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💠 Current CCIP fees: ~$0.58 per transfer* 💠 If SWIFT, DTCC, Euroclear settle on CCIP: 20–30M transfers/day 💠 Fee revenue: $4.2B – $6.3B per year 💠 $LINK buy pressure via PAL/SVR: $1.7B – $2.5B annually 💠 Conservative $LINK price: $57–$86 💠 Global-scale $LINK price: $400+ 💠 With full-stack fees...

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15,342 次观看 • 9 个月前 •via X (Twitter)

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We saved our customers over $1.3 Billion in 2025 alone. That value has helped Airwallex reach $1.2 Billion in ARR, growing 85% YoY. deel, McLaren Mastercard Formula 1 Team , Bolt and 200,000+ other customers trust us because legacy banking wasn't meant for global businesses: • Opening a bank account in a new country takes weeks • SWIFT transfers take 3-5 days • Other platforms convert your money even when you don't want to But with Airwallex you can: 1. Open an account and get paid like a local in 70 countries Most platforms force you to convert your money into your currency and charge you a conversion fee to do it. With Airwallex, your UK client pays you in GBP and it sits in your GBP balance. Your Australian client pays in AUD and it sits in your AUD balance. When you need to pay a UK vendor or run Australian payroll, you can simply pay from the same currency in your Airwallex account which leads to zero conversion fees. 2. Send and receive money on the same day SWIFT takes 3–5 days and hits you with unpredictable fees on every transfer. But over 90% of Airwallex transactions happen on the same day. Since Airwallex uses local rails to move your money, it also happens at near-zero cost. 3. Issue multi-currency cards instantly Airwallex helps you issue multi-currency cards to your employees across the entire world. And every transaction is automatically synced to your accounting system in real-time. 4. Integrate Airwallex in your product SaaS platforms and marketplaces can also use our APIs to offer these financial services to their customers. In fact, many companies are doing it already. But this is just a glimpse of what Airwallex can do. We’re building the all-in-one financial stack your company will ever need. If you're doing $50M+ in revenue, you could save up to $500k in fees. And that's money back into your business. Sign up for a demo here:
2:15

We saved our customers over $1.3 Billion in 2025 alone. That value has helped Airwallex reach $1.2 Billion in ARR, growing 85% YoY. deel, McLaren Mastercard Formula 1 Team , Bolt and 200,000+ other customers trust us because legacy banking wasn't meant for global businesses: • Opening a bank account in a new country takes weeks • SWIFT transfers take 3-5 days • Other platforms convert your money even when you don't want to But with Airwallex you can: 1. Open an account and get paid like a local in 70 countries Most platforms force you to convert your money into your currency and charge you a conversion fee to do it. With Airwallex, your UK client pays you in GBP and it sits in your GBP balance. Your Australian client pays in AUD and it sits in your AUD balance. When you need to pay a UK vendor or run Australian payroll, you can simply pay from the same currency in your Airwallex account which leads to zero conversion fees. 2. Send and receive money on the same day SWIFT takes 3–5 days and hits you with unpredictable fees on every transfer. But over 90% of Airwallex transactions happen on the same day. Since Airwallex uses local rails to move your money, it also happens at near-zero cost. 3. Issue multi-currency cards instantly Airwallex helps you issue multi-currency cards to your employees across the entire world. And every transaction is automatically synced to your accounting system in real-time. 4. Integrate Airwallex in your product SaaS platforms and marketplaces can also use our APIs to offer these financial services to their customers. In fact, many companies are doing it already. But this is just a glimpse of what Airwallex can do. We’re building the all-in-one financial stack your company will ever need. If you're doing $50M+ in revenue, you could save up to $500k in fees. And that's money back into your business. Sign up for a demo here:

Jack Zhang

2,073,289 次观看 • 5 个月前

David Sacks just said what every honest analyst in Silicon Valley is already thinking (Save this). Nobody has ever seen anything like this. Anthropic has grown at 10x per year for three straight years and going into 2026, the conventional wisdom was that the rate of growth had to slow at this level of scale but then the numbers came in. Q1 alone is $10B ARR to $30B, in April, $30B to $44B and that's $96 million in new ARR added every single day. Inference margins are now above 70%, up from 38% last year and the only thing holding them back was compute. That's solved now, the SpaceX deal and others Anthropic has been quietly signing unlocks the supply side. This is exactly why we are bullish on Nebius and AMD. When a single company is adding nearly $100M in ARR per day, the real trade isn't the frontier lab but rather the infrastructure underneath it. Nebius, one of the fastest-growing neoclouds on the planet posted 547% YoY revenue growth in Q4 2025, exited the year with $1.25B ARR, and is guiding for $7–9B ARR by year-end 2026. Their revenue backlog has reached $46B, with projections of $16B in revenue by 2028 and NVIDIA locked in a $2 billion stock buy agreement with them giving Nebius early access to cutting-edge chips while every other cloud scrambles for supply. AMD is the other side of the same coin. Data center revenue hit $5.78B in Q1, up 57% year-over-year with total company revenue at $10.25B, up 38%. Meta has committed to deploying up to 6 gigawatts of AMD Instinct GPUs. Data center GPU revenue is forecast to surge 114% year over year to $15B in 2026. MI400-series chips hit the market in H2 and analysts project segment operating margins climbing to 31% as the next generation ramps. The model is simple, Anthropic is printing revenue and that that revenue pays for compute. That compute flows through companies like Nebius and AMD. This is why Milk Road PRO remains bullish on them and our positions are up massively. Our analysts have broken down the full thesis, the allocations, and the price targets. Go PRO at Milk Road to see everything, link below!
1:03

David Sacks just said what every honest analyst in Silicon Valley is already thinking (Save this). Nobody has ever seen anything like this. Anthropic has grown at 10x per year for three straight years and going into 2026, the conventional wisdom was that the rate of growth had to slow at this level of scale but then the numbers came in. Q1 alone is $10B ARR to $30B, in April, $30B to $44B and that's $96 million in new ARR added every single day. Inference margins are now above 70%, up from 38% last year and the only thing holding them back was compute. That's solved now, the SpaceX deal and others Anthropic has been quietly signing unlocks the supply side. This is exactly why we are bullish on Nebius and AMD. When a single company is adding nearly $100M in ARR per day, the real trade isn't the frontier lab but rather the infrastructure underneath it. Nebius, one of the fastest-growing neoclouds on the planet posted 547% YoY revenue growth in Q4 2025, exited the year with $1.25B ARR, and is guiding for $7–9B ARR by year-end 2026. Their revenue backlog has reached $46B, with projections of $16B in revenue by 2028 and NVIDIA locked in a $2 billion stock buy agreement with them giving Nebius early access to cutting-edge chips while every other cloud scrambles for supply. AMD is the other side of the same coin. Data center revenue hit $5.78B in Q1, up 57% year-over-year with total company revenue at $10.25B, up 38%. Meta has committed to deploying up to 6 gigawatts of AMD Instinct GPUs. Data center GPU revenue is forecast to surge 114% year over year to $15B in 2026. MI400-series chips hit the market in H2 and analysts project segment operating margins climbing to 31% as the next generation ramps. The model is simple, Anthropic is printing revenue and that that revenue pays for compute. That compute flows through companies like Nebius and AMD. This is why Milk Road PRO remains bullish on them and our positions are up massively. Our analysts have broken down the full thesis, the allocations, and the price targets. Go PRO at Milk Road to see everything, link below!

Milk Road AI

184,643 次观看 • 3 个月前

Yesterday I saw Hokanews post the article which is:" The Price of Pi Coin is not $314,159, Here are the actual facts from the words of Dr. Nicolas Kokkalis ". This led to all global community shock and debate. The following is what my understanding of this article and my explanation is to let the community avoid the misleading of Dr.Nicolas Statement! Pay attention! This is only my personal opinion for reference only. I am not representing Dr. Nicolas or Pi Network Core Team!🙏 1. Dr Nicolas never guides or indicates what the Pi Coin price is. He stated one year before: Pi Price was created by pioneers. Therefore Pi Network CT never judges price or indicates/ guide price. They never interrupt pioneer community activities regarding price. They strictly obey USA SEC regulations! If someone uses Dr. Nicolas name to manipulate price, this is a fake statement. It's not Dr. Nicolas Kokkalis statement. 2. Dr Nicolas Kokkalis answered some Pioneers questions regarding 0.01 transaction fees around one year before. Some Pioneers think if $314,159 is Pi price then 0.01 Pi transaction fees are too high. Therefore Dr. Nicolas Kokkalis answered that 0.01 is fixed right now. But it will be adjusted with time. It can be 0.001 or 0.0001. Maybe some day, it can be 1/1 million which is 0.000001 which is the lowest in the Blockchain system. He just gave an example and he said 0.000001 is Pi Network Blockchain function to adjust the transaction fees Lowest limit. 3. There are logic mistakes in Mr. Lu statement. The possibility of 0.000001 doesn't mean it is. How can we change the possibility to reality. This is the critical thing we need to consider! Right now large portions of Pioneers Community accept GCV $314,159. And we can see in China there are 100 million USD transactions to support GCV! However I only see $200 1 transaction to support 1 million USD. If I am wrong, please send me the substantiate proof. 4. Our GCV $314,159 never goes against higher consensus price. $314,159 is safe bottom line to start open mainnet. And we can see many merchant promises to use GCV when this price is fixed which includes real property and cars vehicles, gold ect. 5. If the price can't be united, the Eco system mature time will take a long time. I don't think this is what pioneers and merchants want to see. In addition, the longer close the mainnet, the more pioneers and merchants will lose. I heard from Chinese merchants that they almost used up all their inventory and funds to support GCV. They are waiting for the price to be fixed and open the mainnet so that they can have cash flow to restock. If the open mainnet is delayed, they will have a hard time to support anyone. 6. Not only GCV merchants need to open the mainnet soon, low consensus merchants such as $100 or 1$ price has almost run out of the inventory or fund too. No matter the high or low price for barter, all merchants need cash FiAT when the industry cycle is not built up. I heard one merchant gets 2000 Pi from barter by using a low price,he complained that he has no cash to support anymore. 6. In order to save and help all low and high price merchants, united prices are critical to resolve all cash flow problems. Only when united prices, the outside eco businesses know how to pledge pi to use Blockchain and create the demand of Pi. So merchants and pioneers can have converters use the same price! I call on all global community pioneers and merchants united to GCV together and don't fight prices anymore. It will hurt the community if you can't settle the price down soon! All best wishes and Happy Mother's day to global pioneers! Especially to all mothers🙏🙏🙏🎉🎉🎉 Pi Network #PiNetwork #PiGCV
2:34

Yesterday I saw Hokanews post the article which is:" The Price of Pi Coin is not $314,159, Here are the actual facts from the words of Dr. Nicolas Kokkalis ". This led to all global community shock and debate. The following is what my understanding of this article and my explanation is to let the community avoid the misleading of Dr.Nicolas Statement! Pay attention! This is only my personal opinion for reference only. I am not representing Dr. Nicolas or Pi Network Core Team!🙏 1. Dr Nicolas never guides or indicates what the Pi Coin price is. He stated one year before: Pi Price was created by pioneers. Therefore Pi Network CT never judges price or indicates/ guide price. They never interrupt pioneer community activities regarding price. They strictly obey USA SEC regulations! If someone uses Dr. Nicolas name to manipulate price, this is a fake statement. It's not Dr. Nicolas Kokkalis statement. 2. Dr Nicolas Kokkalis answered some Pioneers questions regarding 0.01 transaction fees around one year before. Some Pioneers think if $314,159 is Pi price then 0.01 Pi transaction fees are too high. Therefore Dr. Nicolas Kokkalis answered that 0.01 is fixed right now. But it will be adjusted with time. It can be 0.001 or 0.0001. Maybe some day, it can be 1/1 million which is 0.000001 which is the lowest in the Blockchain system. He just gave an example and he said 0.000001 is Pi Network Blockchain function to adjust the transaction fees Lowest limit. 3. There are logic mistakes in Mr. Lu statement. The possibility of 0.000001 doesn't mean it is. How can we change the possibility to reality. This is the critical thing we need to consider! Right now large portions of Pioneers Community accept GCV $314,159. And we can see in China there are 100 million USD transactions to support GCV! However I only see $200 1 transaction to support 1 million USD. If I am wrong, please send me the substantiate proof. 4. Our GCV $314,159 never goes against higher consensus price. $314,159 is safe bottom line to start open mainnet. And we can see many merchant promises to use GCV when this price is fixed which includes real property and cars vehicles, gold ect. 5. If the price can't be united, the Eco system mature time will take a long time. I don't think this is what pioneers and merchants want to see. In addition, the longer close the mainnet, the more pioneers and merchants will lose. I heard from Chinese merchants that they almost used up all their inventory and funds to support GCV. They are waiting for the price to be fixed and open the mainnet so that they can have cash flow to restock. If the open mainnet is delayed, they will have a hard time to support anyone. 6. Not only GCV merchants need to open the mainnet soon, low consensus merchants such as $100 or 1$ price has almost run out of the inventory or fund too. No matter the high or low price for barter, all merchants need cash FiAT when the industry cycle is not built up. I heard one merchant gets 2000 Pi from barter by using a low price,he complained that he has no cash to support anymore. 6. In order to save and help all low and high price merchants, united prices are critical to resolve all cash flow problems. Only when united prices, the outside eco businesses know how to pledge pi to use Blockchain and create the demand of Pi. So merchants and pioneers can have converters use the same price! I call on all global community pioneers and merchants united to GCV together and don't fight prices anymore. It will hurt the community if you can't settle the price down soon! All best wishes and Happy Mother's day to global pioneers! Especially to all mothers🙏🙏🙏🎉🎉🎉 Pi Network #PiNetwork #PiGCV

Doris Yin 东方紫莲🪷

38,884 次观看 • 3 年前

This is part 2 of a 2 part post (see part 1 here Below is a structured analysis to demonstrate the validity of using buyers of Veritaseum #SmartMetal to buy into and sell compute from globally aggregated cell phone compute pools - directly compeiting with the big guys - Google, Amazon and Microsoft cloud businesses. We discuss estimates, business model propositions, and potential economic outcomes, but first, see my Executive Global Article on Zero Profit Models( and purchase Veritaseum SmartMetal here - Can you really disintermediate the most profitable revnues of t $6.7 trillion worth of technology cloud providers? Well, the fact that it is among, if not the, most profitable of their revenue drivers is a very material clue! Step 1: Estimating the Number of High-End Smartphones Globally As of early 2025, approximately 7.5 billion smartphones are actively used worldwide. Considering that: About 30% of global smartphones are high-end (comparable or superior to an iPhone X; for instance, Samsung Galaxy S22/S23 Ultra, iPhone 16 Pro Max with A18 chips, and Qualcomm Snapdragon 8 Gen 3 or newer). Thus, approximately 2.25 billion high-end smartphones exist today (30% of 7.5B). Step 2: Aggregate Compute Power Estimation (Idle Capacity) Average Computational Capacity per High-End Smartphone: A high-end phone has roughly: CPU: ~1 to 1.5 TFLOPS GPU: ~1.5 to 2 TFLOPS Average Idle Compute per Phone: 1 TFLOPS (CPU) + 1.5 TFLOPS (GPU) = ~2.5 TFLOPS idle. Total Potential Compute Power: 2.25B smartphones × 2.5 TFLOPS each ≈ 5,625,000,000 TFLOPS (5.625 ExaFLOPS) Comparison to Cloud Vendors: Amazon AWS, Microsoft Azure, Google Cloud combined currently deploy approximately ~1 to 2 ExaFLOPS of continuous computing power. Thus, aggregate idle compute power from high-end smartphones (5.625 ExaFLOPS) exceeds the largest cloud vendors combined by at least 2.8x. Step 3: Proposed Business Model ("Zero Margin Trustless Model") Following Middleton’s economic principles, a decentralized marketplace based on his IP (SmartMetal Rounds and patented protocols) would allow individual users to rent their smartphones’ idle compute power. The economics would follow: Revenue Structure: Compute resources provided by phone owners (children, elderly, economically disadvantaged communities) rented to consumers (AI firms, universities, research institutions, enterprises). Offered at 10% above net cost ("as close to free as possible" per the attached article​Executive Global articl…). Revenue Distribution: SmartMetal Owners (phone owners): Receive 20% of net revenue generated. Platform Cost & Overhead: Costs for electricity, network management, and maintenance (approximately 70% of net revenue). Intellectual Property Licensing (Middleton’s IP): A modest licensing fee—around 10% (aligned with Middleton’s zero-margin, IP-licensing-centric model). Step 4: Revenue Estimation Example Assumptions: Average monthly idle compute contribution per phone: 4 hours/day, 30 days = 120 hours/month. Market price for decentralized high-performance computing: approximately $0.10 per TFLOP-hour. Revenue per Smartphone per Month: Compute provided: 2.5 TFLOPS × 120 hrs = 300 TFLOP-hours Revenue at $0.10 per TFLOP-hour: 300 × $0.10 = $30/month per smartphone Aggregate Monthly and Annual Revenue: Monthly revenue (2.25 billion phones): $30 × 2.25B ≈ $67.5 billion Annual revenue potential: $67.5B × 12 months = $810 billion annually Distribution of Annual Revenue: SmartMetal Round Owners (20%): $810B × 20% ≈ $162 billion/year Operational Cost (70%): $810B × 70% ≈ $567 billion/year Middleton IP Licensing (10%): $810B × 10% ≈ $81 billion/year Thus, the total economic benefit is substantial, particularly transformative for economically disadvantaged participants (children, elderly, developing regions). Step 5: Practical Impact & Social Value Impact on Children & Young Adults: Empowerment through earning potential (around $360 annually per child smartphone owner). Practical, intuitive introduction to economics, technology, and entrepreneurship through gamified interfaces and secure, decentralized platforms. Impact on Elderly and Economically Disadvantaged Communities: Significant supplemental income (potentially exceeding many pension plans or assistance programs). Bridging the technology gap, ensuring inclusive participation in global digital economies. Step 6: Strategic Value & Market Positioning Middleton's patented Zero Margin Trustless Model ("ZMTM")​Executive Global articl… creates a highly attractive, low-cost computational offering. Competing directly with incumbent cloud providers: The computational marketplace can massively disrupt cloud computing with lower fees and broader global reach. Leveraging Middleton’s IP and SmartMetal Rounds, it creates defensible competitive barriers and immense value for early adopters. Step 7: Driving Middleton’s Peer-to-Peer Economy As described in Middleton’s vision​Executive Global articl…, this marketplace underpins a global peer-to-peer economy, transforming idle smartphone resources into meaningful economic output. The P2P economy will leverage: AI-driven autonomous economic agents. Secure blockchain-based IP rights enforcement. Economic democratization by redistributing traditional cloud revenues directly to everyday device owners. Summary & Strategic Conclusion Implementing a decentralized compute platform powered by high-end smartphones and Middleton’s patented Zero Margin Trustless Model presents enormous economic potential, far exceeding current major cloud vendors combined. With annual revenues estimated up to $810 billion, and meaningful income distribution to disadvantaged demographics, this innovative model could dramatically reshape the global computational economy, achieve significant social impacts, and provide the backbone for Middleton’s envisioned peer-to-peer decentralized economy.
8:25

This is part 2 of a 2 part post (see part 1 here Below is a structured analysis to demonstrate the validity of using buyers of Veritaseum #SmartMetal to buy into and sell compute from globally aggregated cell phone compute pools - directly compeiting with the big guys - Google, Amazon and Microsoft cloud businesses. We discuss estimates, business model propositions, and potential economic outcomes, but first, see my Executive Global Article on Zero Profit Models( and purchase Veritaseum SmartMetal here - Can you really disintermediate the most profitable revnues of t $6.7 trillion worth of technology cloud providers? Well, the fact that it is among, if not the, most profitable of their revenue drivers is a very material clue! Step 1: Estimating the Number of High-End Smartphones Globally As of early 2025, approximately 7.5 billion smartphones are actively used worldwide. Considering that: About 30% of global smartphones are high-end (comparable or superior to an iPhone X; for instance, Samsung Galaxy S22/S23 Ultra, iPhone 16 Pro Max with A18 chips, and Qualcomm Snapdragon 8 Gen 3 or newer). Thus, approximately 2.25 billion high-end smartphones exist today (30% of 7.5B). Step 2: Aggregate Compute Power Estimation (Idle Capacity) Average Computational Capacity per High-End Smartphone: A high-end phone has roughly: CPU: ~1 to 1.5 TFLOPS GPU: ~1.5 to 2 TFLOPS Average Idle Compute per Phone: 1 TFLOPS (CPU) + 1.5 TFLOPS (GPU) = ~2.5 TFLOPS idle. Total Potential Compute Power: 2.25B smartphones × 2.5 TFLOPS each ≈ 5,625,000,000 TFLOPS (5.625 ExaFLOPS) Comparison to Cloud Vendors: Amazon AWS, Microsoft Azure, Google Cloud combined currently deploy approximately ~1 to 2 ExaFLOPS of continuous computing power. Thus, aggregate idle compute power from high-end smartphones (5.625 ExaFLOPS) exceeds the largest cloud vendors combined by at least 2.8x. Step 3: Proposed Business Model ("Zero Margin Trustless Model") Following Middleton’s economic principles, a decentralized marketplace based on his IP (SmartMetal Rounds and patented protocols) would allow individual users to rent their smartphones’ idle compute power. The economics would follow: Revenue Structure: Compute resources provided by phone owners (children, elderly, economically disadvantaged communities) rented to consumers (AI firms, universities, research institutions, enterprises). Offered at 10% above net cost ("as close to free as possible" per the attached article​Executive Global articl…). Revenue Distribution: SmartMetal Owners (phone owners): Receive 20% of net revenue generated. Platform Cost & Overhead: Costs for electricity, network management, and maintenance (approximately 70% of net revenue). Intellectual Property Licensing (Middleton’s IP): A modest licensing fee—around 10% (aligned with Middleton’s zero-margin, IP-licensing-centric model). Step 4: Revenue Estimation Example Assumptions: Average monthly idle compute contribution per phone: 4 hours/day, 30 days = 120 hours/month. Market price for decentralized high-performance computing: approximately $0.10 per TFLOP-hour. Revenue per Smartphone per Month: Compute provided: 2.5 TFLOPS × 120 hrs = 300 TFLOP-hours Revenue at $0.10 per TFLOP-hour: 300 × $0.10 = $30/month per smartphone Aggregate Monthly and Annual Revenue: Monthly revenue (2.25 billion phones): $30 × 2.25B ≈ $67.5 billion Annual revenue potential: $67.5B × 12 months = $810 billion annually Distribution of Annual Revenue: SmartMetal Round Owners (20%): $810B × 20% ≈ $162 billion/year Operational Cost (70%): $810B × 70% ≈ $567 billion/year Middleton IP Licensing (10%): $810B × 10% ≈ $81 billion/year Thus, the total economic benefit is substantial, particularly transformative for economically disadvantaged participants (children, elderly, developing regions). Step 5: Practical Impact & Social Value Impact on Children & Young Adults: Empowerment through earning potential (around $360 annually per child smartphone owner). Practical, intuitive introduction to economics, technology, and entrepreneurship through gamified interfaces and secure, decentralized platforms. Impact on Elderly and Economically Disadvantaged Communities: Significant supplemental income (potentially exceeding many pension plans or assistance programs). Bridging the technology gap, ensuring inclusive participation in global digital economies. Step 6: Strategic Value & Market Positioning Middleton's patented Zero Margin Trustless Model ("ZMTM")​Executive Global articl… creates a highly attractive, low-cost computational offering. Competing directly with incumbent cloud providers: The computational marketplace can massively disrupt cloud computing with lower fees and broader global reach. Leveraging Middleton’s IP and SmartMetal Rounds, it creates defensible competitive barriers and immense value for early adopters. Step 7: Driving Middleton’s Peer-to-Peer Economy As described in Middleton’s vision​Executive Global articl…, this marketplace underpins a global peer-to-peer economy, transforming idle smartphone resources into meaningful economic output. The P2P economy will leverage: AI-driven autonomous economic agents. Secure blockchain-based IP rights enforcement. Economic democratization by redistributing traditional cloud revenues directly to everyday device owners. Summary & Strategic Conclusion Implementing a decentralized compute platform powered by high-end smartphones and Middleton’s patented Zero Margin Trustless Model presents enormous economic potential, far exceeding current major cloud vendors combined. With annual revenues estimated up to $810 billion, and meaningful income distribution to disadvantaged demographics, this innovative model could dramatically reshape the global computational economy, achieve significant social impacts, and provide the backbone for Middleton’s envisioned peer-to-peer decentralized economy.

Reggie Middleton, Disruptor-in-Chief

14,751 次观看 • 1 年前

Executive Thesis - Ripple Bank 2025 If Ripple secures bank-like permissions (U.S. national bank charter or state ILC plus key foreign licenses) and runs RL-stablecoins and XRPL rails under a Basel-caliber risk, capital, and compliance stack, it can become a regulated global settlement and asset-services platform. That platform could let central banks, sovereign treasuries, and regulated financial institutions issue, custody, trade, and settle stablecoins and tokenized RWAs (stocks, bonds, commodities, derivatives) with ISO 20022 native messaging, BSA/AML–FATF controls, and Basel III capital/liquidity governance—collapsing today’s slow correspondent chains into a single, high-compliance operating layer. The “Boom” Implications With the right charter(s), prudential regime, and partnerships, Ripple can become a compliance-first global neo-banking platform that (1) absorbs cross-border payment flows from correspondent networks, (2) powers CBDC and sovereign tokenized markets, and (3) monetizes issuance, custody, settlement, and compliance at scale—all inside Basel III, BSA/AML, FATF, and ISO 20022 guardrails. Impact on XRP If Ripple Bank were formally approved and XRP became the primary liquidity and settlement token across its’ regulated ecosystem, the economic demand for XRP would expand exponentially - transforming it from a speculative asset into regulated financial infrastructure. Structural Shift in XRP Demand From Speculative to Utility-backed Demand • XRP’s value today is primarily market-driven by speculation on future adoption. • Under a Ripple Bank framework, XRP becomes a mandatory utility asset — required for: • Settlement liquidity between all tokenized assets on XRPL (CBDCs, stablecoins, RWAs, derivatives). • Transaction fees and compliance verification across billions of high-value financial messages. • Collateral in interbank, treasury, and derivative clearing functions. This converts XRP from “optional” to “indispensable” in regulated settlement flows - similar to how SWIFT messaging depends on correspondent Nostro/Vostro liquidity BUT executed on a frictionless, tokenized rail. Volume & Velocity Effects Token velocity decreases, float demand increases • Basel III and liquidity regulations require prefunded, high-quality settlement collateral. • As banks, sovereigns, and institutions hold XRP as a liquidity reserve (like Tier-1 capital equivalents for tokenized payments), circulating supply falls while volume increases—driving scarcity-driven price appreciation. An Example of Flow Scale • Global wholesale payments ≈ $250T/year. • If 10% settles through Ripple’s bank-backed network using XRP at a 3-day velocity (roughly 120 settlement turns per year): • Required float ≈ $2.1T equivalent demand. • Even at $100/XRP, that implies 20B XRP locked in active liquidity operations. • At today’s 15B non-escrowed supply, value equilibrium could theoretically exceed $140–$200 per token, depending on velocity and collateral requirements. From today’s ~$3 price, this implies a 46x to 66x price surge. Are we ready? Ripple Treasury Department OCC Comptroller Jonathan Gould
0:30

Executive Thesis - Ripple Bank 2025 If Ripple secures bank-like permissions (U.S. national bank charter or state ILC plus key foreign licenses) and runs RL-stablecoins and XRPL rails under a Basel-caliber risk, capital, and compliance stack, it can become a regulated global settlement and asset-services platform. That platform could let central banks, sovereign treasuries, and regulated financial institutions issue, custody, trade, and settle stablecoins and tokenized RWAs (stocks, bonds, commodities, derivatives) with ISO 20022 native messaging, BSA/AML–FATF controls, and Basel III capital/liquidity governance—collapsing today’s slow correspondent chains into a single, high-compliance operating layer. The “Boom” Implications With the right charter(s), prudential regime, and partnerships, Ripple can become a compliance-first global neo-banking platform that (1) absorbs cross-border payment flows from correspondent networks, (2) powers CBDC and sovereign tokenized markets, and (3) monetizes issuance, custody, settlement, and compliance at scale—all inside Basel III, BSA/AML, FATF, and ISO 20022 guardrails. Impact on XRP If Ripple Bank were formally approved and XRP became the primary liquidity and settlement token across its’ regulated ecosystem, the economic demand for XRP would expand exponentially - transforming it from a speculative asset into regulated financial infrastructure. Structural Shift in XRP Demand From Speculative to Utility-backed Demand • XRP’s value today is primarily market-driven by speculation on future adoption. • Under a Ripple Bank framework, XRP becomes a mandatory utility asset — required for: • Settlement liquidity between all tokenized assets on XRPL (CBDCs, stablecoins, RWAs, derivatives). • Transaction fees and compliance verification across billions of high-value financial messages. • Collateral in interbank, treasury, and derivative clearing functions. This converts XRP from “optional” to “indispensable” in regulated settlement flows - similar to how SWIFT messaging depends on correspondent Nostro/Vostro liquidity BUT executed on a frictionless, tokenized rail. Volume & Velocity Effects Token velocity decreases, float demand increases • Basel III and liquidity regulations require prefunded, high-quality settlement collateral. • As banks, sovereigns, and institutions hold XRP as a liquidity reserve (like Tier-1 capital equivalents for tokenized payments), circulating supply falls while volume increases—driving scarcity-driven price appreciation. An Example of Flow Scale • Global wholesale payments ≈ $250T/year. • If 10% settles through Ripple’s bank-backed network using XRP at a 3-day velocity (roughly 120 settlement turns per year): • Required float ≈ $2.1T equivalent demand. • Even at $100/XRP, that implies 20B XRP locked in active liquidity operations. • At today’s 15B non-escrowed supply, value equilibrium could theoretically exceed $140–$200 per token, depending on velocity and collateral requirements. From today’s ~$3 price, this implies a 46x to 66x price surge. Are we ready? Ripple Treasury Department OCC Comptroller Jonathan Gould

Rob Cunningham

10,911 次观看 • 10 个月前

$GRAB Map is The New Google Maps(B2B)🧵 Here is your Free.99 analysis on GrabMap, for those that selling courses for $50-$500/m, if you are using my $GRAB and other analyses, I don't ask for much, at least give me some credit/cite. And yes 99.999% of my posts are Free.99. If you want to support my work, slap the like/repost, as I don't choose to write "Grab or any Ticker is going to x10 x100-x1000" kind of threads or "mark my words" to please the X Algo. Consider Subscribe($0.33/day) if you want to support my work further and get more in-depth analyses! TLDR: GrabMap could generate $7B-$15B a year alone for Grab B2B segment. That is why you are seeing Anthony Tan is mad excited abt this massive opportunity. And it also significantly boost GrabAds long term globally. This precisely proved my point that, Anthony is going to expand to 5B people and we are only 14% thesis realized right now. Grab doesn't have to be just Ride-share/Delivery when expanding! Grab , Southeast Asia's leading AI SuperApp for ride-hailing, food delivery, financial services,Tourism, Dine-Out and more, has developed its proprietary mapping platform, GrabMaps, a massive B2B revenue potential over the next long term, not just in Singapore, Indonesia, Malaysia, Thailand, Philippines, Vietnam, Cambodia, and Myanmar but expanding beyond SEA markets/Customers. 1. GrabMaps: A Strategic Asset GrabMaps is not merely a technological tool but a critical component of Grab's ecosystem, powering its ride-hailing, food delivery, and financial services. Developed in-house, GrabMaps leverages data collected from Grab's vast network of driver-partners across eight SEA countries. This data-driven approach ensures hyper-local customization, addressing the unique challenges of SEA's urban environments, such as narrow alleys, informal roads, and rapid infrastructure changes. The recent announcement of KartaCam2, an upgraded street-level imaging device, marks a significant technological advancement. KartaCam2 enhances data collection by providing higher quality images and more precise location data, which are crucial for maintaining the accuracy and freshness of maps. This breakthrough is part of Grab's broader 2025 AI push, including integrations with OpenAI 's GPT-4o for vision-based mapping and the establishment of an AI Centre of Excellence. These innovations position GrabMaps as a formidable competitor to Google Maps, especially in regions where localized data is paramount. 2. Revenue implications long term The expansion of GrabMaps into B2B services opens up new revenue streams, which could significantly impact Grab's financial performance over the long term. But GrabMap is a brandnew B2B product, and GoogleMap generates around $13-$20B globally. A. Market Opportunity in Southeast Asia ~The SEA market presents a substantial opportunity for GrabMaps. The foodservice market alone is projected to grow from $223.8 billion in 2025 to $416.3 billion by 2030, indicating a robust demand for services that enhance operational efficiencies. Businesses in logistics, e-commerce, and urban planning could benefit from GrabMaps' precise mapping and navigation capabilities, potentially generating revenue through licensing fees, subscription models, and advertising. ~Grab's existing user base of over 46 million monthly transacting users provides a strong foundation for cross-selling B2B solutions, thereby increasing revenue without significant additional marketing costs. B. Competitive Advantage of a Future $500B MC AI SuperApp over Google Map Google Maps, while dominant, may not be as finely tuned for SEA's unique challenges. GrabMaps' hyper-local data and AI-driven enhancements offer a competitive edge, attracting businesses that require accurate and cost-effective mapping solutions. Revenue from B2B services could include: Licensing Fees: Enterprises can license GrabMaps' APIs and SDKs to integrate mapping functionalities into their operations. Subscription Models: Continuous updates and premium features could be offered on a subscription basis. Advertising Revenue: GrabAds, which leverages mapping data, could generate additional income through targeted advertising. C. Global Expansion is Inevitable ~The partnership with Tino in Mongolia is a strategic move to scale GrabMaps internationally. This marks Grab's first major mapping partnership outside SEA, indicating potential for revenue growth in other regions where Google Maps' dominance is less entrenched or where local data needs are acute. ~The use of IoT devices like KartaCam2 and KartaDashCam for real-time data collection could further enhance GrabMaps' value proposition, potentially increasing revenue through premium service offerings in new markets. D. Synergies w/ other businesses Grab's ecosystem approach allows for synergies between GrabMaps and other services like GrabPay, GrabFood, and GrabTransport. For example, businesses using GrabMaps for logistics could also adopt GrabPay for transactions, creating a revenue multiplier effect. 3. Google Map Revenue in Asia ~Total Revenue in Asia-Pacific (2018): Google APAC, based in Singapore, reported $20.24 billion out of the total $21.37 billion revenue in the Asia-Pacific region. This indicates that a significant portion of Google's revenue in Asia is attributed to Singapore, likely due to its role as a hub for Google’s operations. ~Advertising Revenue: In 2018, Google APAC generated $15.8 billion from advertising alone, compared to $4.4 billion from other activities like Google Play. Advertising on Google properties, including Google Maps, is a major revenue driver. ~Market Share in Search Marketing: Google Maps holds a 62.34% market share in the search marketing category, competing with tools like Wix (26.54%) and Google Ads (4.14%). This dominance suggests that a considerable portion of Google’s advertising revenue in Asia is linked to mapping services. For the full fiscal year 2024, Alphabet (Google's parent company) generated $56.82 billion in revenue from the Asia-Pacific (APAC) region. This represented approximately 16.24% of the company's total revenue for the year. If we take a conservative estimate at 25% of $56.82B of Google's total advertising revenue in Asia is related to mapping services= $14.2B. => If GrabMaps secures even 50% of this market share in SEA, it could generate around $7B annually from this segment alone. GrabMap is 4x lower error rate, 10x lower latency, 75% fewer mapping mistakes, and much cheaper than GoogleMap. With OpenAI GPT-4o fine-tuning, GrabMaps hit 80% accuracy for speed limits and lanes13-20% above prior levels excelling in occlusions ( rainy monsoons) where Google relies more on satellite data. Now do you understand why Google and HSBC are clapping $GRAB on search and downgrade? Yes, because GrabMap is a massive threat and Grab Anthony Tan refused to buy $goto since 2020. Conclusion: Grab's expansion of GrabMaps into B2B services represents a strategic move to challenge Google Maps' dominance in Asia, particularly in SEA and future expansion. The revenue implications are substantial, with potential gains from licensing fees, subscription models, advertising, and international expansions. While Google Maps generates billions in revenue, primarily through advertising, GrabMaps' localized and AI-enhanced approach could carve out a significant niche, especially in regions where precise, real-time mapping data is critical. The success of this strategy will depend on Grab's ability to scale internationally, maintain technological superiority, and effectively monetize its B2B offerings. However, the opportunity is clear, and Grab's ecosystem approach positions it well to capitalize on the growing demand for advanced mapping solutions in a rapidly digitalizing world. This move not only enhances Grab's revenue potential but also solidifies its role as a key player in the global tech landscape. Not Financial Advice! Source: Grab Dot Com.
1:26

$GRAB Map is The New Google Maps(B2B)🧵 Here is your Free.99 analysis on GrabMap, for those that selling courses for $50-$500/m, if you are using my $GRAB and other analyses, I don't ask for much, at least give me some credit/cite. And yes 99.999% of my posts are Free.99. If you want to support my work, slap the like/repost, as I don't choose to write "Grab or any Ticker is going to x10 x100-x1000" kind of threads or "mark my words" to please the X Algo. Consider Subscribe($0.33/day) if you want to support my work further and get more in-depth analyses! TLDR: GrabMap could generate $7B-$15B a year alone for Grab B2B segment. That is why you are seeing Anthony Tan is mad excited abt this massive opportunity. And it also significantly boost GrabAds long term globally. This precisely proved my point that, Anthony is going to expand to 5B people and we are only 14% thesis realized right now. Grab doesn't have to be just Ride-share/Delivery when expanding! Grab , Southeast Asia's leading AI SuperApp for ride-hailing, food delivery, financial services,Tourism, Dine-Out and more, has developed its proprietary mapping platform, GrabMaps, a massive B2B revenue potential over the next long term, not just in Singapore, Indonesia, Malaysia, Thailand, Philippines, Vietnam, Cambodia, and Myanmar but expanding beyond SEA markets/Customers. 1. GrabMaps: A Strategic Asset GrabMaps is not merely a technological tool but a critical component of Grab's ecosystem, powering its ride-hailing, food delivery, and financial services. Developed in-house, GrabMaps leverages data collected from Grab's vast network of driver-partners across eight SEA countries. This data-driven approach ensures hyper-local customization, addressing the unique challenges of SEA's urban environments, such as narrow alleys, informal roads, and rapid infrastructure changes. The recent announcement of KartaCam2, an upgraded street-level imaging device, marks a significant technological advancement. KartaCam2 enhances data collection by providing higher quality images and more precise location data, which are crucial for maintaining the accuracy and freshness of maps. This breakthrough is part of Grab's broader 2025 AI push, including integrations with OpenAI 's GPT-4o for vision-based mapping and the establishment of an AI Centre of Excellence. These innovations position GrabMaps as a formidable competitor to Google Maps, especially in regions where localized data is paramount. 2. Revenue implications long term The expansion of GrabMaps into B2B services opens up new revenue streams, which could significantly impact Grab's financial performance over the long term. But GrabMap is a brandnew B2B product, and GoogleMap generates around $13-$20B globally. A. Market Opportunity in Southeast Asia ~The SEA market presents a substantial opportunity for GrabMaps. The foodservice market alone is projected to grow from $223.8 billion in 2025 to $416.3 billion by 2030, indicating a robust demand for services that enhance operational efficiencies. Businesses in logistics, e-commerce, and urban planning could benefit from GrabMaps' precise mapping and navigation capabilities, potentially generating revenue through licensing fees, subscription models, and advertising. ~Grab's existing user base of over 46 million monthly transacting users provides a strong foundation for cross-selling B2B solutions, thereby increasing revenue without significant additional marketing costs. B. Competitive Advantage of a Future $500B MC AI SuperApp over Google Map Google Maps, while dominant, may not be as finely tuned for SEA's unique challenges. GrabMaps' hyper-local data and AI-driven enhancements offer a competitive edge, attracting businesses that require accurate and cost-effective mapping solutions. Revenue from B2B services could include: Licensing Fees: Enterprises can license GrabMaps' APIs and SDKs to integrate mapping functionalities into their operations. Subscription Models: Continuous updates and premium features could be offered on a subscription basis. Advertising Revenue: GrabAds, which leverages mapping data, could generate additional income through targeted advertising. C. Global Expansion is Inevitable ~The partnership with Tino in Mongolia is a strategic move to scale GrabMaps internationally. This marks Grab's first major mapping partnership outside SEA, indicating potential for revenue growth in other regions where Google Maps' dominance is less entrenched or where local data needs are acute. ~The use of IoT devices like KartaCam2 and KartaDashCam for real-time data collection could further enhance GrabMaps' value proposition, potentially increasing revenue through premium service offerings in new markets. D. Synergies w/ other businesses Grab's ecosystem approach allows for synergies between GrabMaps and other services like GrabPay, GrabFood, and GrabTransport. For example, businesses using GrabMaps for logistics could also adopt GrabPay for transactions, creating a revenue multiplier effect. 3. Google Map Revenue in Asia ~Total Revenue in Asia-Pacific (2018): Google APAC, based in Singapore, reported $20.24 billion out of the total $21.37 billion revenue in the Asia-Pacific region. This indicates that a significant portion of Google's revenue in Asia is attributed to Singapore, likely due to its role as a hub for Google’s operations. ~Advertising Revenue: In 2018, Google APAC generated $15.8 billion from advertising alone, compared to $4.4 billion from other activities like Google Play. Advertising on Google properties, including Google Maps, is a major revenue driver. ~Market Share in Search Marketing: Google Maps holds a 62.34% market share in the search marketing category, competing with tools like Wix (26.54%) and Google Ads (4.14%). This dominance suggests that a considerable portion of Google’s advertising revenue in Asia is linked to mapping services. For the full fiscal year 2024, Alphabet (Google's parent company) generated $56.82 billion in revenue from the Asia-Pacific (APAC) region. This represented approximately 16.24% of the company's total revenue for the year. If we take a conservative estimate at 25% of $56.82B of Google's total advertising revenue in Asia is related to mapping services= $14.2B. => If GrabMaps secures even 50% of this market share in SEA, it could generate around $7B annually from this segment alone. GrabMap is 4x lower error rate, 10x lower latency, 75% fewer mapping mistakes, and much cheaper than GoogleMap. With OpenAI GPT-4o fine-tuning, GrabMaps hit 80% accuracy for speed limits and lanes13-20% above prior levels excelling in occlusions ( rainy monsoons) where Google relies more on satellite data. Now do you understand why Google and HSBC are clapping $GRAB on search and downgrade? Yes, because GrabMap is a massive threat and Grab Anthony Tan refused to buy $goto since 2020. Conclusion: Grab's expansion of GrabMaps into B2B services represents a strategic move to challenge Google Maps' dominance in Asia, particularly in SEA and future expansion. The revenue implications are substantial, with potential gains from licensing fees, subscription models, advertising, and international expansions. While Google Maps generates billions in revenue, primarily through advertising, GrabMaps' localized and AI-enhanced approach could carve out a significant niche, especially in regions where precise, real-time mapping data is critical. The success of this strategy will depend on Grab's ability to scale internationally, maintain technological superiority, and effectively monetize its B2B offerings. However, the opportunity is clear, and Grab's ecosystem approach positions it well to capitalize on the growing demand for advanced mapping solutions in a rapidly digitalizing world. This move not only enhances Grab's revenue potential but also solidifies its role as a key player in the global tech landscape. Not Financial Advice! Source: Grab Dot Com.

Mike

120,532 次观看 • 10 个月前

Nebius will be the first trillion dollar neocloud hyperscaler. Most neoclouds are stuck in a single business model, renting bare GPU capacity to whoever will pay for it. Nebius is deliberately building across four layers instead, bare metal, managed infrastructure, inference, and eventually agentic tooling and each layer up the stack dramatically expands who can actually buy from them. Bare metal has maybe a dozen viable customers worldwide, since only the biggest players can even use raw infrastructure at that scale. Managed infrastructure opens that up to hundreds of buyers, while inference reaches thousands of potential customers. Agentic services are still early, but they could eventually serve tens of thousands of developers building on top of the platform. That's the real engine behind a trillion dollar outcome, since a single layer rental business caps out far lower than a company selling into an expanding pyramid of customers at every altitude. There's also a strategic decision buried in how Nebius handles its biggest clients. Serving giants like Meta and Microsoft is a double edged sword, since those companies bring their own full software stack and only need physical infrastructure underneath it, which leaves very thin margin for Nebius to capture on top. Roman was explicit that the company's long term strategy is to avoid over relying on any single hyperscaler and instead build a diversified customer portfolio spanning every layer of the stack, so no single client can dictate terms or growth. He also pushed back on the idea that this business is commodity, arguing that keeping up with what a Meta or Microsoft actually demands from infrastructure at true hyperscale is genuinely difficult, which is exactly why most emerging neoclouds can't even compete for that tier of client. The numbers from this week back up the strategy because revenue came in at 582 million dollars, up 454% year over year, while annualized recurring revenue hit 3.0 billion dollars, up 58% quarter over quarter. Four separate customer contracts signed during the quarter were each worth more than 1 billion dollars in total contract value. Pricing power tells the same story from a different angle. Nebius's newest capacity auction cleared 15% above any price it had ever charged before, and short notice hardware is now going for 40 to 50 million dollars per megawatt, roughly four to five times the 9.8 million dollar per megawatt baseline from earlier deals. That kind of pricing trajectory, paired with a push into higher margin inference and agentic layers, builds a revenue mix that scales well past what a pure infrastructure landlord could ever reach. There are a few other pieces that make Nebius structurally different from the rest of the pack because it owns its full vertical stack, from data center design to server racks to the software layer running on top of all of it. It also has early access to Nvidia's next-generation Vera Rubin platform, following Nvidia's 9.3% stake in the company, and it holds side businesses in autonomous driving through Avride and data infrastructure through ClickHouse and Toloka. Nebius is building far more than a GPU rental business, and I think the market is still underestimating how big that full-stack platform can become. Bullish on Nebius becoming the first trillion-dollar neocloud hyperscaler, make sure to follow Melvin for more AI infrastructure insights, and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can check out the link for more.
3:07

Nebius will be the first trillion dollar neocloud hyperscaler. Most neoclouds are stuck in a single business model, renting bare GPU capacity to whoever will pay for it. Nebius is deliberately building across four layers instead, bare metal, managed infrastructure, inference, and eventually agentic tooling and each layer up the stack dramatically expands who can actually buy from them. Bare metal has maybe a dozen viable customers worldwide, since only the biggest players can even use raw infrastructure at that scale. Managed infrastructure opens that up to hundreds of buyers, while inference reaches thousands of potential customers. Agentic services are still early, but they could eventually serve tens of thousands of developers building on top of the platform. That's the real engine behind a trillion dollar outcome, since a single layer rental business caps out far lower than a company selling into an expanding pyramid of customers at every altitude. There's also a strategic decision buried in how Nebius handles its biggest clients. Serving giants like Meta and Microsoft is a double edged sword, since those companies bring their own full software stack and only need physical infrastructure underneath it, which leaves very thin margin for Nebius to capture on top. Roman was explicit that the company's long term strategy is to avoid over relying on any single hyperscaler and instead build a diversified customer portfolio spanning every layer of the stack, so no single client can dictate terms or growth. He also pushed back on the idea that this business is commodity, arguing that keeping up with what a Meta or Microsoft actually demands from infrastructure at true hyperscale is genuinely difficult, which is exactly why most emerging neoclouds can't even compete for that tier of client. The numbers from this week back up the strategy because revenue came in at 582 million dollars, up 454% year over year, while annualized recurring revenue hit 3.0 billion dollars, up 58% quarter over quarter. Four separate customer contracts signed during the quarter were each worth more than 1 billion dollars in total contract value. Pricing power tells the same story from a different angle. Nebius's newest capacity auction cleared 15% above any price it had ever charged before, and short notice hardware is now going for 40 to 50 million dollars per megawatt, roughly four to five times the 9.8 million dollar per megawatt baseline from earlier deals. That kind of pricing trajectory, paired with a push into higher margin inference and agentic layers, builds a revenue mix that scales well past what a pure infrastructure landlord could ever reach. There are a few other pieces that make Nebius structurally different from the rest of the pack because it owns its full vertical stack, from data center design to server racks to the software layer running on top of all of it. It also has early access to Nvidia's next-generation Vera Rubin platform, following Nvidia's 9.3% stake in the company, and it holds side businesses in autonomous driving through Avride and data infrastructure through ClickHouse and Toloka. Nebius is building far more than a GPU rental business, and I think the market is still underestimating how big that full-stack platform can become. Bullish on Nebius becoming the first trillion-dollar neocloud hyperscaler, make sure to follow Melvin for more AI infrastructure insights, and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can check out the link for more.

Melvin

28,733 次观看 • 10 天前

$GRAB Secret Sauce 🧵 How this company will thrive to $300B MC and beyond! It took me a while to gather the material for this thread. I will link down below other threads I talked extensively on all current and future $GRAB services to avoid making this thread too long. It is very important to understand product roadmap on the SuperApp, and how it will make money over the long-term, and transfer that value creation to shareholders. The closest analogy for new investors to understand is Amazon obsession over customers where $AMZN makes a little bit of money on each transaction to break even, but make the most money on Prime Membership. Or Costco obsession over customers where $COST makes 10-15% margin or lower on most products to break even on operation, but to make the most money on Costco membership fees. Jeff Bezos famously said "investors should invest in the company that obsesses customer experiencein the long term, there's never any misalignment between customer interests and shareholder interests!" The TLDR version: Being Customer Obsessed over Competition. We never heard much where Anthony Tan described or bitter about competition. Because Anthony does pay attention to competition, but he is more focused or obsessed on how to serve customers better at the lowest price possible, those that pay for $GRAB services. It is not just a business, it is a mission from first day of $GRAB or formerly known as MyTeksi. Anthony Tan and Co-founder Hooi Ling Tan both met at a class “Business at the Base of the Pyramid.” This class shaped the years of $GRAB success and today mission, creating a valuable business servicing the mass market, the lower income communities. Now, lets start with Customer Obession. $Grab does not see just users as customers, Anthony Tan views drivers, merchants, and partners are customers as well for long term success of the company. This is a big differentiator that contributed to GRAB success today. A. Hyperfocus on users: Grab emphasizes safety, with 99.9% of rides completed without incidents, and offers affordable options like Saver rides (26% of mobility transactions, 1.5X higher order frequency) alongside high-value services like Premium Rides and GrabUnlimited (3.7X more frequent usage, 2X higher retention). This likely enhances user satisfaction and retention, driving revenue growth, as seen in their Q1 2025 earnings of $773 million, up 18% year-over-year. But it does not stop at rides, it translate this obsession into food/grocery/financial and other services. Anthony Tan centered $GRAB success on affordability and reliability over the long-term since its early startup day. Essentially, the long-term TAM for servicing 2- 3 billion people is to get 30-50% of them on GrabUnlimited. Now it is $4.99 a month, will probably be adjusted to $7-$10 adjusted to inflation 10-15 years from now or around $7-$10B or more subscription revenue straight to net income B. Hyperfocus on Merchants: Grab has significantly focused on merchant growth as a core strategy to expand its ecosystem, particularly through its GrabFood, GrabMart, and financial services like GrabFinance. The reason is simple, these merchants/businesses are bringing in user growth. Businesses also pay GRAB on ea transaction very well, and at the same time using Cheap Loan(provided by Grab) to expand, and pay on GrabAds(this will have the highest margin after GrabUnlimited up to 50-60%). Grab also investing heavily on #AI to help merchants with OpenAI and Anthropic partnerships. The impact is unreal with this core strategy, many merchants today have more than 50-60% of its monhtly sales from $GRAB SuperApp(grew from 10-15% in 2021-2022). This approach has positioned Grab as a leader in Southeast Asia’s on-demand market, with significant potential for further expansion as it continues to innovate and optimize C. Hyperfocus on Drivers: In today world, you will never see $uber or Lyft talking about seeing drivers as customers. GRAB is the only company that sees Drivers as customers, and this focus is critical to maintaining a robust supply of driver-partners to meet consumer demand for ride-hailing, food delivery, and other services. Grab has scaled its driver network significantly since going public day with 5-6m registered driver-partners. Expanding rental/low fee fleets to secure drivers, creating stable employment in its current 8 countries. President Ferdinand R. Marcos Bongbong Marcos recently acknowledged $GRAB's significant impact on employment in the Philippines. All of 8 countries Grab operates in, all presidents and PM have praised Grab contribution on employment in their countries. GRAB makes its the company mission to expand more drivers registered on $GRAB SuperApp. Last Fun Fact, GRAB drivers in its 8 market have much higher income than BA degree holders and in many cases x2 or x3 the average salaries due to Grab Dynamic Pricing to bring supply and demand back to lowest price. AKA when demand is mad high, price will be higher to attract more drivers to bring down price. Drivers financial success is Grab long-term success. Conclusion: Grab's SuperApp success, as evidenced by Q1 2025 financials, is tied to putting customers, drivers, and merchants first. Their focus on safety, affordability, financial inclusion, and upskilling creates a robust ecosystem, reflected in increased MTUs, revenue growth, and profitability. The SuperApp will expand to 3 billion people TAM or more over the long term. 1. User Growth(Transactional Users) 2. GrabAds (expanding beyond SuperApp into Physical Grocery/Fleets) 3. GrabUnlimited( Expanding valuable services/features to make it stupid not to have it) Over the long-term, $GRAB will expand beyond SuperApp. Just like when Amazon has some spare computer capacity and decided to rent it out and became the AWS today, which is a behemoth that's now >4 times bigger than its original shopping business. No, I'm not saying $GRAB is the next Amazon. I'm telling you that with this "Secret Sauce" strategy of customer obsession, Anthony Tan can expand to other ventures with the massive FCF+ and profitable SuperApp to fund it. Disclaimer: I do own a large position in the Private Portfolio, and currently 100% on $GRAB on small public portfolio. This is the public portfolio where I contribute $500-$1000 of my own money. This public portfolio is not intended to be just 100% pure $GRAB, but it is the first position. I will try to keep it under 10 companies, and high quality growth businesses ONLY. I will not bother with garbage or hyped businesses where people just hype x10 x100 x1000 next week/year. You can follow others for that. Everything I wrote here is NOT Financial Advice! Source: Private Sources, Grab Dot Com, Webull, TOS, Bloomberg, Various Asian Media Outlets, Youtube, Anthony Tan, WSJ, Financial Times, Yahoo, Reuters, Jakarta Globe...
12:54

$GRAB Secret Sauce 🧵 How this company will thrive to $300B MC and beyond! It took me a while to gather the material for this thread. I will link down below other threads I talked extensively on all current and future $GRAB services to avoid making this thread too long. It is very important to understand product roadmap on the SuperApp, and how it will make money over the long-term, and transfer that value creation to shareholders. The closest analogy for new investors to understand is Amazon obsession over customers where $AMZN makes a little bit of money on each transaction to break even, but make the most money on Prime Membership. Or Costco obsession over customers where $COST makes 10-15% margin or lower on most products to break even on operation, but to make the most money on Costco membership fees. Jeff Bezos famously said "investors should invest in the company that obsesses customer experiencein the long term, there's never any misalignment between customer interests and shareholder interests!" The TLDR version: Being Customer Obsessed over Competition. We never heard much where Anthony Tan described or bitter about competition. Because Anthony does pay attention to competition, but he is more focused or obsessed on how to serve customers better at the lowest price possible, those that pay for $GRAB services. It is not just a business, it is a mission from first day of $GRAB or formerly known as MyTeksi. Anthony Tan and Co-founder Hooi Ling Tan both met at a class “Business at the Base of the Pyramid.” This class shaped the years of $GRAB success and today mission, creating a valuable business servicing the mass market, the lower income communities. Now, lets start with Customer Obession. $Grab does not see just users as customers, Anthony Tan views drivers, merchants, and partners are customers as well for long term success of the company. This is a big differentiator that contributed to GRAB success today. A. Hyperfocus on users: Grab emphasizes safety, with 99.9% of rides completed without incidents, and offers affordable options like Saver rides (26% of mobility transactions, 1.5X higher order frequency) alongside high-value services like Premium Rides and GrabUnlimited (3.7X more frequent usage, 2X higher retention). This likely enhances user satisfaction and retention, driving revenue growth, as seen in their Q1 2025 earnings of $773 million, up 18% year-over-year. But it does not stop at rides, it translate this obsession into food/grocery/financial and other services. Anthony Tan centered $GRAB success on affordability and reliability over the long-term since its early startup day. Essentially, the long-term TAM for servicing 2- 3 billion people is to get 30-50% of them on GrabUnlimited. Now it is $4.99 a month, will probably be adjusted to $7-$10 adjusted to inflation 10-15 years from now or around $7-$10B or more subscription revenue straight to net income B. Hyperfocus on Merchants: Grab has significantly focused on merchant growth as a core strategy to expand its ecosystem, particularly through its GrabFood, GrabMart, and financial services like GrabFinance. The reason is simple, these merchants/businesses are bringing in user growth. Businesses also pay GRAB on ea transaction very well, and at the same time using Cheap Loan(provided by Grab) to expand, and pay on GrabAds(this will have the highest margin after GrabUnlimited up to 50-60%). Grab also investing heavily on #AI to help merchants with OpenAI and Anthropic partnerships. The impact is unreal with this core strategy, many merchants today have more than 50-60% of its monhtly sales from $GRAB SuperApp(grew from 10-15% in 2021-2022). This approach has positioned Grab as a leader in Southeast Asia’s on-demand market, with significant potential for further expansion as it continues to innovate and optimize C. Hyperfocus on Drivers: In today world, you will never see $uber or Lyft talking about seeing drivers as customers. GRAB is the only company that sees Drivers as customers, and this focus is critical to maintaining a robust supply of driver-partners to meet consumer demand for ride-hailing, food delivery, and other services. Grab has scaled its driver network significantly since going public day with 5-6m registered driver-partners. Expanding rental/low fee fleets to secure drivers, creating stable employment in its current 8 countries. President Ferdinand R. Marcos Bongbong Marcos recently acknowledged $GRAB's significant impact on employment in the Philippines. All of 8 countries Grab operates in, all presidents and PM have praised Grab contribution on employment in their countries. GRAB makes its the company mission to expand more drivers registered on $GRAB SuperApp. Last Fun Fact, GRAB drivers in its 8 market have much higher income than BA degree holders and in many cases x2 or x3 the average salaries due to Grab Dynamic Pricing to bring supply and demand back to lowest price. AKA when demand is mad high, price will be higher to attract more drivers to bring down price. Drivers financial success is Grab long-term success. Conclusion: Grab's SuperApp success, as evidenced by Q1 2025 financials, is tied to putting customers, drivers, and merchants first. Their focus on safety, affordability, financial inclusion, and upskilling creates a robust ecosystem, reflected in increased MTUs, revenue growth, and profitability. The SuperApp will expand to 3 billion people TAM or more over the long term. 1. User Growth(Transactional Users) 2. GrabAds (expanding beyond SuperApp into Physical Grocery/Fleets) 3. GrabUnlimited( Expanding valuable services/features to make it stupid not to have it) Over the long-term, $GRAB will expand beyond SuperApp. Just like when Amazon has some spare computer capacity and decided to rent it out and became the AWS today, which is a behemoth that's now >4 times bigger than its original shopping business. No, I'm not saying $GRAB is the next Amazon. I'm telling you that with this "Secret Sauce" strategy of customer obsession, Anthony Tan can expand to other ventures with the massive FCF+ and profitable SuperApp to fund it. Disclaimer: I do own a large position in the Private Portfolio, and currently 100% on $GRAB on small public portfolio. This is the public portfolio where I contribute $500-$1000 of my own money. This public portfolio is not intended to be just 100% pure $GRAB, but it is the first position. I will try to keep it under 10 companies, and high quality growth businesses ONLY. I will not bother with garbage or hyped businesses where people just hype x10 x100 x1000 next week/year. You can follow others for that. Everything I wrote here is NOT Financial Advice! Source: Private Sources, Grab Dot Com, Webull, TOS, Bloomberg, Various Asian Media Outlets, Youtube, Anthony Tan, WSJ, Financial Times, Yahoo, Reuters, Jakarta Globe...

Mike

209,603 次观看 • 1 年前

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!
1:04

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!

Milk Road AI

28,622 次观看 • 3 个月前

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.
2:41

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.

Alex Kahn

24,432 次观看 • 1 年前

elon musk just sat down with his SpaceX team and explained exactly how they're going to turn humanity into a kardashev type 2 civilization it's one of the most insane things i've ever watched: 1. humanity currently uses less than one trillionth of the sun's energy output. a trillion is a million times a million. on the kardashev scale, the one physicists use to measure how advanced a civilization actually is, we are not even registering. we are effectively non-existent. 2. starship is the first rocket in history designed to be fully reusable. every other mode of transport, cars, planes, ships, bicycles, you take reusability for granted. rockets have always been thrown away after one use. if you had to throw away the plane after every flight, almost nobody would be flying. 3. spacex currently launches 85 to 90% of all mass to orbit on earth. the rest of the world, including most of the us, accounts for maybe 5 to 7%. that's before starship even gets going. 4. the plan is to go from 2,500 tons to orbit per year to a million tons per year. in roughly 3 years. that's not a projection. that's the internal target. 5. data centers are moving to space. by end of next year spacex is targeting 1 gigawatt of ai compute in orbit. then 10x every year after that. 10 gigawatts in 2.5 years. 100 gigawatts in 3.5 years. a terawatt eventually, which is twice the entire electricity consumption of the united states. 6. the ai satellite is actually simpler to build than a starlink satellite. it's mostly solar panels, a radiator, and a rack of gpus. the hard part was already solved building starlink. they're just making it bigger. 7. latency from orbit is about 3 milliseconds. light travels 300 km per millisecond. some people assume orbital compute means high latency. it doesn't. it's 3 milliseconds away. 8. the terafab will be 100 million square feet. ten times the size of the tesla gigafactory texas. the entire global chip industry is on track to hit maybe 100 gigawatts of ai compute per year. a terawatt requires a completely different order of manufacturing. that's why they're building it themselves. 9. to go beyond a terawatt you have to go to the moon. no atmosphere. one-sixth earth's gravity. you manufacture solar panels and radiators directly from moon materials. then you launch ai satellites into deep space using an electromagnetic rail gun. no rocket needed. musk calls this the mass driver. this is the next step on the actual roadmap. 10. if enough mass is going to the moon to run a rail gun operation at that scale, it also means regular people can go. musk's exact words: "i think everyone should go to the moon at least once." 11. the ai satellite has about a terabit of laser link connectivity. it connects to the starlink constellation which then sends data to the ground using frequencies that penetrate clouds and even roofs. the connection never drops regardless of weather. 12. the reference design for the first ai satellites is built around nvidia rubin and GB300 chips. but the architecture is open. google TPUs, amazon trainium, any chip can go up. spacex is building the infrastructure, not locking in the compute. 13. spacex is the only operator on earth with experience running a constellation at the scale of 10,000 satellites. nobody else is even close. that operational knowledge is a moat that cannot be replicated quickly.
31:08

elon musk just sat down with his SpaceX team and explained exactly how they're going to turn humanity into a kardashev type 2 civilization it's one of the most insane things i've ever watched: 1. humanity currently uses less than one trillionth of the sun's energy output. a trillion is a million times a million. on the kardashev scale, the one physicists use to measure how advanced a civilization actually is, we are not even registering. we are effectively non-existent. 2. starship is the first rocket in history designed to be fully reusable. every other mode of transport, cars, planes, ships, bicycles, you take reusability for granted. rockets have always been thrown away after one use. if you had to throw away the plane after every flight, almost nobody would be flying. 3. spacex currently launches 85 to 90% of all mass to orbit on earth. the rest of the world, including most of the us, accounts for maybe 5 to 7%. that's before starship even gets going. 4. the plan is to go from 2,500 tons to orbit per year to a million tons per year. in roughly 3 years. that's not a projection. that's the internal target. 5. data centers are moving to space. by end of next year spacex is targeting 1 gigawatt of ai compute in orbit. then 10x every year after that. 10 gigawatts in 2.5 years. 100 gigawatts in 3.5 years. a terawatt eventually, which is twice the entire electricity consumption of the united states. 6. the ai satellite is actually simpler to build than a starlink satellite. it's mostly solar panels, a radiator, and a rack of gpus. the hard part was already solved building starlink. they're just making it bigger. 7. latency from orbit is about 3 milliseconds. light travels 300 km per millisecond. some people assume orbital compute means high latency. it doesn't. it's 3 milliseconds away. 8. the terafab will be 100 million square feet. ten times the size of the tesla gigafactory texas. the entire global chip industry is on track to hit maybe 100 gigawatts of ai compute per year. a terawatt requires a completely different order of manufacturing. that's why they're building it themselves. 9. to go beyond a terawatt you have to go to the moon. no atmosphere. one-sixth earth's gravity. you manufacture solar panels and radiators directly from moon materials. then you launch ai satellites into deep space using an electromagnetic rail gun. no rocket needed. musk calls this the mass driver. this is the next step on the actual roadmap. 10. if enough mass is going to the moon to run a rail gun operation at that scale, it also means regular people can go. musk's exact words: "i think everyone should go to the moon at least once." 11. the ai satellite has about a terabit of laser link connectivity. it connects to the starlink constellation which then sends data to the ground using frequencies that penetrate clouds and even roofs. the connection never drops regardless of weather. 12. the reference design for the first ai satellites is built around nvidia rubin and GB300 chips. but the architecture is open. google TPUs, amazon trainium, any chip can go up. spacex is building the infrastructure, not locking in the compute. 13. spacex is the only operator on earth with experience running a constellation at the scale of 10,000 satellites. nobody else is even close. that operational knowledge is a moat that cannot be replicated quickly.

Jaynit

78,558 次观看 • 2 个月前

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026
0:49

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

Mike

145,992 次观看 • 2 个月前

I am extremely bullish on silver prices long term. There are clearly supply shortages in this market which has caused the increase in the silver prices from $30 last year to over $70 today. The issue is global silver mine supply. It is in decline. All of the mines in the world produced 900 million oz in 2015. In 2026 it will be about 820 million oz. Production is steadily declining. The best mines have been found and depleted. Meanwhile silver demand is still increasing. EVs, solar panels, electronics, Ai chips, etc. I have invested in physical silver, but I also invest in a few silver mining stocks. Most mining stocks are struggling just to maintain current silver production. The key is to find the companies that can increase production. My top silver stock in my portfolio is Aya Gold & Silver (ticker AYASF). The reason why is because this company is one of the few that can seriously increase it's production in the coming years. They are already producing 6 million oz of silver per year from their first mine, Zgounder. They mine at a cost of $20 per oz, they are selling their silver at over $70 per oz. That is over $50 per oz profit margins on 6 million oz. They are building their next mine, Boumadine, which is currently projected to produce 37 million oz AgEq in 2030. So this is a company that will increase revenue and profits by about 6x to 7x even if gold and silver prices remain at current levels. If gold and silver prices increase from here, the upside for AYASF is even higher. Here is a brief clip from an interview last week where the CEO, Benoit La Salle, walks through the numbers and the comparison to other silver miners. Benoit has built multiple mines in his career and he is doing it again with Aya (ticker AYASF). I will leave the link to the full interview in the replies below. This is just a brief clip. Bookmark this post. I will be posting about Aya regularly in the coming years.
2:16

I am extremely bullish on silver prices long term. There are clearly supply shortages in this market which has caused the increase in the silver prices from $30 last year to over $70 today. The issue is global silver mine supply. It is in decline. All of the mines in the world produced 900 million oz in 2015. In 2026 it will be about 820 million oz. Production is steadily declining. The best mines have been found and depleted. Meanwhile silver demand is still increasing. EVs, solar panels, electronics, Ai chips, etc. I have invested in physical silver, but I also invest in a few silver mining stocks. Most mining stocks are struggling just to maintain current silver production. The key is to find the companies that can increase production. My top silver stock in my portfolio is Aya Gold & Silver (ticker AYASF). The reason why is because this company is one of the few that can seriously increase it's production in the coming years. They are already producing 6 million oz of silver per year from their first mine, Zgounder. They mine at a cost of $20 per oz, they are selling their silver at over $70 per oz. That is over $50 per oz profit margins on 6 million oz. They are building their next mine, Boumadine, which is currently projected to produce 37 million oz AgEq in 2030. So this is a company that will increase revenue and profits by about 6x to 7x even if gold and silver prices remain at current levels. If gold and silver prices increase from here, the upside for AYASF is even higher. Here is a brief clip from an interview last week where the CEO, Benoit La Salle, walks through the numbers and the comparison to other silver miners. Benoit has built multiple mines in his career and he is doing it again with Aya (ticker AYASF). I will leave the link to the full interview in the replies below. This is just a brief clip. Bookmark this post. I will be posting about Aya regularly in the coming years.

Wall Street Mav

64,431 次观看 • 4 个月前

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!
3:09

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

102,223 次观看 • 7 个月前

Magic LUM and the Booster ⚡️🐙🪷 The birth of Magic LUM and the start of the Booster are together the most anticipated events for ShimmerSea so far! Magic LUM being the Governance Token of the protocol is the canter stage token on ShimmerSea. The activation of the Booster marks the start of a new token economic model in DeFi!🧵👇 🌐 📰 Magic LUM 🪷 Compared to $LUM which is the reward token of ShimmerSea, $MLUM is the Governance token of ShimmerSea and is therefore limited to 1 million tokens only!💎 Besides being limited in supply the token gives holders voting opportunities. These voting opportunities range from smaller every-day decisions like emission rates for $LUM in farms, to larger strategic decisions impacting the future of the protocol. Finally, holders get to earn all protocol revenue in USDT through a special staking pool, which will go live on IOTA EVM. This includes revenue from trading fees on the DEX and the NFT marketplace, deposit fees, and revenue from fair launches. Until #IOTA EVM a Lumi locked $MLUM->$LUM staking pool will be available. Magic LUM Start and Distribution Magic LUM will start trading shortly before the Booster goes live. This Friday the 1st of March 2024, at 1pm CET the MLUM-USDT trading pool will go live. The pool will have a total liquidity of 50.000 USDT and 2.500 $MLUM. This makes an initial $MLUM price of 20$. Be cautious in the first days of trading as the total liquidity with 100.000 $ value is comparably low! High volatility is to be expected! The initial liquidity will be locked with Hedgey. All other $MLUM tokens will either be locked in the Booster, locked with Hedgey 🦔 or only minted later on IOTA EVM. On IOTA EVM the team allocation will be locked in the $MLUM staking pool for another minimum of 6 months and a maximum of 12 months. All other not yet distributed $MLUM tokens will be locked in the new Booster contract on IOTA EVM where they will be distributed over the coming years. Magic LUM distribution: Booster — 79.5 % Team — 20 % Treasury — 0.5 % Shortly after trading starts on the 1st of March, 2024 at 4pm CET, the Booster goes live and everyone can start refining their $LUM to $MLUM. The first month the Booster will have an increased $MLUM emission rate! On March the 2nd at 8pm the MLUM-USDT farm will go live. The Booster ⚡️ The Booster is a special staking pool in which you can refine $LUM to $MLUM. The smart contract works similar to a staking pool with some important modifications. Just like a staking pool a fixed amount of $MLUM tokens is distributed per second through the Booster. The amount of $MLUM a staker earns depends on his share of $LUM allocated into the smart contract. Different to a standard staking pool, the Booster burns the allocated $LUM on a percent basis. This means that your $LUM stake is burned away while the booster rewards you with $MLUM. The emission rate of the Booster in the first month is 30.000 $MLUM and continues with 13.200 $MLUM per month. Security is at the centre of what we do at ShimmerSea. Therefore, the Smart Contract Booster has been audited twice by HashEx Blockchain Security and as well has been audited by AuditOne. Timeline The birth of $MLUM and the activation of the Booster are two events that happen in sequence. Following all the dates around the launch can be seen: 📅 Friday - 01.03.2024 🕚 11:00am CET - $MLUM gets listed 🕐 1:00pm CET - $MLUM/ $USDT pool start 🕗 8:00pm CET - The Booster goes live! 📅 Saturday - 02.03.2024 🕗 8:00pm CET - $MLUM/ $USDT Farm starts - $LUM/ $USDT Farm starts - $MLUM -> $LUM staking pool Outlook With Magic LUM and the Booster also new farms are being issued for Magic LUM and LUM. The new farms are rewarding liquidity pools with USDT. This will give both tokens, $LUM and $MLUM more price stability. On the horizon we can already see the next big event approaching: ShimmerSea scaling to IOTA EVM. With this, ShimmerSea will evolve to a multi-chain DEX! With this magic evolution the last major puzzle piece of the protocol tokenomics will also go live: The $MLUM staking pool which distributes protocol revenue on IOTA EVM!💥 #ShimmerEVM
0:48

Magic LUM and the Booster ⚡️🐙🪷 The birth of Magic LUM and the start of the Booster are together the most anticipated events for ShimmerSea so far! Magic LUM being the Governance Token of the protocol is the canter stage token on ShimmerSea. The activation of the Booster marks the start of a new token economic model in DeFi!🧵👇 🌐 📰 Magic LUM 🪷 Compared to $LUM which is the reward token of ShimmerSea, $MLUM is the Governance token of ShimmerSea and is therefore limited to 1 million tokens only!💎 Besides being limited in supply the token gives holders voting opportunities. These voting opportunities range from smaller every-day decisions like emission rates for $LUM in farms, to larger strategic decisions impacting the future of the protocol. Finally, holders get to earn all protocol revenue in USDT through a special staking pool, which will go live on IOTA EVM. This includes revenue from trading fees on the DEX and the NFT marketplace, deposit fees, and revenue from fair launches. Until #IOTA EVM a Lumi locked $MLUM->$LUM staking pool will be available. Magic LUM Start and Distribution Magic LUM will start trading shortly before the Booster goes live. This Friday the 1st of March 2024, at 1pm CET the MLUM-USDT trading pool will go live. The pool will have a total liquidity of 50.000 USDT and 2.500 $MLUM. This makes an initial $MLUM price of 20$. Be cautious in the first days of trading as the total liquidity with 100.000 $ value is comparably low! High volatility is to be expected! The initial liquidity will be locked with Hedgey. All other $MLUM tokens will either be locked in the Booster, locked with Hedgey 🦔 or only minted later on IOTA EVM. On IOTA EVM the team allocation will be locked in the $MLUM staking pool for another minimum of 6 months and a maximum of 12 months. All other not yet distributed $MLUM tokens will be locked in the new Booster contract on IOTA EVM where they will be distributed over the coming years. Magic LUM distribution: Booster — 79.5 % Team — 20 % Treasury — 0.5 % Shortly after trading starts on the 1st of March, 2024 at 4pm CET, the Booster goes live and everyone can start refining their $LUM to $MLUM. The first month the Booster will have an increased $MLUM emission rate! On March the 2nd at 8pm the MLUM-USDT farm will go live. The Booster ⚡️ The Booster is a special staking pool in which you can refine $LUM to $MLUM. The smart contract works similar to a staking pool with some important modifications. Just like a staking pool a fixed amount of $MLUM tokens is distributed per second through the Booster. The amount of $MLUM a staker earns depends on his share of $LUM allocated into the smart contract. Different to a standard staking pool, the Booster burns the allocated $LUM on a percent basis. This means that your $LUM stake is burned away while the booster rewards you with $MLUM. The emission rate of the Booster in the first month is 30.000 $MLUM and continues with 13.200 $MLUM per month. Security is at the centre of what we do at ShimmerSea. Therefore, the Smart Contract Booster has been audited twice by HashEx Blockchain Security and as well has been audited by AuditOne. Timeline The birth of $MLUM and the activation of the Booster are two events that happen in sequence. Following all the dates around the launch can be seen: 📅 Friday - 01.03.2024 🕚 11:00am CET - $MLUM gets listed 🕐 1:00pm CET - $MLUM/ $USDT pool start 🕗 8:00pm CET - The Booster goes live! 📅 Saturday - 02.03.2024 🕗 8:00pm CET - $MLUM/ $USDT Farm starts - $LUM/ $USDT Farm starts - $MLUM -> $LUM staking pool Outlook With Magic LUM and the Booster also new farms are being issued for Magic LUM and LUM. The new farms are rewarding liquidity pools with USDT. This will give both tokens, $LUM and $MLUM more price stability. On the horizon we can already see the next big event approaching: ShimmerSea scaling to IOTA EVM. With this, ShimmerSea will evolve to a multi-chain DEX! With this magic evolution the last major puzzle piece of the protocol tokenomics will also go live: The $MLUM staking pool which distributes protocol revenue on IOTA EVM!💥 #ShimmerEVM

MagicSea

12,945 次观看 • 2 年前

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!
4:46

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

84,951 次观看 • 2 个月前

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!
13:03

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!

Mike

301,322 次观看 • 3 个月前

Chamath Palihapitiya just dropped the number that explains the entire AI infrastructure trade (Save this). A gigawatt of compute now costs $100 billion and when he started his Arizona data center project it was $4 to $5 billion, it has gone up 20x in a single investment cycle. The implication is not just that AI infrastructure is expensive but rather that the capital barrier to owning meaningful compute has become so high that only a handful of entities in the world can actually build it and the companies who got there early are sitting on what may be the most durable pricing power in the history of the technology industry. This is the neocloud trade. The neocloud market, purpose-built GPU cloud providers like CoreWeave, Nebius, and Lambda Labs was worth $35 billion in 2026 and is projected to reach $236 billion by 2031, compounding at 46% annually. For context, that is faster growth than cloud computing itself posted in its first decade. The reason is very simple, hyperscalers like AWS, Azure, and Google are building for everything, storage, databases, enterprise software, networking and their GPU pricing reflects the overhead of that full-stack infrastructure. Neoclouds build for one thing only, AI compute. The result is a 60% to 85% cost advantage on the same Nvidia silicon, bare metal H100s at $0.78 to $2.79 per GPU-hour on a neocloud versus $3.43 to $5.07 per GPU-hour on a hyperscaler. That spread does not close as AI demand scales but rather it widens, because hyperscalers have to amortize legacy infrastructure and margin expectations that neoclouds do not carry. Gartner projects that by 2030, neoclouds will capture 20% of the $267 billion AI cloud market, and Vultr's own analysis says at least 80% of GPU market share by end of 2026 will be held by a small group of scaled neocloud providers. Now zoom into Nebius specifically, because it is the most interesting publicly traded proxy for this trade. Nebius is the infrastructure arm of the former Yandex Russia's equivalent of Google rebuilt from the ground up after Russia's invasion of Ukraine by Arkady Volozh and relisted on Nasdaq in October 2024. The team that built it already knew how to run internet-scale infrastructure at the lowest possible cost, which is exactly the operational DNA a neocloud requires. In Q1 2026, Nebius reported revenue of $399 million and already generating serious cash on a young business with revenue growing nearly eightfold year-over-year. Then in March 2026, Meta signed a five-year infrastructure agreement with Nebius worth up to $27 billion, $12 billion in committed dedicated GPU capacity deployments beginning early 2027, plus up to $15 billion more tied to Meta purchasing Nebius's unsold third-party capacity. The deal will be executed on one of the first large-scale deployments of Nvidia's Vera Rubin platform, the next-generation architecture after Blackwell making Nebius one of a tiny number of operators in the world with confirmed priority access to the most advanced AI hardware available. Following the contract, Nebius guided to $7 to $9 billion in annualized recurring revenue for 2026 representing 540% year-over-year growth. Chamath Palihapitiya point about the $100 billion capital moat is the bear case for new entrants and the bull case for incumbents. No one can afford to build the next CoreWeave or Nebius from scratch at current hardware and power costs. The companies that are already built, already contracted, and already deploying Nvidia's latest silicon have a moat that compounds with every GPU generation cycle because they get allocations first, they deploy fastest, and their customers re-sign rather than wait for a new operator that does not yet exist. Come join Milk Road Pro for our full breakdown, the complete neocloud competitive landscape, how to think about Nebius's valuation versus CoreWeave and AI entire thesis. Link below.
0:20

Chamath Palihapitiya just dropped the number that explains the entire AI infrastructure trade (Save this). A gigawatt of compute now costs $100 billion and when he started his Arizona data center project it was $4 to $5 billion, it has gone up 20x in a single investment cycle. The implication is not just that AI infrastructure is expensive but rather that the capital barrier to owning meaningful compute has become so high that only a handful of entities in the world can actually build it and the companies who got there early are sitting on what may be the most durable pricing power in the history of the technology industry. This is the neocloud trade. The neocloud market, purpose-built GPU cloud providers like CoreWeave, Nebius, and Lambda Labs was worth $35 billion in 2026 and is projected to reach $236 billion by 2031, compounding at 46% annually. For context, that is faster growth than cloud computing itself posted in its first decade. The reason is very simple, hyperscalers like AWS, Azure, and Google are building for everything, storage, databases, enterprise software, networking and their GPU pricing reflects the overhead of that full-stack infrastructure. Neoclouds build for one thing only, AI compute. The result is a 60% to 85% cost advantage on the same Nvidia silicon, bare metal H100s at $0.78 to $2.79 per GPU-hour on a neocloud versus $3.43 to $5.07 per GPU-hour on a hyperscaler. That spread does not close as AI demand scales but rather it widens, because hyperscalers have to amortize legacy infrastructure and margin expectations that neoclouds do not carry. Gartner projects that by 2030, neoclouds will capture 20% of the $267 billion AI cloud market, and Vultr's own analysis says at least 80% of GPU market share by end of 2026 will be held by a small group of scaled neocloud providers. Now zoom into Nebius specifically, because it is the most interesting publicly traded proxy for this trade. Nebius is the infrastructure arm of the former Yandex Russia's equivalent of Google rebuilt from the ground up after Russia's invasion of Ukraine by Arkady Volozh and relisted on Nasdaq in October 2024. The team that built it already knew how to run internet-scale infrastructure at the lowest possible cost, which is exactly the operational DNA a neocloud requires. In Q1 2026, Nebius reported revenue of $399 million and already generating serious cash on a young business with revenue growing nearly eightfold year-over-year. Then in March 2026, Meta signed a five-year infrastructure agreement with Nebius worth up to $27 billion, $12 billion in committed dedicated GPU capacity deployments beginning early 2027, plus up to $15 billion more tied to Meta purchasing Nebius's unsold third-party capacity. The deal will be executed on one of the first large-scale deployments of Nvidia's Vera Rubin platform, the next-generation architecture after Blackwell making Nebius one of a tiny number of operators in the world with confirmed priority access to the most advanced AI hardware available. Following the contract, Nebius guided to $7 to $9 billion in annualized recurring revenue for 2026 representing 540% year-over-year growth. Chamath Palihapitiya point about the $100 billion capital moat is the bear case for new entrants and the bull case for incumbents. No one can afford to build the next CoreWeave or Nebius from scratch at current hardware and power costs. The companies that are already built, already contracted, and already deploying Nvidia's latest silicon have a moat that compounds with every GPU generation cycle because they get allocations first, they deploy fastest, and their customers re-sign rather than wait for a new operator that does not yet exist. Come join Milk Road Pro for our full breakdown, the complete neocloud competitive landscape, how to think about Nebius's valuation versus CoreWeave and AI entire thesis. Link below.

Milk Road AI

139,047 次观看 • 2 个月前

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!
1:06

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

Mike

711,006 次观看 • 10 个月前