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The $1 trillion narrative around NVIDIA becomes clearer when structured against actual numbers and timelines. 1. 𝗧𝗵𝗲 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲 In 2020, NVIDIA generated ~$10.9B in annual revenue 2. 𝗧𝗵𝗲 𝗴𝗿𝗼𝘄𝘁𝗵 𝗰𝘂𝗿𝘃𝗲 FY2022 → ~$26.97B FY2024 → ~$60.9B FY2025 → ~$130.5B FY2026 → ~$215.9B ~𝟮𝟬𝘅 𝗴𝗿𝗼𝘄𝘁𝗵 𝗶𝗻 𝘀𝗶𝘅 𝘆𝗲𝗮𝗿𝘀 🤯 3.... 𝗧𝗵𝗲 𝗱𝗲𝗺𝗮𝗻𝗱 𝗲𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 At GTC, Jensen Huang outlined: ~$1 trillion in cumulative demand for Blackwell and Vera Rubin through 2027 up from ~$500B projected through 2026 just one year ago Demand expectations doubled within 12 months 4. 𝗧𝗵𝗲 𝘀𝗰𝗮𝗹𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 ~$1T across two product lines ≈ ~4.6x FY2026 revenue This excludes gaming, automotive, and other segments Even ~50% conversion implies a pathway toward ~$500B annual revenue within this decade 5. 𝗧𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗯𝗮𝗰𝗸𝗱𝗿𝗼𝗽 Top cloud companies are expected to invest ~$700B in data center infrastructure in 2026 Long-term projections point toward ~$3T–$4T annually by 2030 6. 𝗧𝗵𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗹𝗼𝗼𝗽 AI adoption → rising inference demand Inference demand → infrastructure investment Infrastructure → higher capability Capability → increased usage 𝐀 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝𝐢𝐧𝐠 𝐜𝐲𝐜𝐥𝐞 7. 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 Compute evolves into a primary economic layer Every AI interaction consumes it Every enterprise integrating AI scales it 𝗙𝗶𝗻𝗮𝗹 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The next decade aligns growth with access to compute And the companies controlling it define the pace and limits of that growthshow more

Jaymin Shah

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306,435 次观看 • 3 个月前 •via X (Twitter)

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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!

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 次观看 • 23 天前

"I see through 2027, at least $1 trillion" of demand for Blackwell and Rubin, Nvidia CEO Jensen Huang says.

"I see through 2027, at least $1 trillion" of demand for Blackwell and Rubin, Nvidia CEO Jensen Huang says.

Yahoo Finance

191,519 次观看 • 3 个月前

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Dustin

28,395 次观看 • 4 个月前

NEWS: Nvidia CEO Jensen Huang just announced that he sees at least $1 trillion in revenue by 2027, expects computing demand to exceed that. “We are now a computing platform that runs all of AI.”

NEWS: Nvidia CEO Jensen Huang just announced that he sees at least $1 trillion in revenue by 2027, expects computing demand to exceed that. “We are now a computing platform that runs all of AI.”

Sawyer Merritt

340,344 次观看 • 3 个月前

Nvidia CEO Jensen Huang predicts $1 trillion in annual spend on AI infrastructure by the end of the decade. This is the biggest boom in history. With the Trump administration enabling easier permitting and new power generation, it’s time to build!

Nvidia CEO Jensen Huang predicts $1 trillion in annual spend on AI infrastructure by the end of the decade. This is the biggest boom in history. With the Trump administration enabling easier permitting and new power generation, it’s time to build!

David Sacks

431,427 次观看 • 1 年前

Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!

Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!

Milk Road AI

14,578 次观看 • 18 天前

"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

NVIDIA

81,834 次观看 • 3 个月前

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.

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 次观看 • 1 个月前

We’re seeing massive investment in U.S. manufacturing and innovation. • Apple is increasing its commitment from $500B to $600B to build in America, not just design here. • Micron is investing $200B for production of advanced memory chips. • IBM announced a $150B investment over the next five years. • NVIDIA said it will invest $500B in AI infrastructure. • Softbank, OpenAI, and Oracle are bringing together $500B investment in AI infrastructure. Trillions are coming back, and this is just the beginning.

We’re seeing massive investment in U.S. manufacturing and innovation. • Apple is increasing its commitment from $500B to $600B to build in America, not just design here. • Micron is investing $200B for production of advanced memory chips. • IBM announced a $150B investment over the next five years. • NVIDIA said it will invest $500B in AI infrastructure. • Softbank, OpenAI, and Oracle are bringing together $500B investment in AI infrastructure. Trillions are coming back, and this is just the beginning.

Treasury Secretary Scott Bessent

222,569 次观看 • 11 个月前

"Nvidia is positioned perfectly to thrive on the coding agent wave" and the explosion in inference demand, says tae kim. "I met with Ian Buck and dozens of engineers at Meta, Google, and Nvidia. All of them are seeing crazy inference demand and AI compute shortages." "People are building bots to pick up any kind of B200 GPUs they can find, but they're waiting weeks or months." "Jensen is very prescient. He probably saw this demand months away. He locked up all the supply agreements for memory, CoWoS, and connectors ahead of time to take advantage of the coding-assistant boom." "It's almost like a gold rush. You see OpenAI pivoting toward it, Anthropic is obviously thriving on it. Billions of ARR every few weeks." "Jensen acquired Groq's assets and people. And the combination of integrating Groq's technology with Vera Rubin lets Nvidia serve this tremendous wave of compute demand economically."

"Nvidia is positioned perfectly to thrive on the coding agent wave" and the explosion in inference demand, says tae kim. "I met with Ian Buck and dozens of engineers at Meta, Google, and Nvidia. All of them are seeing crazy inference demand and AI compute shortages." "People are building bots to pick up any kind of B200 GPUs they can find, but they're waiting weeks or months." "Jensen is very prescient. He probably saw this demand months away. He locked up all the supply agreements for memory, CoWoS, and connectors ahead of time to take advantage of the coding-assistant boom." "It's almost like a gold rush. You see OpenAI pivoting toward it, Anthropic is obviously thriving on it. Billions of ARR every few weeks." "Jensen acquired Groq's assets and people. And the combination of integrating Groq's technology with Vera Rubin lets Nvidia serve this tremendous wave of compute demand economically."

TBPN

32,912 次观看 • 3 个月前

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

Dami-Defi

16,080 次观看 • 2 个月前

Nvidia CEO Jensen Huang said the company could generate at least $1 trillion in revenue from its AI chips by 2027, as it ramps up competition in the fast-growing real-time AI computing market. Read:

Nvidia CEO Jensen Huang said the company could generate at least $1 trillion in revenue from its AI chips by 2027, as it ramps up competition in the fast-growing real-time AI computing market. Read:

Reuters

50,979 次观看 • 3 个月前

$1 TRILLION in orders. 21 months still to go. Jensen Huang at GTC just casually redefined what demand looks like in the AI era. This is not a drill.

$1 TRILLION in orders. 21 months still to go. Jensen Huang at GTC just casually redefined what demand looks like in the AI era. This is not a drill.

Lark Davis

29,770 次观看 • 3 个月前

📣 CoreWeave and NVIDIA are accelerating the build out of AI factories—token generators that directly drive revenue. This includes adoption of and support for: ✅ NVIDIA Vera Rubin deployment, starting in 2H 2026 ✅ NVIDIA Vera CPU and BlueField-4 STX ✅ NVIDIA Dynamo for inference and NVIDIA Agent Toolkit and OpenShell for long-running agents ✅ NVIDIA DSX Air for digital twins of AI factories Learn more ➡️

📣 CoreWeave and NVIDIA are accelerating the build out of AI factories—token generators that directly drive revenue. This includes adoption of and support for: ✅ NVIDIA Vera Rubin deployment, starting in 2H 2026 ✅ NVIDIA Vera CPU and BlueField-4 STX ✅ NVIDIA Dynamo for inference and NVIDIA Agent Toolkit and OpenShell for long-running agents ✅ NVIDIA DSX Air for digital twins of AI factories Learn more ➡️

NVIDIA AI Infrastructure

10,213 次观看 • 2 个月前

From Nvidia CEO Jensen Huang at GTC 2026; "Computing Demand has increased by 1M times in the last 2 years"

From Nvidia CEO Jensen Huang at GTC 2026; "Computing Demand has increased by 1M times in the last 2 years"

CNET

8,251,590 次观看 • 3 个月前

Jensen Huang: OpenAI & Anthropic are struggling to keep up with demand. The *exponential* growth in AI compute, adoption, and applications is immense - never seen in humanity. Supporting their scaling is critical.

Jensen Huang: OpenAI & Anthropic are struggling to keep up with demand. The exponential growth in AI compute, adoption, and applications is immense - never seen in humanity. Supporting their scaling is critical.

Chubby♨️

44,006 次观看 • 7 个月前

AI INFRASTRUCTURE/ENERGY DEMAND STOCKS TO KEEP ON WATCH FOR 2026 Ex-CEO $GOOGL Eric Schmidt on energy demand driven by AI: "We need energy and the numbers are profound. What we need from you is energy in all forms. People are planning 10 gigawatt data centers" $IREN: owns and develops vertically integrated data centers for hyperscalers $CIFR: large power footprint, 3.4 GW pipeline for AI workloads + HPC $WULF: high capacity, low cost data center expansion across Texas and New York $NBIS: AI compute and cloud services translating energy into revenue $CLSK: America’s Bitcoin Miner transitioning to AI infrastructure with a massive power footprint across 30+ data center sites $BITF: owns and operates power sites positioned for AI workloads, yet to sign with a hyperscaler but could come this year $GLXY: multi-gigawatt data center infrastructure at scale

AI INFRASTRUCTURE/ENERGY DEMAND STOCKS TO KEEP ON WATCH FOR 2026 Ex-CEO $GOOGL Eric Schmidt on energy demand driven by AI: "We need energy and the numbers are profound. What we need from you is energy in all forms. People are planning 10 gigawatt data centers" $IREN: owns and develops vertically integrated data centers for hyperscalers $CIFR: large power footprint, 3.4 GW pipeline for AI workloads + HPC $WULF: high capacity, low cost data center expansion across Texas and New York $NBIS: AI compute and cloud services translating energy into revenue $CLSK: America’s Bitcoin Miner transitioning to AI infrastructure with a massive power footprint across 30+ data center sites $BITF: owns and operates power sites positioned for AI workloads, yet to sign with a hyperscaler but could come this year $GLXY: multi-gigawatt data center infrastructure at scale

Small Cap Snipa

116,034 次观看 • 6 个月前

Amazon, Kroger, Vanguard, BlackRock ~220%+ Growth and $120M+ Revenue Target in 2026: Why NextNRG (NASDAQ: $NXXT) Looks Massively Undervalued NextNRG Inc. has entered a clear hypergrowth phase while still trading at a valuation that implies far more modest expectations. 2024 revenue: ~$28M 2025 run-rate: ~$80–85M (+185–200% YoY growth) 2026 projected revenue: $120M+ Active clients: 700+ including Amazon, Kroger Investors include Vanguard, BlackRock.. Valuation: ~1× forward revenue, compared with 5–15× for energy infrastructure peers With triple-digit YoY growth already demonstrated, visibility toward $120M+ in 2026 revenue, and direct exposure to grid instability, AI-driven power demand, and fleet electrification, NextNRG appears positioned for continued expansion while remaining materially undervalued at current levels. DYOR

Amazon, Kroger, Vanguard, BlackRock ~220%+ Growth and $120M+ Revenue Target in 2026: Why NextNRG (NASDAQ: $NXXT) Looks Massively Undervalued NextNRG Inc. has entered a clear hypergrowth phase while still trading at a valuation that implies far more modest expectations. 2024 revenue: ~$28M 2025 run-rate: ~$80–85M (+185–200% YoY growth) 2026 projected revenue: $120M+ Active clients: 700+ including Amazon, Kroger Investors include Vanguard, BlackRock.. Valuation: ~1× forward revenue, compared with 5–15× for energy infrastructure peers With triple-digit YoY growth already demonstrated, visibility toward $120M+ in 2026 revenue, and direct exposure to grid instability, AI-driven power demand, and fleet electrification, NextNRG appears positioned for continued expansion while remaining materially undervalued at current levels. DYOR

Victor Renard

22,343,671 次观看 • 5 个月前

Foxconn posted a 17% rise in Q3 profit, driven by AI server demand, and forecasts significant year-on-year growth for 2026 revenue

Foxconn posted a 17% rise in Q3 profit, driven by AI server demand, and forecasts significant year-on-year growth for 2026 revenue

Reuters

20,584 次观看 • 7 个月前