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Arvind Krishna on Masters of Scale: the AI data center math doesn't work. IBM's CEO runs the numbers. 125 GW of committed AI data centers implies $8-12T in capex. That requires roughly $1T in annual profit. Which requires $4T in incremental AI revenue. His words: "I don't see the...

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The AI bubble burst will be worse than the dotcom bubble. None of these companies are profitable without government subsidies, or communism. IBM CEO Arvind Krishna says there is "no way" to turn a profit on the $8 trillion currently being committed to AI data centers. Krishna notes that at $80 billion per gigawatt, a company committing to 20-30 GW faces $1.5 trillion in CapEx, requiring $800 billion in annual profit just to cover interest and depreciation of chips that must be replaced every 5 years. 0/10 of the top AI startups are profitable!
0:48

The AI bubble burst will be worse than the dotcom bubble. None of these companies are profitable without government subsidies, or communism. IBM CEO Arvind Krishna says there is "no way" to turn a profit on the $8 trillion currently being committed to AI data centers. Krishna notes that at $80 billion per gigawatt, a company committing to 20-30 GW faces $1.5 trillion in CapEx, requiring $800 billion in annual profit just to cover interest and depreciation of chips that must be replaced every 5 years. 0/10 of the top AI startups are profitable!

Truth_teller 🇷🇺

12,925 Aufrufe • vor 4 Monaten

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

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

Milk Road AI

21,141 Aufrufe • vor 2 Monaten

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud
1:31

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,663 Aufrufe • vor 1 Monat

.Jon Callaghan (Founder, True Ventures) on the scale of the AI boom: “Our numbers are 2 to 4 trillion of CapEx going into the AI wave over the next five years. In every prior wave (Web2, mobile, cloud) the value created at the application layer on top of that CapEx was 5 to 10x.” “We’re talking about markets that haven’t been invented yet. Net new consumer behaviors. Net new enterprise behaviors. At an unprecedented level.” For founders and investors, he calls it “the vintage of a lifetime.”
1:44

.Jon Callaghan (Founder, True Ventures) on the scale of the AI boom: “Our numbers are 2 to 4 trillion of CapEx going into the AI wave over the next five years. In every prior wave (Web2, mobile, cloud) the value created at the application layer on top of that CapEx was 5 to 10x.” “We’re talking about markets that haven’t been invented yet. Net new consumer behaviors. Net new enterprise behaviors. At an unprecedented level.” For founders and investors, he calls it “the vintage of a lifetime.”

TBPN

23,562 Aufrufe • vor 1 Jahr

The reason we’re all in on AI agents at Box is because agents can now process all of the unstructured data in an enterprise at scale. Companies are sitting on mountains of valuable information that they can tap into for the first time.
1:27

The reason we’re all in on AI agents at Box is because agents can now process all of the unstructured data in an enterprise at scale. Companies are sitting on mountains of valuable information that they can tap into for the first time.

Aaron Levie

73,694 Aufrufe • vor 6 Monaten

DAN IVES JUST GAVE HIS TAKE ON WHERE THE QUANTUM COMPUTING RACE ACTUALLY STANDS The Wedbush analyst was asked what inning we're in. His answer: "We're maybe in the dugout in warmups, getting ready for the first inning. The game hasn't started." Ives calls quantum a "derivative play on AI" that will eventually become the next level pushing AI forward. He points to names like IonQ $IONQ and D-Wave $QBTS that he says have "phenomenal technologies" still building toward scale. He's most bullish on IBM $IBM, framing its quantum and AI bets as a real turnaround: "This is just the start of these turnarounds, and IBM continues to be one that has massive upside." He says CEO Arvind Krishna is "way underappreciated" for seeing where the technology is heading, putting him in the same tier as Jensen Huang. On the runway ahead, Ives pegs AI spending at $1 trillion over the next 3-4 years and says we're only 15%-20% through it. His bigger picture: "This is how we're going to get Nasdaq 30,000." Dan Ives
5:34

DAN IVES JUST GAVE HIS TAKE ON WHERE THE QUANTUM COMPUTING RACE ACTUALLY STANDS The Wedbush analyst was asked what inning we're in. His answer: "We're maybe in the dugout in warmups, getting ready for the first inning. The game hasn't started." Ives calls quantum a "derivative play on AI" that will eventually become the next level pushing AI forward. He points to names like IonQ $IONQ and D-Wave $QBTS that he says have "phenomenal technologies" still building toward scale. He's most bullish on IBM $IBM, framing its quantum and AI bets as a real turnaround: "This is just the start of these turnarounds, and IBM continues to be one that has massive upside." He says CEO Arvind Krishna is "way underappreciated" for seeing where the technology is heading, putting him in the same tier as Jensen Huang. On the runway ahead, Ives pegs AI spending at $1 trillion over the next 3-4 years and says we're only 15%-20% through it. His bigger picture: "This is how we're going to get Nasdaq 30,000." Dan Ives

WOLF

112,286 Aufrufe • vor 3 Monaten

AI Bull vs Bear #1: 60-70% gross margins on AI inference. Gavin Baker Gavin Baker of Atreides ran the math on BG2 Pod: inference revenue exits 2026 above $200B, and he calls $300B a low estimate for 2027. Ed Zitron's Ed Zitron bear case says costs scale with revenue, so the curve never bends. Baker's number says the unit economics already work at the inference layer. The catch: that is gross margin, not the whole P&L. It does not answer OpenAI's $21B operating loss. Where the bull margin math meets Zitron's receipts:
5:16

AI Bull vs Bear #1: 60-70% gross margins on AI inference. Gavin Baker Gavin Baker of Atreides ran the math on BG2 Pod: inference revenue exits 2026 above $200B, and he calls $300B a low estimate for 2027. Ed Zitron's Ed Zitron bear case says costs scale with revenue, so the curve never bends. Baker's number says the unit economics already work at the inference layer. The catch: that is gross margin, not the whole P&L. It does not answer OpenAI's $21B operating loss. Where the bull margin math meets Zitron's receipts:

Podcast Alpha

15,261 Aufrufe • vor 2 Monaten

“We have an AI bubble.” Edward Dowd just revealed an uncomfortable truth about AI data center spending. GDP growth for Q4 2025 came in at 0.7%. But … “If you back out the data center AI GDP number, about 1.5%, which doesn’t really add to the labor force, we were negative GDP.” AI capital expenditure has been “driving a lot of the GDP, and especially Nvidia.” But Dowd warns that that’s about to change. “AI CapEx is gonna be rolling over very soon.” “We just made the power complex that much more expensive.” “We don’t have enough electricity and water to fund all this CapEx for AI.” “It’s already starting to show signs of cracking because the debt markets are questioning the ROI on the whole complex.” “There’s not enough revenue to support this CapEx spend.” Edward Dowd Jeffrey A Tucker
1:46

“We have an AI bubble.” Edward Dowd just revealed an uncomfortable truth about AI data center spending. GDP growth for Q4 2025 came in at 0.7%. But … “If you back out the data center AI GDP number, about 1.5%, which doesn’t really add to the labor force, we were negative GDP.” AI capital expenditure has been “driving a lot of the GDP, and especially Nvidia.” But Dowd warns that that’s about to change. “AI CapEx is gonna be rolling over very soon.” “We just made the power complex that much more expensive.” “We don’t have enough electricity and water to fund all this CapEx for AI.” “It’s already starting to show signs of cracking because the debt markets are questioning the ROI on the whole complex.” “There’s not enough revenue to support this CapEx spend.” Edward Dowd Jeffrey A Tucker

Brownstone Institute

23,331 Aufrufe • vor 1 Monat

Gene Munster on RiskReversal Media: AI is in the second inning. 10x wealth creation still ahead. The first inning: build the brain. Chips ($NVDA, $MU), data centers, energy. That's where we are. The second inning: inference. Applications multiply. Enterprise unlocks. Agentic AI (AI that completes multi-step tasks autonomously) reaches scale. That second inning hasn't started. That's why Gene's 2027 CapEx call is 40-60% when the street is at 22%. The spending has to continue before the applications exist. Street estimates for $MU and $NVDA in fiscal 2027 don't model a 40% CapEx year. If Gene is right, those numbers move up. The full second-inning thesis and the specific signals to watch in Q3 2026: Source: Risk Reversal Podcast -
4:31

Gene Munster on RiskReversal Media: AI is in the second inning. 10x wealth creation still ahead. The first inning: build the brain. Chips ($NVDA, $MU), data centers, energy. That's where we are. The second inning: inference. Applications multiply. Enterprise unlocks. Agentic AI (AI that completes multi-step tasks autonomously) reaches scale. That second inning hasn't started. That's why Gene's 2027 CapEx call is 40-60% when the street is at 22%. The spending has to continue before the applications exist. Street estimates for $MU and $NVDA in fiscal 2027 don't model a 40% CapEx year. If Gene is right, those numbers move up. The full second-inning thesis and the specific signals to watch in Q3 2026: Source: Risk Reversal Podcast -

Podcast Alpha

12,349 Aufrufe • vor 2 Monaten

This is the best take I have heard around the existing AI landscape today Fantastic explanation around the constraints at OpenAI and Anthropic, focus on enterprise business models, and the doomerism narrative in AI From former AI czar David Sacks "Even though Anthropic's revenue has followed this exponential graph very predictably, it cannot do that ever. The reason is that as you hit new levels of scale, you encounter new problems. You are simply going to run out of compute, electricity, data centers, and infrastructure. There are limits to the physical world you are going to hit. And there is already evidence that Anthropic is hitting some of those limits."
4:27

This is the best take I have heard around the existing AI landscape today Fantastic explanation around the constraints at OpenAI and Anthropic, focus on enterprise business models, and the doomerism narrative in AI From former AI czar David Sacks "Even though Anthropic's revenue has followed this exponential graph very predictably, it cannot do that ever. The reason is that as you hit new levels of scale, you encounter new problems. You are simply going to run out of compute, electricity, data centers, and infrastructure. There are limits to the physical world you are going to hit. And there is already evidence that Anthropic is hitting some of those limits."

Boring_Business

236,448 Aufrufe • vor 4 Monaten

The enterprise AI switch has flipped! I loved jamming w/ Ben Scharfstein who is as sharp as they come on building complex AI for the enterprise. He runs product for one of the best kept secrets in SV - the Scale AI Enterprise Applications Business - which has been a remarkable success over the past ~12 months. He shared some of his key learnings from the Scale Enterprise journey - notably how to sell+implement massive deals, what it’s like building forward deployed teams from scratch, and how delivering complex products & services is changing at the enterprise. I had a ton of fun and hope you enjoy this ep as much as I did.
47:14

The enterprise AI switch has flipped! I loved jamming w/ Ben Scharfstein who is as sharp as they come on building complex AI for the enterprise. He runs product for one of the best kept secrets in SV - the Scale AI Enterprise Applications Business - which has been a remarkable success over the past ~12 months. He shared some of his key learnings from the Scale Enterprise journey - notably how to sell+implement massive deals, what it’s like building forward deployed teams from scratch, and how delivering complex products & services is changing at the enterprise. I had a ton of fun and hope you enjoy this ep as much as I did.

Joe Schmidt IV

77,724 Aufrufe • vor 1 Jahr

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA
1:06

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA

Nick Dorsey

40,200 Aufrufe • vor 5 Monaten

Chief AI Officer at Meta a few months ago broke down the Meta Flywheel for AI. Meta holds an asymmetric advantage over pure-play AI competitors like OpenAI or Anthropic due to its 3.5B daily active users. Launching AI agents to half the global population generates immediate scale and revenue, which justifies massive capex on infrastructure, which in turn trains the next generation of frontier models. $META
1:26

Chief AI Officer at Meta a few months ago broke down the Meta Flywheel for AI. Meta holds an asymmetric advantage over pure-play AI competitors like OpenAI or Anthropic due to its 3.5B daily active users. Launching AI agents to half the global population generates immediate scale and revenue, which justifies massive capex on infrastructure, which in turn trains the next generation of frontier models. $META

CapexAndChill

21,742 Aufrufe • vor 1 Monat

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp
1:22

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp

Scale Network

18,177 Aufrufe • vor 12 Tagen

Palantir co-founder Joe Lonsdale just went on CNBC and said something that is hard to argue with, "It's crazy to think he's not going to win" (Save this). Joe Lonsdale is talking about Elon Musk, SpaceX and the single most consequential infrastructure race happening in the world right now. His core argument is that, Musk is the best hardware builder in the world at exactly the moment when trillions of dollars are flowing into hardware. SpaceX's S-1 IPO filing which dropped last month revealed the company spent approximately $20 billion on its AI division in 2025 alone, representing roughly 60% of total capital expenditure. The filing claims SpaceX has identified the largest actionable total addressable market in human history at $28.5 trillion, with $22.7 trillion of that attributed to enterprise AI applications. But the part of Lonsdale's argument that most people missed is the regulatory angle. Every regulation making it harder to build data centers on Earth, every zoning fight, every environmental objection, every piece of legislation used to slow down AI infrastructure directly benefits Musk because his answer to all of it is to build in space, where those rules simply do not apply. Every obstacle that slows down a terrestrial data center is, in practice, an argument for the orbital alternative that only SpaceX is currently positioned to deliver at scale. Bullish on Space.
0:31

Palantir co-founder Joe Lonsdale just went on CNBC and said something that is hard to argue with, "It's crazy to think he's not going to win" (Save this). Joe Lonsdale is talking about Elon Musk, SpaceX and the single most consequential infrastructure race happening in the world right now. His core argument is that, Musk is the best hardware builder in the world at exactly the moment when trillions of dollars are flowing into hardware. SpaceX's S-1 IPO filing which dropped last month revealed the company spent approximately $20 billion on its AI division in 2025 alone, representing roughly 60% of total capital expenditure. The filing claims SpaceX has identified the largest actionable total addressable market in human history at $28.5 trillion, with $22.7 trillion of that attributed to enterprise AI applications. But the part of Lonsdale's argument that most people missed is the regulatory angle. Every regulation making it harder to build data centers on Earth, every zoning fight, every environmental objection, every piece of legislation used to slow down AI infrastructure directly benefits Musk because his answer to all of it is to build in space, where those rules simply do not apply. Every obstacle that slows down a terrestrial data center is, in practice, an argument for the orbital alternative that only SpaceX is currently positioned to deliver at scale. Bullish on Space.

Milk Road AI

199,309 Aufrufe • vor 2 Monaten

Elon is a genius and there's no other way to explain what he built. The bears biggest argument against SpaceX's $1.75–2 trillion IPO valuation has always been the orbital data center play. Space based compute is unproven, technically complex, and expensive at a scale that defies easy modeling. SpaceX's own S-1 acknowledged it directly that these data centers may not achieve commercial viability. Critics were right to flag it as speculative but what they missed was that Elon was already building the hedge. While the debate about orbital compute was still theoretical, he was assembling the largest terrestrial AI infrastructure footprint on Earth. Colossus 1, now leased to Anthropic houses 230,000+ GPUs at 500 MW. Colossus 2 (Macro-Hard) runs 550,000 Blackwell GPUs targeting over 1 gigawatt enough to power roughly 750,000 American homes. The third facility, Macro-Harder, adds another ~500 MW of capacity across 810,000 square feet in Southaven, Mississippi, pushing the total campus toward 2 gigawatts and over 1 million GPUs. He secured all of this, the land, the power contracts, the chips before the rest of the market understood that power and compute would become the single most constrained resource in the AI economy. Now that constraint is everywhere and he owns the supply. Starlink is already generating $11.4 billion in annual revenue with 63% EBITDA margins and 10 million+ subscribers. The company is projecting $20 billion in total revenue across the combined entity in 2026 and that existing cash engine now funds the entire AI buildout and the Anthropic compute deal alone is estimated to generate an incremental $4–5 billion in revenue this year. The orbital data center bet may still pay off on top of all of this. But it doesn't have to, the terrestrial capacity alone, at current utilization rates, subsidizes Grok training, generates hyperscaler revenue, and floors the SpaceX valuation story against every bear case scenario. Elon didn't just build rockets but rather he built the infrastructure layer that the entire AI industry now depends on and then started selling access to the people who need it most. Go PRO at Milk Road to see how our analysts are positioning for what could become the trade of a lifetime, the SpaceX IPO and the AI infrastructure supercycle. Link below!
1:05

Elon is a genius and there's no other way to explain what he built. The bears biggest argument against SpaceX's $1.75–2 trillion IPO valuation has always been the orbital data center play. Space based compute is unproven, technically complex, and expensive at a scale that defies easy modeling. SpaceX's own S-1 acknowledged it directly that these data centers may not achieve commercial viability. Critics were right to flag it as speculative but what they missed was that Elon was already building the hedge. While the debate about orbital compute was still theoretical, he was assembling the largest terrestrial AI infrastructure footprint on Earth. Colossus 1, now leased to Anthropic houses 230,000+ GPUs at 500 MW. Colossus 2 (Macro-Hard) runs 550,000 Blackwell GPUs targeting over 1 gigawatt enough to power roughly 750,000 American homes. The third facility, Macro-Harder, adds another ~500 MW of capacity across 810,000 square feet in Southaven, Mississippi, pushing the total campus toward 2 gigawatts and over 1 million GPUs. He secured all of this, the land, the power contracts, the chips before the rest of the market understood that power and compute would become the single most constrained resource in the AI economy. Now that constraint is everywhere and he owns the supply. Starlink is already generating $11.4 billion in annual revenue with 63% EBITDA margins and 10 million+ subscribers. The company is projecting $20 billion in total revenue across the combined entity in 2026 and that existing cash engine now funds the entire AI buildout and the Anthropic compute deal alone is estimated to generate an incremental $4–5 billion in revenue this year. The orbital data center bet may still pay off on top of all of this. But it doesn't have to, the terrestrial capacity alone, at current utilization rates, subsidizes Grok training, generates hyperscaler revenue, and floors the SpaceX valuation story against every bear case scenario. Elon didn't just build rockets but rather he built the infrastructure layer that the entire AI industry now depends on and then started selling access to the people who need it most. Go PRO at Milk Road to see how our analysts are positioning for what could become the trade of a lifetime, the SpaceX IPO and the AI infrastructure supercycle. Link below!

Milk Road AI

63,920 Aufrufe • vor 3 Monaten

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

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

Crypto Rover

58,862 Aufrufe • vor 3 Monaten

"My biggest fear is most people haven't truly seen AI yet." -Rob Thomas, SVP and CCO at IBM The version of AI that truly changes the business runs on your own data. Rob Thomas tells Patrick Moorhead exactly what that looks like in practice at IBM Think 2026.
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"My biggest fear is most people haven't truly seen AI yet." -Rob Thomas, SVP and CCO at IBM The version of AI that truly changes the business runs on your own data. Rob Thomas tells Patrick Moorhead exactly what that looks like in practice at IBM Think 2026.

Six Five Media

126,905 Aufrufe • vor 3 Monaten

#WATCH | Delhi: When asked about his reaction on private capital expenditure (CAPEX) not coming in and growth is happening only because of Govt CAPEX over the last 3-4 years, Neelkanth Mishra, Executive Director for India at the World Bank Headquarters, says, "In the economy, investment to GDP, as reported by the CSO or the MoSPI, is 32% of GDP. Central government CAPEX is 3.2% of GDP. So the central government is one-tenth of overall CAPEX. Central government CAPEX as a percentage of GDP, in the last three years, has moved by 10 basis points. So you think that all of CAPEX is happening with the centre? Just because it is in the narrative and every media house is based in Delhi, it doesn't mean that the only central government is handling the economy. A large part of CAPEX happens when houses are built. Household CAPEX, capital formation, is where people add a room to their house or a floor to their house. Then someone gets a son who is married, someone, two brothers separate, and they build separate floors. Those are things which are at a population scale substantial for capital formation. Look at what is happening with steel. I mean, the other day I saw this compilation of some 100 million tons of steel capacity being planned in Odisha. That's massive. That is how CAPEX happens... CAPEX has picked up power generation. Corporate credit is starting to pick up. All these narratives that private CAPEX is not happening are like this..."
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#WATCH | Delhi: When asked about his reaction on private capital expenditure (CAPEX) not coming in and growth is happening only because of Govt CAPEX over the last 3-4 years, Neelkanth Mishra, Executive Director for India at the World Bank Headquarters, says, "In the economy, investment to GDP, as reported by the CSO or the MoSPI, is 32% of GDP. Central government CAPEX is 3.2% of GDP. So the central government is one-tenth of overall CAPEX. Central government CAPEX as a percentage of GDP, in the last three years, has moved by 10 basis points. So you think that all of CAPEX is happening with the centre? Just because it is in the narrative and every media house is based in Delhi, it doesn't mean that the only central government is handling the economy. A large part of CAPEX happens when houses are built. Household CAPEX, capital formation, is where people add a room to their house or a floor to their house. Then someone gets a son who is married, someone, two brothers separate, and they build separate floors. Those are things which are at a population scale substantial for capital formation. Look at what is happening with steel. I mean, the other day I saw this compilation of some 100 million tons of steel capacity being planned in Odisha. That's massive. That is how CAPEX happens... CAPEX has picked up power generation. Corporate credit is starting to pick up. All these narratives that private CAPEX is not happening are like this..."

ANI

26,742 Aufrufe • vor 2 Monaten

The AI bubble argument has the causality backwards. Cerebras CEO Andrew Feldman was in the 1990s fiber optic wave. He knows what a bubble looks like. In fiber, supply was built ahead of demand - companies bet on a future that hadn't arrived. AI infrastructure is the opposite: data centers can't be built fast enough to meet demand that is already here. Cerebras has $25B in backlog. NVIDIA has a backlog. AMD has a backlog. That is not a bubble. That is a queue. If you are short AI infrastructure on bubble thesis, Feldman's framing is the counter-argument you need to engage with. Feldman's full structural argument - including the fiber optic and railroad comparisons:
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The AI bubble argument has the causality backwards. Cerebras CEO Andrew Feldman was in the 1990s fiber optic wave. He knows what a bubble looks like. In fiber, supply was built ahead of demand - companies bet on a future that hadn't arrived. AI infrastructure is the opposite: data centers can't be built fast enough to meet demand that is already here. Cerebras has $25B in backlog. NVIDIA has a backlog. AMD has a backlog. That is not a bubble. That is a queue. If you are short AI infrastructure on bubble thesis, Feldman's framing is the counter-argument you need to engage with. Feldman's full structural argument - including the fiber optic and railroad comparisons:

Podcast Alpha

16,010 Aufrufe • vor 3 Monaten