Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

$META is already using AI to improve its ad engine which makes it a clear winner in the AI economy. The market still wants proof that its massive infrastructure spend can create revenue beyond better ads which is why the compute business matters. If Meta monetizes just 20% of... show more

Shay Boloor

427,876 subscribers

81,247 views • 1 day ago •via X (Twitter)

Finance Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

$NBIS is not building a business around renting bare metal to hyperscalers. $NBIS is building toward becoming a hyperscaler itself. The CEO explained that the $27B deal with $META is effectively split into two parts: • $12B for providing bare metal capacity to $META • $15B of backstop capacity allowing $NBIS to leverage $META commitment to secure lower-cost financing for its own cloud infrastructure $NBIS isn't just selling capacity. It is using $META’s financial strength to accelerate the buildout of its own AI cloud platform while targeting thousands of AI companies and enterprises. This deal is bigger than revenue. It is strengthening the foundation for what $NBIS wants to become.

$NBIS is not building a business around renting bare metal to hyperscalers. $NBIS is building toward becoming a hyperscaler itself. The CEO explained that the $27B deal with $META is effectively split into two parts: • $12B for providing bare metal capacity to $META • $15B of backstop capacity allowing $NBIS to leverage $META commitment to secure lower-cost financing for its own cloud infrastructure $NBIS isn't just selling capacity. It is using $META’s financial strength to accelerate the buildout of its own AI cloud platform while targeting thousands of AI companies and enterprises. This deal is bigger than revenue. It is strengthening the foundation for what $NBIS wants to become.

Daniel

39,676 views • 1 month ago

$CBRS is a very unique company because it keeps a huge amount of compute and memory very close together which helps it deliver extremely fast inference and makes the architecture well positioned for agentic AI workloads where fast tokens matter more. OpenAI validated the tec but the stock is getting hit because we still don't have enough visibility into how Cerebras turns that technical advantage into a broad, diversified and high-margin business. Customer concentration remains a massive risk especially with so much revenue tied to its own investor and the company still has to solve the physical infrastructure side of the business across power, cooling, data center capacity and deployment timing. If $NVDA is trading at a below-market multiple while still growing faster than Cerebras then why buy the Nvidia alternative instead of just owning Nvidia?

$CBRS is a very unique company because it keeps a huge amount of compute and memory very close together which helps it deliver extremely fast inference and makes the architecture well positioned for agentic AI workloads where fast tokens matter more. OpenAI validated the tec but the stock is getting hit because we still don't have enough visibility into how Cerebras turns that technical advantage into a broad, diversified and high-margin business. Customer concentration remains a massive risk especially with so much revenue tied to its own investor and the company still has to solve the physical infrastructure side of the business across power, cooling, data center capacity and deployment timing. If $NVDA is trading at a below-market multiple while still growing faster than Cerebras then why buy the Nvidia alternative instead of just owning Nvidia?

Shay Boloor

190,537 views • 13 days ago

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

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

21,645 views • 4 days ago

Meta signed a 20 billion dollar compute deal in April and by June was quietly trying to sell the leftovers, and Dan Niles went on CNBC to explain why that flip means the AI building boom may have already peaked. That timeline should stop you. For two years, the whole AI bet ran on one idea: that nobody has enough compute, and everyone wants more, and the demand never ends. Then Meta signed a 20 billion dollar deal in April. By June they were shopping the extra capacity to their rivals. Niles explained what broke. Companies used to burn as many tokens as they could to look busy. Then they started cutting. His example is the one that sticks. Using the smartest model to summarize your emails is like driving a Ferrari to the corner store for milk. You do not need it. Coinbase cut its AI spend in half while it used the tools more. Uber burned its whole budget in four months, then slashed it. When the biggest spenders suddenly have compute to sell, the story cracks. Everyone is still pricing these stocks like demand only climbs. The people paying the bills just found the off switch.

Meta signed a 20 billion dollar compute deal in April and by June was quietly trying to sell the leftovers, and Dan Niles went on CNBC to explain why that flip means the AI building boom may have already peaked. That timeline should stop you. For two years, the whole AI bet ran on one idea: that nobody has enough compute, and everyone wants more, and the demand never ends. Then Meta signed a 20 billion dollar deal in April. By June they were shopping the extra capacity to their rivals. Niles explained what broke. Companies used to burn as many tokens as they could to look busy. Then they started cutting. His example is the one that sticks. Using the smartest model to summarize your emails is like driving a Ferrari to the corner store for milk. You do not need it. Coinbase cut its AI spend in half while it used the tools more. Uber burned its whole budget in four months, then slashed it. When the biggest spenders suddenly have compute to sell, the story cracks. Everyone is still pricing these stocks like demand only climbs. The people paying the bills just found the off switch.

Ihtesham Ali

65,321 views • 5 days ago

Facebook $META CEO Mark Zuckerberg just announced the release of its newest version of Meta AI "We believe Meta AI is now the most intelligent AI assistant that you can freely use"

Facebook $META CEO Mark Zuckerberg just announced the release of its newest version of Meta AI "We believe Meta AI is now the most intelligent AI assistant that you can freely use"

Evan

519,009 views • 2 years ago

$$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.$

$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

181,980 views • 7 months ago

On Rogan, Zuckerberg says he doesn't know what the CFPB is or why it's investigating Meta. The truth is the Consumer Financial Protection Bureau defends people from corporate abuse. Meta has been accused of illegally using people's financial data in its ad business.

On Rogan, Zuckerberg says he doesn't know what the CFPB is or why it's investigating Meta. The truth is the Consumer Financial Protection Bureau defends people from corporate abuse. Meta has been accused of illegally using people's financial data in its ad business.

More Perfect Union

2,578,279 views • 1 year ago

SoftBank is creating the biggest data center. On Fox Business, Arm CEO and SoftBank Group International CEO Rene Haas was asked what he thinks about Meta selling compute, which could mean a compute supply glut. Haas disagreed, pointing to SpaceX's compute deal with Anthropic as proof of real demand, not oversupply. He said Meta's situation is similar, and that only a handful of companies, SpaceX, Meta, and SoftBank, have the financial muscle to compete at gigawatt scale, which is why SoftBank is moving in.

SoftBank is creating the biggest data center. On Fox Business, Arm CEO and SoftBank Group International CEO Rene Haas was asked what he thinks about Meta selling compute, which could mean a compute supply glut. Haas disagreed, pointing to SpaceX's compute deal with Anthropic as proof of real demand, not oversupply. He said Meta's situation is similar, and that only a handful of companies, SpaceX, Meta, and SoftBank, have the financial muscle to compete at gigawatt scale, which is why SoftBank is moving in.

Lark Davis

34,017 views • 5 days ago

Here's what Zuckerberg doesn't want you to know: Meta is willfully pirating droves of copyrighted content to train its AI models And then the company turns to its lawyers & lobbyists to get off scot-free. Meta doesn't care about the law - it cares about profit

Here's what Zuckerberg doesn't want you to know: Meta is willfully pirating droves of copyrighted content to train its AI models And then the company turns to its lawyers & lobbyists to get off scot-free. Meta doesn't care about the law - it cares about profit

Josh Hawley

516,743 views • 11 months ago

I still own $IREN because I think the market is underestimating both the $NVDA alignment and how hard its power footprint is to replicate. If the company can turn ~5 GW of secured power into Nvidia-aligned AI cloud capacity then the market may start valuing the 10x gap between its power base and near-term AI cloud needs.

I still own $IREN because I think the market is underestimating both the $NVDA alignment and how hard its power footprint is to replicate. If the company can turn ~5 GW of secured power into Nvidia-aligned AI cloud capacity then the market may start valuing the 10x gap between its power base and near-term AI cloud needs.

Shay Boloor

1,314,709 views • 1 month ago

$ORCL is borrowing aggressively to build AI data centers for a customer base heavily tied to OpenAI and the market is starting to question whether Oracle can fund that buildout without $MSFT, $GOOGL, $AMZN or $META level cash generation. The upside is that OCI now has one of the largest AI backlogs in the market at ~$640B which gives Oracle a real shot at becoming the fourth major AI cloud platform if that demand converts into durable revenue. If cloud growth stays above 50% and customer prepayments ease the funding burden then this selloff could eventually look like the painful capital cycle before the harvest phase.

$ORCL is borrowing aggressively to build AI data centers for a customer base heavily tied to OpenAI and the market is starting to question whether Oracle can fund that buildout without $MSFT, $GOOGL, $AMZN or $META level cash generation. The upside is that OCI now has one of the largest AI backlogs in the market at ~$640B which gives Oracle a real shot at becoming the fourth major AI cloud platform if that demand converts into durable revenue. If cloud growth stays above 50% and customer prepayments ease the funding burden then this selloff could eventually look like the painful capital cycle before the harvest phase.

Shay Boloor

117,893 views • 7 days ago

$IREN owns and develops large-scale, power-dense data center campuses in energy-rich regions for AI cloud, GPU clusters and build-to-suit data centers. Today’s 800MW Australia announcement reinforces the thesis that IREN can turn massive power capacity into AI infrastructure revenue faster than the market expected.

$IREN owns and develops large-scale, power-dense data center campuses in energy-rich regions for AI cloud, GPU clusters and build-to-suit data centers. Today’s 800MW Australia announcement reinforces the thesis that IREN can turn massive power capacity into AI infrastructure revenue faster than the market expected.

Shay Boloor

164,255 views • 1 month ago

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.

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

120,233 views • 5 months ago

Meta Platforms Inc. has selected Pacific Investment Management Co. and Blue Owl Capital Inc. to lead a $29B financing for its data center expansion in rural Louisiana as the race for AI infrastructure heats up.

Meta Platforms Inc. has selected Pacific Investment Management Co. and Blue Owl Capital Inc. to lead a $29B financing for its data center expansion in rural Louisiana as the race for AI infrastructure heats up.

Bloomberg

35,197 views • 11 months ago

Druckenmiller nailed this The present is already priced. You have to position where the fundamentals are going That is why I leaned into AI & space in 2023 when it became clear compute would become a utility & defense would expand beyond Earth See it early & size it correctly

Druckenmiller nailed this The present is already priced. You have to position where the fundamentals are going That is why I leaned into AI & space in 2023 when it became clear compute would become a utility & defense would expand beyond Earth See it early & size it correctly

Shay Boloor

187,465 views • 8 months ago

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter just said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Milk Road Pro remains massively bullish on Nebius, we called it early, we are up huge on the position, and we continue to track every development across AI infrastructure before it becomes obvious to the rest of the market. Come join us to see our full Nebius thesis and every other position in the portfolio, link below!

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter just said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Milk Road Pro remains massively bullish on Nebius, we called it early, we are up huge on the position, and we continue to track every development across AI infrastructure before it becomes obvious to the rest of the market. Come join us to see our full Nebius thesis and every other position in the portfolio, link below!

Milk Road AI

216,498 views • 1 month ago

I’ve seen that when AI moves from prototype to production, infrastructure becomes strategy. A business cannot improvise at the edge if its network still hesitates at the core. The challenge is not to scale AI, but to let it perform without friction. T-Mobile Business Partner

I’ve seen that when AI moves from prototype to production, infrastructure becomes strategy. A business cannot improvise at the edge if its network still hesitates at the core. The challenge is not to scale AI, but to let it perform without friction. T-Mobile Business Partner

Antonio Grasso

10,947 views • 10 months ago

Sundar Pichai reaffirms his belief that AI is the most profound technology humanity will ever create—surpassing even fire, electricity, or the internet. He reflects on whether that view could be influenced by recency bias but ultimately argues that AI’s speed, scope, and potential set it apart from anything in history. What makes AI truly different, Pichai says, is its ability to improve itself and accelerate the act of creation. Unlike past technologies that enhanced human effort, AI can independently generate ideas, build solutions, and evolve. This recursive, compounding effect may place it in a league of its own, making it, in the long run, the greatest productivity multiplier ever.

Sundar Pichai reaffirms his belief that AI is the most profound technology humanity will ever create—surpassing even fire, electricity, or the internet. He reflects on whether that view could be influenced by recency bias but ultimately argues that AI’s speed, scope, and potential set it apart from anything in history. What makes AI truly different, Pichai says, is its ability to improve itself and accelerate the act of creation. Unlike past technologies that enhanced human effort, AI can independently generate ideas, build solutions, and evolve. This recursive, compounding effect may place it in a league of its own, making it, in the long run, the greatest productivity multiplier ever.

Wes Roth

57,483 views • 1 year ago

Oracle predicted that the AI data center boom will power its revenue above Wall Street estimates well into 2027, sending its shares up 8.3% in extended trading

Oracle predicted that the AI data center boom will power its revenue above Wall Street estimates well into 2027, sending its shares up 8.3% in extended trading

Reuters

39,456 views • 4 months ago