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Open-source vs. frontier AI is usually a theoretical debate. DataCamp has to make that choice in production - while building an AI-native tutor for a platform serving more than 19 million learners. My co-founder Peter and I sat down with DataCamp Co-Founder and CEO Jonathan Cornelissen and Chief AI... Officer Yusuf Saber to discuss: – which models actually work in production – whether open models are really 10x cheaper – hidden infrastructure and engineering costs – caching, latency, quality, and hallucinations – privacy, data residency, and vendor lock-in – what open models still cannot do A genuinely practical conversation about what happens when AI leaves the demo and meets real users, real costs, and real failure modes. Full interview down below + YouTube in comment sectionshow more

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Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

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Uber CEO Dara Khosrowshahi: "We Blew Through Our AI Budget in a Quarter" In response, Uber is doing three things: -Slowing headcount growth as AI makes engineers more productive -Reserving expensive frontier models for exploration only and shifting production workloads to cheaper open source models -Capping each employee at $1,500/month per AI coding tool

Uber CEO Dara Khosrowshahi: "We Blew Through Our AI Budget in a Quarter" In response, Uber is doing three things: -Slowing headcount growth as AI makes engineers more productive -Reserving expensive frontier models for exploration only and shifting production workloads to cheaper open source models -Capping each employee at $1,500/month per AI coding tool

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Europe talks constantly about AI sovereignty. But in practice, much of the continent still seems to want American AI with a European postcode. Together with Superintelligence co-founder Peter Thum, I sat down with PandaOS co-founders Philipp Türker and Marco Szeidenleder to examine what technological sovereignty actually means when European companies remain dependent on US models, cloud providers, chips, APIs, and infrastructure. The conversation began with Europe’s recent Anthropic warning shot: what happens when access to a frontier model can suddenly disappear, prices change, or a provider becomes unavailable? Philipp and Marco argue that sovereignty should not mean attempting to own every layer of the AI stack. It means maintaining control over the systems and data that are essential to your business—and preserving the ability to switch providers without rebuilding your entire operation. Have fun! YouTube down below

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Rohan Paul

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> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI

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As agentic automation helps developers get more done, the role is evolving toward higher-level decisions about what to build, how systems should work, and where taste and creativity matter most. Databricks Co-founder Patrick Wendell, OpenClaw creator Peter Steinberger 🦞, and Codex engineering lead Tibo discuss what that shift means for enterprises building, governing, and scaling fleets of AI agents in production. Watch the full conversation:

As agentic automation helps developers get more done, the role is evolving toward higher-level decisions about what to build, how systems should work, and where taste and creativity matter most. Databricks Co-founder Patrick Wendell, OpenClaw creator Peter Steinberger 🦞, and Codex engineering lead Tibo discuss what that shift means for enterprises building, governing, and scaling fleets of AI agents in production. Watch the full conversation:

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NVIDIA CEO, Jensen Huang, warns that America's lead in AI is far from secure. He breaks down the US-China AI competition into what he calls a "five-layer cake." And while the US dominates some layers, Jensen sees critical vulnerabilities in others... 1) Energy: China has twice as much as the US despite a smaller economy. Which "makes no sense" to Jensen. 2) Chips: The US is "generations ahead," but Jensen warns against complacency. "Anybody who thinks China can't manufacture is missing the big idea." 3) Infrastructure: Standing up a data center in the US takes about three years. In China? "They can build a hospital in a weekend." 4) Models: US frontier models are "unquestionably world class," but "China is well ahead, way ahead on open source." 5) Applications: Public sentiment differs sharply. Ask both populations whether AI will do more good than harm, and "in their case 80% would say AI will do more good than harm. In our case, it'd be the other way around." Jensen's warning is clear. Leading in chips and frontier models isn't enough when you're behind on energy, infrastructure speed, open source, and public trust. Winning the AI race requires strength across the entire stack, and right now, the US has work to do.

NVIDIA CEO, Jensen Huang, warns that America's lead in AI is far from secure. He breaks down the US-China AI competition into what he calls a "five-layer cake." And while the US dominates some layers, Jensen sees critical vulnerabilities in others... 1) Energy: China has twice as much as the US despite a smaller economy. Which "makes no sense" to Jensen. 2) Chips: The US is "generations ahead," but Jensen warns against complacency. "Anybody who thinks China can't manufacture is missing the big idea." 3) Infrastructure: Standing up a data center in the US takes about three years. In China? "They can build a hospital in a weekend." 4) Models: US frontier models are "unquestionably world class," but "China is well ahead, way ahead on open source." 5) Applications: Public sentiment differs sharply. Ask both populations whether AI will do more good than harm, and "in their case 80% would say AI will do more good than harm. In our case, it'd be the other way around." Jensen's warning is clear. Leading in chips and frontier models isn't enough when you're behind on energy, infrastructure speed, open source, and public trust. Winning the AI race requires strength across the entire stack, and right now, the US has work to do.

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Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

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$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

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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

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

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Our CEO and Co-Founder, Anastasios Nikolas Angelopoulos, joined Harry Stebbings on 20VC to share his thoughts on some of the most consequential technical, economic, and security questions shaping AI: - U.S. vs China competition in frontier and open-weight models - The future of the U.S. open-source AI ecosystem - Kimi K3 release and what it signals about China’s model capabilities - Gravity of the recent cyber security incidents - Which neo-labs will breakthrough into durable businesses - The next generation of business moats: proprietary data, network effects, and ownership of the intelligence stack Watch the full podcast below.

Our CEO and Co-Founder, Anastasios Nikolas Angelopoulos, joined Harry Stebbings on 20VC to share his thoughts on some of the most consequential technical, economic, and security questions shaping AI: - U.S. vs China competition in frontier and open-weight models - The future of the U.S. open-source AI ecosystem - Kimi K3 release and what it signals about China’s model capabilities - Gravity of the recent cyber security incidents - Which neo-labs will breakthrough into durable businesses - The next generation of business moats: proprietary data, network effects, and ownership of the intelligence stack Watch the full podcast below.

Arena.ai

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$🇨🇳 CHINA IS ALMOST READY TO UNDERCUT THE WORLD'S BEST AI MODELS. Six months ago, that sounded impossible. Today GLM-5.2 is open source and costs a fraction of frontier models. Zhipu's co-founder says Fable-class Chinese AI comes even sooner than people think. This is the same playbook China ran on solar, on steel, on EVs. Build it cheaper. Give it away. Take the market. If China delivers frontier AI at a fraction of the price, the economics holding up the U.S. AI market start to crack.$

🇨🇳 CHINA IS ALMOST READY TO UNDERCUT THE WORLD'S BEST AI MODELS. Six months ago, that sounded impossible. Today GLM-5.2 is open source and costs a fraction of frontier models. Zhipu's co-founder says Fable-class Chinese AI comes even sooner than people think. This is the same playbook China ran on solar, on steel, on EVs. Build it cheaper. Give it away. Take the market. If China delivers frontier AI at a fraction of the price, the economics holding up the U.S. AI market start to crack.

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What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

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.River AI has a new vision for personal AI. We co-led River’s $1.1B funding round because we believe in Igor Babuschkin's ambition to build open-weight models that individuals can train and improve continuously. Watch Hemant Taneja in conversation with Igor. Chapters 02:22 — Co-Founding xAI 04:36 — An AI Experience You Own 05:59 — Betting Early on Open Weights 07:47 — How Do You Make Money on Free Models? 09:25 — Why the US Needs Its Own Open Models 11:27 — Enterprises Want to Control Their AI 13:36 — What Is Continual Learning? 16:56 — An API That Updates the Weights 19:17 — A Personal AI for Everyone 20:46 — The 20-Minute Training Run

.River AI has a new vision for personal AI. We co-led River’s $1.1B funding round because we believe in Igor Babuschkin's ambition to build open-weight models that individuals can train and improve continuously. Watch Hemant Taneja in conversation with Igor. Chapters 02:22 — Co-Founding xAI 04:36 — An AI Experience You Own 05:59 — Betting Early on Open Weights 07:47 — How Do You Make Money on Free Models? 09:25 — Why the US Needs Its Own Open Models 11:27 — Enterprises Want to Control Their AI 13:36 — What Is Continual Learning? 16:56 — An API That Updates the Weights 19:17 — A Personal AI for Everyone 20:46 — The 20-Minute Training Run

General Catalyst

110,829 görüntüleme • 15 gün önce

VIDEO | Delhi: Yann LeCun (Yann LeCun), AI pioneer and former Chief AI Scientist at Meta, says, “It’s enormous and very impressive, with so much activity and many interesting one-on-one interactions and forums. I think many countries should come together to build frontier models using data available across different regions of the world, which is not accessible to proprietary models. This could help open-source AI models become better than closed, proprietary ones... India should invest more in research, incentivise more students to pursue advanced degrees and PhDs, and encourage them to stay in India. Given the changing geopolitical situation, it is becoming harder to study in certain countries, which makes developing local expertise even more important. Being a leader in technology requires vibrant research activity, which can only happen with strong funding for academic research and PhD programmes.” He visited Puch AI stall with Puch AI co-founder & CEO Siddharth Bhatia (Siddharth Bhatia) at Bharat Mandapam during India AI Impact Summit. #PTIAtAIImpactSummit #IndiaAIImpactSummit2026 #AIImpactSummit2026 (Full video available on

VIDEO | Delhi: Yann LeCun (Yann LeCun), AI pioneer and former Chief AI Scientist at Meta, says, “It’s enormous and very impressive, with so much activity and many interesting one-on-one interactions and forums. I think many countries should come together to build frontier models using data available across different regions of the world, which is not accessible to proprietary models. This could help open-source AI models become better than closed, proprietary ones... India should invest more in research, incentivise more students to pursue advanced degrees and PhDs, and encourage them to stay in India. Given the changing geopolitical situation, it is becoming harder to study in certain countries, which makes developing local expertise even more important. Being a leader in technology requires vibrant research activity, which can only happen with strong funding for academic research and PhD programmes.” He visited Puch AI stall with Puch AI co-founder & CEO Siddharth Bhatia (Siddharth Bhatia) at Bharat Mandapam during India AI Impact Summit. #PTIAtAIImpactSummit #IndiaAIImpactSummit2026 #AIImpactSummit2026 (Full video available on

Press Trust of India

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vLLM runs on half a million GPUs at any given moment. Most people have never heard of it. Simon Mo, co-founder and CEO of Inferact and lead maintainer of vLLM, sits down with a16z’s Matt Bornstein and Elena Burger to discuss what it takes to actually run open models in production, the advantages of open models, Simon’s mission at Inferact, and more. 00:00 Intro 01:46 When open source became critical infrastructure 08:55 Day zero model releases, and the drama behind them 14:59 What Kimi K3 actually buys you 18:56 Why open model licenses are changing 22:24 The pharmaceutical analogy for funding model training 26:16 If GPUs got 99% cheaper 29:08 Why vLLM, OpenRouter, and Ollama all started before ChatGPT 35:42 Building a company on an open source project 39:48 The inventor of RoPE removing RoPE Simon Mo Matt Bornstein Elena

vLLM runs on half a million GPUs at any given moment. Most people have never heard of it. Simon Mo, co-founder and CEO of Inferact and lead maintainer of vLLM, sits down with a16z’s Matt Bornstein and Elena Burger to discuss what it takes to actually run open models in production, the advantages of open models, Simon’s mission at Inferact, and more. 00:00 Intro 01:46 When open source became critical infrastructure 08:55 Day zero model releases, and the drama behind them 14:59 What Kimi K3 actually buys you 18:56 Why open model licenses are changing 22:24 The pharmaceutical analogy for funding model training 26:16 If GPUs got 99% cheaper 29:08 Why vLLM, OpenRouter, and Ollama all started before ChatGPT 35:42 Building a company on an open source project 39:48 The inventor of RoPE removing RoPE Simon Mo Matt Bornstein Elena

a16z

209,110 görüntüleme • 20 gün önce

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 Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

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 Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

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