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Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners...

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Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.
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Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.

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As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

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America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."
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America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

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60,135 Aufrufe • vor 3 Monaten

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Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

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Matt Turck

64,952 Aufrufe • vor 1 Monat

"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.
26:06

"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.

OriginTrail

227,307 Aufrufe • vor 2 Monaten

In Signal Dialogues #04, we explore what it takes to build the foundational layer of voice AI from the ground up. Karan Goel, Co-founder and CEO of Cartesia, sits down with Aakrit Vaish to unpack the bet behind one of the most ambitious infrastructure plays in AI. Karan and his co-founders came out of Stanford's AI Lab, where Albert Gu (now Cartesia's Chief Scientist) co-authored Mamba and pioneered State Space Models, the architecture they believe will define the next generation of real-time, interactive AI. Backed by Kleiner Perkins, Index Ventures, Lightspeed and NVIDIA with over $120M raised, Cartesia is building voice models engineered for real-time latency and efficiency at scale. If you're a founder, researcher or builder thinking about voice, infrastructure or the next architectural shift in AI, this one's for you. Timestamps: 00:00 - Introduction 02:46 - What Cartesia Does: Interactive AI Models 03:24 - Founding Story: From Stanford PhDs to Cartesia 04:03 - The Unique Insight: Efficiency and Beyond Transformers 09:07 - Voice Company, Neolab, or Big Lab? 14:57 - State Space Models and Algorithmic Innovation 26:04 - The Four Big Bets in Interactive AI 30:24 - Products, Customers, and Going Full-Stack 41:44 - The BPO Disruption: India's Call Center Future 54:17 - Competition, Moats, and the Long Game Karan Goel Aakrit Vaish Cartesia
1:01:29

In Signal Dialogues #04, we explore what it takes to build the foundational layer of voice AI from the ground up. Karan Goel, Co-founder and CEO of Cartesia, sits down with Aakrit Vaish to unpack the bet behind one of the most ambitious infrastructure plays in AI. Karan and his co-founders came out of Stanford's AI Lab, where Albert Gu (now Cartesia's Chief Scientist) co-authored Mamba and pioneered State Space Models, the architecture they believe will define the next generation of real-time, interactive AI. Backed by Kleiner Perkins, Index Ventures, Lightspeed and NVIDIA with over $120M raised, Cartesia is building voice models engineered for real-time latency and efficiency at scale. If you're a founder, researcher or builder thinking about voice, infrastructure or the next architectural shift in AI, this one's for you. Timestamps: 00:00 - Introduction 02:46 - What Cartesia Does: Interactive AI Models 03:24 - Founding Story: From Stanford PhDs to Cartesia 04:03 - The Unique Insight: Efficiency and Beyond Transformers 09:07 - Voice Company, Neolab, or Big Lab? 14:57 - State Space Models and Algorithmic Innovation 26:04 - The Four Big Bets in Interactive AI 30:24 - Products, Customers, and Going Full-Stack 41:44 - The BPO Disruption: India's Call Center Future 54:17 - Competition, Moats, and the Long Game Karan Goel Aakrit Vaish Cartesia

Activate

12,489 Aufrufe • vor 2 Monaten

This is amazing research result! Xun Huang and team trained a bidirectional teacher model for motion control, and used Self-Forcing to make it autoregressive. Xun recently gave a great talk at the Real Time Video AI summit about this work, and a few other projects around video world models. Here is the recording.
29:48

This is amazing research result! Xun Huang and team trained a bidirectional teacher model for motion control, and used Self-Forcing to make it autoregressive. Xun recently gave a great talk at the Real Time Video AI summit about this work, and a few other projects around video world models. Here is the recording.

Eric is Daydreaming

52,796 Aufrufe • vor 9 Monaten

AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!
1:41

AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!

Jason Cui

36,205 Aufrufe • vor 3 Monaten

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
1:14:20

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 Aufrufe • vor 4 Monaten