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🔥 Tyler on $CIFR’s Direct Compute Vision When asked about direct compute, Tyler revealed they’re exploring a 56 MW GPU pilot program. He’s evaluating options to rent, lease, or return GPUs for credit — a model that keeps hardware fresh every few years. But he emphasized one thing: it...

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$NVDA CEO Jensen Huang revealed the Five Layer AI Model These 5 layers include: 1) Chips - $AMD $AVGO $NVDA 2) Memory - $MU $SNDK $DRAM 3) Photonics - $AAOI $AXTI $AEHR 4) Compute - $CIFR $IREN $NBIS 5) Energy - $BE $FLNC $TE
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$NVDA CEO Jensen Huang revealed the Five Layer AI Model These 5 layers include: 1) Chips - $AMD $AVGO $NVDA 2) Memory - $MU $SNDK $DRAM 3) Photonics - $AAOI $AXTI $AEHR 4) Compute - $CIFR $IREN $NBIS 5) Energy - $BE $FLNC $TE

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Day 97. Miners + AI compute plays always snap back the fastest. Difficulty at ATHs, AI datacenter spend exploding, and HUT/IREN/CIFR expanding while others retreat. This is the setup for violent mean reversion. The reversal will be faster than the drawdown.
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Day 97. Miners + AI compute plays always snap back the fastest. Difficulty at ATHs, AI datacenter spend exploding, and HUT/IREN/CIFR expanding while others retreat. This is the setup for violent mean reversion. The reversal will be faster than the drawdown.

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“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever
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“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

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🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.
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🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.

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If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.
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If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.

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Testing image-to-CAD for SR Platform on a new set of GPUs 🔥 We are working with lium.io (SN51) to showcase the efficiency of decentralized compute for SR Platform — from CAD generation to RL training and sim-to-real validation What it means: > Flexible, on-demand rental for us. Pay with crypto. > Diverse GPU options, powered by Openτensor Foundaτion's incentive alignment. > Lower equipment cost → higher margin for the network. Onchain robots, onchain compute.

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Compute is the new oil, bottlenecked by GPU scarcity. Enter The Render Network — connecting idle consumer GPUs into a decentralized compute network. This is DePIN on Solana.
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Jensen Huang’s interview had a few important tells. The $NVDA GPU depreciation overhang on AI utilities like $NBIS, $IREN, $CIFR & $CRWV is fading right as hyperscaler CapEx + GPU demand re-accelerate. This is still early innings of the largest industrial buildout of our time.
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Jensen Huang’s interview had a few important tells. The $NVDA GPU depreciation overhang on AI utilities like $NBIS, $IREN, $CIFR & $CRWV is fading right as hyperscaler CapEx + GPU demand re-accelerate. This is still early innings of the largest industrial buildout of our time.

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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
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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

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10 stocks to buy and hold for the next 10 years: 1/ IREN Iren is redefining data center infrastructure by building THE world’s most efficient platforms for powering AI compute with 100% renewable energy. As the CEO says, the industry is growing so rapidly, "They can't meet the demand". - 3GW Contracted Power - $9.7B $MSFT Contract - Next Hyperscaler deal is inevitable
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10 stocks to buy and hold for the next 10 years: 1/ IREN Iren is redefining data center infrastructure by building THE world’s most efficient platforms for powering AI compute with 100% renewable energy. As the CEO says, the industry is growing so rapidly, "They can't meet the demand". - 3GW Contracted Power - $9.7B $MSFT Contract - Next Hyperscaler deal is inevitable

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$NVDA CEO Jensen Huang is saying there’s no bubble because AI manufactures intelligence in real time which means every query consumes fresh compute & forces the system to scale its GPU base as usage grows. That is why demand for GPUs & power keeps accelerating & why this looks like a genuine industrial build-out rather than a bubble.
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$NVDA CEO Jensen Huang is saying there’s no bubble because AI manufactures intelligence in real time which means every query consumes fresh compute & forces the system to scale its GPU base as usage grows. That is why demand for GPUs & power keeps accelerating & why this looks like a genuine industrial build-out rather than a bubble.

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The Future of AI Compute is Here! AI runs on compute power—but there's a crisis. Enter Exabits, the AI Compute Refinery. Optimized, scalable, & powered by $EXA. Own the infrastructure behind the AI revolution. Watch now! 🎥
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The Future of AI Compute is Here! AI runs on compute power—but there's a crisis. Enter Exabits, the AI Compute Refinery. Optimized, scalable, & powered by $EXA. Own the infrastructure behind the AI revolution. Watch now! 🎥

Exabits

359,053 Aufrufe • vor 1 Jahr

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.
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Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Aakash Gupta

31,285 Aufrufe • vor 2 Monaten

AI DATA CENTERS IN SPACE? Chamath warns that as $GOOGL, $META, $MSFT & $AMZN add gigawatts of $NVDA GPU capacity, the grid can’t keep up & power costs could double. He says limitless solar energy & zero cooling could power the next wave of AI compute.
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AI DATA CENTERS IN SPACE? Chamath warns that as $GOOGL, $META, $MSFT & $AMZN add gigawatts of $NVDA GPU capacity, the grid can’t keep up & power costs could double. He says limitless solar energy & zero cooling could power the next wave of AI compute.

Shay Boloor

476,263 Aufrufe • vor 8 Monaten

When I say $NVDA isn’t just a hardware company anymore, it’s not tongue-in-cheek. They’re turning compute into a utility by building AI factories. Here’s Jensen explaining how NVIDIA supplies both the tools and the blueprint for AI infrastructure 👇
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When I say $NVDA isn’t just a hardware company anymore, it’s not tongue-in-cheek. They’re turning compute into a utility by building AI factories. Here’s Jensen explaining how NVIDIA supplies both the tools and the blueprint for AI infrastructure 👇

Shay Boloor

52,062 Aufrufe • vor 1 Jahr