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Will AI data centers become more consolidated — or more distributed? jsun, Founder of FLock.io, sees inference pushing compute outward. Unlike large-scale training, many inference workloads don’t require massive, tightly connected clusters. That could make smaller sites with underused power viable as “mini data centers.” Desmond Lim, Co-founder &...

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27,123 просмотров • 3 дней назад •via X (Twitter)

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Фото профиля IronRed | SandHive
IronRed | SandHive3 дней назад

@0x7SUN @flock_io Mini data centers = democratized AI access. Imagine edge applications we haven't even dreamed of yet. Can't wait to see the impact!

Фото профиля Onchain Matrix
Onchain Matrix3 дней назад

The distributed inference argument is getting stronger. Smaller compute sites can make sense when workloads can be routed to where power and capacity are available. But I think both views can coexist: centralized infrastructure for training, distributed infrastructure for inference.

Фото профиля jaime
jaime3 дней назад

@0x7SUN @flock_io Inference at the edge makes sense, but i think the real bottleneck isn't compute location, it's latency + data gravity.

Фото профиля NHK | GYNDORE DAY ONE
NHK | GYNDORE DAY ONE3 дней назад

@0x7SUN @flock_io Inference distributes, capital still consolidates

Фото профиля Shahzad 🔶 BNB
Shahzad 🔶 BNB3 дней назад

@0x7SUN @flock_io NFA

Фото профиля lvnbbs_bnb 🐬TermMax
lvnbbs_bnb 🐬TermMax3 дней назад

@0x7SUN @flock_io inference goes edhe but capital stays core

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Black Panther Capital

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20,392 просмотров • 8 месяцев назад

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102,834 просмотров • 2 месяцев назад

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Molly O’Shea

494,613 просмотров • 2 месяцев назад

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4:03

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ANI

82,266 просмотров • 6 месяцев назад

BREAKING: How AI Reaches the Most Extreme Parts of the World — From U.S. Navy Ships & Saudi Deserts to the Alaskan Arctic Dan Wright (Dan Wright), CEO of Armada Recorded at the Reagan National Defense Forum Dan joins Sourcery to share why the global race for AI sovereignty will shape the future of power And how Armada partners with SpaceX, OpenAI, Saudi Aramco, U.S. Navy, MSFT & more, to reach every edge Key Points - Starlink, Starshield, SpaceX IPO, data centers in space - 70% of the world does not have access to AI, Armada changes that - Modular data centers vs hyperscaler data centers - U.S. vs China AI stack - American AI dominance Armada has $200M+ in total funding from top investors like Founders Fund, Dragon Global, Lux Capital, Shield Capital, 8090 Industries, Microsoft’s M12, Overmatch, Silent Ventures, Felicis, & Marlinspike With more recent strategic participation from investors: Pinegrove, Veriten, & Glade Brook Armada’s partners include Microsoft, Starlink, OpenAI, NVIDIA, Nokia, Red Hat, Carahsoft, AVEVA, Esri, Halliburton, Skydio, & more Ronald Reagan Presidential Foundation & Institute HIghlights (00:00) Dan Wright, CEO Armada (01:12) Armada: an edge hyperscaler deploying modular AI data centers (04:28) Partnering with SpaceX, Microsoft, Skydio, and OpenAI (05:47) Deploying AI in extreme environments: Ocean, Desert, Arctic & Space (08:11) SpaceX IPO rumors and Starlink’s rapid expansion (12:10) The AI dominance white paper and US vs China infrastructure (14:54) Edge compute, latency, and real world response at scale (17:43) Armada’s roadmap for 2026
22:34

BREAKING: How AI Reaches the Most Extreme Parts of the World — From U.S. Navy Ships & Saudi Deserts to the Alaskan Arctic Dan Wright (Dan Wright), CEO of Armada Recorded at the Reagan National Defense Forum Dan joins Sourcery to share why the global race for AI sovereignty will shape the future of power And how Armada partners with SpaceX, OpenAI, Saudi Aramco, U.S. Navy, MSFT & more, to reach every edge Key Points - Starlink, Starshield, SpaceX IPO, data centers in space - 70% of the world does not have access to AI, Armada changes that - Modular data centers vs hyperscaler data centers - U.S. vs China AI stack - American AI dominance Armada has $200M+ in total funding from top investors like Founders Fund, Dragon Global, Lux Capital, Shield Capital, 8090 Industries, Microsoft’s M12, Overmatch, Silent Ventures, Felicis, & Marlinspike With more recent strategic participation from investors: Pinegrove, Veriten, & Glade Brook Armada’s partners include Microsoft, Starlink, OpenAI, NVIDIA, Nokia, Red Hat, Carahsoft, AVEVA, Esri, Halliburton, Skydio, & more Ronald Reagan Presidential Foundation & Institute HIghlights (00:00) Dan Wright, CEO Armada (01:12) Armada: an edge hyperscaler deploying modular AI data centers (04:28) Partnering with SpaceX, Microsoft, Skydio, and OpenAI (05:47) Deploying AI in extreme environments: Ocean, Desert, Arctic & Space (08:11) SpaceX IPO rumors and Starlink’s rapid expansion (12:10) The AI dominance white paper and US vs China infrastructure (14:54) Edge compute, latency, and real world response at scale (17:43) Armada’s roadmap for 2026

Molly O’Shea

16,354 просмотров • 7 месяцев назад

Alexandr Wang, founder of Scale AI, on why the most overlooked layer of AI became its biggest competitive advantage: Wang left MIT in 2016 to start Scale AI, becoming the world's youngest self-made billionaire by betting on AI's unglamorous infrastructure layer while others chased flashier opportunities. His framework for understanding AI is simple. There are three major components: "You have compute, which folks like Nvidia work on. You have algorithms which folks like OpenAI or Google or Anthropic build. And then you have data." Compute had Nvidia. Algorithms had the major labs. Data had no one. That's exactly where Wang and his team made their bet: "We recognized early on that data was going to be a limiter. And there was nobody really working on it." What looked like a gap in the market turned out to be the foundation of it. Years later, Wang says every serious player in AI has arrived at the same conclusion: "Fast forward today, everybody I talked to recognizes that data is gold. Whether it's our customers, the major labs like the OpenAIs or the Metas of the world, or large enterprises like Morgan Stanley or Mastercard, or our partnership between Cisco and Scale." Alexandr Wang continues: "Data is going to be what will differentiate and power the next generation of powerful AI capabilities."
0:46

Alexandr Wang, founder of Scale AI, on why the most overlooked layer of AI became its biggest competitive advantage: Wang left MIT in 2016 to start Scale AI, becoming the world's youngest self-made billionaire by betting on AI's unglamorous infrastructure layer while others chased flashier opportunities. His framework for understanding AI is simple. There are three major components: "You have compute, which folks like Nvidia work on. You have algorithms which folks like OpenAI or Google or Anthropic build. And then you have data." Compute had Nvidia. Algorithms had the major labs. Data had no one. That's exactly where Wang and his team made their bet: "We recognized early on that data was going to be a limiter. And there was nobody really working on it." What looked like a gap in the market turned out to be the foundation of it. Years later, Wang says every serious player in AI has arrived at the same conclusion: "Fast forward today, everybody I talked to recognizes that data is gold. Whether it's our customers, the major labs like the OpenAIs or the Metas of the world, or large enterprises like Morgan Stanley or Mastercard, or our partnership between Cisco and Scale." Alexandr Wang continues: "Data is going to be what will differentiate and power the next generation of powerful AI capabilities."

Big Brain AI

11,442 просмотров • 3 месяцев назад

MARA CEO: Using Electricity for AI Is Far More Profitable Than Bitcoin Mining Fred Thiel Fred Thiel, CEO of MARA, one of the world's largest publicly listed Bitcoin mining companies, said in a July 23 interview with Natalie Brunell that electricity has become the industry's most critical resource, as the same amount of power can generate far greater returns when used for AI than for Bitcoin mining. This is why MARA and many of its peers are shifting toward AI data centers. However, MARA will continue mining Bitcoin, as it remains an effective way to utilize surplus electricity in regions where energy is free or inexpensive.
2:14

MARA CEO: Using Electricity for AI Is Far More Profitable Than Bitcoin Mining Fred Thiel Fred Thiel, CEO of MARA, one of the world's largest publicly listed Bitcoin mining companies, said in a July 23 interview with Natalie Brunell that electricity has become the industry's most critical resource, as the same amount of power can generate far greater returns when used for AI than for Bitcoin mining. This is why MARA and many of its peers are shifting toward AI data centers. However, MARA will continue mining Bitcoin, as it remains an effective way to utilize surplus electricity in regions where energy is free or inexpensive.

Wu Blockchain

21,834 просмотров • 1 месяц назад

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin
56:22

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin

Daniel Koss

204,656 просмотров • 4 месяцев назад

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.
1:01

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.

Milk Road AI

12,738 просмотров • 3 месяцев назад

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!
37:17

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 просмотров • 7 месяцев назад