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Cyber ethnography is decoding the internet's hidden subcultures - and taste is becoming the only moat that matters in the era of AGI. Our full conversation with Ruby Justice Thelot, cyber ethnographer and NYU professor, where he tells us why "trends" are usually just three TikToks in a trench...

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NYU Prof. Ruby Thelot on why everyone suddenly cares about taste: "I wrote a book called A Few Essays on Taste, where I make the case that taste is not just aesthetic preference. Taste is a way for people to consume virtuously. The question of taste emerged in the 17th century, at a time where people needed guidance as to how to consume properly." "In 1688, the Bill of Rights happens in England after the Glorious Revolution, and the nobility's powers are restrained. As they lose political power, they also lose cultural power. At the same time you have the growth of the British colonies, immense amounts of wealth being generated, and suddenly you have this nouvelle bourgeoisie, this new elite. And there's a belief that this emergence of excessive luxury will lead to the decay of society." "It is in the vacuum, in the reduction of the cultural power of the previous nobility, that the question of taste emerges. What's interesting to me is that we're in a similar position now. We're entering a similar industrial revolution. We're in the midst of immense amounts of wealth being created, and we're asking ourselves, how do we consume virtuously? That is why the question of taste is emerging. It mirrors 17 and 18th century England." ruby justice thelot Nancy
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NYU Prof. Ruby Thelot on why everyone suddenly cares about taste: "I wrote a book called A Few Essays on Taste, where I make the case that taste is not just aesthetic preference. Taste is a way for people to consume virtuously. The question of taste emerged in the 17th century, at a time where people needed guidance as to how to consume properly." "In 1688, the Bill of Rights happens in England after the Glorious Revolution, and the nobility's powers are restrained. As they lose political power, they also lose cultural power. At the same time you have the growth of the British colonies, immense amounts of wealth being generated, and suddenly you have this nouvelle bourgeoisie, this new elite. And there's a belief that this emergence of excessive luxury will lead to the decay of society." "It is in the vacuum, in the reduction of the cultural power of the previous nobility, that the question of taste emerges. What's interesting to me is that we're in a similar position now. We're entering a similar industrial revolution. We're in the midst of immense amounts of wealth being created, and we're asking ourselves, how do we consume virtuously? That is why the question of taste is emerging. It mirrors 17 and 18th century England." ruby justice thelot Nancy

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NYU Prof. Ruby Thelot on how to tell a real trend from three TikToks in a trench coat: "This is one of the hardest things to do on the internet. I like to say, is this a real thing, or is this three TikToks and a trench coat? Because oftentimes what feels like a trend, what feels like a novel social behavior, can just be super micro, super small." "What we do at 131 is a lot of quantitative analysis of social media to see how prevalent this thing really is. There's been articles about this thing called heteropessimism, the aversion that certain straight women have about dating men. And I was like, okay, is this a real thing? So we took the top posts about dating on TikTok, anything over a million views, took a look at 2,000 videos, and identified, did heteropessimism grow since 2020?" "We found that dating content is mostly positive. Our beloved journalists, because of their algorithms, are getting hit with a lot of that content, and then they think it's a trend. But when I take a look at the data of the top 2,000 videos across five years, it's actually kind of decreased over time, and most dating content is, for the most part, positive." ruby justice thelot
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NYU Prof. Ruby Thelot on how to tell a real trend from three TikToks in a trench coat: "This is one of the hardest things to do on the internet. I like to say, is this a real thing, or is this three TikToks and a trench coat? Because oftentimes what feels like a trend, what feels like a novel social behavior, can just be super micro, super small." "What we do at 131 is a lot of quantitative analysis of social media to see how prevalent this thing really is. There's been articles about this thing called heteropessimism, the aversion that certain straight women have about dating men. And I was like, okay, is this a real thing? So we took the top posts about dating on TikTok, anything over a million views, took a look at 2,000 videos, and identified, did heteropessimism grow since 2020?" "We found that dating content is mostly positive. Our beloved journalists, because of their algorithms, are getting hit with a lot of that content, and then they think it's a trend. But when I take a look at the data of the top 2,000 videos across five years, it's actually kind of decreased over time, and most dating content is, for the most part, positive." ruby justice thelot

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NYU Prof. Ruby Thelot on the split he keeps hearing across America: everyone says they hate AI, and everyone says they love chat "I find that Americans don't like AI. AI is a very broad umbrella here, because they all use chat, and they'll say, I love chat. But they don't like AI. There's a bit of a divergence there. There's the boogeyman of the data center, the boogeyman of, I'm gonna lose my job." "If you take a look at an Anthropic survey, they surveyed 85,000 people to ask how they were using AI for career advancement or life management. When I talk to mothers in Bryan, Texas, they say, oh yeah, I had a leak in my faucet, I took a photo, and in about an hour or two I was able to fix that thing." "But to them it's kind of like Google. It's not the thing that replaces their work and puts them on unemployment. So there's a language issue here around what does this technology do for me, versus what is the negative impact that's coming in a somewhat near potential future." ruby justice thelot Nancy

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“The Chinese are skeptical of our calls for international cooperation on AI. They think they're in a race with us. And if they think they're in a race with us, it's kind of hard for us not to be in a race with them. And that's just the way it is.” “And at the end of the day, the Chinese government is a dictatorship that's nationalist and socialist and doesn't really want to cooperate with us much. They want to beat us. They want to compete with us. So, we can try. It's worth trying.” “But the record shows, according to 100% of the evidence, that when you sign an AI or a nuclear or a cyber-hacking document with the Chinese government, it turns out not to be held up. It turns out that they're not actually trustworthy.”
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“The Chinese are skeptical of our calls for international cooperation on AI. They think they're in a race with us. And if they think they're in a race with us, it's kind of hard for us not to be in a race with them. And that's just the way it is.” “And at the end of the day, the Chinese government is a dictatorship that's nationalist and socialist and doesn't really want to cooperate with us much. They want to beat us. They want to compete with us. So, we can try. It's worth trying.” “But the record shows, according to 100% of the evidence, that when you sign an AI or a nuclear or a cyber-hacking document with the Chinese government, it turns out not to be held up. It turns out that they're not actually trustworthy.”

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Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50
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Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50

Dan Shipper

187,107 Aufrufe • vor 1 Jahr

Open source AI just became the biggest policy fight in tech. Our full conversation with Ben Horowitz, where he tells Theo and Sofia why banning open source is nearly impossible, why it's historically the safer path, the risk of one company controlling AI and why distillation isn't the threat people claim. 01:21 The Nvidia-backed open letter on open-weight AI 01:49 The OpenAI–Hugging Face hacking incident 02:27 Why you can't actually ban open source 03:05 Historically, open source has been safer (Linux vs. Windows) 05:17 Jensen's Twitter account and the letter's significance 07:25 China, "sleeper agent" models, and national security risk 09:23 Anthropic defying the US government 15:18 The danger of building your business on Anthropic 18:51 BYD, EVs, and rebuilding US manufacturing 21:30 Why would a company ever release an open model? 23:58 The DeepSeek moment: why nothing actually changed 28:25 Paying 100x more for an AI researcher vs. a janitor 29:06 AI art, the "slop" problem, and a coming renaissance benahorowitz.eth Theo Jaffee sof 𓋹
33:01

Open source AI just became the biggest policy fight in tech. Our full conversation with Ben Horowitz, where he tells Theo and Sofia why banning open source is nearly impossible, why it's historically the safer path, the risk of one company controlling AI and why distillation isn't the threat people claim. 01:21 The Nvidia-backed open letter on open-weight AI 01:49 The OpenAI–Hugging Face hacking incident 02:27 Why you can't actually ban open source 03:05 Historically, open source has been safer (Linux vs. Windows) 05:17 Jensen's Twitter account and the letter's significance 07:25 China, "sleeper agent" models, and national security risk 09:23 Anthropic defying the US government 15:18 The danger of building your business on Anthropic 18:51 BYD, EVs, and rebuilding US manufacturing 21:30 Why would a company ever release an open model? 23:58 The DeepSeek moment: why nothing actually changed 28:25 Paying 100x more for an AI researcher vs. a janitor 29:06 AI art, the "slop" problem, and a coming renaissance benahorowitz.eth Theo Jaffee sof 𓋹

MTS

11,246 Aufrufe • vor 1 Monat

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science
49:06

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

69,801 Aufrufe • vor 3 Monaten

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?
26:21

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,577 Aufrufe • vor 1 Monat

I sat down with Jordi Visser to discuss why bitcoin is the purest AI trade available in the market, breakdown of energy infrastructure, how to invest amid the government shutdown, the Fed/interest rates, and why prediction markets and tokenization are two big themes moving forward. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:04 - Why bitcoin is the purest AI trade 11:43 - The impact of new energy infrastructure 16:53 - How Base Power is building a decentralized grid and other opportunities for investors 23:49 - Why retail spots themes before institutions 32:33 - Retail orange-pilling each other in public 37:25 - What is the impact of government shutdown on financial markets? 40:25 - The rise of prediction markets and tokenization 49:28 - Where to follow Jordi on the internet
50:43

I sat down with Jordi Visser to discuss why bitcoin is the purest AI trade available in the market, breakdown of energy infrastructure, how to invest amid the government shutdown, the Fed/interest rates, and why prediction markets and tokenization are two big themes moving forward. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:04 - Why bitcoin is the purest AI trade 11:43 - The impact of new energy infrastructure 16:53 - How Base Power is building a decentralized grid and other opportunities for investors 23:49 - Why retail spots themes before institutions 32:33 - Retail orange-pilling each other in public 37:25 - What is the impact of government shutdown on financial markets? 40:25 - The rise of prediction markets and tokenization 49:28 - Where to follow Jordi on the internet

Anthony Pompliano 🌪

110,012 Aufrufe • vor 11 Monaten

NYU Prof. Ruby Thelot on why the AI backlash is simple: "Techno-optimism will be on the rise once the claimed benefits of the technology are aligned with the desires and interests of American constituents. What do Americans want? A wife, some food, some clothes to feed their children. In the words of Gil Scott-Heron. It's simple." "Even Dave Chappelle says this in one of his specials, you never get between a man and his bread. So I don't know who thought it was a good idea to go on national television and tell people they're gonna lose their job. If I'm an American, which I'm not, I wouldn't want to lose my job, because that's how I feed my family." "The thing I love with doing all my research is that I get to talk to people who put in very blunt terms what I could have written a five-page report on. But it's quite simple. People wanna be employed." ruby justice thelot Nancy
1:18

NYU Prof. Ruby Thelot on why the AI backlash is simple: "Techno-optimism will be on the rise once the claimed benefits of the technology are aligned with the desires and interests of American constituents. What do Americans want? A wife, some food, some clothes to feed their children. In the words of Gil Scott-Heron. It's simple." "Even Dave Chappelle says this in one of his specials, you never get between a man and his bread. So I don't know who thought it was a good idea to go on national television and tell people they're gonna lose their job. If I'm an American, which I'm not, I wouldn't want to lose my job, because that's how I feed my family." "The thing I love with doing all my research is that I get to talk to people who put in very blunt terms what I could have written a five-page report on. But it's quite simple. People wanna be employed." ruby justice thelot Nancy

MTS

45,005 Aufrufe • vor 1 Monat

What happens when a global recording artist is also a self taught technologist? In this episode of IsraelTech, Yoel Israel sits down with global artist Nissim Black to explore the intersection of music, technology, AI, media, and spirituality. Before becoming an internationally known artist, Nissim grew up watching computers being built in his home and experimenting with music production equipment as a kid. That early exposure shaped the way he approaches creativity and technology today. They discuss how podcasting and music production rely on many of the same tools, how AI is already transforming the music industry, and why independent creators now have more power than traditional media. The conversation also dives into deeper questions about morality, media narratives, and why technology itself is not the real risk. The real question is how people choose to use it. This episode covers everything from AI generated music to the future of media platforms and why storytelling still matters more than ever. Hosted by Yoel Israel
1:10:17

What happens when a global recording artist is also a self taught technologist? In this episode of IsraelTech, Yoel Israel sits down with global artist Nissim Black to explore the intersection of music, technology, AI, media, and spirituality. Before becoming an internationally known artist, Nissim grew up watching computers being built in his home and experimenting with music production equipment as a kid. That early exposure shaped the way he approaches creativity and technology today. They discuss how podcasting and music production rely on many of the same tools, how AI is already transforming the music industry, and why independent creators now have more power than traditional media. The conversation also dives into deeper questions about morality, media narratives, and why technology itself is not the real risk. The real question is how people choose to use it. This episode covers everything from AI generated music to the future of media platforms and why storytelling still matters more than ever. Hosted by Yoel Israel

IsraelTech

27,666 Aufrufe • vor 6 Monaten