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Nvidia $NVDA is at roughly a $160B run rate for the data center. In an interview with Caroline Hyde from Bloomberg Technology, I discuss the path to a $300B run rate plus the constraints that Nvidia and the broader AI market face as we approach future generations of GPUs.

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Beth Kindig

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905,452 просмотров • 1 год назад •via X (Twitter)

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There will be a moment when companies are able to monetize AI to where capex costs are absorbed. In terms of the ability to monetize AI, this is being proven as we speak with OpenAI exhibiting the fastest run rate from $0 to $20 billion in all of tech’s history. I joined Bloomberg Technology to discuss how the incoming AI monetization wave can help absorb elevated capex costs, and how the AI accelerator market will widen beyond Nvidia $NVDA and benefit other firms such as Broadcom $AVGO.
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There will be a moment when companies are able to monetize AI to where capex costs are absorbed. In terms of the ability to monetize AI, this is being proven as we speak with OpenAI exhibiting the fastest run rate from $0 to $20 billion in all of tech’s history. I joined Bloomberg Technology to discuss how the incoming AI monetization wave can help absorb elevated capex costs, and how the AI accelerator market will widen beyond Nvidia $NVDA and benefit other firms such as Broadcom $AVGO.

Beth Kindig

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Last week, I joined Bloomberg Tech in-studio in New York to talk about the critical shortages we are seeing from the AI data center buildout, and how to navigate AI stocks in this complex market. Ed Ludlow Caroline Hyde $NVDA $AMD
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Last week, I joined Bloomberg Tech in-studio in New York to talk about the critical shortages we are seeing from the AI data center buildout, and how to navigate AI stocks in this complex market. Ed Ludlow Caroline Hyde $NVDA $AMD

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Though US export restrictions threaten a major market for Nvidia in China, I think it’s a bullish demand signal for $NVDA. Below I explain why and what I see for the rest of 2025 for the AI chip leader. Bloomberg Technology
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Though US export restrictions threaten a major market for Nvidia in China, I think it’s a bullish demand signal for $NVDA. Below I explain why and what I see for the rest of 2025 for the AI chip leader. Bloomberg Technology

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AI adoption surging - but how do we expand deployment beyond hyperscalers? At #GTC25, Patrick Moorhead & Cisco's Kevin Wollenweber discuss Cisco’s vision for bringing #AI to all PLUS what the announcement of the Cisco Secure AI Factory with NVIDIA could mean for the future. $CSCO $NVDA
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TOM LEE SAYS NVIDIA $NVDA IS A SENTIMENT BAROMETER FOR THE AI TRADE He thinks the stock’s valuation is still far from signaling peak AI enthusiasm: - Thomas (Tom) Lee (not drummer) FundstratDirect.com agrees that NVIDIA’s valuation multiple is capped - He says the stock’s multiple has remained stuck rather than aggressively re-rating higher - Investors cannot be truly euphoric about AI until NVIDIA follows a Cisco-like path and re-rates toward the broader market Traders still price a 73% chance that NVIDIA remains the world’s largest company by market cap at the end of 2026.
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TOM LEE SAYS NVIDIA $NVDA IS A SENTIMENT BAROMETER FOR THE AI TRADE He thinks the stock’s valuation is still far from signaling peak AI enthusiasm: - Thomas (Tom) Lee (not drummer) FundstratDirect.com agrees that NVIDIA’s valuation multiple is capped - He says the stock’s multiple has remained stuck rather than aggressively re-rating higher - Investors cannot be truly euphoric about AI until NVIDIA follows a Cisco-like path and re-rates toward the broader market Traders still price a 73% chance that NVIDIA remains the world’s largest company by market cap at the end of 2026.

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"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.
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"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.

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$NVDA - NVIDIA CEO ON CHINA Nvidia CEO says China is a “bonus opportunity” for the future. Currently, the company is focused on strong global demand, which is skyrocketing. If China becomes a key market, it would benefit Nvidia, the U.S., and the world.
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$NVDA - NVIDIA CEO ON CHINA Nvidia CEO says China is a “bonus opportunity” for the future. Currently, the company is focused on strong global demand, which is skyrocketing. If China becomes a key market, it would benefit Nvidia, the U.S., and the world.

*Walter Bloomberg

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🚨 NVIDIA IS GETTING HIT FROM BOTH SIDES CHINA JUST COMPLETELY BANNED NVIDIA CHIPS BEIJING CANCELED THE GREEN LIGHT FOR TOP CHINESE COMPANIES TO BUY $NVDA CHIPS CHINA WOULD RATHER BUILD ITS OWN AI SUPPLY CHAIN THAN DEPEND ON AMERICA AT THE SAME TIME, NVIDIA CUT ITS INITIAL SUPPORT FOR OPENAI’S OHIO DATA CENTER FROM $250 BILLION TO LESS THAN $120 BILLION CHINA IS BACKING AWAY FROM NVIDIA AND NVIDIA IS STARTING TO BACK AWAY FROM THE AI BOOM THIS IS NOT JUST ABOUT ONE CHIPMAKER THE ENTIRE AI TRADE IS STARTING TO CHANGE
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🚨 NVIDIA IS GETTING HIT FROM BOTH SIDES CHINA JUST COMPLETELY BANNED NVIDIA CHIPS BEIJING CANCELED THE GREEN LIGHT FOR TOP CHINESE COMPANIES TO BUY $NVDA CHIPS CHINA WOULD RATHER BUILD ITS OWN AI SUPPLY CHAIN THAN DEPEND ON AMERICA AT THE SAME TIME, NVIDIA CUT ITS INITIAL SUPPORT FOR OPENAI’S OHIO DATA CENTER FROM $250 BILLION TO LESS THAN $120 BILLION CHINA IS BACKING AWAY FROM NVIDIA AND NVIDIA IS STARTING TO BACK AWAY FROM THE AI BOOM THIS IS NOT JUST ABOUT ONE CHIPMAKER THE ENTIRE AI TRADE IS STARTING TO CHANGE

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Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.
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Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

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641,147 просмотров • 6 месяцев назад

The Financial Times reported that NVIDIA is going to backstop a $50B lease for a data center in Texas. But tae kim argues the headline doesn't tell the full story. The $50B figure only applies if NVIDIA renews the lease after 15 years, bringing the total lease value to $50B over 30 years, or roughly $1B to $2B/year. A $1B to $2B annual commitment is "a rounding error" for NVIDIA, which is generating roughly $320B in annual revenue, according to Tae Kim. "This is not a story, but this is how people run with sensationalized headlines and people panic and freak out."
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The Financial Times reported that NVIDIA is going to backstop a $50B lease for a data center in Texas. But tae kim argues the headline doesn't tell the full story. The $50B figure only applies if NVIDIA renews the lease after 15 years, bringing the total lease value to $50B over 30 years, or roughly $1B to $2B/year. A $1B to $2B annual commitment is "a rounding error" for NVIDIA, which is generating roughly $320B in annual revenue, according to Tae Kim. "This is not a story, but this is how people run with sensationalized headlines and people panic and freak out."

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Carwan Rasoal NVIDIA NVIDIA AI The idea that there is a trinity between training, omniverse, and run time is absolute genius. 🔼
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Carwan Rasoal NVIDIA NVIDIA AI The idea that there is a trinity between training, omniverse, and run time is absolute genius. 🔼

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Gene Munster Gene Munster on Nvidia: "I expect that next year, calendar 27, will probably grow at 90%. That means growth rate in calendar 27 will be the same as it is in calendar 26...we are probably in the 3rd inning of this buildout. I think we are very early." 90% from CY26 is close to $800 billion in revenue. Via Bloomberg TV $NVDA $GOOG $MSFT $AMZN $META
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Gene Munster Gene Munster on Nvidia: "I expect that next year, calendar 27, will probably grow at 90%. That means growth rate in calendar 27 will be the same as it is in calendar 26...we are probably in the 3rd inning of this buildout. I think we are very early." 90% from CY26 is close to $800 billion in revenue. Via Bloomberg TV $NVDA $GOOG $MSFT $AMZN $META

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Class is in session with NVIDIA AI. Join Ivan Nardini and Katja Sirazitdinova for a hands-on course that walks through the end-to-end journey of building, profiling, and scaling JAX models on NVIDIA GPUs—from initial setup to production serving →
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Class is in session with NVIDIA AI. Join Ivan Nardini and Katja Sirazitdinova for a hands-on course that walks through the end-to-end journey of building, profiling, and scaling JAX models on NVIDIA GPUs—from initial setup to production serving →

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Zuck META AI update. 2GW data center, could power 1M - 2M Nvidia Blackwell GPUs. A similar number of GPUs from Elon's xAI. Then other LLM providers like OpenAI, etc. The race is still well and live.
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Zuck META AI update. 2GW data center, could power 1M - 2M Nvidia Blackwell GPUs. A similar number of GPUs from Elon's xAI. Then other LLM providers like OpenAI, etc. The race is still well and live.

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