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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

45,175 subscribers

14,751 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Delta Lima
Delta Limavor 1 Jahr

@Veritaseuminc Thank you Reggie🇨🇦

Profilbild von Rainmaker
Rainmakervor 1 Jahr

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:

Profilbild von Duney
Duneyvor 1 Jahr

@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, 😆

Profilbild von hagop belerian
hagop belerianvor 1 Jahr

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

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AbsurdNerdvor 1 Jahr

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

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Wade Bowersvor 1 Jahr

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

Profilbild von Reggie Middleton US11196566 US11895246 US12231579
Reggie Middleton US11196566 US11895246 US12231579vor 1 Jahr

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

Profilbild von Articulate Mumbler
Articulate Mumblervor 1 Jahr

@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.

Profilbild von Coach Sifu Krypto
Coach Sifu Kryptovor 1 Jahr

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

Profilbild von wolf face
wolf facevor 1 Jahr

@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

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Mike

187,491 Aufrufe • vor 8 Monaten

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
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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

69,744 Aufrufe • vor 2 Monaten

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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 Aufrufe • vor 2 Monaten

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

74,945 Aufrufe • vor 2 Monaten

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

761,973 Aufrufe • vor 2 Monaten

Elon Musk's biggest competitor is secretly paying him $1.25 BILLION per month. SpaceX just revealed its financials for the first time in 23 years of existence. And buried deep in the S-1 is a detail that changes how you should think about the entire AI race. Anthropic, the company building Claude, the company that positions itself as OpenAI's biggest threat, the company valued at over $100 billion, is paying SpaceX $1.25 billion EVERY SINGLE MONTH for compute capacity through May 2029. That is $15 billion a year flowing directly from Elon's top AI competitor into Elon's bank account. Think about what that means: Every time Anthropic trains a new model, improves Claude, or lands an enterprise customer, a massive chunk of that revenue goes straight to the guy who owns the competing AI product. Anthropic is literally funding the war against itself. And that's just the beginning of what this filing reveals... The entire SpaceX IPO is structured around a bet most people haven't figured out yet. In 2025, SpaceX spent $20 billion in capex. 60% of that, roughly $12 billion, went to AI infrastructure. Rockets and satellites got the leftovers. In Q1 2026 alone, $7.7 billion out of $10 billion in total capex went to AI. The "rocket company" is spending like an AI company. Meanwhile, xAI, the division that houses Grok, generated $3.2 billion in revenue for the full year of 2025. But its R&D costs TRIPLED to $5 billion. It's burning cash at a pace that would have destroyed it as a standalone company. Which is exactly why Elon merged it into SpaceX two months before filing the IPO. And Starlink is the engine that makes the whole thing work: $11.4 billion in revenue, $4.4 billion in operating profit, and 10.3 million subscribers across 164 countries. It's one of the most profitable subscription businesses on the planet right now. But the average revenue per user DROPPED from $99 per month in 2023 to $66 per month in March 2026. Subscribers quadrupled but each one is paying a third less. Starlink is growing by getting cheaper. SpaceX has lost $37 BILLION since it was founded. Net loss in 2025 was $4.9 billion. This is a company that has never turned an annual profit in 23 years of operation, and it is about to IPO at a $1.75 trillion valuation. And the total addressable market SpaceX claims in the filing is $28.5 trillion. That is a QUARTER of global GDP. So here is what investors are actually buying when this IPO prices: They are buying the most profitable satellite internet business in history, stapled to an AI lab that is burning cash, wrapped inside a Mars colonization pitch that requires building a permanent city on another planet, funded by monthly billion-dollar payments from a direct competitor who has no other option for compute at that scale. This is the kind of thing only Elon could pull off.
2:03

Elon Musk's biggest competitor is secretly paying him $1.25 BILLION per month. SpaceX just revealed its financials for the first time in 23 years of existence. And buried deep in the S-1 is a detail that changes how you should think about the entire AI race. Anthropic, the company building Claude, the company that positions itself as OpenAI's biggest threat, the company valued at over $100 billion, is paying SpaceX $1.25 billion EVERY SINGLE MONTH for compute capacity through May 2029. That is $15 billion a year flowing directly from Elon's top AI competitor into Elon's bank account. Think about what that means: Every time Anthropic trains a new model, improves Claude, or lands an enterprise customer, a massive chunk of that revenue goes straight to the guy who owns the competing AI product. Anthropic is literally funding the war against itself. And that's just the beginning of what this filing reveals... The entire SpaceX IPO is structured around a bet most people haven't figured out yet. In 2025, SpaceX spent $20 billion in capex. 60% of that, roughly $12 billion, went to AI infrastructure. Rockets and satellites got the leftovers. In Q1 2026 alone, $7.7 billion out of $10 billion in total capex went to AI. The "rocket company" is spending like an AI company. Meanwhile, xAI, the division that houses Grok, generated $3.2 billion in revenue for the full year of 2025. But its R&D costs TRIPLED to $5 billion. It's burning cash at a pace that would have destroyed it as a standalone company. Which is exactly why Elon merged it into SpaceX two months before filing the IPO. And Starlink is the engine that makes the whole thing work: $11.4 billion in revenue, $4.4 billion in operating profit, and 10.3 million subscribers across 164 countries. It's one of the most profitable subscription businesses on the planet right now. But the average revenue per user DROPPED from $99 per month in 2023 to $66 per month in March 2026. Subscribers quadrupled but each one is paying a third less. Starlink is growing by getting cheaper. SpaceX has lost $37 BILLION since it was founded. Net loss in 2025 was $4.9 billion. This is a company that has never turned an annual profit in 23 years of operation, and it is about to IPO at a $1.75 trillion valuation. And the total addressable market SpaceX claims in the filing is $28.5 trillion. That is a QUARTER of global GDP. So here is what investors are actually buying when this IPO prices: They are buying the most profitable satellite internet business in history, stapled to an AI lab that is burning cash, wrapped inside a Mars colonization pitch that requires building a permanent city on another planet, funded by monthly billion-dollar payments from a direct competitor who has no other option for compute at that scale. This is the kind of thing only Elon could pull off.

Ricardo

208,631 Aufrufe • vor 3 Monaten

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,273 Aufrufe • vor 9 Monaten

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.
0:54

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.

Milk Road AI

485,762 Aufrufe • vor 3 Monaten

Nebius is going to be a Trillion-dollar company! Twelve months ago, Nebius was trading near $18 per share with roughly $55 million in quarterly revenue. Today the stock trades above $225, quarterly revenue just came in at $399 million, up 684% year over year and the company has a contracted revenue backlog that would make most Fortune 500 companies envious. But the current market cap, sitting around $56 billion, prices in almost none of what is actually coming. The first reason Nebius reaches a trillion is the Meta deal alone. In March, Nebius signed a five year agreement with Meta worth up to $27 billion, one of the largest infrastructure contracts Meta has ever signed with any company under which Nebius will provide $12 billion in dedicated AI capacity across multiple locations, with Meta also having committed to purchase up to an additional $15 billion in third-party capacity over the same period. That contract barely starts until 2027, which means the revenue impact is not yet reflected in any trailing metric. The second reason is Microsoft, which is currently receiving its first deployment phases from Nebius and is expected to contribute at full annual run rate starting in 2027. Between Meta and Microsoft alone, Nebius has signed agreements worth more than $46 billion in total contracted value before a single additional customer is counted. The third reason is the ARR trajectory, which is the fastest revenue ramp of any infrastructure company in the public markets. Nebius ended 2025 at $1.25 billion in ARR and is guiding to $7–9 billion ARR by year-end 2026. Wall Street analysts project revenue growing 523% in 2026 and another 206% in 2027. One of the company's own institutional shareholders has already suggested the year-end ARR could come in more than twice the guided range if the Meta and Microsoft ramps hit their timelines. The fourth reason is Nvidia's direct involvement. Nvidia made a $2 billion strategic equity investment in Nebius and has given Nebius early access to the Vera Rubin platform, its next generation GPU architecture as part of the delivery commitments to Meta. The fifth reason is the capacity buildout, which is being funded by the revenue itself. Nebius invested $2.5 billion in capex in Q1 alone, CEO Arkady Volozh has guided for $16–20 billion in total investment for 2026, and contracted capacity is now on track to exceed 4 GW by year end with new owned sites in Pennsylvania at 1.2 GW and Finland at 310 MW now under development. The more capacity they build, the more they can sell and demand continues to outpace supply at every stage of the buildout. When you run the math on a business with $7–9 billion in ARR exiting 2026, a $27 billion Meta contract that begins in earnest in 2027, a Microsoft relationship at full run rate, 206% analyst projected growth in 2027, and a structural relationship with Nvidia that gives it hardware access no competitor can match, a trillion-dollar valuation within three to four years is not a moonshot. It is the base case if the compounding holds, and every data point so far suggests it is. 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 at the link in bio/below!
1:04

Nebius is going to be a Trillion-dollar company! Twelve months ago, Nebius was trading near $18 per share with roughly $55 million in quarterly revenue. Today the stock trades above $225, quarterly revenue just came in at $399 million, up 684% year over year and the company has a contracted revenue backlog that would make most Fortune 500 companies envious. But the current market cap, sitting around $56 billion, prices in almost none of what is actually coming. The first reason Nebius reaches a trillion is the Meta deal alone. In March, Nebius signed a five year agreement with Meta worth up to $27 billion, one of the largest infrastructure contracts Meta has ever signed with any company under which Nebius will provide $12 billion in dedicated AI capacity across multiple locations, with Meta also having committed to purchase up to an additional $15 billion in third-party capacity over the same period. That contract barely starts until 2027, which means the revenue impact is not yet reflected in any trailing metric. The second reason is Microsoft, which is currently receiving its first deployment phases from Nebius and is expected to contribute at full annual run rate starting in 2027. Between Meta and Microsoft alone, Nebius has signed agreements worth more than $46 billion in total contracted value before a single additional customer is counted. The third reason is the ARR trajectory, which is the fastest revenue ramp of any infrastructure company in the public markets. Nebius ended 2025 at $1.25 billion in ARR and is guiding to $7–9 billion ARR by year-end 2026. Wall Street analysts project revenue growing 523% in 2026 and another 206% in 2027. One of the company's own institutional shareholders has already suggested the year-end ARR could come in more than twice the guided range if the Meta and Microsoft ramps hit their timelines. The fourth reason is Nvidia's direct involvement. Nvidia made a $2 billion strategic equity investment in Nebius and has given Nebius early access to the Vera Rubin platform, its next generation GPU architecture as part of the delivery commitments to Meta. The fifth reason is the capacity buildout, which is being funded by the revenue itself. Nebius invested $2.5 billion in capex in Q1 alone, CEO Arkady Volozh has guided for $16–20 billion in total investment for 2026, and contracted capacity is now on track to exceed 4 GW by year end with new owned sites in Pennsylvania at 1.2 GW and Finland at 310 MW now under development. The more capacity they build, the more they can sell and demand continues to outpace supply at every stage of the buildout. When you run the math on a business with $7–9 billion in ARR exiting 2026, a $27 billion Meta contract that begins in earnest in 2027, a Microsoft relationship at full run rate, 206% analyst projected growth in 2027, and a structural relationship with Nvidia that gives it hardware access no competitor can match, a trillion-dollar valuation within three to four years is not a moonshot. It is the base case if the compounding holds, and every data point so far suggests it is. 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 at the link in bio/below!

Milk Road AI

48,673 Aufrufe • vor 3 Monaten

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.
0:32

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.

George Noble

120,284 Aufrufe • vor 7 Monaten

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?
0:26

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?

Ricardo

167,613 Aufrufe • vor 4 Monaten

$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 Aufrufe • vor 10 Monaten

Cerebras just IPO’d and the stock already ran up over 100% (Save this). For the entire 70 year history of the semiconductor industry, every company on earth has followed the same process. You take a dinner plate sized silicon wafer, put hundreds of tiny chips onto it, and dice it up like a pizza. Nvidia does it this way, AMD does it this way, Intel has done it this way for six decades and everyone who tried to break that convention failed. Until Cerebras asked the most annoyingly obvious question in the industry’s history, what if you just didn’t cut it? The result is the Wafer Scale Engine, a single chip 56 times larger than Nvidia’s H100 and it fundamentally changes the physics of how AI inference works. The reason this matters is not the size, it’s the bandwidth. Every time an AI model generates a single word, it has to reach into memory, pull weights, multiply them together, and produce a prediction and when you’re running millions of concurrent sessions at once, the bottleneck is not raw processing power but how fast data moves between memory and compute. Nvidia’s H100 moves data at roughly 3 terabytes per second, while Cerebras’ WSE-3 moves data at 21 petabytes per second, roughly 7,000 times faster because memory and compute live on the same enormous piece of silicon and data barely has to travel at all. That gap is exactly why OpenAI went from 150 tokens per second on traditional GPUs to 2,000 tokens per second on Cerebras hardware, and why AWS integrated Cerebras into Bedrock to deliver roughly 5x more inference capacity in the same physical footprint. The macro setup is making the trade even more urgent. South Korea DRAM export prices recently jumped 35%, flash memory surged 47%, and SSD pricing spiked nearly 140% and every single one of those increases hits Nvidia-based infrastructure directly, because the H100 requires 80GB of the most expensive, most contested memory in the AI supply chain. Cerebras’ WSE-3 uses zero external HBM memory, baking 44GB of SRAM directly into the wafer itself which means as memory pricing goes parabolic, every CFO evaluating AI infrastructure is suddenly looking much more seriously at the architecture that sidesteps that cost entirely. The demand is already showing up in the backlog. Cerebras ended 2025 with $24.6 billion in remaining performance obligations for a company doing just over $500 million in annual revenue, that is a number that implies years of contracted growth already sitting on the books. The IPO was 20x oversubscribed, the price range was raised twice before listing, and shares opened 89% above their listing price on a $5.55 billion raise that made it the largest semiconductor IPO in history. The risks are real and worth naming. 86% of 2025 revenue came from two entities with UAE ties, U.S. revenue actually fell 34% to $187 million, and the $20 billion OpenAI contract is conditional, if Cerebras misses delivery milestones, OpenAI can terminate and trigger repayment demands on a $1 billion loan facility. And yet the market is valuing Cerebras at roughly 91x trailing revenue, richer than Nvidia, AMD, and Arm combined. What investors are betting on is not that Cerebras beats Nvidia, it is that the inference supercycle is large enough to support an entirely different architecture optimized for a different workload, and that $24.6 billion in contracted backlog converts to diversified revenue before the market starts asking harder questions. CEO Andrew Feldman said this took a decade of late nights to get right, everyone who tried to copy it failed and given that the entire inference economy is now running through exactly the bottleneck Cerebras was built to eliminate, the market is starting to believe him.
2:04

Cerebras just IPO’d and the stock already ran up over 100% (Save this). For the entire 70 year history of the semiconductor industry, every company on earth has followed the same process. You take a dinner plate sized silicon wafer, put hundreds of tiny chips onto it, and dice it up like a pizza. Nvidia does it this way, AMD does it this way, Intel has done it this way for six decades and everyone who tried to break that convention failed. Until Cerebras asked the most annoyingly obvious question in the industry’s history, what if you just didn’t cut it? The result is the Wafer Scale Engine, a single chip 56 times larger than Nvidia’s H100 and it fundamentally changes the physics of how AI inference works. The reason this matters is not the size, it’s the bandwidth. Every time an AI model generates a single word, it has to reach into memory, pull weights, multiply them together, and produce a prediction and when you’re running millions of concurrent sessions at once, the bottleneck is not raw processing power but how fast data moves between memory and compute. Nvidia’s H100 moves data at roughly 3 terabytes per second, while Cerebras’ WSE-3 moves data at 21 petabytes per second, roughly 7,000 times faster because memory and compute live on the same enormous piece of silicon and data barely has to travel at all. That gap is exactly why OpenAI went from 150 tokens per second on traditional GPUs to 2,000 tokens per second on Cerebras hardware, and why AWS integrated Cerebras into Bedrock to deliver roughly 5x more inference capacity in the same physical footprint. The macro setup is making the trade even more urgent. South Korea DRAM export prices recently jumped 35%, flash memory surged 47%, and SSD pricing spiked nearly 140% and every single one of those increases hits Nvidia-based infrastructure directly, because the H100 requires 80GB of the most expensive, most contested memory in the AI supply chain. Cerebras’ WSE-3 uses zero external HBM memory, baking 44GB of SRAM directly into the wafer itself which means as memory pricing goes parabolic, every CFO evaluating AI infrastructure is suddenly looking much more seriously at the architecture that sidesteps that cost entirely. The demand is already showing up in the backlog. Cerebras ended 2025 with $24.6 billion in remaining performance obligations for a company doing just over $500 million in annual revenue, that is a number that implies years of contracted growth already sitting on the books. The IPO was 20x oversubscribed, the price range was raised twice before listing, and shares opened 89% above their listing price on a $5.55 billion raise that made it the largest semiconductor IPO in history. The risks are real and worth naming. 86% of 2025 revenue came from two entities with UAE ties, U.S. revenue actually fell 34% to $187 million, and the $20 billion OpenAI contract is conditional, if Cerebras misses delivery milestones, OpenAI can terminate and trigger repayment demands on a $1 billion loan facility. And yet the market is valuing Cerebras at roughly 91x trailing revenue, richer than Nvidia, AMD, and Arm combined. What investors are betting on is not that Cerebras beats Nvidia, it is that the inference supercycle is large enough to support an entirely different architecture optimized for a different workload, and that $24.6 billion in contracted backlog converts to diversified revenue before the market starts asking harder questions. CEO Andrew Feldman said this took a decade of late nights to get right, everyone who tried to copy it failed and given that the entire inference economy is now running through exactly the bottleneck Cerebras was built to eliminate, the market is starting to believe him.

Milk Road AI

30,441 Aufrufe • vor 3 Monaten

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!
3:41

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 Aufrufe • vor 2 Monaten

$HIMS| Adjustment on Growth toward 2030🧵 Not Financial Advice! FY2025: Revenue: $2.35B or 58% YoY (weightloss $740), Core $1.61B FY2026: Revenue $3.2B(36%) where weightloss may be down by 10-15% or flat. FY2027: Revenue $4.16B(30%) FY2028: Revenue: $5.2B(25%) FY2029 Revenue: $6.5B(25%) FY2030 Revenue: $8.12B(25%) I expect management to ramp up buyback from FCF generation while company is trading at under 2x P/S andrewdudum. The discontinuation of Hims & Hers' compounded oral semaglutide pill in early February 2026(after 2 days), prompted by FDA regulatory actions and legal pressures from Novo Nordisk, introduces near-term challenges to the weight loss segment but does not derail the company's broader growth trajectory, as it pivots aggressively toward diversification and high-potential expansions The weight loss category bolstered by liraglutide injectables, generic semaglutide in Canada, and non-GLP-1 personalized kits retains strong momentum, contributing approximately 31% of total revenue in 2025 and projected to grow at 15-20% annually through 2030, down from prior 60%+ rates but still adding $150-250 million yearly through cross-selling and retention. Offsetting this moderation are ambitious new expansions: international markets, now accounting for an initial 5-10% of revenue but scaling to 20% by 2030 via Canada entry (projected 10% growth contribution in 2026 from generic semaglutide and Livewell acquisition) and Europe/UK via Zava (adding 8-12% incremental growth through telehealth in Germany, France, and Ireland); diagnostics and labs, launched in late 2025 with Quest Diagnostics partnership and YourBio Health's pain-free blood sampling tech, offering 50-120 biomarker tests across heart, metabolism, hormones, inflammation, and stress, expected to generate 12-18% of total revenue by 2027 and ramp to a standalone $1 billion segment by 2030. Preventive care and longevity initiatives, set for full 2026 rollout including peptide manufacturing (via acquired U.S. facility, contributing 10-15% to growth through vertical integration and supply control), coenzymes, GLP/GIP blends for performance and recovery, and a $325 million Grail investment enabling multi-cancer early detection blood tests (projected to add 8-12% revenue uplift starting in 2026 by enhancing subscription retention); and hormone health expansions like menopause/perimenopause and low testosterone treatments, already driving 10% of 2025 growth and poised for 20-25% annual expansion through data-driven personalization. Multi-cancer early detection (MCED) blood testing via the Galleri® test from GRAIL in the prior breakdown, even though it was bundled under longevity/preventive care. This is a significant new offering launched on February 4, 2026, providing subscribers (via the Labs platform) access to a simple annual blood test that screens for signals shared by over 50 types of cancer (including hard-to-detect ones like pancreatic, liver, ovarian, and lung) before symptoms appear. Hims & Hers is offering it at a discounted ~$700 (vs. retail $949), following their participation in GRAIL's $325 million private placement investment in late 2025, which strengthens the partnership and positions this as a core pillar of proactive/longevity care. This could help push Average growth to 30-35% vs 28.2%(my above revised projection). These levers, combined with a subscriber base exceeding 2.5 million (up 31% YoY) and AI-enhanced platform efficiency under new CTO leadership, support an upward revision to growth rates targeting 22-25% CAGR from 2026-2030 to meet the company's $6.5 billion revenue goal, far outpacing prior conservative estimates of mid-teens expansion. This high-growth scenario assumes execution on global scaling, regulatory navigation (FDA approvals for compounded alternatives), and margin recovery to 74-78% via vertical integration, positioning Hims & Hers as a comprehensive digital health ecosystem rather than a GLP-1-dependent player, with potential upside from emerging trends like peptide demand (up 144% in Google searches) and proactive wellness adoption. Not Financial Advice!
3:31

$HIMS| Adjustment on Growth toward 2030🧵 Not Financial Advice! FY2025: Revenue: $2.35B or 58% YoY (weightloss $740), Core $1.61B FY2026: Revenue $3.2B(36%) where weightloss may be down by 10-15% or flat. FY2027: Revenue $4.16B(30%) FY2028: Revenue: $5.2B(25%) FY2029 Revenue: $6.5B(25%) FY2030 Revenue: $8.12B(25%) I expect management to ramp up buyback from FCF generation while company is trading at under 2x P/S andrewdudum. The discontinuation of Hims & Hers' compounded oral semaglutide pill in early February 2026(after 2 days), prompted by FDA regulatory actions and legal pressures from Novo Nordisk, introduces near-term challenges to the weight loss segment but does not derail the company's broader growth trajectory, as it pivots aggressively toward diversification and high-potential expansions The weight loss category bolstered by liraglutide injectables, generic semaglutide in Canada, and non-GLP-1 personalized kits retains strong momentum, contributing approximately 31% of total revenue in 2025 and projected to grow at 15-20% annually through 2030, down from prior 60%+ rates but still adding $150-250 million yearly through cross-selling and retention. Offsetting this moderation are ambitious new expansions: international markets, now accounting for an initial 5-10% of revenue but scaling to 20% by 2030 via Canada entry (projected 10% growth contribution in 2026 from generic semaglutide and Livewell acquisition) and Europe/UK via Zava (adding 8-12% incremental growth through telehealth in Germany, France, and Ireland); diagnostics and labs, launched in late 2025 with Quest Diagnostics partnership and YourBio Health's pain-free blood sampling tech, offering 50-120 biomarker tests across heart, metabolism, hormones, inflammation, and stress, expected to generate 12-18% of total revenue by 2027 and ramp to a standalone $1 billion segment by 2030. Preventive care and longevity initiatives, set for full 2026 rollout including peptide manufacturing (via acquired U.S. facility, contributing 10-15% to growth through vertical integration and supply control), coenzymes, GLP/GIP blends for performance and recovery, and a $325 million Grail investment enabling multi-cancer early detection blood tests (projected to add 8-12% revenue uplift starting in 2026 by enhancing subscription retention); and hormone health expansions like menopause/perimenopause and low testosterone treatments, already driving 10% of 2025 growth and poised for 20-25% annual expansion through data-driven personalization. Multi-cancer early detection (MCED) blood testing via the Galleri® test from GRAIL in the prior breakdown, even though it was bundled under longevity/preventive care. This is a significant new offering launched on February 4, 2026, providing subscribers (via the Labs platform) access to a simple annual blood test that screens for signals shared by over 50 types of cancer (including hard-to-detect ones like pancreatic, liver, ovarian, and lung) before symptoms appear. Hims & Hers is offering it at a discounted ~$700 (vs. retail $949), following their participation in GRAIL's $325 million private placement investment in late 2025, which strengthens the partnership and positions this as a core pillar of proactive/longevity care. This could help push Average growth to 30-35% vs 28.2%(my above revised projection). These levers, combined with a subscriber base exceeding 2.5 million (up 31% YoY) and AI-enhanced platform efficiency under new CTO leadership, support an upward revision to growth rates targeting 22-25% CAGR from 2026-2030 to meet the company's $6.5 billion revenue goal, far outpacing prior conservative estimates of mid-teens expansion. This high-growth scenario assumes execution on global scaling, regulatory navigation (FDA approvals for compounded alternatives), and margin recovery to 74-78% via vertical integration, positioning Hims & Hers as a comprehensive digital health ecosystem rather than a GLP-1-dependent player, with potential upside from emerging trends like peptide demand (up 144% in Google searches) and proactive wellness adoption. Not Financial Advice!

Mike

273,519 Aufrufe • vor 6 Monaten

Everything Elon said about Optimus on the Q4 2024 earnings call: ⦿ I see a path for Tesla to be the most valuable company in the world, possibly bigger than the next five companies combined, overwhelmingly due to autonomous vehicles and autonomous humanoid robots. ⦿ The training compute needed for Optimus will ultimately probably be 10× what is needed for cars. Humanoids likely have 1,000× more useS than a car, which doesn't mean training scales by 1,000×, but probably close to 10×. The training compute will scale progressively as Optimus becomes more productive. ⦿ Long-term, Optimus has the potential to generate $10 trillion in revenue. In that scenario, we can support a lot of training compute. Even $500 billion in training compute is a good deal (chuckles). ⦿ There's a lot of uncertainty with timing because several aspects are being iterated simultaneously. The internal plan is for roughly 10,000 robots to be built this year, but we'll more likely produce several thousand. ⦿ I'm confident those several thousand robots will be able to do useful things. ⦿ The lessons from Production V1 will inform the changes in Production V2, which we expect to launch around mid-next year. ⦿ Our goal, aspirationally, is to ramp 10× every year, but perhaps we end up with 5× growth per year. With that kind of growth, it won't be many years before we're making 100 million robots a year. ⦿ The off-the-shelf components didn't work well, so we had to design everything in-house, including the most sophisticated hand ever made. Optimus will be able to play a piano and thread a needle. ⦿ My long-term prediction is that Optimus will overwhelmingly be the value of the company. ⦿ Optimus is not design-locked. It is rapidly evolving in a good direction. Tesla has by far the best humanoid robotics engineers in the world. Tesla also has all the other necessary ingredients: battery pack, power electronics, charging, communications, real-world AI, and the ability to scale production. ⦿ What other companies are missing is real-world AI and the ability to scale to millions of units a year. ⦿ This year, we aim to use Optimus internally at Tesla. We can easily use several thousand robots at Tesla for repetitive tasks, such as loading sheet metal at the welding line. ⦿ The Production V1 line is roughly 1,000 units per month. The Production V2, launching around mid-next year, will be for 10k units per month. The line after that will be for 100k units a month. Of course, it takes time for any given line to reach its maximum potential. ⦿ A very rough guess: we'll start delivering Optimus to companies outside of Tesla in the second half of 2026. The ramp is going to be exponential, and demand will not be a problem. ⦿ Once we're above 1 million units per year, the production cost of Optimus will be less than $20,000. Its total mass and complexity are much lower than a car. At a similar production volume to the Model Y, Optimus should be about half the cost of a Model Y. ⦿ The price is a different matter than cost. The price of Optimus will be set by market demand. [This is by far the longest Elon has ever spent discussing Optimus on an earnings call.]
12:28

Everything Elon said about Optimus on the Q4 2024 earnings call: ⦿ I see a path for Tesla to be the most valuable company in the world, possibly bigger than the next five companies combined, overwhelmingly due to autonomous vehicles and autonomous humanoid robots. ⦿ The training compute needed for Optimus will ultimately probably be 10× what is needed for cars. Humanoids likely have 1,000× more useS than a car, which doesn't mean training scales by 1,000×, but probably close to 10×. The training compute will scale progressively as Optimus becomes more productive. ⦿ Long-term, Optimus has the potential to generate $10 trillion in revenue. In that scenario, we can support a lot of training compute. Even $500 billion in training compute is a good deal (chuckles). ⦿ There's a lot of uncertainty with timing because several aspects are being iterated simultaneously. The internal plan is for roughly 10,000 robots to be built this year, but we'll more likely produce several thousand. ⦿ I'm confident those several thousand robots will be able to do useful things. ⦿ The lessons from Production V1 will inform the changes in Production V2, which we expect to launch around mid-next year. ⦿ Our goal, aspirationally, is to ramp 10× every year, but perhaps we end up with 5× growth per year. With that kind of growth, it won't be many years before we're making 100 million robots a year. ⦿ The off-the-shelf components didn't work well, so we had to design everything in-house, including the most sophisticated hand ever made. Optimus will be able to play a piano and thread a needle. ⦿ My long-term prediction is that Optimus will overwhelmingly be the value of the company. ⦿ Optimus is not design-locked. It is rapidly evolving in a good direction. Tesla has by far the best humanoid robotics engineers in the world. Tesla also has all the other necessary ingredients: battery pack, power electronics, charging, communications, real-world AI, and the ability to scale production. ⦿ What other companies are missing is real-world AI and the ability to scale to millions of units a year. ⦿ This year, we aim to use Optimus internally at Tesla. We can easily use several thousand robots at Tesla for repetitive tasks, such as loading sheet metal at the welding line. ⦿ The Production V1 line is roughly 1,000 units per month. The Production V2, launching around mid-next year, will be for 10k units per month. The line after that will be for 100k units a month. Of course, it takes time for any given line to reach its maximum potential. ⦿ A very rough guess: we'll start delivering Optimus to companies outside of Tesla in the second half of 2026. The ramp is going to be exponential, and demand will not be a problem. ⦿ Once we're above 1 million units per year, the production cost of Optimus will be less than $20,000. Its total mass and complexity are much lower than a car. At a similar production volume to the Model Y, Optimus should be about half the cost of a Model Y. ⦿ The price is a different matter than cost. The price of Optimus will be set by market demand. [This is by far the longest Elon has ever spent discussing Optimus on an earnings call.]

The Humanoid Hub

96,475 Aufrufe • vor 1 Jahr

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

299,024 Aufrufe • vor 3 Monaten

🚨A 25 YEAR OLD BUILT THE FASTEST GROWING SOFTWARE COMPANY IN HISTORY.. WITH ZERO MARKETING SPEND.. AND SPACEX JUST OFFERED $60 BILLION TO BUY IT.. His name is Michael Truell.. He started coding at 11.. Interned at Google at 18.. Dropped out of MIT to start a company that built AI tools for mechanical engineering.. That company failed.. So he pivoted.. And built Cursor.. An AI-powered code editor that writes software for you.. Here's how fast it grew.. $100 million in annual revenue in 12 months.. Fastest in SaaS history.. Broke every record ever set by Slack, Zoom, and Wiz.. $500 million by month 21.. $1 billion by November 2025.. $2 billion by February 2026.. Projected to hit $6 billion by end of year.. Zero marketing spend.. Not a single dollar.. Pure word of mouth from developers who couldn't stop talking about it.. Over 1 billion lines of code accepted per day.. Used by 70% of Fortune 1000 companies.. Every single one of Nvidia's 40,000 engineers uses it.. Coinbase hit 100% adoption among their developers.. And he did this with a team of four MIT co-founders.. One of them was a three-time International Math Olympiad competitor from Pakistan.. Another was a college squash captain with zero startup experience who built the entire product strategy.. They spent zero on sales.. Zero on ads.. Zero on growth hacking.. The product sold itself.. But here's where the story takes a turn nobody expected.. Even at $50 billion valuation.. Even generating billions in revenue.. They hit a wall.. Not a market wall.. A physics wall.. They couldn't get enough GPUs to train their next AI model.. The physical chips didn't exist in sufficient quantities for them to buy.. Money couldn't solve the problem.. Enter Elon Musk.. On April 21.. SpaceX announced a deal to potentially acquire Cursor for $60 billion.. The largest acquisition option in tech history.. The structure is insane.. SpaceX gives Cursor immediate access to Colossus.. xAI's supercomputer equivalent to one million Nvidia H100 GPUs.. For nine months of joint development.. At the end.. SpaceX can buy the company for $60 billion.. If they don't buy it.. They owe Cursor a $10 billion breakup fee.. The largest breakup fee in corporate history.. Think about what that means for Cursor.. Either they get acquired for $60 billion.. Or they walk away with $10 billion in cash and nine months of free training on the most powerful supercomputer on earth.. There is no losing scenario.. And here's why Musk wants it.. SpaceX is preparing for an IPO at $1.75 trillion.. The biggest IPO ever.. But aerospace alone can't justify that number.. By merging xAI into SpaceX.. And now acquiring Cursor.. Musk transforms SpaceX from a rocket company into an AI empire that owns the compute, the models, and the developer tools.. Cursor is the missing piece.. The application layer that puts xAI's models into the daily workflow of every Fortune 500 engineering team.. Oh and one more thing.. In 2022.. FTX's trading firm Alameda Research made a seed investment in Cursor.. During the FTX bankruptcy.. Liquidators sold that stake for $200,000.. That stake is now worth approximately $3 billion.. Sam Bankman-Fried called it the worst liquidation decision in venture capital history.. From a prison cell.. A failed mechanical engineering startup.. Pivoted by four kids from MIT.. Zero marketing.. Zero sales team.. Built the fastest growing software company in history.. And now SpaceX is writing a $60 billion check for it.. This is the most insane founder story in Silicon Valley history.. And most people haven't even heard of Michael Truell.
27:39

🚨A 25 YEAR OLD BUILT THE FASTEST GROWING SOFTWARE COMPANY IN HISTORY.. WITH ZERO MARKETING SPEND.. AND SPACEX JUST OFFERED $60 BILLION TO BUY IT.. His name is Michael Truell.. He started coding at 11.. Interned at Google at 18.. Dropped out of MIT to start a company that built AI tools for mechanical engineering.. That company failed.. So he pivoted.. And built Cursor.. An AI-powered code editor that writes software for you.. Here's how fast it grew.. $100 million in annual revenue in 12 months.. Fastest in SaaS history.. Broke every record ever set by Slack, Zoom, and Wiz.. $500 million by month 21.. $1 billion by November 2025.. $2 billion by February 2026.. Projected to hit $6 billion by end of year.. Zero marketing spend.. Not a single dollar.. Pure word of mouth from developers who couldn't stop talking about it.. Over 1 billion lines of code accepted per day.. Used by 70% of Fortune 1000 companies.. Every single one of Nvidia's 40,000 engineers uses it.. Coinbase hit 100% adoption among their developers.. And he did this with a team of four MIT co-founders.. One of them was a three-time International Math Olympiad competitor from Pakistan.. Another was a college squash captain with zero startup experience who built the entire product strategy.. They spent zero on sales.. Zero on ads.. Zero on growth hacking.. The product sold itself.. But here's where the story takes a turn nobody expected.. Even at $50 billion valuation.. Even generating billions in revenue.. They hit a wall.. Not a market wall.. A physics wall.. They couldn't get enough GPUs to train their next AI model.. The physical chips didn't exist in sufficient quantities for them to buy.. Money couldn't solve the problem.. Enter Elon Musk.. On April 21.. SpaceX announced a deal to potentially acquire Cursor for $60 billion.. The largest acquisition option in tech history.. The structure is insane.. SpaceX gives Cursor immediate access to Colossus.. xAI's supercomputer equivalent to one million Nvidia H100 GPUs.. For nine months of joint development.. At the end.. SpaceX can buy the company for $60 billion.. If they don't buy it.. They owe Cursor a $10 billion breakup fee.. The largest breakup fee in corporate history.. Think about what that means for Cursor.. Either they get acquired for $60 billion.. Or they walk away with $10 billion in cash and nine months of free training on the most powerful supercomputer on earth.. There is no losing scenario.. And here's why Musk wants it.. SpaceX is preparing for an IPO at $1.75 trillion.. The biggest IPO ever.. But aerospace alone can't justify that number.. By merging xAI into SpaceX.. And now acquiring Cursor.. Musk transforms SpaceX from a rocket company into an AI empire that owns the compute, the models, and the developer tools.. Cursor is the missing piece.. The application layer that puts xAI's models into the daily workflow of every Fortune 500 engineering team.. Oh and one more thing.. In 2022.. FTX's trading firm Alameda Research made a seed investment in Cursor.. During the FTX bankruptcy.. Liquidators sold that stake for $200,000.. That stake is now worth approximately $3 billion.. Sam Bankman-Fried called it the worst liquidation decision in venture capital history.. From a prison cell.. A failed mechanical engineering startup.. Pivoted by four kids from MIT.. Zero marketing.. Zero sales team.. Built the fastest growing software company in history.. And now SpaceX is writing a $60 billion check for it.. This is the most insane founder story in Silicon Valley history.. And most people haven't even heard of Michael Truell.

Evan Luthra

989,346 Aufrufe • vor 4 Monaten

Wall Street is WRONG about Oracle. $ORCL is being pitched as the "fourth hyperscaler." The AI infrastructure play of a lifetime. 35 out of 46 analysts have a buy rating. Consensus price target is $246. The stock is at $172. Down 47% from its September high. Now let me explain what the bulls aren't telling you and why this will end HORRIBLY: Oracle's non-current debt has ballooned to $124.7 billion. Up from $85.3 billion a year ago. A 46% increase in 12 months. Total liabilities sit at $206 billion against shareholders' equity of $39 billion. That's a 5-to-1 leverage ratio on a company being pitched as a "safe" infrastructure play. But that $124.7 billion isn't even the full picture... Oracle has been using project financing structures (loans repaid from projected future cashflow) to keep tens of billions more in borrowing off its balance sheet entirely. So when analysts quote Oracle's debt load, they're UNDERSTATING the actual exposure by a meaningful margin. Interest expense jumped 32% YOY. Free cash flow is negative $24.7 billion on a trailing basis. The company is spending $48 billion a year in capex while generating roughly $17 billion in operating cash flow. They issued $43 billion in senior notes in 9 months. They are borrowing at a pace that would make a leveraged buyout firm nervous. And what did they get for all that spending? They fired 30,000 people. On March 31st, Oracle sent an email at 6 AM to tens of thousands of employees telling them their roles were eliminated. 18% of the global workforce gone in a single morning. TD Cowen estimates the layoffs save $8 to $10 billion in annual cash flow. Which tells you everything about the math: Oracle can't fund $50 billion in AI capex AND keep 162,000 people on payroll. So the people went. Net income was up 95% last quarter. The stock is still down 47% from its high. Mr. Market is telling you something. The earnings look great on paper partly because Oracle extended the useful life of its servers to 6 years, reducing depreciation expense by billions. I've been flagging this accounting game across the hyperscalers for months. It flatters the income statement while the balance sheet quietly deteriorates. Now let's talk about the $553 billion in Remaining Performance Obligations that every bull cites as the "reason" to own this stock: Roughly $300 billion of that is a SINGLE contract with OpenAI through the Stargate project. Revenue doesn't start flowing until 2027. And OpenAI itself expects to lose over $167 billion through 2028 even if it hits $100 billion in annual revenue. So Oracle is borrowing $125+ billion to build data centers for a customer that cannot even fund its own operations. And the data centers themselves are significantly behind schedule: The flagship Stargate campus in Abilene has been under construction since mid-2024. 2 years later, only 2 of 8 planned buildings are operational, covering about 200 megawatts of the planned 1.2 gigawatts. The remaining Stargate sites across Wisconsin, New Mexico, Michigan, and other locations are in the earliest stages of development. The total estimated cost to build out Oracle's 7 gigawatts of planned Stargate capacity runs around $340 billion. And lenders are already getting nervous. The Wall Street Journal reported that additional capacity at Abilene originally earmarked for OpenAI ended up going to Microsoft instead - because the banks financing the build were uncomfortable with their credit exposure to OpenAI as the ultimate customer. When your LENDERS don't trust your tenant's ability to pay, then there's SERIOUS issue. And by the time those data centers are fully built, the GPUs inside them will already be approaching obsolescence anyway. Nvidia releases new architectures annually. Each generation delivers dramatically more compute per watt. The hardware goes obsolete in 3 years but the debt used to buy it gets repaid over a much longer horizon. The AI infrastructure buildout is a treadmill, not a revolution. Oracle is the purest expression of that thesis. - $206 billion in reported liabilities. - Billions more hidden off-balance-sheet. - Negative $25 billion in free cash flow. - 30,000 people fired to fund the capex. - A single unprofitable customer behind over half the backlog. - Data centers years behind schedule. And 35 analysts saying buy. This doesn't sound right, does it?
1:22

Wall Street is WRONG about Oracle. $ORCL is being pitched as the "fourth hyperscaler." The AI infrastructure play of a lifetime. 35 out of 46 analysts have a buy rating. Consensus price target is $246. The stock is at $172. Down 47% from its September high. Now let me explain what the bulls aren't telling you and why this will end HORRIBLY: Oracle's non-current debt has ballooned to $124.7 billion. Up from $85.3 billion a year ago. A 46% increase in 12 months. Total liabilities sit at $206 billion against shareholders' equity of $39 billion. That's a 5-to-1 leverage ratio on a company being pitched as a "safe" infrastructure play. But that $124.7 billion isn't even the full picture... Oracle has been using project financing structures (loans repaid from projected future cashflow) to keep tens of billions more in borrowing off its balance sheet entirely. So when analysts quote Oracle's debt load, they're UNDERSTATING the actual exposure by a meaningful margin. Interest expense jumped 32% YOY. Free cash flow is negative $24.7 billion on a trailing basis. The company is spending $48 billion a year in capex while generating roughly $17 billion in operating cash flow. They issued $43 billion in senior notes in 9 months. They are borrowing at a pace that would make a leveraged buyout firm nervous. And what did they get for all that spending? They fired 30,000 people. On March 31st, Oracle sent an email at 6 AM to tens of thousands of employees telling them their roles were eliminated. 18% of the global workforce gone in a single morning. TD Cowen estimates the layoffs save $8 to $10 billion in annual cash flow. Which tells you everything about the math: Oracle can't fund $50 billion in AI capex AND keep 162,000 people on payroll. So the people went. Net income was up 95% last quarter. The stock is still down 47% from its high. Mr. Market is telling you something. The earnings look great on paper partly because Oracle extended the useful life of its servers to 6 years, reducing depreciation expense by billions. I've been flagging this accounting game across the hyperscalers for months. It flatters the income statement while the balance sheet quietly deteriorates. Now let's talk about the $553 billion in Remaining Performance Obligations that every bull cites as the "reason" to own this stock: Roughly $300 billion of that is a SINGLE contract with OpenAI through the Stargate project. Revenue doesn't start flowing until 2027. And OpenAI itself expects to lose over $167 billion through 2028 even if it hits $100 billion in annual revenue. So Oracle is borrowing $125+ billion to build data centers for a customer that cannot even fund its own operations. And the data centers themselves are significantly behind schedule: The flagship Stargate campus in Abilene has been under construction since mid-2024. 2 years later, only 2 of 8 planned buildings are operational, covering about 200 megawatts of the planned 1.2 gigawatts. The remaining Stargate sites across Wisconsin, New Mexico, Michigan, and other locations are in the earliest stages of development. The total estimated cost to build out Oracle's 7 gigawatts of planned Stargate capacity runs around $340 billion. And lenders are already getting nervous. The Wall Street Journal reported that additional capacity at Abilene originally earmarked for OpenAI ended up going to Microsoft instead - because the banks financing the build were uncomfortable with their credit exposure to OpenAI as the ultimate customer. When your LENDERS don't trust your tenant's ability to pay, then there's SERIOUS issue. And by the time those data centers are fully built, the GPUs inside them will already be approaching obsolescence anyway. Nvidia releases new architectures annually. Each generation delivers dramatically more compute per watt. The hardware goes obsolete in 3 years but the debt used to buy it gets repaid over a much longer horizon. The AI infrastructure buildout is a treadmill, not a revolution. Oracle is the purest expression of that thesis. - $206 billion in reported liabilities. - Billions more hidden off-balance-sheet. - Negative $25 billion in free cash flow. - 30,000 people fired to fund the capex. - A single unprofitable customer behind over half the backlog. - Data centers years behind schedule. And 35 analysts saying buy. This doesn't sound right, does it?

George Noble

58,284 Aufrufe • vor 4 Monaten

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."
31:16

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

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