Loading video...

Video Failed to Load

Go Home

SN4 // Targon // Novelty Search E064 “Decentralized compute is probably the oldest idea in decentralized AI..." See how SN4 by Manifold make privacy-preserving AI workloads possible to run on Bittensor. Highlights: - TEE Live for 6 months - Added confidential virtual machines (CVMs) with Intel TDX & AMD...

opentensor's profile picture

Openτensor Foundaτion

173,886 subscribers

12,863 views • 9 months ago •via X (Twitter)

Science & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

“We’ve had over 16,000 confidential workloads deployed this year.” On the latest Novelty Search, Targon shared that average revenue per user grew from $83 to $5,000, with SN4 supporting both Web2 training runs and Bittensor subnets. They also detailed Targon Tower, a high-performance computer for the home designed to run AI models locally and sell idle compute back to Targon’s confidential network. Hosted by const Full episode in the first comment
0:34

“We’ve had over 16,000 confidential workloads deployed this year.” On the latest Novelty Search, Targon shared that average revenue per user grew from $83 to $5,000, with SN4 supporting both Web2 training runs and Bittensor subnets. They also detailed Targon Tower, a high-performance computer for the home designed to run AI models locally and sell idle compute back to Targon’s confidential network. Hosted by const Full episode in the first comment

Openτensor Foundaτion

16,083 views • 18 days 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 :
53:45

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

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization
1:25

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization

The Crypto GEMs

69,984 views • 1 year ago

Introducing the SKALE Agent for Pinata! A powerful AI agent that unlocks SKALE on Base chains with 6 integrated skills designed for AI agents & x402 payments: SKALE-Bridge Bridge USDC from dozens of chains (Polygon, Base, Optimism, Monad, Arbitrum, Avalanche, and more) directly to SKALE Base in seconds via Trails and SKALE Native Bridge. SKALE-Deploy Automated contract deployment with native RNG integration, MTM (Multi-Transaction Mode) for batching transactions in seconds, and instant connection to canonical ERC-8004 contracts. Launch AI agents fast. SKALE-Bite BITE v1: Encrypted mempool transactions—no front-running, no MEV. BITE v2: Conditional encrypted txs that decrypt ONLY when specific on-chain conditions are met. Privacy by default. SKALE-Partners Direct access to ecosystem tools like Xona Agent for AI-generated images, videos, and creative content—all payable through x402. SKALE-x402 Create x402 resources and enable HTTP 402 payments through multiple facilitators. Monetize AI services with gasless pay-per-use models. OWS Wallets Open Wallet Standard integration—create unlimited EVM-compatible wallets for your agent with ZERO private key management. Secure, local-first, autonomous. Your AI agent. Fully equipped. Ready to execute on-SKALE = on-chain. Deploy your agent ▶️
1:05

Introducing the SKALE Agent for Pinata! A powerful AI agent that unlocks SKALE on Base chains with 6 integrated skills designed for AI agents & x402 payments: SKALE-Bridge Bridge USDC from dozens of chains (Polygon, Base, Optimism, Monad, Arbitrum, Avalanche, and more) directly to SKALE Base in seconds via Trails and SKALE Native Bridge. SKALE-Deploy Automated contract deployment with native RNG integration, MTM (Multi-Transaction Mode) for batching transactions in seconds, and instant connection to canonical ERC-8004 contracts. Launch AI agents fast. SKALE-Bite BITE v1: Encrypted mempool transactions—no front-running, no MEV. BITE v2: Conditional encrypted txs that decrypt ONLY when specific on-chain conditions are met. Privacy by default. SKALE-Partners Direct access to ecosystem tools like Xona Agent for AI-generated images, videos, and creative content—all payable through x402. SKALE-x402 Create x402 resources and enable HTTP 402 payments through multiple facilitators. Monetize AI services with gasless pay-per-use models. OWS Wallets Open Wallet Standard integration—create unlimited EVM-compatible wallets for your agent with ZERO private key management. Secure, local-first, autonomous. Your AI agent. Fully equipped. Ready to execute on-SKALE = on-chain. Deploy your agent ▶️

SKALE

466,476 views • 3 months ago

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation
37:00

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 views • 1 year ago

No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇
1:17

No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇

Galileo

78,922 views • 1 year ago

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing
45:43

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

MR SHIFT 🦁

41,057 views • 8 months ago

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.
0:29

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

NeuralAI

241,287 views • 2 years ago

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
2:49

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 views • 5 months ago

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:
0:44

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:

Satya Nadella

907,624 views • 8 months ago

💡 What is a Gaia Domain?! Gaia Domains were created to make AI infrastructure truly sovereign. Each domain is a programmable environment you own and control on-chain, where your logic, models, and data stay in your hands at all times. With Gaia, you set the rules: choose which AI agents to deploy, configure your workflows, and decide how others can interact with your domain. Domains integrate seamlessly with smart contracts, other domains, and a wide range of decentralized tools is simple and secure. Every part of your domain operates on Gaia’s decentralized node network, protecting your work from centralized servers or closed AI systems. Thankfully, getting started is quick. Domains are ready out of the box with open-source language models and SDKs, so you can launch and iterate fast. Their modular design makes them easy to adapt, and EVM compatibility means they fit right into the broader DeAI ecosystem. Earning is built in at every step: you can run services, sell access, or enable delegated tasks with all transactions managed on-chain for total transparency. Gaia Domains open new ground for developers, DAOs, founders, researchers, and enterprises seeking to decentralize their AI stack. Questions? Reach out to our DevRels Tobiloba 🦀 Harish Kotra 🥑 meowy🦀! Get started here: In this video, Tobiloba 🦀 demonstrates how to integrate Gaia's public domains for AI agents, applications, chatbots, and frameworks, or even interact directly via the Gaia chat UI 🔽
8:25

💡 What is a Gaia Domain?! Gaia Domains were created to make AI infrastructure truly sovereign. Each domain is a programmable environment you own and control on-chain, where your logic, models, and data stay in your hands at all times. With Gaia, you set the rules: choose which AI agents to deploy, configure your workflows, and decide how others can interact with your domain. Domains integrate seamlessly with smart contracts, other domains, and a wide range of decentralized tools is simple and secure. Every part of your domain operates on Gaia’s decentralized node network, protecting your work from centralized servers or closed AI systems. Thankfully, getting started is quick. Domains are ready out of the box with open-source language models and SDKs, so you can launch and iterate fast. Their modular design makes them easy to adapt, and EVM compatibility means they fit right into the broader DeAI ecosystem. Earning is built in at every step: you can run services, sell access, or enable delegated tasks with all transactions managed on-chain for total transparency. Gaia Domains open new ground for developers, DAOs, founders, researchers, and enterprises seeking to decentralize their AI stack. Questions? Reach out to our DevRels Tobiloba 🦀 Harish Kotra 🥑 meowy🦀! Get started here: In this video, Tobiloba 🦀 demonstrates how to integrate Gaia's public domains for AI agents, applications, chatbots, and frameworks, or even interact directly via the Gaia chat UI 🔽

Gaia 🌱

15,829 views • 1 year ago

NEAR AI Ecosystem – “What Did You Ship This Week?” Ep. 7 A big week across the NEAR AI ecosystem with updates on personalized data agents, TypeScript tooling, crowdsourced AI data, and non-custodial flows for token coordination + much more. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:00 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI explains how their platform lets users earn stablecoins and points by uploading and verifying native language audio clips. Data builders can now claim earnings directly to a NEAR Protocol wallet. Public beta is live. 09:35 – Matt Hamilton from ASIMOV Protocol demos their CLI tools for uploading structured datasets to NEAR, plus new knowledge graph visualizations and early experiments transcribing voice into semantic graphs. 15:55 – @tahpot_au from Verida 💜 shows how developers can access private user data for AI agents using the Verida Vault. Users retain full control over their data, with API keys gated by permission. 25:25 – Zahidul Islam from Jutsu | Work Smarter with AI Agents drops updates on the NEAR AI TypeScript SDK. One-command agent deployment, fully local dev support, and matching features with the Python SDK. Also demoed new AI-generated video tooling with Postt. 34:20 – Matt Lockyer from Proximity Labs shares the launch of their Base naming agent Basednames. Users post on X to mint names, pay with Bankr, and finalize with verified chain signatures powered by NEAR. All non-custodial. All live on Base. 50:35 – Charles previews Divvy Agent. Users can set portfolio growth and swap thresholds with natural language. Latest update adds persistent memory, improved LLM matching, and MPC integration for safe, automated execution. 59:36 – Cameron.near🇺🇸 closes out with updates from the NEAR AI core team. Hiring in progress, governance agents coming soon, and progress on privacy tooling and agent infra powering the NEAR AI Hub. The future is NEAR - the blockchain for AI. Catch the full replay:
1:03:20

NEAR AI Ecosystem – “What Did You Ship This Week?” Ep. 7 A big week across the NEAR AI ecosystem with updates on personalized data agents, TypeScript tooling, crowdsourced AI data, and non-custodial flows for token coordination + much more. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:00 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI explains how their platform lets users earn stablecoins and points by uploading and verifying native language audio clips. Data builders can now claim earnings directly to a NEAR Protocol wallet. Public beta is live. 09:35 – Matt Hamilton from ASIMOV Protocol demos their CLI tools for uploading structured datasets to NEAR, plus new knowledge graph visualizations and early experiments transcribing voice into semantic graphs. 15:55 – @tahpot_au from Verida 💜 shows how developers can access private user data for AI agents using the Verida Vault. Users retain full control over their data, with API keys gated by permission. 25:25 – Zahidul Islam from Jutsu | Work Smarter with AI Agents drops updates on the NEAR AI TypeScript SDK. One-command agent deployment, fully local dev support, and matching features with the Python SDK. Also demoed new AI-generated video tooling with Postt. 34:20 – Matt Lockyer from Proximity Labs shares the launch of their Base naming agent Basednames. Users post on X to mint names, pay with Bankr, and finalize with verified chain signatures powered by NEAR. All non-custodial. All live on Base. 50:35 – Charles previews Divvy Agent. Users can set portfolio growth and swap thresholds with natural language. Latest update adds persistent memory, improved LLM matching, and MPC integration for safe, automated execution. 59:36 – Cameron.near🇺🇸 closes out with updates from the NEAR AI core team. Hiring in progress, governance agents coming soon, and progress on privacy tooling and agent infra powering the NEAR AI Hub. The future is NEAR - the blockchain for AI. Catch the full replay:

NEARWEEK

35,143 views • 1 year ago

Ep. 20 | Free The Money | How NEAR Intents Fix DeFi UX In this episode, I sit down with Harshit Tiwari (chronear), Head of Ecosystem Strategy at the NEAR Foundation, to break down NEAR Intents, one of the fastest-growing cross-chain protocols. At its core, NEAR Intents allows users to seamlessly move assets across chains without needing to think about bridges, liquidity, or execution. Instead of manually swapping tokens, users simply express what they want (an “intent”), and a network of competing solvers executes the trade, optimizing for the best price and fastest settlement. A major innovation discussed is confidential intents, which bring privacy to on chain trading, something historically missing in crypto. This is especially critical for institutional adoption, where large players require discretion similar to traditional finance (e.g. dark pools). Looking ahead, Harshit predicts a major shift toward AI-driven economies, where autonomous agents transact on behalf of users. Within 12–18 months, AI agents could dominate on-chain activity, executing trades, payments, and real-world tasks through intent-based systems. NEAR is building toward: • Tokenized equities • An open-source AI stack — NEAR AI is already live today • A world where users own their data, not centralized platforms • And a model where ecosystem activity ultimately feeds back into value accrual for NEAR holders Sign up for ITrustCapitol with this link for $100 funding bonus. See why people are opening a tax-advantaged Crypto, Gold & Silver IRA for their future: If you’re being moved onto a platform you didn’t choose, with higher fees, there are better options. iTrustCapital gives you more control, better pricing, and now a limited time 2% match to make the move even more compelling. 00:00 Intro: NEAR = Universal Transaction Layer for AI Economy 01:00 What Are NEAR Intents? (Fixing Cross-Chain Fragmentation) 02:26 Confidential Intents: Bringing Privacy to On-Chain Trading 04:50 Institutional Adoption Is Already Here (What Changes Next) 05:38 Chain Abstraction + User-First UX (Why This Wins) 06:32 Competing with Coinbase & Binance UX (DeFi’s Big Unlock) 06:58 iTrustCapital Ad (Gold, Crypto, IRA Strategy) 08:00 Why CEX UX Still Dominates (and How NEAR Fixes It) 09:35 One Interface for Everything: The NEAR “Super App” Vision 10:13 Intents = Decentralized Fiverr (Solvers Compete for Best Price) 11:24 AI Agents Will Transact More Than Humans (12–18 Months?) 13:10 Decentralized AI vs Big Tech (Who Controls Your Data?) 14:24 NEAR AI: User-Owned, Open-Source, Verifiable AI 15:54 NEAR Tokenomics: Value Accrual + Buybacks Explained 17:37 Deflationary Token Model (Why Supply Could Shrink) 18:19 Stocks & Commodities Coming to NEAR Intents 20:29 “Trillions of AI Agents” Thesis Explained 22:43 Regulation, AI, and Working With Governments
25:59

Ep. 20 | Free The Money | How NEAR Intents Fix DeFi UX In this episode, I sit down with Harshit Tiwari (chronear), Head of Ecosystem Strategy at the NEAR Foundation, to break down NEAR Intents, one of the fastest-growing cross-chain protocols. At its core, NEAR Intents allows users to seamlessly move assets across chains without needing to think about bridges, liquidity, or execution. Instead of manually swapping tokens, users simply express what they want (an “intent”), and a network of competing solvers executes the trade, optimizing for the best price and fastest settlement. A major innovation discussed is confidential intents, which bring privacy to on chain trading, something historically missing in crypto. This is especially critical for institutional adoption, where large players require discretion similar to traditional finance (e.g. dark pools). Looking ahead, Harshit predicts a major shift toward AI-driven economies, where autonomous agents transact on behalf of users. Within 12–18 months, AI agents could dominate on-chain activity, executing trades, payments, and real-world tasks through intent-based systems. NEAR is building toward: • Tokenized equities • An open-source AI stack — NEAR AI is already live today • A world where users own their data, not centralized platforms • And a model where ecosystem activity ultimately feeds back into value accrual for NEAR holders Sign up for ITrustCapitol with this link for $100 funding bonus. See why people are opening a tax-advantaged Crypto, Gold & Silver IRA for their future: If you’re being moved onto a platform you didn’t choose, with higher fees, there are better options. iTrustCapital gives you more control, better pricing, and now a limited time 2% match to make the move even more compelling. 00:00 Intro: NEAR = Universal Transaction Layer for AI Economy 01:00 What Are NEAR Intents? (Fixing Cross-Chain Fragmentation) 02:26 Confidential Intents: Bringing Privacy to On-Chain Trading 04:50 Institutional Adoption Is Already Here (What Changes Next) 05:38 Chain Abstraction + User-First UX (Why This Wins) 06:32 Competing with Coinbase & Binance UX (DeFi’s Big Unlock) 06:58 iTrustCapital Ad (Gold, Crypto, IRA Strategy) 08:00 Why CEX UX Still Dominates (and How NEAR Fixes It) 09:35 One Interface for Everything: The NEAR “Super App” Vision 10:13 Intents = Decentralized Fiverr (Solvers Compete for Best Price) 11:24 AI Agents Will Transact More Than Humans (12–18 Months?) 13:10 Decentralized AI vs Big Tech (Who Controls Your Data?) 14:24 NEAR AI: User-Owned, Open-Source, Verifiable AI 15:54 NEAR Tokenomics: Value Accrual + Buybacks Explained 17:37 Deflationary Token Model (Why Supply Could Shrink) 18:19 Stocks & Commodities Coming to NEAR Intents 20:29 “Trillions of AI Agents” Thesis Explained 22:43 Regulation, AI, and Working With Governments

Bri Teresi

29,602 views • 4 months ago

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.
0:58

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

Ricardo

594,643 views • 6 months ago

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.
1:42

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.

Bull Theory

26,668 views • 1 month ago

🚀 Expanding the Renta Network Ecosystem: Welcoming New Partners! 🎉 At Renta Network, we believe that strategic partnerships are key to the growth of Web3 rentals and tokenized assets. We’ve previously highlighted our strong allies – check out the full list here 👉 🚀 Now, let’s introduce our new partners who are driving Renta Network forward! - HyperGPT – AI-powered Web3 infrastructure for intelligent automation. - UQUID | Digital Commerce Infrastructure – Bridging DeFi with real-world payments through crypto solutions. - Conduit – Simplifying blockchain development with modular rollups. - DAOBase 🐝 – Enhancing decentralized governance with AI-DAO tools. - @UniLendFinance – Enabling permissionless lending and borrowing in DeFi. - @BitDocAI – AI-driven document management for secure smart contracts. - Lance Walley – Transforming real-world asset financing with DeFi liquidity. - @RoxiAI_bot – AI bot for automated blockchain analytics and insights. - LIFT⚡️ – Leveraging AI for real-time data processing and DeFi optimization. - Kima Network – Bridging traditional finance and blockchain seamlessly. - Hooked Protocol🪝 – Gamifying Web3 adoption for mass engagement. - Polyhedra – Enabling privacy-focused zero-knowledge (ZK) solutions for Web3. - Warden – Strengthening cross-chain security with trustless verification. - Owlto Finance(🦉💛🦉) – Enhancing cross-chain liquidity and fast asset transfers. - Clearpool – Providing institutional-grade credit solutions for DeFi. - jennifer oinn.io – A Web3-native networking and collaboration platform. - Novastro 2.0 🔜 – AI-powered RWA tokenization and next-gen DeFi solutions. - Plena Finance – The ultimate crypto super app optimizing the DeFi experience. 💡 With these powerful new partnerships, we are moving even faster: expanding AI integrations, scaling RWA tokenization, increasing liquidity, and bringing new opportunities to Web3 rentals. 📢 Stay tuned – the best is yet to come! 🚀 - 🔔 Don’t miss out on important updates and giveaways! Follow us on Twitter — Renta Network If you're a beginner, start here ➡️ ⬅️ 📢 Join Our Ambassador Program - 📈 Trade here: All relevant links -- #RentaNetwork #Partnerships #Web3 #Blockchain #Crypto #DeFi #AI #PropertyNFT
0:23

🚀 Expanding the Renta Network Ecosystem: Welcoming New Partners! 🎉 At Renta Network, we believe that strategic partnerships are key to the growth of Web3 rentals and tokenized assets. We’ve previously highlighted our strong allies – check out the full list here 👉 🚀 Now, let’s introduce our new partners who are driving Renta Network forward! - HyperGPT – AI-powered Web3 infrastructure for intelligent automation. - UQUID | Digital Commerce Infrastructure – Bridging DeFi with real-world payments through crypto solutions. - Conduit – Simplifying blockchain development with modular rollups. - DAOBase 🐝 – Enhancing decentralized governance with AI-DAO tools. - @UniLendFinance – Enabling permissionless lending and borrowing in DeFi. - @BitDocAI – AI-driven document management for secure smart contracts. - Lance Walley – Transforming real-world asset financing with DeFi liquidity. - @RoxiAI_bot – AI bot for automated blockchain analytics and insights. - LIFT⚡️ – Leveraging AI for real-time data processing and DeFi optimization. - Kima Network – Bridging traditional finance and blockchain seamlessly. - Hooked Protocol🪝 – Gamifying Web3 adoption for mass engagement. - Polyhedra – Enabling privacy-focused zero-knowledge (ZK) solutions for Web3. - Warden – Strengthening cross-chain security with trustless verification. - Owlto Finance(🦉💛🦉) – Enhancing cross-chain liquidity and fast asset transfers. - Clearpool – Providing institutional-grade credit solutions for DeFi. - jennifer oinn.io – A Web3-native networking and collaboration platform. - Novastro 2.0 🔜 – AI-powered RWA tokenization and next-gen DeFi solutions. - Plena Finance – The ultimate crypto super app optimizing the DeFi experience. 💡 With these powerful new partnerships, we are moving even faster: expanding AI integrations, scaling RWA tokenization, increasing liquidity, and bringing new opportunities to Web3 rentals. 📢 Stay tuned – the best is yet to come! 🚀 - 🔔 Don’t miss out on important updates and giveaways! Follow us on Twitter — Renta Network If you're a beginner, start here ➡️ ⬅️ 📢 Join Our Ambassador Program - 📈 Trade here: All relevant links -- #RentaNetwork #Partnerships #Web3 #Blockchain #Crypto #DeFi #AI #PropertyNFT

Renta Network

103,000 views • 1 year ago

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!
7:18

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!

Mike

141,018 views • 9 months ago

NEAR AI Ecosystem - “What Did You Ship This Week?” Ep. 9 A week of steady momentum across the NEAR AI stack. From Open Agent Alliance expansion and new agent standards to browser-native private data operators and OAuth-powered crossposting infrastructure, builders continue pushing open-source AI forward. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:36 – AVB from Open Agent Alliance (OAA) shares updates on the OAA. After its ETHDenver launch, OAA is ramping up with a renewed website, active working groups around agent discovery, secure communication, and decentralized payments. Their mission: build a truly open, interoperable agent ecosystem across Web3 and beyond. 09:18 – play_tingz unveils their AI-powered gaming creation platform. Using simple text prompts, users can instantly generate 3D games without coding. 18:44 – shudong from MIZU demos MIZU Agent, a browser-native AI operator that lives in your existing browser environment. It accesses private data securely without re-logins, supporting agent-native interactions like navigating social media and automating tasks locally. 28:53 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI introduces new features including live quizzes for contributor skill validation, Hot Wallet integration for seamless Web3 reward claims, and Discord Connect to strengthen expert communities. PublicAI continues expanding its decentralized data sourcing platform. 40:40 – elliot braem from open crosspost shows CrossPost, an OAuth-integrated platform letting NEAR accounts securely control social media posting. Users can sign actions with their wallets via function call access keys, enabling bots and agents to crosspost content without needing direct account access. 52:50 – Matt Lockyer from Proximity Labs announces the Bankr x Shade Agent hackathon. Developers are invited to extend BankerBot's functionality with new Shade Agents that leverage NEAR Chain Signatures for decentralized agent-to-agent transactions. Submissions open through May 3rd. 1:01:37 – Wrap-up by Cameron.near🇺🇸 with updates on upcoming NEAR AI releases, including auditor agents for discovery and security + private inference compute networks. The future is NEAR – the blockchain for AI. Catch the full replay:
1:03:23

NEAR AI Ecosystem - “What Did You Ship This Week?” Ep. 9 A week of steady momentum across the NEAR AI stack. From Open Agent Alliance expansion and new agent standards to browser-native private data operators and OAuth-powered crossposting infrastructure, builders continue pushing open-source AI forward. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:36 – AVB from Open Agent Alliance (OAA) shares updates on the OAA. After its ETHDenver launch, OAA is ramping up with a renewed website, active working groups around agent discovery, secure communication, and decentralized payments. Their mission: build a truly open, interoperable agent ecosystem across Web3 and beyond. 09:18 – play_tingz unveils their AI-powered gaming creation platform. Using simple text prompts, users can instantly generate 3D games without coding. 18:44 – shudong from MIZU demos MIZU Agent, a browser-native AI operator that lives in your existing browser environment. It accesses private data securely without re-logins, supporting agent-native interactions like navigating social media and automating tasks locally. 28:53 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI introduces new features including live quizzes for contributor skill validation, Hot Wallet integration for seamless Web3 reward claims, and Discord Connect to strengthen expert communities. PublicAI continues expanding its decentralized data sourcing platform. 40:40 – elliot braem from open crosspost shows CrossPost, an OAuth-integrated platform letting NEAR accounts securely control social media posting. Users can sign actions with their wallets via function call access keys, enabling bots and agents to crosspost content without needing direct account access. 52:50 – Matt Lockyer from Proximity Labs announces the Bankr x Shade Agent hackathon. Developers are invited to extend BankerBot's functionality with new Shade Agents that leverage NEAR Chain Signatures for decentralized agent-to-agent transactions. Submissions open through May 3rd. 1:01:37 – Wrap-up by Cameron.near🇺🇸 with updates on upcoming NEAR AI releases, including auditor agents for discovery and security + private inference compute networks. The future is NEAR – the blockchain for AI. Catch the full replay:

NEARWEEK

30,822 views • 1 year ago

DePIN & Post Web Demo Days are coming, June 18th & 25th. Deploying real products into the ecosystem. Meet our DePIN Base Camp: 🌬️ Aira Labs – Building the world's largest community-powered platform for air quality data. 🔐 Beacon Protocol – Unlocking new agent capabilities by enabling access to private and topical datasets while protecting ownership and rewarding creators. ⚡ Combinder – Aggregating idle electrical devices into a global pool of micro-flexibility to generate revenue in energy markets. 📡 Decen Space – Making satellite communication cheaper, more accessible, and more reliable. 📶 Depinsim – The world’s first decentralized connectivity protocol, enabling borderless communication and payments via crypto eSIM. 🌦️ SkyX – Revolutionizing weather forecasting with AI, blockchain, and decentralized data collection. The Post Web is happening now, see who's building it: 🧑‍🤝‍🧑 @Openformat_tech - The community co-pilot for coordinating, understanding and rewarding your web3 community 🧠 Datai Network - A web3 data layer powering on-chain AI. 🎭 Alias - A next-gen ecosystem guiding IP, entertainment, creators, and content into the era of interactive 3D AI agents. 💱 Fiet Protocol by Usher Labs - building Fiet Protocol - Fiet Protocol delivers decentralised fiat-to-stablecoin exchange with OTC-matching or lower fees and passive yield. 🌀 @duffleinc - Intent-based Abstraction Layer that allows users to swap tokens across any chain without gas fees and/or manual signature approvals 🏀 LootMogul - AI Native, Blockchain Secured, Meme Coin Enabled with 400+ NBA, NFL, UFC athletes 💳 NettyWorth - AI-loan Infrastructure unlocking liquidity from Digital and Real-World Assets with AI-Agents & DeFi Credit Scores.
0:36

DePIN & Post Web Demo Days are coming, June 18th & 25th. Deploying real products into the ecosystem. Meet our DePIN Base Camp: 🌬️ Aira Labs – Building the world's largest community-powered platform for air quality data. 🔐 Beacon Protocol – Unlocking new agent capabilities by enabling access to private and topical datasets while protecting ownership and rewarding creators. ⚡ Combinder – Aggregating idle electrical devices into a global pool of micro-flexibility to generate revenue in energy markets. 📡 Decen Space – Making satellite communication cheaper, more accessible, and more reliable. 📶 Depinsim – The world’s first decentralized connectivity protocol, enabling borderless communication and payments via crypto eSIM. 🌦️ SkyX – Revolutionizing weather forecasting with AI, blockchain, and decentralized data collection. The Post Web is happening now, see who's building it: 🧑‍🤝‍🧑 @Openformat_tech - The community co-pilot for coordinating, understanding and rewarding your web3 community 🧠 Datai Network - A web3 data layer powering on-chain AI. 🎭 Alias - A next-gen ecosystem guiding IP, entertainment, creators, and content into the era of interactive 3D AI agents. 💱 Fiet Protocol by Usher Labs - building Fiet Protocol - Fiet Protocol delivers decentralised fiat-to-stablecoin exchange with OTC-matching or lower fees and passive yield. 🌀 @duffleinc - Intent-based Abstraction Layer that allows users to swap tokens across any chain without gas fees and/or manual signature approvals 🏀 LootMogul - AI Native, Blockchain Secured, Meme Coin Enabled with 400+ NBA, NFL, UFC athletes 💳 NettyWorth - AI-loan Infrastructure unlocking liquidity from Digital and Real-World Assets with AI-Agents & DeFi Credit Scores.

Outlier Ventures

18,908 views • 1 year ago

Only a few hours left to secure your spot on MVB 9! Apply before 23:59 (UTC) to accelerate your project's growth. Apply now via the link below: 🔗 Not sure if you should join? Read all about below 👇 The Most Valuable Builder (MVB) program is a 4 week accelerator and is jointly run by BNB Chain, Yzi Labs & CoinMarketCap Labs. MVB supports early-stage Web3 projects on the BNB Chain, offering a tailored curriculum to help them grow and build sustainable businesses. The MVB program offers 1:1 mentorship, fireside chats with industry leaders, networking with founders, and growth resources. Participants also receive the BNB Chain Launch-as-a-Service package, valued up to $300,000 & investment opportunities by Binance Labs. So far over 131 projects have been nurtured from 7 cohorts, with 75 of those receiving funding. Our last season, season 8, included 35 projects across DeFi, infra & AI. What’s New in MVB Season 9? 🔸 AI-First: focus on AI innovations like DeFi, AI games, companions, data labeling, and decentralized AI. 🔸 Quarterly: program runs every quarter for more Web3 innovator opportunities. 🔸 Networking: 2-day event in Hong Kong for connecting with peers, mentors, and speakers. 🔸 BIA Winners: top projects from BIA events (Dubai, Bangkok, Denver) get direct entry to MVB Season 9. This season, the project sectors we particularly welcome include, but are not limited to: • AI Applications: Intention based DeFi, AI first Game, DeSoc, AI Agent & Companions, Data labeling, DataDAO • Infra and Tooling: Identity, MPC Wallet, Social Graph, AgentKit, Agent Hosting, AI Training, Inference, DevTools, Privacy Computing, and Streaming Indexing Criteria for applying to MVB: ✅ The majority of business and transaction volume is captured on BNB Chain ✅ Smart contract for core business is deployed on BNB Chain ✅ The project token is initially launched on BNB Chain How to apply to be a part of MVB 9: Please complete the form using the link below. Once we receive your application, it will be reviewed, and we will interview applicants. You will then be notified of our decision. 📝 Apply here: 🗓️ Application window: Dec 13 - Jan 13, 23:59 (UTC)
1:52

Only a few hours left to secure your spot on MVB 9! Apply before 23:59 (UTC) to accelerate your project's growth. Apply now via the link below: 🔗 Not sure if you should join? Read all about below 👇 The Most Valuable Builder (MVB) program is a 4 week accelerator and is jointly run by BNB Chain, Yzi Labs & CoinMarketCap Labs. MVB supports early-stage Web3 projects on the BNB Chain, offering a tailored curriculum to help them grow and build sustainable businesses. The MVB program offers 1:1 mentorship, fireside chats with industry leaders, networking with founders, and growth resources. Participants also receive the BNB Chain Launch-as-a-Service package, valued up to $300,000 & investment opportunities by Binance Labs. So far over 131 projects have been nurtured from 7 cohorts, with 75 of those receiving funding. Our last season, season 8, included 35 projects across DeFi, infra & AI. What’s New in MVB Season 9? 🔸 AI-First: focus on AI innovations like DeFi, AI games, companions, data labeling, and decentralized AI. 🔸 Quarterly: program runs every quarter for more Web3 innovator opportunities. 🔸 Networking: 2-day event in Hong Kong for connecting with peers, mentors, and speakers. 🔸 BIA Winners: top projects from BIA events (Dubai, Bangkok, Denver) get direct entry to MVB Season 9. This season, the project sectors we particularly welcome include, but are not limited to: • AI Applications: Intention based DeFi, AI first Game, DeSoc, AI Agent & Companions, Data labeling, DataDAO • Infra and Tooling: Identity, MPC Wallet, Social Graph, AgentKit, Agent Hosting, AI Training, Inference, DevTools, Privacy Computing, and Streaming Indexing Criteria for applying to MVB: ✅ The majority of business and transaction volume is captured on BNB Chain ✅ Smart contract for core business is deployed on BNB Chain ✅ The project token is initially launched on BNB Chain How to apply to be a part of MVB 9: Please complete the form using the link below. Once we receive your application, it will be reviewed, and we will interview applicants. You will then be notified of our decision. 📝 Apply here: 🗓️ Application window: Dec 13 - Jan 13, 23:59 (UTC)

BNB Chain

85,288 views • 1 year ago