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Can open source prevent AI power from concentrating? MTS host sophia dew visits the Open Source AI Summit in San Francisco to ask researchers and founders working at different layers of AI. Co-author of Attention Is All You Need, Lukasz Kaiser, argues power concentration is a property of the...

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103,450 Aufrufe • vor 17 Tagen •via X (Twitter)

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Profilbild von sophia dew
sophia dewvor 17 Tagen

@lukaszkaiser nothing like interviewing all my fave open source builders in one day

Profilbild von Taylor Lorenz
Taylor Lorenzvor 17 Tagen

@sophiadew @lukaszkaiser Omg fomo!! This is awesome

Profilbild von Matt White
Matt Whitevor 17 Tagen

@sophiadew @lukaszkaiser Thank you for having me, really enjoyed the conversation!

Profilbild von Winston B.
Winston B.vor 17 Tagen

Open weights moved the concentration rather than preventing it. US models went from 74% of OpenRouter tokens in June 2025 to 20% a year later, and China's commerce ministry is now consulting Alibaba, ByteDance and Zhipu on export controls for its most advanced weights. Openness holds right up until a government decides it has a lead worth protecting.

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China is trying to pop the US AI bubble. Xi just announced China will provide 30 countries with 5,000 AI training and seminar opportunities while building international AI application cooperation centers. China and the US have a different definition of "winning" the AI race. The US strategy: Spend trillions building and controlling the AI infrastructure the world will depend on through a proprietary ecosystem. China is taking the opposite approach: Build cheaper open-source models with similar performance and encourage the world to build on top of them. The bet is simple: The winner of AI will not necessarily be whoever owns the infrastructure. .... It will be whoever builds the best companies on top of it. Just like in the post-dot-com era... The biggest winners were companies like Amazon, Google, and Microsoft that built businesses on top of open infrastructure.

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197,172 Aufrufe • vor 1 Monat

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.
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What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

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“We have a crisis of open source models in the Western world. Outside of China, there are no good open source models. We don't even have any in the US now. The talent, the capital and the focus to be best-in-class at pre-training, mid-training, post-training is an extremely scarce skillset. The answer for a lot of countries may just be to take an open weights model from China, post-train or fine-tune their own version, and have that be what they start from." Everett Randle Why does the West have such poor open source models and is it a national security threat to rely on open-source Chinese models Demis Hassabis Yann LeCun Aidan Gomez Lilian Weng Jan Leike

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63,738 Aufrufe • vor 3 Monaten

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0:26

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

30,392 Aufrufe • vor 1 Monat

.Marc Andreessen 🇺🇸 says the winner of the US-China AI race could dictate what all 8 billion people on Earth use — but open source introduces a third possibility: "The stakes are basically: what is the world going to run on?" "In the long run, somebody's gonna win. And the world will either be running on American AI or be running on Chinese AI." "The open source thing is super fascinating because it throws a wrench into all of this. It raises a third possibility that neither the US nor China are going to be the platform — it may just be open source." Marc Andreessen with Jeetu Patel at Cisco AI Summit
3:41

.Marc Andreessen 🇺🇸 says the winner of the US-China AI race could dictate what all 8 billion people on Earth use — but open source introduces a third possibility: "The stakes are basically: what is the world going to run on?" "In the long run, somebody's gonna win. And the world will either be running on American AI or be running on Chinese AI." "The open source thing is super fascinating because it throws a wrench into all of this. It raises a third possibility that neither the US nor China are going to be the platform — it may just be open source." Marc Andreessen with Jeetu Patel at Cisco AI Summit

a16z

101,985 Aufrufe • vor 7 Monaten

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1:14

The open-source & open-weight frontier is 7 months behind the closed-model frontier. Let’s talk about it. Check out last week’s episode for the reasons why you might want to consider open-source & open-weight models. Welcome to the fourth episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! We have been discussing open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Please leave all your questions about open-source & open-weight AI in the comments! This is a super important topic that is novel to most people, and it can be challenging to grasp - so rest assured your questions are shared with many others!

Harper Carroll

17,279 Aufrufe • vor 18 Tagen

Matt Bornstein says open source is how AI companies stop being wrappers: "A year ago, a bunch of smaller companies or new application companies were trying to figure out, 'How do I really build an AI without just being a wrapper on top of OpenAI?' The answer to that question turned out to be open source." "This is what Cursor did. This is what Decagon and Harvey are in the process of doing now. A bunch of really strong application-level startups made the determination: we can't build just on closed source." "We need to do our own mid-training, our own post-training, our own inference and deployment tricks. All of that means it must be built on top of open source. The closed-source vendors won't give you the access to do this." "Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. Some of the most innovative products and applications now really depend on this very deeply." Matt Bornstein
1:15

Matt Bornstein says open source is how AI companies stop being wrappers: "A year ago, a bunch of smaller companies or new application companies were trying to figure out, 'How do I really build an AI without just being a wrapper on top of OpenAI?' The answer to that question turned out to be open source." "This is what Cursor did. This is what Decagon and Harvey are in the process of doing now. A bunch of really strong application-level startups made the determination: we can't build just on closed source." "We need to do our own mid-training, our own post-training, our own inference and deployment tricks. All of that means it must be built on top of open source. The closed-source vendors won't give you the access to do this." "Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. Some of the most innovative products and applications now really depend on this very deeply." Matt Bornstein

a16z

40,507 Aufrufe • vor 1 Monat

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31:59

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860,331 Aufrufe • vor 8 Monaten

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1:22:57

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Matt Turck

56,954 Aufrufe • vor 2 Monaten

Perplexity CEO on China catching up in AI: “Whatever you did to not let them catch up didn’t even matter. They ended up catching up anyway. What’s more dangerous is they have the best open-source model. And all the American developers are building on that.” That was DeepSeek. Now just dropped GLM-5.2: • MIT open weights • 1M context • 81.0 on Terminal-Bench 2.1 • within a few points of Claude Opus 4.8 The open-source AI race is not theory anymore.
2:47

Perplexity CEO on China catching up in AI: “Whatever you did to not let them catch up didn’t even matter. They ended up catching up anyway. What’s more dangerous is they have the best open-source model. And all the American developers are building on that.” That was DeepSeek. Now just dropped GLM-5.2: • MIT open weights • 1M context • 81.0 on Terminal-Bench 2.1 • within a few points of Claude Opus 4.8 The open-source AI race is not theory anymore.

Vadim

650,826 Aufrufe • vor 3 Monaten

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34:01

Big day for American open-source AI. For the launch of Laguna S, I sat down with Eiso Kant to discuss its architecture, the economics of open weights, and the question of who gets to build intelligence. Timestamps: 0:00 Intro 1:50 Why Poolside started opening its models: the oligopoly on intelligence 4:28 Getting nerd-sniped by Karpathy, building LLMs before anyone cared 11:20 Laguna S: 118B parameters, 8B active, built in 8 weeks 13:05 The future of software engineering: behaviors over IQ 14:25 Sliding window attention, 1M context, the model factory 15:50 Being an American open-source lab 20:47 The economics of open weights 24:22 Who gets to build intelligence? The 12–18 month window 30:28 Erdős 397 in 30 minutes 31:48 Extracting transcripts with a debugger Congrats to the Poolside team on the launch!

Madison Kanna

64,611 Aufrufe • vor 2 Monaten

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
27:58

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

119,268 Aufrufe • vor 5 Monaten

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?
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🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

Matt Turck

93,416 Aufrufe • vor 1 Monat