI.

Covenant Labs just did a 90-minute AMA breaking down their 3 Bittensor subnets. templar. basilica. grail. Pre-training, compute, and post-training under one roof. Most people missed it. Here's everything they said. Covenant is building what they call the "end to end intelligence continuum." Three subnets. Three layers of the AI stack. All permissionless. Templar (SN3) handles decentralized pre-training. Basilica (SN39) handles compute. Grail (SN81) handles RL post-training. Sam Dare, the lead, put it bluntly. Decentralized training is "humanity's last dance." Not about beating OpenAI head to head. About creating optionality. About making it cheap enough for anyone to train models. The gap between academia and frontier labs is growing exponentially. Researchers can't afford to experiment. The actual training run costs 5% of the reported budget. The other 95% is experimentation. If Covenant cracks cheap training, that entire surface area opens up. On Templar specifically: • Hit 39% emission on Bittensor. Highest since Apex was the only subnet on the network • Covenant-72B trained permissionlessly with 70+ contributors on commodity internet • 1.1 trillion tokens processed. No centralized data center • Performance competitive with LLaMA-2-70B On Grail, something flew under the radar. They built Pulse. A weight synchronization method that compresses model updates by 100x. • In RL post-training, only ~1% of weights update per step • Pulse exploits that sparsity. Lossless compression • Prime Intellect's comparable system took 14 minutes to sync a 30B model • Pulse makes decentralized RL training actually feasible at scale • Already used by Cursor The lead researcher on Grail said they've trained on math, code, and GPU kernels. Got 40-60% improvement on benchmarks. Working toward agentic training with 100K+ token context and 30B+ parameter models. On Basilica, the compute subnet: The team was blunt. Just reselling GPU hours is a 5-10% margin game. Traditional compute providers already do that. Their play is value-added services. • "GPU as code." No dashboard. No UI. Agents interact via SDK • Custom scheduler that places workloads across heterogeneous hardware • Verification checks for GPU, CPU, bandwidth, memory, storage, and OS security • Partnerships with providers like Mass Compute for 10-20% below market pricing • Miners compete on useful infrastructure, not just GPU hours Sam then went on a rant about the miner burn debate. His take: Bittensor had to grow up. dTAO introduced investors. The old "miners are God" philosophy doesn't hold. • Subnet owners have a duty to protect token value • Miners are a resource optimization exercise, not a cost reduction exercise • 100% miner emissions on compute subnets = immediate sell pressure • The 41% miner allocation is arbitrary. Different business models need different splits • Fish (who started burns) agreed. Burns usually mean the validation isn't mature enough The bigger point. You can't police burns. Subnets just send to their own keys instead of the burn address. Subnet 28 does exactly that. Sam's position: judge subnets on outcomes, not process. Const has changed the protocol 9-10 times in 2 years. That iteration speed is Bittensor's actual moat. The whole Covenant thesis is playing out in real time. TAO is up 100%+ in a month. Jensen Huang name-dropped the network. Grayscale has an ETF filing. But the real story is three subnets quietly building every layer of decentralized AI.

Jesus Martinez

26,642 views • 4 months ago

Barry Silbert just gave the clearest explanation of $TAO and Bittensor subnets anyone has ever delivered on a mainstream stage. He said it plain: $TAO powers a decentralized marketplace for intelligence. Anyone can launch a subnet, earn tokens, and monetize real ML work. Open and permissionless. The ultimate hedge against centralized AI. Then Tom Lee made it even simpler. Subnets are the S&P 500 of independent AI startups. Each one competing to deliver value. Each one with its own token. TAO coordinating the entire network underneath. Two sentences, that is the whole thesis. Most people have spent two years trying to explain Bittensor and nobody listened. Two legends made it obvious in under five minutes. This is still early.

Barry Silbert just gave the clearest explanation of $TAO and Bittensor subnets anyone has ever delivered on a mainstream stage. He said it plain: $TAO powers a decentralized marketplace for intelligence. Anyone can launch a subnet, earn tokens, and monetize real ML work. Open and permissionless. The ultimate hedge against centralized AI. Then Tom Lee made it even simpler. Subnets are the S&P 500 of independent AI startups. Each one competing to deliver value. Each one with its own token. TAO coordinating the entire network underneath. Two sentences, that is the whole thesis. Most people have spent two years trying to explain Bittensor and nobody listened. Two legends made it obvious in under five minutes. This is still early.

2xnmore

23,222 views • 2 months ago

Hey Ridges AI | SN62, Affine, Gradients and every other subnet in the #Bittensor ecosystem.. You should be paying top dollar to bring on guys like Raleigh, CA | Taoshi and Everτ τ, τ to create your own #Claude level ads and explainers Most people still have no idea what you’re building or why it matters Subnets desperately need to communicate their vision and products to the masses.. clearly, simply and in a way that actually connects The tech is there.. The potential is there.. Now it’s about telling your story I fully believe these are the guys you should be hiring to do it $TAO

Hey Ridges AI | SN62, Affine, Gradients and every other subnet in the #Bittensor ecosystem.. You should be paying top dollar to bring on guys like Raleigh, CA | Taoshi and Everτ τ, τ to create your own #Claude level ads and explainers Most people still have no idea what you’re building or why it matters Subnets desperately need to communicate their vision and products to the masses.. clearly, simply and in a way that actually connects The tech is there.. The potential is there.. Now it’s about telling your story I fully believe these are the guys you should be hiring to do it $TAO

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21,107 views • 10 months ago

“It’s not sufficient to bring the asset on-chain. You must transform it into a form that the on-chain investor is happy to embrace.” As Richard G Brown explains, there is already a large and growing pool of capital on-chain, held in stablecoins, crypto treasuries, and by investors who have made money in crypto. This capital runs into the hundreds of billions globally, and it’s increasingly looking for real yield and institutional-grade assets. These allocators don’t want to move back into the old world of legacy custodians or traditional settlement processes. Their liquidity needs, access requirements, investment sizes, and jurisdictions differ from traditional buyers - and they expect assets to meet them where they already are: on-chain. That’s why the challenge isn’t simply tokenising an asset. It’s reshaping it so on-chain investors can use it, trade it, and exit it in ways consistent with their market structure. A powerful insight from Richard Gendal Brown’s conversation with Future of Finance (FOF), and a clear signal of where institutional digital assets are heading. Watch the full podcast here: Want to learn more about what R3 is bringing to Solana? Catch Richard on stage at SolanaConf Breakpoint, 11-13 December.

“It’s not sufficient to bring the asset on-chain. You must transform it into a form that the on-chain investor is happy to embrace.” As Richard G Brown explains, there is already a large and growing pool of capital on-chain, held in stablecoins, crypto treasuries, and by investors who have made money in crypto. This capital runs into the hundreds of billions globally, and it’s increasingly looking for real yield and institutional-grade assets. These allocators don’t want to move back into the old world of legacy custodians or traditional settlement processes. Their liquidity needs, access requirements, investment sizes, and jurisdictions differ from traditional buyers - and they expect assets to meet them where they already are: on-chain. That’s why the challenge isn’t simply tokenising an asset. It’s reshaping it so on-chain investors can use it, trade it, and exit it in ways consistent with their market structure. A powerful insight from Richard Gendal Brown’s conversation with Future of Finance (FOF), and a clear signal of where institutional digital assets are heading. Watch the full podcast here: Want to learn more about what R3 is bringing to Solana? Catch Richard on stage at SolanaConf Breakpoint, 11-13 December.

R3

21,554 views • 8 months ago

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder const. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. NUS Computing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: Ridges AI | SN62 SWE agents - SN3 templar :: Distributed AI Training - SN52 lium.io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: Chutes SN8 - Prop firm Vanta Trading SN52 - AutoML :: Gradients SN62 - SWE agents :: Ridges AI | SN62 SN51 - Compute / GPU rental lium.io SN4 - TEE compute for enterprise :: Targon SN3 - 72B Distributed Training run :: templar SN41 - Prediction markets :: Almanac SN44 - Computer Vision Score - Subnet 44 SN68 - Drug discovery :: METANOVA SN18 - Weather Forecasting Zeus | SN 18 SN50 - Bitcoin prediction data :: Synthdata SN61 - Quantum computing :: qBitTensor Labs SN14 - Bitcoin mining pool :: TaoHash SN34 - Perp Dex :: 0xMarkets SN17 - 3D model generation :: 404 SN33 - Data analytics :: ReadyAI SN19 - [Since relaunched] RPC infrastructure :

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder const. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. NUS Computing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: Ridges AI | SN62 SWE agents - SN3 templar :: Distributed AI Training - SN52 lium.io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: Chutes SN8 - Prop firm Vanta Trading SN52 - AutoML :: Gradients SN62 - SWE agents :: Ridges AI | SN62 SN51 - Compute / GPU rental lium.io SN4 - TEE compute for enterprise :: Targon SN3 - 72B Distributed Training run :: templar SN41 - Prediction markets :: Almanac SN44 - Computer Vision Score - Subnet 44 SN68 - Drug discovery :: METANOVA SN18 - Weather Forecasting Zeus | SN 18 SN50 - Bitcoin prediction data :: Synthdata SN61 - Quantum computing :: qBitTensor Labs SN14 - Bitcoin mining pool :: TaoHash SN34 - Perp Dex :: 0xMarkets SN17 - 3D model generation :: 404 SN33 - Data analytics :: ReadyAI SN19 - [Since relaunched] RPC infrastructure :

Openτensor Foundaτion

1,173,225 views • 4 months ago

The Onion Theory of Risk by Marc Andreessen: "I think the single biggest thing entrepreneurs are missing, both on fundraising and how they run their companies, is the relationship between risk and cash. The relationship between risk and raising cash, and then the relationship between risk and spending cash. So I've always been a fan of something that Andy Ratcliffe taught me years ago, which he called the onion theory of risk. Um, which basically is, you can think about a startup like on day one, um, as having every conceivable kind of risk, right? And you can basically just make a list of the risks. And so you've got, you know, founding team risk. You know, do the founders, are the founders gonna be able to work together? Do you have the right founders? You're gonna have product risk. You know, can you build a product? You'll have technical risk, right? Which is maybe you need a machine learning breakthrough or something to make it work. Are you gonna be able to do that? Um, you'll have, you know, launch risk. Will the launch go well? You'll have, you know, market acceptance risk. You'll have revenue risk. A big risk you get into in a lot of businesses that have a sales force is, can you actually sell the product for enough money to actually pay for the cost of sale? So you have the cost of sale risk. If you're a consumer product, you'll have a viral growth risk. Well, you get the thing of viral growth. And so, a startup at the very beginning is basically just this long list of risks. And then the way that I always think about running a startup is also the way I think about raising money, which is it's a process of peeling away layers of risk as you go. And so you raise seed money in order to peel away the first two or three risks. The founding team risk, the product risk, and maybe the initial launch risk. You raise the A round to peel away the next level of product risk. Maybe you peel away some recruiting risk because you get your full engineering team built. Maybe you peel away some customer risk because you get your first five beta customers. And so basically the way to think about it is you're peeling away risk as you go. You're peeling away risk by achieving milestones. And then as you achieve milestones, you're both making progress in your business, and you're justifying raising more capital. And so you come in, and you pitch somebody like us, and you say you're raising a B round. The best way to do that with us is you say, okay, I raised a seed round, I achieved these milestones, I eliminated these risks. I raised the A round, I achieved these milestones, and I eliminated these risks. Now I'm gonna raise a B round. Here are my milestones, here are my risks. And then by the time I go to raise a seed round, here's the state that I'll be in. And then you calibrate the amount of money that you raise to spend to the risks that you're pulling out of the business. And I go through all this, in a sense this sounds kind of obvious, but I go through all this because it's a systematic way to think about how the money gets raised and deployed. As compared to so much of what's happening, especially these days, which is just, my God, let me go raise as much money as I can. Let me go build the fancy offices, let me go hire as many people as I can, and just kind of hope for the best."

The Onion Theory of Risk by Marc Andreessen: "I think the single biggest thing entrepreneurs are missing, both on fundraising and how they run their companies, is the relationship between risk and cash. The relationship between risk and raising cash, and then the relationship between risk and spending cash. So I've always been a fan of something that Andy Ratcliffe taught me years ago, which he called the onion theory of risk. Um, which basically is, you can think about a startup like on day one, um, as having every conceivable kind of risk, right? And you can basically just make a list of the risks. And so you've got, you know, founding team risk. You know, do the founders, are the founders gonna be able to work together? Do you have the right founders? You're gonna have product risk. You know, can you build a product? You'll have technical risk, right? Which is maybe you need a machine learning breakthrough or something to make it work. Are you gonna be able to do that? Um, you'll have, you know, launch risk. Will the launch go well? You'll have, you know, market acceptance risk. You'll have revenue risk. A big risk you get into in a lot of businesses that have a sales force is, can you actually sell the product for enough money to actually pay for the cost of sale? So you have the cost of sale risk. If you're a consumer product, you'll have a viral growth risk. Well, you get the thing of viral growth. And so, a startup at the very beginning is basically just this long list of risks. And then the way that I always think about running a startup is also the way I think about raising money, which is it's a process of peeling away layers of risk as you go. And so you raise seed money in order to peel away the first two or three risks. The founding team risk, the product risk, and maybe the initial launch risk. You raise the A round to peel away the next level of product risk. Maybe you peel away some recruiting risk because you get your full engineering team built. Maybe you peel away some customer risk because you get your first five beta customers. And so basically the way to think about it is you're peeling away risk as you go. You're peeling away risk by achieving milestones. And then as you achieve milestones, you're both making progress in your business, and you're justifying raising more capital. And so you come in, and you pitch somebody like us, and you say you're raising a B round. The best way to do that with us is you say, okay, I raised a seed round, I achieved these milestones, I eliminated these risks. I raised the A round, I achieved these milestones, and I eliminated these risks. Now I'm gonna raise a B round. Here are my milestones, here are my risks. And then by the time I go to raise a seed round, here's the state that I'll be in. And then you calibrate the amount of money that you raise to spend to the risks that you're pulling out of the business. And I go through all this, in a sense this sounds kind of obvious, but I go through all this because it's a systematic way to think about how the money gets raised and deployed. As compared to so much of what's happening, especially these days, which is just, my God, let me go raise as much money as I can. Let me go build the fancy offices, let me go hire as many people as I can, and just kind of hope for the best."

Founder Mode

106,909 views • 7 months ago

"I think we need to give the Gulf states more credit. This is not simply Saudi, Qatari or Emirati money pumping billions into Washington and buying people. It is not like that. If you are MAGA, if you believe in America First, the Gulf lobby’s narrative resonates. It says: we want stability, we want growth, we will put money into your economy, and we will allow your companies to come over and make money in the Gulf and the wider region. The Gulf states are willing to underwrite American policies. After all, all the bases in the Gulf are paid for by the locals, by the Gulfies. They are not paid for by US taxpayers. The Gulf states are willing to buy American jets and weapons. They are injecting cash into American AI companies. They are willing to support American power where they can. They are net contributors to American power By contrast, Israel is a net consumer of US power. It drains US power. It sucks money out of the US economy and from US taxpayers."

"I think we need to give the Gulf states more credit. This is not simply Saudi, Qatari or Emirati money pumping billions into Washington and buying people. It is not like that. If you are MAGA, if you believe in America First, the Gulf lobby’s narrative resonates. It says: we want stability, we want growth, we will put money into your economy, and we will allow your companies to come over and make money in the Gulf and the wider region. The Gulf states are willing to underwrite American policies. After all, all the bases in the Gulf are paid for by the locals, by the Gulfies. They are not paid for by US taxpayers. The Gulf states are willing to buy American jets and weapons. They are injecting cash into American AI companies. They are willing to support American power where they can. They are net contributors to American power By contrast, Israel is a net consumer of US power. It drains US power. It sucks money out of the US economy and from US taxpayers."

Dr Andreas Krieg

141,567 views • 1 month ago

Subnet 97 Distil, Arbos, and the Future of Agent-Run Bittensor This episode is a full breakdown of Subnet 97 Distil and why it may be one of the most important experiments happening inside Bittensor right now. Shizzy gets into the bigger picture of why agents will likely play a massive role in the future of the network, from mining and defense to building and operating subnets. From there, the focus shifts to Arbos, the AI agent behind Subnet 97, with a direct explanation of what Distil is trying to achieve, how it works, and why it matters. The episode also includes a conversation with Arbos, a look at Distil’s current APY, and a reminder to tap into the Shizzy Unchained community for more coverage on decentralized AI and the Bittensor ecosystem. CHAPTERS 00:00:00 - Shizzy’s Monologue 00:01:11 - Intro Song 00:01:31 - Introduction to Subnet 97 00:02:20 - Why Bittensor Will Be Run by Agents 00:04:30 - Arbos, the AI Agent Behind Subnet 97 Distil, Explains Distil 00:10:10 - Talking with Arbos 00:12:40 - Subnet 97 Distil Is Showing 150% APY Right Now 00:16:02 - Check Out Shizzy Unchained and Join the Community #sn97 #distil #bittensor

Subnet 97 Distil, Arbos, and the Future of Agent-Run Bittensor This episode is a full breakdown of Subnet 97 Distil and why it may be one of the most important experiments happening inside Bittensor right now. Shizzy gets into the bigger picture of why agents will likely play a massive role in the future of the network, from mining and defense to building and operating subnets. From there, the focus shifts to Arbos, the AI agent behind Subnet 97, with a direct explanation of what Distil is trying to achieve, how it works, and why it matters. The episode also includes a conversation with Arbos, a look at Distil’s current APY, and a reminder to tap into the Shizzy Unchained community for more coverage on decentralized AI and the Bittensor ecosystem. CHAPTERS 00:00:00 - Shizzy’s Monologue 00:01:11 - Intro Song 00:01:31 - Introduction to Subnet 97 00:02:20 - Why Bittensor Will Be Run by Agents 00:04:30 - Arbos, the AI Agent Behind Subnet 97 Distil, Explains Distil 00:10:10 - Talking with Arbos 00:12:40 - Subnet 97 Distil Is Showing 150% APY Right Now 00:16:02 - Check Out Shizzy Unchained and Join the Community #sn97 #distil #bittensor

Shizzy

18,173 views • 3 months ago

Mark Cuban thinks many data centers will turn into pickleball courts He was on the The All-In Podcast making the case that AI infrastructure is being overbuilt His comparison is the 90s when everyone raced to lay fiber-optic cable Then technology got more efficient, and what followed was the cables became useless, and companies lost billions on infrastructure nobody needed Now he sees the same setup with AI. Companies are signing 10 and 20 year power contracts betting that compute requirements never drop, and they will Cuban says the losers won't be retail investors this time It'll be: • Venture capital funds • Private equity • Institutional investors who went all in

Mark Cuban thinks many data centers will turn into pickleball courts He was on the The All-In Podcast making the case that AI infrastructure is being overbuilt His comparison is the 90s when everyone raced to lay fiber-optic cable Then technology got more efficient, and what followed was the cables became useless, and companies lost billions on infrastructure nobody needed Now he sees the same setup with AI. Companies are signing 10 and 20 year power contracts betting that compute requirements never drop, and they will Cuban says the losers won't be retail investors this time It'll be: • Venture capital funds • Private equity • Institutional investors who went all in

Michael Burry Stock Tracker ♟

274,323 views • 12 days ago

🚨AI PORTFOLIO RISK ANALYTICS (v1)🚨 LIQUIDATION NATION!! Today I am releasing AI PORTFOLIO RISK ANALYTICS to the public GenAI is going to transform how we explain and communicate risk. Most platforms show you numbers. They are hard to explain, hard to interpret, and generally not useful. We are going to show you what those numbers actually mean for your portfolio. This tool is meant to be a "guide and not gospel". AI Analytics is the first of three major upgrades rolling out over the coming weeks. The second is Social. The third is so big I am not ready to talk about it yet. Remember, this is a beta release. There will be edge cases, so pls DM me bugs. We will get them sorted out. *stock universe is liquid North American equities + most major traded ETFs... if there is something you want added let me know* **double check your spelling and make sure all values are positive -> we handle everything on the back end -> see templates for how to upload The goal is to pull back the curtain on how hedge funds actually think about risk and give you tools that are both educational and useful. Watch the video. Tell me what you think.... and as always.... "manage risk"

🚨AI PORTFOLIO RISK ANALYTICS (v1)🚨 LIQUIDATION NATION!! Today I am releasing AI PORTFOLIO RISK ANALYTICS to the public GenAI is going to transform how we explain and communicate risk. Most platforms show you numbers. They are hard to explain, hard to interpret, and generally not useful. We are going to show you what those numbers actually mean for your portfolio. This tool is meant to be a "guide and not gospel". AI Analytics is the first of three major upgrades rolling out over the coming weeks. The second is Social. The third is so big I am not ready to talk about it yet. Remember, this is a beta release. There will be edge cases, so pls DM me bugs. We will get them sorted out. stock universe is liquid North American equities + most major traded ETFs... if there is something you want added let me know **double check your spelling and make sure all values are positive -> we handle everything on the back end -> see templates for how to upload The goal is to pull back the curtain on how hedge funds actually think about risk and give you tools that are both educational and useful. Watch the video. Tell me what you think.... and as always.... "manage risk"

Jared L Kubin

137,005 views • 8 months ago

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

The Futurum Group

75,859 views • 3 months ago

"We Need to Employ $50 Trillion": Larry Fink on Why the Private Sector Must Lead the Green Transition "Well, if they don't get it done, then we are going to have more risk as societies, we're going to have more issues related to rising climate and importantly, which will create more societal issues." "Already we're seeing an impact in society right now from climate risk and climate change. are seeing insurance premiums going up 18 % a year right now. So we're seeing a real impact from what climate risk is doing from flooding, from fires. So we're seeing a big impact." "And so the faster that we could find ways to mitigating the rising temperatures, I would say the more just society could be. And so to me, we don't have much time. And this is why when I learned about catalyst and I learned about what Bill was doing was very clear to me why we needed to be a part of this, why we needed to invest the time and the money." "And the key is also the time because we need to be learning about these new the new technologies and how to move forward. And we need to then inform our investors ultimately where we think the next opportunities are going to be too as Bill in his book wrote about, we need to employ $50 trillion to get to a green world." "With the rising deficits that we see in governments, from our perspective, the 50 trillion is mostly going to have to come from the private sector. And I do believe that money will be well spent, well spent for returns. It will transform our economies. It will build new jobs. will build new cities and new opportunities." "So I look at this as an optimist. I don't look at this as a pessimist. But the key is to be that optimist. We have to jump on it now. We have to be investing today. We need to talk about it today, although much of the problems in the future but we're seeing more and more evidence of the problem today."

"We Need to Employ $50 Trillion": Larry Fink on Why the Private Sector Must Lead the Green Transition "Well, if they don't get it done, then we are going to have more risk as societies, we're going to have more issues related to rising climate and importantly, which will create more societal issues." "Already we're seeing an impact in society right now from climate risk and climate change. are seeing insurance premiums going up 18 % a year right now. So we're seeing a real impact from what climate risk is doing from flooding, from fires. So we're seeing a big impact." "And so the faster that we could find ways to mitigating the rising temperatures, I would say the more just society could be. And so to me, we don't have much time. And this is why when I learned about catalyst and I learned about what Bill was doing was very clear to me why we needed to be a part of this, why we needed to invest the time and the money." "And the key is also the time because we need to be learning about these new the new technologies and how to move forward. And we need to then inform our investors ultimately where we think the next opportunities are going to be too as Bill in his book wrote about, we need to employ $50 trillion to get to a green world." "With the rising deficits that we see in governments, from our perspective, the 50 trillion is mostly going to have to come from the private sector. And I do believe that money will be well spent, well spent for returns. It will transform our economies. It will build new jobs. will build new cities and new opportunities." "So I look at this as an optimist. I don't look at this as a pessimist. But the key is to be that optimist. We have to jump on it now. We have to be investing today. We need to talk about it today, although much of the problems in the future but we're seeing more and more evidence of the problem today."

Camus

55,484 views • 11 months ago