正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Generative UI, built for you on demand, principally requires fast inference. Logan Kilpatrick Logan Kilpatrick on how AI could reshape software: agents handle transactional tasks, while new interfaces unlock richer experiences. Speed changes the interface. Presented with Cerebras // Alycia Cary

Sarah Chieng

27,808 subscribers

17,591 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

"Most of the real world is actually the long tail. And how do you learn the long tail as cheaply as possible is one of the most important questions right now." Sara Hooker on why GPUs — built for dense, predictable matmuls — are the wrong tool for the world AI is actually moving into. Presented with Cerebras and Alycia Cary

"Most of the real world is actually the long tail. And how do you learn the long tail as cheaply as possible is one of the most important questions right now." Sara Hooker on why GPUs — built for dense, predictable matmuls — are the wrong tool for the world AI is actually moving into. Presented with Cerebras and Alycia Cary

Sarah Chieng

20,850 次观看 • 2 个月前

New generative UI experiences in the Google Gemini and AI Mode in Search use Gemini 3’s multimodal understanding capabilities to make entire user interfaces for you from scratch — dynamically delivering what you need in a visual and interactive way. Google Research software engineer Yaniv Leviathan shares more about the process of building generative UI (hint: so much fun that the team couldn't stop playing with it) and how he's been using it so far.

New generative UI experiences in the Google Gemini and AI Mode in Search use Gemini 3’s multimodal understanding capabilities to make entire user interfaces for you from scratch — dynamically delivering what you need in a visual and interactive way. Google Research software engineer Yaniv Leviathan shares more about the process of building generative UI (hint: so much fun that the team couldn't stop playing with it) and how he's been using it so far.

Google

138,435 次观看 • 9 个月前

What does AI actually change for builders: It’s not just speed. It’s ambition. The old question was whether an idea was even possible. Google DeepMind's Logan Kilpatrick says he now catches himself wanting to push the same idea ten steps further. The hard part becomes resetting your own ceiling on ambition. What do you build when the only constraint left is how big you let yourself dream?

What does AI actually change for builders: It’s not just speed. It’s ambition. The old question was whether an idea was even possible. Google DeepMind's Logan Kilpatrick says he now catches himself wanting to push the same idea ten steps further. The hard part becomes resetting your own ceiling on ambition. What do you build when the only constraint left is how big you let yourself dream?

Sequoia Capital

13,389 次观看 • 2 个月前

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

Pratham

33,920 次观看 • 7 个月前

"Maybe the right UI for AI is a generative UI." Here's my new interview with Stephen Chau featuring a live demo of Cove's ground-breaking visual AI interface that breaks free from chatbots. We had a great chat about: - Real world use cases for visual AI interfaces - Generative UI and just-in-time AI apps - How Stephen scaled Uber Eats to $25B ARR Some quotes from Stephen Chau: "In computing, we moved from the command line to graphic user interfaces. Chatbots are like the command line era of AI. What's next is this..." "Imagine the entire layout of the workspace and all its contents is something made just in time, based on what you're trying to do..." "Our goal with Uber Eats was to build a business that's the same size as the Rides business. Here's what mattered..." 📌 Watch now:

"Maybe the right UI for AI is a generative UI." Here's my new interview with Stephen Chau featuring a live demo of Cove's ground-breaking visual AI interface that breaks free from chatbots. We had a great chat about: - Real world use cases for visual AI interfaces - Generative UI and just-in-time AI apps - How Stephen scaled Uber Eats to $25B ARR Some quotes from Stephen Chau: "In computing, we moved from the command line to graphic user interfaces. Chatbots are like the command line era of AI. What's next is this..." "Imagine the entire layout of the workspace and all its contents is something made just in time, based on what you're trying to do..." "Our goal with Uber Eats was to build a business that's the same size as the Rides business. Here's what mattered..." 📌 Watch now:

Peter Yang

74,721 次观看 • 1 年前

Nimo Infinity 🎉 - Introducing a New AI Browser, Purpose-Built for Productivity. One Canvas for All Your AI Apps and Agents. Nimo We were totally broke in 2024, but we didn’t want to give up. We built Nimo Infinity to rethink the interface for AI. Beautiful design, one canvas for all your apps, and built-in memory and context, all deeply focused on productivity. Comment ‘Nimo Infinity’ 👇 below to get early access with 100 free tasks. Our mission is to build an entirely new AI OS that is secure, intelligent, and interactive. Today, we are taking the first step with an AI Browser. We’ve spent the last 7 years working on Nimo Spatial OS and ultra-portable spatial computers. That experience has shaped how we approach the AI Browser with a completely new interface, purpose-built for AI.

Nimo Infinity 🎉 - Introducing a New AI Browser, Purpose-Built for Productivity. One Canvas for All Your AI Apps and Agents. Nimo We were totally broke in 2024, but we didn’t want to give up. We built Nimo Infinity to rethink the interface for AI. Beautiful design, one canvas for all your apps, and built-in memory and context, all deeply focused on productivity. Comment ‘Nimo Infinity’ 👇 below to get early access with 100 free tasks. Our mission is to build an entirely new AI OS that is secure, intelligent, and interactive. Today, we are taking the first step with an AI Browser. We’ve spent the last 7 years working on Nimo Spatial OS and ultra-portable spatial computers. That experience has shaped how we approach the AI Browser with a completely new interface, purpose-built for AI.

rohildev

27,345 次观看 • 1 年前

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,811 次观看 • 11 个月前

How does an AI model actually learn to see? 🤖 Learn about the tech behind native multimodality, how models reason over visual data like documents and video, and the future of proactive AI assistants with Logan Kilpatrick and Gemini Model Behavior Product Lead, Ani Baddepudi. ↓ Timestamps: 01:12 Why Gemini is natively multimodal 02:23 The technology behind multimodal models 05:15 Video understanding with Gemini 2.5 09:25 Deciding what to build next 13:23 Building new product experiences with multimodal AI 17:15 The vision for proactive assistants 24:13 Improving video usability with variable FPS and frame tokenization 27:35 What’s next for Gemini’s multimodal development 31:47 Deep dive on Gemini’s document understanding capabilities 37:56 The teamwork and collaboration behind Gemini 40:56 What’s next with model behavior

How does an AI model actually learn to see? 🤖 Learn about the tech behind native multimodality, how models reason over visual data like documents and video, and the future of proactive AI assistants with Logan Kilpatrick and Gemini Model Behavior Product Lead, Ani Baddepudi. ↓ Timestamps: 01:12 Why Gemini is natively multimodal 02:23 The technology behind multimodal models 05:15 Video understanding with Gemini 2.5 09:25 Deciding what to build next 13:23 Building new product experiences with multimodal AI 17:15 The vision for proactive assistants 24:13 Improving video usability with variable FPS and frame tokenization 27:35 What’s next for Gemini’s multimodal development 31:47 Deep dive on Gemini’s document understanding capabilities 37:56 The teamwork and collaboration behind Gemini 40:56 What’s next with model behavior

Google AI

58,703 次观看 • 1 年前

Personalized agents are the next step in AI adoption, learning our habits and specificities, and supporting us throughout the day. Specialized agents, on the other hand, will be for-hire experts, built to excel at a single function. From business advisory to creative tasks, nutritional advice or even perfecting a sourdough, each one can be tuned based on the creator's preferences, experiences and life history. These on-demand utilities will make expert knowledge widely available for much lower costs, while simultaneously expanding a creator's reach, supporting new income streams. All of this running on blockchain rails - the critical trust layer - ensuring commercial arrangements operate seamlessly, agents have on-chain IDs to verify their credentials, and escrow as standard to protect consumers. The exponential economy is fast-approaching, and it's coming to VeChain, soon. $VET

Personalized agents are the next step in AI adoption, learning our habits and specificities, and supporting us throughout the day. Specialized agents, on the other hand, will be for-hire experts, built to excel at a single function. From business advisory to creative tasks, nutritional advice or even perfecting a sourdough, each one can be tuned based on the creator's preferences, experiences and life history. These on-demand utilities will make expert knowledge widely available for much lower costs, while simultaneously expanding a creator's reach, supporting new income streams. All of this running on blockchain rails - the critical trust layer - ensuring commercial arrangements operate seamlessly, agents have on-chain IDs to verify their credentials, and escrow as standard to protect consumers. The exponential economy is fast-approaching, and it's coming to VeChain, soon. $VET

VeChain

10,221 次观看 • 2 个月前

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

Ivan Burazin

22,655 次观看 • 4 个月前

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

MTS

27,935 次观看 • 3 个月前

Crypto built the root of trust. AI needs it. At Permissionless Wealth (自由财富) IV’s Open Source AI Summit presented by NEAR Protocol, Sreeram Kannan dropped the most important update in Crypto x AI this year. ☁️ Eigencloud Supercharging Verifiable AI. ☁️ Crypto built the foundation for digital property, programmable money, and trustless execution. But it was never built to run AI. EigenCloud changes that. For the first time, you can deploy unstoppable, verifiable AI agents, not behind an API key, but as sovereign entities that own, earn, and act onchain. 🔥 Smart contracts that think. 🔥 AI that can stake. 🔥 Agents that can own. This is not a demo. It’s a new reality. And it’s the most powerful unlock in crypto since Ethereum. EigenCloud Timestamps:⁣ 0:10 Introduction⁣ 0:32 EigenLayer → EigenCloud 1:53 Autonomous Verifiable Services 3:06 Evolving from Infra to Apps⁣ to AI 5:41 EigenCloud Architecture 9:30 Bringing AI Onchain⁣ with EigenVerify 12:01 Supercharge AI 15:44 Sovereign AI Agents 17:48 Making Autonomous Agents Practical⁣ 18:37 AI Inference, Model, and Agents 19:03 Deep stack of Verifiability 21:15 Call to Builders: Build Powerful Expressive Apps with Agents

Crypto built the root of trust. AI needs it. At Permissionless Wealth (自由财富) IV’s Open Source AI Summit presented by NEAR Protocol, Sreeram Kannan dropped the most important update in Crypto x AI this year. ☁️ Eigencloud Supercharging Verifiable AI. ☁️ Crypto built the foundation for digital property, programmable money, and trustless execution. But it was never built to run AI. EigenCloud changes that. For the first time, you can deploy unstoppable, verifiable AI agents, not behind an API key, but as sovereign entities that own, earn, and act onchain. 🔥 Smart contracts that think. 🔥 AI that can stake. 🔥 Agents that can own. This is not a demo. It’s a new reality. And it’s the most powerful unlock in crypto since Ethereum. EigenCloud Timestamps:⁣ 0:10 Introduction⁣ 0:32 EigenLayer → EigenCloud 1:53 Autonomous Verifiable Services 3:06 Evolving from Infra to Apps⁣ to AI 5:41 EigenCloud Architecture 9:30 Bringing AI Onchain⁣ with EigenVerify 12:01 Supercharge AI 15:44 Sovereign AI Agents 17:48 Making Autonomous Agents Practical⁣ 18:37 AI Inference, Model, and Agents 19:03 Deep stack of Verifiability 21:15 Call to Builders: Build Powerful Expressive Apps with Agents

EigenCloud

65,722 次观看 • 1 年前

For the longest time, AI could create songs. But turning those songs into actual music videos still felt like work. Now, huge budgets, filming crews, characters/cast, editing software, visual effects, transitions, timing... Then I tried Freebeat AI. What surprised me wasn't that it generated a music video. It was how little work I had to do. Now, with Freebeat's new Realtime Creator Challenge launching on June 26, creators are competing to see just how fast music videos can be built with AI. ⚡🧵 👇

For the longest time, AI could create songs. But turning those songs into actual music videos still felt like work. Now, huge budgets, filming crews, characters/cast, editing software, visual effects, transitions, timing... Then I tried Freebeat AI. What surprised me wasn't that it generated a music video. It was how little work I had to do. Now, with Freebeat's new Realtime Creator Challenge launching on June 26, creators are competing to see just how fast music videos can be built with AI. ⚡🧵 👇

Aina Ai | Tools & Updates

33,901 次观看 • 2 个月前

GEMINI 3 LAUNCH IS HERE I got a SNEAK PEEK at Gemini 3 with Logan Kilpatrick (Google Deepmind), and it might be the most POWERFUL vibe-coding tool on the planet. A little breakdown: 1. Anyone can build 3D and casual games now You can vibecode full, playable 3D video games generated in minutes. Actual games with physics, characters, controls, and loops you can remix instantly. Pure insanity. I can see founders and brands spinning up games on the fly to ride trends and drive growth. 2. Intelligent apps are becoming the default We built apps where reasoning, memory, and multi-step planning were baked in from the start. Once you’re building apps with ACTUAL intelligence baked in, there’s a whole wave of new opportunities that weren’t possible before. 3. Gemini acts like a creative partner You describe the idea, Gemini fills in the gaps, challenges decisions, proposes alternatives, and iterates in real time. 4. Vibe coding hits a new level Gemini 3 can generate assets, code, game logic, UI, and narrative in one flow. Tools like Claude and Cursor feel fast. This feels like the next layer, the one where a single builder can compete with full teams. Logan Kilpatrick and I pushed Google Gemini 3 hard, and the outputs were solid. A few times we had to give it a few extra prompts but it took feedback really well. I think 1 year ago, a lot of people discounted Google in the AI arms race. Can you discount them anymore? Doubt it. After this, it feels like they at best leading, at worst leading. What do you think of Google's AI efforts/Gemini 3 My biggest takeaway was how it just felt like Gemini 3 had a little more vibe coding horsepower than anything I’ve used.

GEMINI 3 LAUNCH IS HERE I got a SNEAK PEEK at Gemini 3 with Logan Kilpatrick (Google Deepmind), and it might be the most POWERFUL vibe-coding tool on the planet. A little breakdown: 1. Anyone can build 3D and casual games now You can vibecode full, playable 3D video games generated in minutes. Actual games with physics, characters, controls, and loops you can remix instantly. Pure insanity. I can see founders and brands spinning up games on the fly to ride trends and drive growth. 2. Intelligent apps are becoming the default We built apps where reasoning, memory, and multi-step planning were baked in from the start. Once you’re building apps with ACTUAL intelligence baked in, there’s a whole wave of new opportunities that weren’t possible before. 3. Gemini acts like a creative partner You describe the idea, Gemini fills in the gaps, challenges decisions, proposes alternatives, and iterates in real time. 4. Vibe coding hits a new level Gemini 3 can generate assets, code, game logic, UI, and narrative in one flow. Tools like Claude and Cursor feel fast. This feels like the next layer, the one where a single builder can compete with full teams. Logan Kilpatrick and I pushed Google Gemini 3 hard, and the outputs were solid. A few times we had to give it a few extra prompts but it took feedback really well. I think 1 year ago, a lot of people discounted Google in the AI arms race. Can you discount them anymore? Doubt it. After this, it feels like they at best leading, at worst leading. What do you think of Google's AI efforts/Gemini 3 My biggest takeaway was how it just felt like Gemini 3 had a little more vibe coding horsepower than anything I’ve used.

GREG ISENBERG

73,804 次观看 • 9 个月前

NEW: Cerebras $CBRS CEO Andrew Feldman (Andrew Feldman) "When the chip on your shoulder is the largest chip the world has ever seen." "The demand for AI has outpaced everybody's expectation & everybody's forecast. & so everybody's chasing. They're chasing chips, memory, or data centers." We get into the chip 58x larger than any other, $20B OpenAI deal signed in 4.5 weeks, & what's actually going on with the 'big AI deals' Recorded 2 months after Cerebras' $5.5B IPO at a $56B valuation, where a first-day pop briefly hit ~$95B before settling toward ~$60B. We cover: › A "Cambrian explosion" of new chip architectures › $20B+ OpenAI deal: 750MW of inference compute over 3 years › Why inference, not training, is where the value is now › "We are behind" on the data center build-out › Free tokens & circular deals: "These are drug pushers" › Creating 1,000 millionaires › Nvidia's balance sheet & market strength › Sovereign AI & owning the stack › Co-design & data centers in space › A real 25-year path to ending cancer Cerebras builds AI infrastructure for training & inference. It went public in May 2026, & its products include inference, Wafer Scale Engine, AI supercomputers, AI model services, cloud, systems, & processors. Filmed at the Raise Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Andrew Feldman, Co-Founder & CEO at Cerebras Systems (00:49) Why hardware suddenly became the coolest industry in tech (01:51) What changed at Raise AI Summit (03:01) Inside the $20 billion Cerebras - OpenAI deal (05:50) What actually changes two months after an IPO (06:32) Turning 1,000 employees into millionaires (07:52) Staying sane during an AI gold rush (10:44) Life after the IPO plateau (11:45) The truth about the global data center shortage (12:56) Why data centers are borrowing jet engines for power (14:44) Are data centers really headed to space? (15:37) The shift to designing chips & software together (17:39) The biggest misconception about co-designing chips & software (18:31) Inside SpaceX's multi-billion dollar AI deals (20:06) NVIDIA's playbook for locking out competitors (20:54) The hidden cost behind free tokens (22:52) Andrew's response to Karp's sovereign AI thesis (24:18) AI's biggest win might be curing cancer (26:50) Peptides & biohacking (28:00) How AI could finally fix the broken education problem (29:46) The mentors who shaped Andrew Feldman's career

NEW: Cerebras $CBRS CEO Andrew Feldman (Andrew Feldman) "When the chip on your shoulder is the largest chip the world has ever seen." "The demand for AI has outpaced everybody's expectation & everybody's forecast. & so everybody's chasing. They're chasing chips, memory, or data centers." We get into the chip 58x larger than any other, $20B OpenAI deal signed in 4.5 weeks, & what's actually going on with the 'big AI deals' Recorded 2 months after Cerebras' $5.5B IPO at a $56B valuation, where a first-day pop briefly hit ~$95B before settling toward ~$60B. We cover: › A "Cambrian explosion" of new chip architectures › $20B+ OpenAI deal: 750MW of inference compute over 3 years › Why inference, not training, is where the value is now › "We are behind" on the data center build-out › Free tokens & circular deals: "These are drug pushers" › Creating 1,000 millionaires › Nvidia's balance sheet & market strength › Sovereign AI & owning the stack › Co-design & data centers in space › A real 25-year path to ending cancer Cerebras builds AI infrastructure for training & inference. It went public in May 2026, & its products include inference, Wafer Scale Engine, AI supercomputers, AI model services, cloud, systems, & processors. Filmed at the Raise Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Andrew Feldman, Co-Founder & CEO at Cerebras Systems (00:49) Why hardware suddenly became the coolest industry in tech (01:51) What changed at Raise AI Summit (03:01) Inside the $20 billion Cerebras - OpenAI deal (05:50) What actually changes two months after an IPO (06:32) Turning 1,000 employees into millionaires (07:52) Staying sane during an AI gold rush (10:44) Life after the IPO plateau (11:45) The truth about the global data center shortage (12:56) Why data centers are borrowing jet engines for power (14:44) Are data centers really headed to space? (15:37) The shift to designing chips & software together (17:39) The biggest misconception about co-designing chips & software (18:31) Inside SpaceX's multi-billion dollar AI deals (20:06) NVIDIA's playbook for locking out competitors (20:54) The hidden cost behind free tokens (22:52) Andrew's response to Karp's sovereign AI thesis (24:18) AI's biggest win might be curing cancer (26:50) Peptides & biohacking (28:00) How AI could finally fix the broken education problem (29:46) The mentors who shaped Andrew Feldman's career

Molly O’Shea

494,613 次观看 • 1 个月前

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 次观看 • 1 年前

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Fireside Alpha

282,905 次观看 • 1 个月前

MEET THE NVIDIA KILLER: OpenAI bet $10 BILLION on this company that makes chips 20x faster than Nvidia's. If this plays out as expected, it’s over for Nvidia. Cerebras Systems just locked in 750 megawatts of computing power to OpenAI through 2028. For reference: that's equivalent to the annual power consumption of 600,000 US homes. The deal? Over $10 billion. Here's what nobody understands: Cerebras doesn't make normal chips. Nvidia sells you thousands of tiny chips that you connect together. Cerebras makes ONE chip. A single wafer-scale processor the size of a dinner plate. 900,000 AI cores. 4 trillion transistors. All on one piece of silicon. The result? When OpenAI tested it, Cerebras ran inference 20X FASTER than Nvidia GPUs. That's not incremental improvement. That's a different category of performance. But here's where the story gets wild: Four months ago, Cerebras was a struggling company. Their IPO filing revealed that 87% of their revenue came from ONE customer: G42, a UAE-based AI firm. The US government launched a national security review. G42 had ties to Huawei. Ties to China. The IPO collapsed. Investors panicked. Cerebras withdrew their filing in October 2025. Most startups would've been dead. Instead, Cerebras did the opposite. They raised $1.1 billion at an $8.1 billion valuation. Kicked G42 out of the cap table entirely. Got CFIUS clearance. Then landed the OpenAI deal. Now they're raising ANOTHER $1 billion at a $22 billion valuation. They more than DOUBLED their valuation in 4 months. From near-death to $22 billion. While getting rid of their biggest customer. Why OpenAI chose them: ChatGPT has 900 million weekly users. Sam Altman keeps saying they have a "severe shortage" of compute. They need SPEED, not just power. When you ask ChatGPT a question, there's a loop happening: You send request → model thinks → sends response back Nvidia chips are fast at training models. Cerebras chips are built specifically for inference. For real-time responses. For the exact bottleneck OpenAI is trying to solve. Sachin Katti from OpenAI said it best: "Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people." In other words: "We need this to scale ChatGPT." The competitive landscape just shifted: Nvidia announced a $100 billion deal with OpenAI in September. But it's still not finalized. Meanwhile, Cerebras closed their deal before Thanksgiving. And it's ALREADY being deployed. Here's the part that should terrify Nvidia: In December, Nvidia bought Groq for $20 billion. Groq makes fast inference chips. Just like Cerebras. So why would Nvidia spend $20 billion buying a competitor to something they supposedly already dominate? Because they know what's coming. Inference is the new battleground. And Cerebras is winning it. The IPO is coming Q2 2026. After this OpenAI deal, Cerebras now has: ✓ IBM contracts ✓ Department of Energy contracts ✓ OpenAI locked in for 3 years ✓ $22 billion valuation ✓ CFIUS clearance ✓ Zero customer concentration risk They went from 87% revenue dependency on one customer to the most diversified chip company outside Nvidia. In four months. The lesson? Smart money doesn't follow headlines. It follows where the AI leaders are actually spending. OpenAI didn't announce this deal for publicity. They need Cerebras hardware to scale ChatGPT. That's a $10 billion vote of confidence. While everyone's watching Nvidia stock, the real war is happening in inference. And the company with ONE giant chip just beat the company with thousands of tiny ones. What do you think happens when Cerebras IPOs?

MEET THE NVIDIA KILLER: OpenAI bet $10 BILLION on this company that makes chips 20x faster than Nvidia's. If this plays out as expected, it’s over for Nvidia. Cerebras Systems just locked in 750 megawatts of computing power to OpenAI through 2028. For reference: that's equivalent to the annual power consumption of 600,000 US homes. The deal? Over $10 billion. Here's what nobody understands: Cerebras doesn't make normal chips. Nvidia sells you thousands of tiny chips that you connect together. Cerebras makes ONE chip. A single wafer-scale processor the size of a dinner plate. 900,000 AI cores. 4 trillion transistors. All on one piece of silicon. The result? When OpenAI tested it, Cerebras ran inference 20X FASTER than Nvidia GPUs. That's not incremental improvement. That's a different category of performance. But here's where the story gets wild: Four months ago, Cerebras was a struggling company. Their IPO filing revealed that 87% of their revenue came from ONE customer: G42, a UAE-based AI firm. The US government launched a national security review. G42 had ties to Huawei. Ties to China. The IPO collapsed. Investors panicked. Cerebras withdrew their filing in October 2025. Most startups would've been dead. Instead, Cerebras did the opposite. They raised $1.1 billion at an $8.1 billion valuation. Kicked G42 out of the cap table entirely. Got CFIUS clearance. Then landed the OpenAI deal. Now they're raising ANOTHER $1 billion at a $22 billion valuation. They more than DOUBLED their valuation in 4 months. From near-death to $22 billion. While getting rid of their biggest customer. Why OpenAI chose them: ChatGPT has 900 million weekly users. Sam Altman keeps saying they have a "severe shortage" of compute. They need SPEED, not just power. When you ask ChatGPT a question, there's a loop happening: You send request → model thinks → sends response back Nvidia chips are fast at training models. Cerebras chips are built specifically for inference. For real-time responses. For the exact bottleneck OpenAI is trying to solve. Sachin Katti from OpenAI said it best: "Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people." In other words: "We need this to scale ChatGPT." The competitive landscape just shifted: Nvidia announced a $100 billion deal with OpenAI in September. But it's still not finalized. Meanwhile, Cerebras closed their deal before Thanksgiving. And it's ALREADY being deployed. Here's the part that should terrify Nvidia: In December, Nvidia bought Groq for $20 billion. Groq makes fast inference chips. Just like Cerebras. So why would Nvidia spend $20 billion buying a competitor to something they supposedly already dominate? Because they know what's coming. Inference is the new battleground. And Cerebras is winning it. The IPO is coming Q2 2026. After this OpenAI deal, Cerebras now has: ✓ IBM contracts ✓ Department of Energy contracts ✓ OpenAI locked in for 3 years ✓ $22 billion valuation ✓ CFIUS clearance ✓ Zero customer concentration risk They went from 87% revenue dependency on one customer to the most diversified chip company outside Nvidia. In four months. The lesson? Smart money doesn't follow headlines. It follows where the AI leaders are actually spending. OpenAI didn't announce this deal for publicity. They need Cerebras hardware to scale ChatGPT. That's a $10 billion vote of confidence. While everyone's watching Nvidia stock, the real war is happening in inference. And the company with ONE giant chip just beat the company with thousands of tiny ones. What do you think happens when Cerebras IPOs?

Ricardo

28,088 次观看 • 7 个月前