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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 -...

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Reggie Middleton, Disruptor-in-Chief

44,525 subscribers

14,728 görüntüleme • 1 yıl önce •via X (Twitter)

EğitimBilim & TeknolojiFinans#SmartMetal
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Delta Lima1 yıl önce

@Veritaseuminc Thank you Reggie🇨🇦

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Rainmaker1 yıl önce

Strategy validation made smarter! Discover why Walk-Forward Validation is the ultimate stress test for ML in finance. With this technique, your strategy will confidently tackle changing market dynamics. Full code and article on my Substack:

Duney profil fotoğrafı
Duney1 yıl önce

@Veritaseuminc It crossed my mind that owning a physical round with embedded access 100% prevents scammers from trying to gain access to whatever value I hold. I mean, they can keep sending me emails, but now they will have to send me a 1oz silver round with a fake barcode. Pls do, 😆

hagop belerian profil fotoğrafı
hagop belerian1 yıl önce

@Veritaseuminc I never got my rounds , not complaining but just asking

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AbsurdNerd1 yıl önce

@Veritaseuminc @grok how much could I earn and per 10000 dollar investment?

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Wade Bowers1 yıl önce

@Veritaseuminc Why on centralized Ethereum when Bitcon layer II exists? Doesn't it defeat the purpose of immutability?

Reggie Middleton US11196566 US11895246 US12231579 profil fotoğrafı
Reggie Middleton US11196566 US11895246 US122315791 yıl önce

@Veritaseuminc Our IP is platform agnostic. I seriously doubt if any of the platforms are truly immutable, just to a degree....

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Articulate Mumbler1 yıl önce

@Veritaseuminc The Girl Friday AI would have to be very user friendly, even including a basic user manual for the elderly, children and easily distracted. The creative imagination can't begin without a basic understanding.

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Coach Sifu Krypto1 yıl önce

@Veritaseuminc Damn' @ReggieMiddleton , it's not even muh birthday and you ask if we would like to have a personal 'Jarvis'?!?!?!?? Uh Yea!!!

wolf face profil fotoğrafı
wolf face1 yıl önce

@Veritaseuminc This is essentially a raffle. At $300 a piece, if you are not able to implement the AI... you essentially lost and way overpaid for your ticket. If you are able to work it out then you get the winning ticket

Benzer Videolar

Your phone is idle. Meanwhile, cloud companies generate hundreds of billions of dollars per year doing what your device can help power for free. With Acurast, you can use your phone to provide compute, and get rewarded for it. At the same time, developers use this to run decentralized workloads. Everyone wins. No cloud giant. No middleman. No vague invoices. 👉Join now
1:03

Your phone is idle. Meanwhile, cloud companies generate hundreds of billions of dollars per year doing what your device can help power for free. With Acurast, you can use your phone to provide compute, and get rewarded for it. At the same time, developers use this to run decentralized workloads. Everyone wins. No cloud giant. No middleman. No vague invoices. 👉Join now

Acurast

13,739 görüntüleme • 1 ay önce

Cloud compute is costly, centralized, and trust-heavy. Most Devs see no alternative to Big Tech. We Interviewed Acurast founder Alessandro De Carli ☁️ 🫡 Alessandro De Carli (Acurast) on turning smartphones into a global decentralized compute network you can earn from. Timestamps: 0:09 Decentralized compute powered by smartphones 1:09 Acurast mission: onboard the masses without “feeling crypto” 2:36 Secure element and TEE basics 3:16 Verifiable compute and anti cheat via device attestation 4:16 Confidential compute offload work without tampering or inspection 14:51 How to get involved install the app opt in foreground compute 23:41 OpenClaw on Acurast containers enable it soon
30:22

Cloud compute is costly, centralized, and trust-heavy. Most Devs see no alternative to Big Tech. We Interviewed Acurast founder Alessandro De Carli ☁️ 🫡 Alessandro De Carli (Acurast) on turning smartphones into a global decentralized compute network you can earn from. Timestamps: 0:09 Decentralized compute powered by smartphones 1:09 Acurast mission: onboard the masses without “feeling crypto” 2:36 Secure element and TEE basics 3:16 Verifiable compute and anti cheat via device attestation 4:16 Confidential compute offload work without tampering or inspection 14:51 How to get involved install the app opt in foreground compute 23:41 OpenClaw on Acurast containers enable it soon

Crypto Coin Show

14,504 görüntüleme • 3 ay önce

Attention Rebels! 📣 We're bringing the decentralized and confidential Cloud Rebellion to iPhone users with the launch of Processor Lite for iOS. Starting today, you can download Acurast Processor Lite on the Apple App Store. Your iPhone can now contribute compute power to a global, decentralized, confidential cloud — and earn rewards for it! 📱
0:20

Attention Rebels! 📣 We're bringing the decentralized and confidential Cloud Rebellion to iPhone users with the launch of Processor Lite for iOS. Starting today, you can download Acurast Processor Lite on the Apple App Store. Your iPhone can now contribute compute power to a global, decentralized, confidential cloud — and earn rewards for it! 📱

Acurast

96,605 görüntüleme • 1 yıl önce

$AMD Valuation at $70-$100B Revenue in 2026🧵 As of December 4, 2025, AMD's stock trades at approximately $220, with a market cap of $355billion. Revised Valuation with $70B Revenue Earnings Per Share (EPS): Assuming a 40% operating margin (consistent with historical trends, probably higher), $70 billion in revenue translates to $28 billion in operating income. After taxes and interest, net income could be $20 billion, or $12.50 EPS Forward P/E: At 50x-60x (a premium due to growth), the stock price could reach $650-$750 EV/EBITDA: With $28 billion EBITDA, at 40x, EV is $1.12 trillion. Subtracting $5 billion net debt, equity value is $1.115 trillion, or $697 per share. Revised Valuation with $100B Revenue EPS: $100 billion revenue at 45% margin yields $45 billion operating income, $35 billion net income, or $22 EPS. Forward P/E: At 50x-70x, the stock price could reach $1,100-$1,540 EV/EBITDA: $40 billion EBITDA at 45x EV/EBITDA yields $1.8 trillion EV. Subtracting $5 billion net debt, equity value is $1.795 trillion, or $1,122 per share. The market's willingness to assign a high P/E multiple to AMD will be based on the anticipation that these partnerships will translate into substantial revenue and earnings growth. The P/E ratio for the semiconductor industry is approximately 58.57, a significant increase from previous years because of AI CapEx Growth and we are only 2nd year of 10 years cycle. Hence, if $AMD grew to $70B-$100B revenue in 2026, 50x-70x P/E is justified. AMD's existing partnerships with OpenAI , $Meta, $MSFT, $AMZN, $GOOGL, $DELL, $HPE, $SMCI,xAI , Oracle, Vulture combined with new collaborations with international 40+ countries like Saudi,UAE form a solid foundation for revenue growth. The OpenAI deal alone could contribute $25 billion to $28 billion(2026), while Meta's expanded allocation and Oracle's increased orders with the rest add substantial upside of 1m+ GPUs(FY2026) . Technological Leadership: The MI450 GPU, with its superior inference and training capabilities, positions AMD to disrupt Nvidia's market dominance. Benchmarks show 1.5-2x performance advantages at 35-50% lower TCO, making it an attractive choice for hyperscalers. The ROCm platform's maturity, supporting day-zero integration for major AI models, closes the software gap with CUDA, enhancing AMD's competitiveness. In conclusion, AMD's combination of strategic partnerships, technological leadership, and favorable market dynamics positions it to achieve $70 billion to $100 billion in revenue by 2026. This growth is not merely aspirational but grounded in real demand signals and execution capabilities. While risks remain, the upside potential is significant, making AMD a the best AI Name in this AI Supercycle trading at extreme cheap valuation. Not Financial Advice!
1:37

$AMD Valuation at $70-$100B Revenue in 2026🧵 As of December 4, 2025, AMD's stock trades at approximately $220, with a market cap of $355billion. Revised Valuation with $70B Revenue Earnings Per Share (EPS): Assuming a 40% operating margin (consistent with historical trends, probably higher), $70 billion in revenue translates to $28 billion in operating income. After taxes and interest, net income could be $20 billion, or $12.50 EPS Forward P/E: At 50x-60x (a premium due to growth), the stock price could reach $650-$750 EV/EBITDA: With $28 billion EBITDA, at 40x, EV is $1.12 trillion. Subtracting $5 billion net debt, equity value is $1.115 trillion, or $697 per share. Revised Valuation with $100B Revenue EPS: $100 billion revenue at 45% margin yields $45 billion operating income, $35 billion net income, or $22 EPS. Forward P/E: At 50x-70x, the stock price could reach $1,100-$1,540 EV/EBITDA: $40 billion EBITDA at 45x EV/EBITDA yields $1.8 trillion EV. Subtracting $5 billion net debt, equity value is $1.795 trillion, or $1,122 per share. The market's willingness to assign a high P/E multiple to AMD will be based on the anticipation that these partnerships will translate into substantial revenue and earnings growth. The P/E ratio for the semiconductor industry is approximately 58.57, a significant increase from previous years because of AI CapEx Growth and we are only 2nd year of 10 years cycle. Hence, if $AMD grew to $70B-$100B revenue in 2026, 50x-70x P/E is justified. AMD's existing partnerships with OpenAI , $Meta, $MSFT, $AMZN, $GOOGL, $DELL, $HPE, $SMCI,xAI , Oracle, Vulture combined with new collaborations with international 40+ countries like Saudi,UAE form a solid foundation for revenue growth. The OpenAI deal alone could contribute $25 billion to $28 billion(2026), while Meta's expanded allocation and Oracle's increased orders with the rest add substantial upside of 1m+ GPUs(FY2026) . Technological Leadership: The MI450 GPU, with its superior inference and training capabilities, positions AMD to disrupt Nvidia's market dominance. Benchmarks show 1.5-2x performance advantages at 35-50% lower TCO, making it an attractive choice for hyperscalers. The ROCm platform's maturity, supporting day-zero integration for major AI models, closes the software gap with CUDA, enhancing AMD's competitiveness. In conclusion, AMD's combination of strategic partnerships, technological leadership, and favorable market dynamics positions it to achieve $70 billion to $100 billion in revenue by 2026. This growth is not merely aspirational but grounded in real demand signals and execution capabilities. While risks remain, the upside potential is significant, making AMD a the best AI Name in this AI Supercycle trading at extreme cheap valuation. Not Financial Advice!

Mike

187,491 görüntüleme • 7 ay önce

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk
6:02

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk

Milk Road AI

69,240 görüntüleme • 26 gün önce

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.
0:22

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.

Crypto Rover

58,862 görüntüleme • 1 ay önce

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

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

The Crypto GEMs

69,984 görüntüleme • 1 yıl önce

INTERNET COMPUTER GOES DEFLATIONARY AS MISSION 70 KICKS IN DFINITY Foundation's Mission 70 governance proposal, which passed with 53%+ support, is now actively cutting $ICP's annual inflation by up to 70%, dropping the rate from 9.72% to between 2.92% and 5.42% by year-end. The mechanism has teeth. 20% of all revenue from the upcoming Cloud Engines product will permanently burn $ICP, with another 80% routed to node providers. NNS voting rewards have been capped, and node provider incentives restructured to align with actual usage. With Cloud Engines demoing May 10 and the deflationary engine already running, $ICP's setup is shifting from "speculative AI compute play" to "tokens burned per unit of enterprise revenue."
0:22

INTERNET COMPUTER GOES DEFLATIONARY AS MISSION 70 KICKS IN DFINITY Foundation's Mission 70 governance proposal, which passed with 53%+ support, is now actively cutting $ICP's annual inflation by up to 70%, dropping the rate from 9.72% to between 2.92% and 5.42% by year-end. The mechanism has teeth. 20% of all revenue from the upcoming Cloud Engines product will permanently burn $ICP, with another 80% routed to node providers. NNS voting rewards have been capped, and node provider incentives restructured to align with actual usage. With Cloud Engines demoing May 10 and the deflationary engine already running, $ICP's setup is shifting from "speculative AI compute play" to "tokens burned per unit of enterprise revenue."

BSCN

35,641 görüntüleme • 2 ay önce

We’re thrilled to announce that Gata Decentralized GPU Cloud is now LIVE! Start today → Built to accelerate the decentralization of the open AI economy, Gata enables anyone, anywhere to access decentralized GPU infrastructure, to build, train, and deploy AI models with full transparency and efficiency. GPU credits are now available for purchase directly with $GATA, creating a seamless bridge between compute and the decentralized economy.
0:24

We’re thrilled to announce that Gata Decentralized GPU Cloud is now LIVE! Start today → Built to accelerate the decentralization of the open AI economy, Gata enables anyone, anywhere to access decentralized GPU infrastructure, to build, train, and deploy AI models with full transparency and efficiency. GPU credits are now available for purchase directly with $GATA, creating a seamless bridge between compute and the decentralized economy.

Gata

48,232 görüntüleme • 8 ay önce

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

138,185 görüntüleme • 27 gün önce

The market is watching xAI charge $50 billion per gigawatt and the rest of the neocloud sector run up is just getting started (Save this). According to Gavin Baker of Atreides Management, this is the most important number in AI infrastructure right now, xAI is monetizing compute at $50 billion per gigawatt on the Google deal, 2 to 3 times what any neocloud competitor charges. Google is paying $920 million per month for access to roughly 110,000 Nvidia GPUs through June 2029, and Anthropic is paying $1.25 billion per month for Colossus 1's 300 megawatts. Baker's point is simple that stop tracking rocket launches, stop tracking GPU orders, model gigawatt additions. At $50 billion per gigawatt, every new gigawatt that xAI energizes over the next 12 months is a revenue event that the market has not yet priced in. But this is not just an xAI story but rather why neocloud stocks are one of the most mispriced assets in the entire AI stack. Neoclouds charge $17 to $25 billion per gigawatt in contract value, a dramatic discount to xAI's pricing, but still an extraordinary business model when the underlying infrastructure costs $9 to $12 million per megawatt to operate and customers are signing 5-year locked contracts. H100 GPU-hours from neoclouds like Nebius at $2.95 per GPU-hour are 66% cheaper than hyperscaler rates, which is the structural reason enterprise AI teams are shifting spend to neoclouds at an accelerating pace. The neocloud market is projected to grow 69% annually through 2030 to reach nearly $180 billion and right now only a handful of public companies offer direct exposure to it. Nebius is the standout among the publicly traded neoclouds. It reported Q1 2026 AI cloud revenue of $399 million, an 841% increase year over year beating estimates, with its CEO stating that demand continues to exceed available capacity and customers are actively being turned away. Nebius commands a 20 to 25% revenue premium over peers thanks to its full-stack software offering, European sovereign positioning, and data residency advantages that physically prevent hyperscalers from competing for a large portion of its customer base. It has $49 billion in contracted backlog with Meta, Microsoft, and Nvidia meaning its revenue trajectory for the next three to five years is not a forecast, it is a schedule. The competitive moat is in power, permits, and speed exactly what xAI has proven is the true bottleneck. Jensen Huang said publicly that xAI deploys data centers faster than anyone else in the ecosystem, and Baker called out that this deployment speed advantage directly translates to monetization speed, every week of earlier energization at these pricing levels is worth hundreds of millions in revenue. Neoclouds with secured power, permits, and long-term customer contracts are not in a fair race against companies still waiting on grid connections and zoning approvals. The companies with the most locked in gigawatts coming online in 2026 and 2027 are about to have very good years.
1:14

The market is watching xAI charge $50 billion per gigawatt and the rest of the neocloud sector run up is just getting started (Save this). According to Gavin Baker of Atreides Management, this is the most important number in AI infrastructure right now, xAI is monetizing compute at $50 billion per gigawatt on the Google deal, 2 to 3 times what any neocloud competitor charges. Google is paying $920 million per month for access to roughly 110,000 Nvidia GPUs through June 2029, and Anthropic is paying $1.25 billion per month for Colossus 1's 300 megawatts. Baker's point is simple that stop tracking rocket launches, stop tracking GPU orders, model gigawatt additions. At $50 billion per gigawatt, every new gigawatt that xAI energizes over the next 12 months is a revenue event that the market has not yet priced in. But this is not just an xAI story but rather why neocloud stocks are one of the most mispriced assets in the entire AI stack. Neoclouds charge $17 to $25 billion per gigawatt in contract value, a dramatic discount to xAI's pricing, but still an extraordinary business model when the underlying infrastructure costs $9 to $12 million per megawatt to operate and customers are signing 5-year locked contracts. H100 GPU-hours from neoclouds like Nebius at $2.95 per GPU-hour are 66% cheaper than hyperscaler rates, which is the structural reason enterprise AI teams are shifting spend to neoclouds at an accelerating pace. The neocloud market is projected to grow 69% annually through 2030 to reach nearly $180 billion and right now only a handful of public companies offer direct exposure to it. Nebius is the standout among the publicly traded neoclouds. It reported Q1 2026 AI cloud revenue of $399 million, an 841% increase year over year beating estimates, with its CEO stating that demand continues to exceed available capacity and customers are actively being turned away. Nebius commands a 20 to 25% revenue premium over peers thanks to its full-stack software offering, European sovereign positioning, and data residency advantages that physically prevent hyperscalers from competing for a large portion of its customer base. It has $49 billion in contracted backlog with Meta, Microsoft, and Nvidia meaning its revenue trajectory for the next three to five years is not a forecast, it is a schedule. The competitive moat is in power, permits, and speed exactly what xAI has proven is the true bottleneck. Jensen Huang said publicly that xAI deploys data centers faster than anyone else in the ecosystem, and Baker called out that this deployment speed advantage directly translates to monetization speed, every week of earlier energization at these pricing levels is worth hundreds of millions in revenue. Neoclouds with secured power, permits, and long-term customer contracts are not in a fair race against companies still waiting on grid connections and zoning approvals. The companies with the most locked in gigawatts coming online in 2026 and 2027 are about to have very good years.

Milk Road AI

74,554 görüntüleme • 25 gün önce

ANTHROPIC HAS SURPASSED OPENAI IN REVENUE In April 2026, Anthropic reached a $30 billion annualized revenue run rate, ahead of OpenAI’s roughly $25 billion. The company’s annualized revenue grew from around $9 billion at the end of 2025 to $14 billion in February and $30 billion just two months later, driven by strong enterprise adoption of Claude. Unlike OpenAI, which generates much of its revenue through ChatGPT, Anthropic’s business is heavily focused on enterprise and developer customers.
0:23

ANTHROPIC HAS SURPASSED OPENAI IN REVENUE In April 2026, Anthropic reached a $30 billion annualized revenue run rate, ahead of OpenAI’s roughly $25 billion. The company’s annualized revenue grew from around $9 billion at the end of 2025 to $14 billion in February and $30 billion just two months later, driven by strong enterprise adoption of Claude. Unlike OpenAI, which generates much of its revenue through ChatGPT, Anthropic’s business is heavily focused on enterprise and developer customers.

CryptoGoos

14,610 görüntüleme • 14 gün önce

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️
0:49

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

NVIDIA

88,885 görüntüleme • 3 ay önce

This is WILD! One week before SpaceX's historic IPO, Google signed a deal to pay SpaceX $920 million per month from October 2026 through June 2029 for access to 110,000 Nvidia GPUs, CPUs, and related infrastructure (Save this). That is $11 billion per year and up to $30 billion over the life of the contract. This comes less than a month after Anthropic committed $1.25 billion per month for full access to the Colossus 1 data center in Memphis, 200,000+ GPUs, 300+ megawatts of power capacity, through 2029. Two of the most consequential AI labs in the world combined committed value over $70 billion. The question that haunted SpaceX's IPO roadshow was why did Elon keep spending billions constructing Colossus, Macro Hard and Macro Harder, three facilities totaling nearly 2 gigawatts of AI compute when xAI's revenue wasn't yet on the same trajectory as OpenAI or Anthropic? Wall Street was pricing in a risk that Elon was building capacity ahead of revenue which would mean sustained cash burn without a clear payback timeline. That concern was legitimate on its face, because xAI had been aggressive on model development but had not yet demonstrated the enterprise revenue numbers to justify the infrastructure cost. The answer is that the compute itself was always the product. Amazon has AWS, Microsoft has Azure, Google has Google Cloud, Elon just confirmed that he has been quietly building the fourth major hyperscale AI cloud and his first two paying customers are Google and Anthropic, the very companies most aggressively competing in the AI race. xAI's Colossus facility in Memphis was built at a speed that no traditional data center developer could match, it went from groundbreaking to operational in roughly 122 days. That is what happens when you have direct Nvidia relationships, a construction operation built around SpaceX-style execution, and a founder who treats infrastructure buildout the same way he treats rocket launches: compress every timeline and eliminate every bottleneck. The result is that SpaceX now has three operational facilities, Colossus, Macro Hard, and Macro Harder with Macro Hard and Macro Harder in Blackwell architecture running 1.2 gigawatts combined. Colossus 1, built on H100s and optimized for inference, is the facility that went to Anthropic first. The Blackwell-era facilities are where the next-generation training workloads happen and Google's deal suggests they are renting into that capacity as it comes online through the second half of 2026. Elon's compute leasing business would generate approximately $45 billion in incremental annual revenue on top of the mid-$20 billion range analysts had been modeling for SpaceX more than enough to fully subsidize the infrastructure investment and take the financial pressure off xAI delivering immediate AI product revenue. That changes the entire valuation conversation of SpaceX completely! Milk road remains bullish on Space and come join Milk Road Pro and get our full SpaceX IPO breakdown, how we're thinking about the $1.75 trillion valuation and our entire AI thesis. Link below!
1:59

This is WILD! One week before SpaceX's historic IPO, Google signed a deal to pay SpaceX $920 million per month from October 2026 through June 2029 for access to 110,000 Nvidia GPUs, CPUs, and related infrastructure (Save this). That is $11 billion per year and up to $30 billion over the life of the contract. This comes less than a month after Anthropic committed $1.25 billion per month for full access to the Colossus 1 data center in Memphis, 200,000+ GPUs, 300+ megawatts of power capacity, through 2029. Two of the most consequential AI labs in the world combined committed value over $70 billion. The question that haunted SpaceX's IPO roadshow was why did Elon keep spending billions constructing Colossus, Macro Hard and Macro Harder, three facilities totaling nearly 2 gigawatts of AI compute when xAI's revenue wasn't yet on the same trajectory as OpenAI or Anthropic? Wall Street was pricing in a risk that Elon was building capacity ahead of revenue which would mean sustained cash burn without a clear payback timeline. That concern was legitimate on its face, because xAI had been aggressive on model development but had not yet demonstrated the enterprise revenue numbers to justify the infrastructure cost. The answer is that the compute itself was always the product. Amazon has AWS, Microsoft has Azure, Google has Google Cloud, Elon just confirmed that he has been quietly building the fourth major hyperscale AI cloud and his first two paying customers are Google and Anthropic, the very companies most aggressively competing in the AI race. xAI's Colossus facility in Memphis was built at a speed that no traditional data center developer could match, it went from groundbreaking to operational in roughly 122 days. That is what happens when you have direct Nvidia relationships, a construction operation built around SpaceX-style execution, and a founder who treats infrastructure buildout the same way he treats rocket launches: compress every timeline and eliminate every bottleneck. The result is that SpaceX now has three operational facilities, Colossus, Macro Hard, and Macro Harder with Macro Hard and Macro Harder in Blackwell architecture running 1.2 gigawatts combined. Colossus 1, built on H100s and optimized for inference, is the facility that went to Anthropic first. The Blackwell-era facilities are where the next-generation training workloads happen and Google's deal suggests they are renting into that capacity as it comes online through the second half of 2026. Elon's compute leasing business would generate approximately $45 billion in incremental annual revenue on top of the mid-$20 billion range analysts had been modeling for SpaceX more than enough to fully subsidize the infrastructure investment and take the financial pressure off xAI delivering immediate AI product revenue. That changes the entire valuation conversation of SpaceX completely! Milk road remains bullish on Space and come join Milk Road Pro and get our full SpaceX IPO breakdown, how we're thinking about the $1.75 trillion valuation and our entire AI thesis. Link below!

Milk Road AI

760,588 görüntüleme • 1 ay önce

OpenAI CFO Sarah Friar: The business case for AI is simple: more compute, more revenue OpenAI scaled from 200 megawatts to 2 gigawatts, and ARR climbed from $2 billion to over $20 billion, yet we remain absolutely constrained on compute "the signal from everywhere is real... we are in a paradigm shift"
1:54

OpenAI CFO Sarah Friar: The business case for AI is simple: more compute, more revenue OpenAI scaled from 200 megawatts to 2 gigawatts, and ARR climbed from $2 billion to over $20 billion, yet we remain absolutely constrained on compute "the signal from everywhere is real... we are in a paradigm shift"

Haider.

18,109 görüntüleme • 5 ay önce

$INFERRA Just came across Inferra and this one actually feels really interesting. 9ufM9TJd1UEmi9awnGfxCkCHAgQ3JZ5Sw6YxeSeEASY They're building a decentralized marketplace where AI compute becomes a tradable commodity. Instead of letting GPU power sit idle, providers can list verified compute capacity while users can buy, sell, trade, or redeem it for actual compute access. What caught my attention is that they're approaching compute like a real market, with spot trading, futures, and eventually inference markets. As AI demand keeps exploding, compute is becoming one of the most valuable resources in the industry. Projects focused on making that resource more liquid and accessible make a lot of sense to me. High risk as marketcap is tiny. But definitely an interesting compute bet I aped small. DYOR. RR
0:40

$INFERRA Just came across Inferra and this one actually feels really interesting. 9ufM9TJd1UEmi9awnGfxCkCHAgQ3JZ5Sw6YxeSeEASY They're building a decentralized marketplace where AI compute becomes a tradable commodity. Instead of letting GPU power sit idle, providers can list verified compute capacity while users can buy, sell, trade, or redeem it for actual compute access. What caught my attention is that they're approaching compute like a real market, with spot trading, futures, and eventually inference markets. As AI demand keeps exploding, compute is becoming one of the most valuable resources in the industry. Projects focused on making that resource more liquid and accessible make a lot of sense to me. High risk as marketcap is tiny. But definitely an interesting compute bet I aped small. DYOR. RR

Crypto Ideology ✍🏻

13,657 görüntüleme • 26 gün önce

We asked Flood about his dynamic relationship with Arthur Hayes on Hyperliquid. "People fundamentally misprice Hyperliquid and Arthur is one of the people who actually has the best pricing and probably the most edge" 🦣 "The global perpetual swap market is anywhere from $20-30 billion in revenue a year, and Binance conservatively makes $10 billion a year in revenue." "Hyperliquid does about one-sixth to one-tenth the volume that Binance does on perps—if Hyperliquid is making a billion a year, that puts Binance at conservatively $10 billion annually just from perps."
1:22

We asked Flood about his dynamic relationship with Arthur Hayes on Hyperliquid. "People fundamentally misprice Hyperliquid and Arthur is one of the people who actually has the best pricing and probably the most edge" 🦣 "The global perpetual swap market is anywhere from $20-30 billion in revenue a year, and Binance conservatively makes $10 billion a year in revenue." "Hyperliquid does about one-sixth to one-tenth the volume that Binance does on perps—if Hyperliquid is making a billion a year, that puts Binance at conservatively $10 billion annually just from perps."

The Rollup

83,293 görüntüleme • 1 yıl önce

📁 Sarah Friar, CFO of OpenAI, says AI growth is measured in megawatts. 200 MW turned into 2 billion in revenue. 600 MW into 6 billion. 2 GW into more than 20 billion. The relationship is direct, more compute means more revenue. The real constraint is time. Decisions made today define capacity for 2028, 2029 and 2030. The industry has already moved, global hardware investment jumped by over 220 billion.
1:55

📁 Sarah Friar, CFO of OpenAI, says AI growth is measured in megawatts. 200 MW turned into 2 billion in revenue. 600 MW into 6 billion. 2 GW into more than 20 billion. The relationship is direct, more compute means more revenue. The real constraint is time. Decisions made today define capacity for 2028, 2029 and 2030. The industry has already moved, global hardware investment jumped by over 220 billion.

Jon Hernandez

59,693 görüntüleme • 5 ay önce

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!
4:04

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Milk Road AI

21,141 görüntüleme • 26 gün önce