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Agentic workloads generate 20-50x more tokens than chat. That multiplier hits compute, storage, and orchestration simultaneously. Google Cloud's Mark Lohmeyer (who owns all of Compute) and Patrick Moorhead break down how GC is building infrastructure to absorb that pressure across the full AI Hypercomputer stack from Google Cloud Next 2026.

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10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

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UBS estimates that 48% of Google Cloud's 2027 revenues will come from Anthropic and OpenAI, who represent 70% of AI revenues across Amazon, Google and Microsoft. For hyperscalers to meet expectations, AI labs must raise hundreds of billions of dollars in the next three years.

UBS estimates that 48% of Google Cloud's 2027 revenues will come from Anthropic and OpenAI, who represent 70% of AI revenues across Amazon, Google and Microsoft. For hyperscalers to meet expectations, AI labs must raise hundreds of billions of dollars in the next three years.

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BREAKING: Elon Musk says Google and Anthropic are leasing AI compute from SpaceX amid a growing power shortage, adding that SpaceX has brought AI compute online better than anyone else so far. “Even before next year, there are challenges with power, which is why Google, Anthropic and many other companies are leasing compute from SpaceX. We’ve been able to bring AI compute online better than anyone else so far. Constructing our own power plants is the only way we were able to do it”

BREAKING: Elon Musk says Google and Anthropic are leasing AI compute from SpaceX amid a growing power shortage, adding that SpaceX has brought AI compute online better than anyone else so far. “Even before next year, there are challenges with power, which is why Google, Anthropic and many other companies are leasing compute from SpaceX. We’ve been able to bring AI compute online better than anyone else so far. Constructing our own power plants is the only way we were able to do it”

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Building the world's largest AI compute DePIN means has tens of thousands of independent node operators around the world. Providing alternatives to centralized cloud providers is more than bringing transactions for compute on-chain, it is tapping into the distributed compute that already exists in the world and making it available to innovators and builders advancing AI and ML. video from our community member leeland アブス ✳️

Building the world's largest AI compute DePIN means has tens of thousands of independent node operators around the world. Providing alternatives to centralized cloud providers is more than bringing transactions for compute on-chain, it is tapping into the distributed compute that already exists in the world and making it available to innovators and builders advancing AI and ML. video from our community member leeland アブス ✳️

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Had a great conversation with Richard Seroter, Senior Director and Chief Evangelist at Google Cloud, during Google I/O Connect India in Bangalore today. We spoke about how he uses Gemini in his day to day work, how Google is handling the massive compute demand for AI, why defining goals matters more than writing perfect specs, and what's next for Gemini.

Had a great conversation with Richard Seroter, Senior Director and Chief Evangelist at Google Cloud, during Google I/O Connect India in Bangalore today. We spoke about how he uses Gemini in his day to day work, how Google is handling the massive compute demand for AI, why defining goals matters more than writing perfect specs, and what's next for Gemini.

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INTERNET COMPUTER SET TO DEMO "CLOUD ENGINES" DFINITY Foundation prepares to unveil "cloud engines" on May 10, 2026, marking the fifth anniversary of the Internet Computer network. According to the Founder's statement, the protocol will also reveal agentic builds, AI nodes, alongside other new features... The demonstration features a full-stack compute technology, including autonomous agentic builds and dedicated AI nodes designed for decentralized scaling. "This is a very big deal for ICP, cloud and agentic R&D. Engines will be the 1st frontier cloud/compute technology the wild." This launch positions $ICP as a primary infrastructure provider for frontier cloud R&D by enabling machine-to-machine logic at the protocol level. The release aims to transition traditional cloud workloads into a trustless, GPU-backed environment for the next generation of autonomous applications.

INTERNET COMPUTER SET TO DEMO "CLOUD ENGINES" DFINITY Foundation prepares to unveil "cloud engines" on May 10, 2026, marking the fifth anniversary of the Internet Computer network. According to the Founder's statement, the protocol will also reveal agentic builds, AI nodes, alongside other new features... The demonstration features a full-stack compute technology, including autonomous agentic builds and dedicated AI nodes designed for decentralized scaling. "This is a very big deal for ICP, cloud and agentic R&D. Engines will be the 1st frontier cloud/compute technology the wild." This launch positions $ICP as a primary infrastructure provider for frontier cloud R&D by enabling machine-to-machine logic at the protocol level. The release aims to transition traditional cloud workloads into a trustless, GPU-backed environment for the next generation of autonomous applications.

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The pilot phase of enterprise AI is ending. What separates the organizations that scale from those that stall is infrastructure, data readiness, and governance built before models are ever deployed. Patrick Moorhead and Daniel Newman sit down with Varun Chhabra of Dell Technologies and Jason Schroedl of NVIDIA at Dell Technologies World 2026 to break down new approaches to agentic AI deployment, investments in data readiness, and how enterprises can expand AI capabilities while maintaining control over security, costs and operations.

The pilot phase of enterprise AI is ending. What separates the organizations that scale from those that stall is infrastructure, data readiness, and governance built before models are ever deployed. Patrick Moorhead and Daniel Newman sit down with Varun Chhabra of Dell Technologies and Jason Schroedl of NVIDIA at Dell Technologies World 2026 to break down new approaches to agentic AI deployment, investments in data readiness, and how enterprises can expand AI capabilities while maintaining control over security, costs and operations.

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AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

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"You end up having to use AI to fight AI." — Francis DeSouza, COO and President of Security Products, Google Cloud shared that AI can take 30-minute investigations down to one minute. That's threat hunting at a scale that humans can't match. Listen to the full conversation with Patrick Moorhead and Google's Francis DeSouza on why the SOC just changed permanently.

"You end up having to use AI to fight AI." — Francis DeSouza, COO and President of Security Products, Google Cloud shared that AI can take 30-minute investigations down to one minute. That's threat hunting at a scale that humans can't match. Listen to the full conversation with Patrick Moorhead and Google's Francis DeSouza on why the SOC just changed permanently.

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Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

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$NVDA is evolving into the platform behind the entire AI factory spanning compute, CPUs, networking, interconnect, software, rack-scale systems and now the financing that helps customers build them. Owning more of that stack lets Nvidia capture more of every dollar spent on AI infrastructure while sitting across the bottlenecks that determine how quickly the entire market can scale.

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Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

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Extra outtake clip from latest Bg2 Pod with Jensen Brad Gerstner Contrary to popular hypersensationalist rhetoric -- that we are in a massive AI glut -- we are likely in a stretch of structural compute shortage. Google announced in May that tokens had grown 50x y/y, and doubled again by July 2025 (100x) to 1 quadrillion monthly tokens. In that period - algorithmic and hardware advances improved efficiencies by ~10-15x - which means Google had to increase accelerated compute dedicated to token generation by 3-10x. Our estimate is that Google increased accelerated compute by ~3x during the period - which means that they had to pull compute from training, recommenders, etc to allocate to token generation. Significant algorithmic advancements (Flash Attention, quantization, MoE), and infrastructure investments (prompt caching, batching) have driven much of that efficiency, but counting on the hardware to get better is something the industry is counting / relying on. We used to be able to ride Moore's Law / Dennard scaling to improve compute per watt. But now... we have to rely on $NVDA / hardware ecosystem (Google, $AMD, etc) to drive 2-4x improvement per generation (Huang's Law). People underestimate the strain of exponentials on human systems - that are hard coded to think linearly... The total global accelerated compute base is probably ~8 GW of installed capacity on my math and analysts have estimated growth to increase to 10x to ~80 GW globally by 2030. Even assuming all of that capex gets done, and Nvidia continues to push yearly roadmap (generating 2x y/y performance uplift), the 10x power increase should equate to ~50x increase in compute. We just had 100x AI usage increase in 1 year from Google's testimony. OpenAI, Google, Anthropic are in structural shortage of compute - each bit they bring online is fully invested in serving their users. And that is without even expanding into true video / world models, robotics, or long horizon thinking to find novel breakthroughs. What could change this trajectory? If the algorithmic efficiencies we are gaining from new breakthroughs outstrip the exponential increase in current demand -- and FUTURE demand. Investors hyperventilated at DeepSeek's release earlier this year, but their gains were outweighed by the increase in demand created by reasoning.

Extra outtake clip from latest Bg2 Pod with Jensen Brad Gerstner Contrary to popular hypersensationalist rhetoric -- that we are in a massive AI glut -- we are likely in a stretch of structural compute shortage. Google announced in May that tokens had grown 50x y/y, and doubled again by July 2025 (100x) to 1 quadrillion monthly tokens. In that period - algorithmic and hardware advances improved efficiencies by ~10-15x - which means Google had to increase accelerated compute dedicated to token generation by 3-10x. Our estimate is that Google increased accelerated compute by ~3x during the period - which means that they had to pull compute from training, recommenders, etc to allocate to token generation. Significant algorithmic advancements (Flash Attention, quantization, MoE), and infrastructure investments (prompt caching, batching) have driven much of that efficiency, but counting on the hardware to get better is something the industry is counting / relying on. We used to be able to ride Moore's Law / Dennard scaling to improve compute per watt. But now... we have to rely on $NVDA / hardware ecosystem (Google, $AMD, etc) to drive 2-4x improvement per generation (Huang's Law). People underestimate the strain of exponentials on human systems - that are hard coded to think linearly... The total global accelerated compute base is probably ~8 GW of installed capacity on my math and analysts have estimated growth to increase to 10x to ~80 GW globally by 2030. Even assuming all of that capex gets done, and Nvidia continues to push yearly roadmap (generating 2x y/y performance uplift), the 10x power increase should equate to ~50x increase in compute. We just had 100x AI usage increase in 1 year from Google's testimony. OpenAI, Google, Anthropic are in structural shortage of compute - each bit they bring online is fully invested in serving their users. And that is without even expanding into true video / world models, robotics, or long horizon thinking to find novel breakthroughs. What could change this trajectory? If the algorithmic efficiencies we are gaining from new breakthroughs outstrip the exponential increase in current demand -- and FUTURE demand. Investors hyperventilated at DeepSeek's release earlier this year, but their gains were outweighed by the increase in demand created by reasoning.

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The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

Drey

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Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

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“When we start consuming 4 to 5 quintillion tokens per year, 70% of the tokens are going to be generated by agents,” DigitalOcean CEO Paddy Srinivasan said. AI token demand is projected to surge by 2030, with agents expected to be one of the biggest drivers, according to Srinivasan. That shift means agentic workloads could become “multiple orders of magnitude bigger” than human-driven workflows, with agents requiring “a significant amount of compute: GPUs, CPUs, databases, memory, and cloud primitives all in one cloud.” Supported by DigitalOcean.

“When we start consuming 4 to 5 quintillion tokens per year, 70% of the tokens are going to be generated by agents,” DigitalOcean CEO Paddy Srinivasan said. AI token demand is projected to surge by 2030, with agents expected to be one of the biggest drivers, according to Srinivasan. That shift means agentic workloads could become “multiple orders of magnitude bigger” than human-driven workflows, with agents requiring “a significant amount of compute: GPUs, CPUs, databases, memory, and cloud primitives all in one cloud.” Supported by DigitalOcean.

The Information

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Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

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AI agents move fast. So should your search foundation. Introducing the next generation of Amazon OpenSearch Serverless. We rebuilt OpenSearch Serverless from the ground up for agentic AI and unpredictable workloads: - Create resources in seconds - Autoscale up to 20x faster than before - Usage-based pricing with decoupled storage and compute - No idle costs with scale to zero - Up to 60% cost savings vs. provisioned clusters for peak capacity Learn more.

AI agents move fast. So should your search foundation. Introducing the next generation of Amazon OpenSearch Serverless. We rebuilt OpenSearch Serverless from the ground up for agentic AI and unpredictable workloads: - Create resources in seconds - Autoscale up to 20x faster than before - Usage-based pricing with decoupled storage and compute - No idle costs with scale to zero - Up to 60% cost savings vs. provisioned clusters for peak capacity Learn more.

Amazon Web Services

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