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Ten days after winning the Fields Medal — math's highest honor — Jacob Tsimerman joins to explain why he's leaving academia temporarily for OpenAI's AI safety team, and why he's grieving even as he does it. The conversation covers why math is one of the first fields being radically... show more

Curt Jaimungal

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50,330 просмотров • 17 дней назад •via X (Twitter)

Наука и технологии Образование Новости и политика

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Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Dwarkesh Patel

290,780 просмотров • 1 месяц назад

Mo Gawdat believes up to 30% of jobs in certain sectors could disappear by 2028. That stopped me in my tracks. Mo was one of the first people to come on this podcast and warn me about AI, long before most of the world was talking about it. At the time, it felt early. Now, it feels like the world is catching up to what he was seeing. I’m still trying to understand what AI actually means for our lives. Not just whether it can write emails, create images or make us more productive. I mean what it does to jobs. What it does to power. What it does to education. What it does to human connection… That’s why I wanted to have this discussion with Mo again. What makes Mo worth listening to is that he saw these systems inside Google years before most of us had even heard the term AI. His book *Scary Smart* now feels like it was written for this exact moment. Let me explain why this discussion matters. Mo believes we’re not just entering an AI revolution. We’re entering a period where AI, robotics, economics, surveillance, digital currencies and global instability are all colliding at the same time. That’s a lot for any of us to process. We spoke about: - The jobs Mo believes are most at risk from AI. - Why he believes that AI is actually underhyped! - The mistake almost everyone is making with ChatGPT. - The prediction that changed even his own view of the future. The part that stayed with me was this idea that human connection may become the real currency. Because if AI can produce the information, write the report, analyse the data, then what is left? I don’t think this conversation gives neat answers. That’s probably why it’s worth watching. It helped me think more honestly about what’s coming.

Mo Gawdat believes up to 30% of jobs in certain sectors could disappear by 2028. That stopped me in my tracks. Mo was one of the first people to come on this podcast and warn me about AI, long before most of the world was talking about it. At the time, it felt early. Now, it feels like the world is catching up to what he was seeing. I’m still trying to understand what AI actually means for our lives. Not just whether it can write emails, create images or make us more productive. I mean what it does to jobs. What it does to power. What it does to education. What it does to human connection… That’s why I wanted to have this discussion with Mo again. What makes Mo worth listening to is that he saw these systems inside Google years before most of us had even heard the term AI. His book Scary Smart now feels like it was written for this exact moment. Let me explain why this discussion matters. Mo believes we’re not just entering an AI revolution. We’re entering a period where AI, robotics, economics, surveillance, digital currencies and global instability are all colliding at the same time. That’s a lot for any of us to process. We spoke about: - The jobs Mo believes are most at risk from AI. - Why he believes that AI is actually underhyped! - The mistake almost everyone is making with ChatGPT. - The prediction that changed even his own view of the future. The part that stayed with me was this idea that human connection may become the real currency. Because if AI can produce the information, write the report, analyse the data, then what is left? I don’t think this conversation gives neat answers. That’s probably why it’s worth watching. It helped me think more honestly about what’s coming.

Steven Bartlett

27,882 просмотров • 2 месяцев назад

Why is AI suddenly cracking all kinds of previously unsolved problems in math? My guess is that it’s because: A) Math is verifiable so it's easier for models to improve B) Theoretical math highly benefits from chain of thought reasoning C) The answers and breakthroughs can be tested and verified by expert humans. But this leads to another question: what if progress in other fields is moving just as fast and we can't see it?

Why is AI suddenly cracking all kinds of previously unsolved problems in math? My guess is that it’s because: A) Math is verifiable so it's easier for models to improve B) Theoretical math highly benefits from chain of thought reasoning C) The answers and breakthroughs can be tested and verified by expert humans. But this leads to another question: what if progress in other fields is moving just as fast and we can't see it?

nxthompson

17,404 просмотров • 24 дней назад

Rapidly advancing AI models have sparked breakthroughs in long-standing math problems. From solving problems at the International Math Olympiad to disproving decades-old conjectures - some in the math world are now questioning whether they have a future in the field. But not Professor Shayan Oveis Gharan, who won the 2026 Abacus Medal for his work on the theory of algorithms. He believes the power of AI is harnessed not as replacement for human mathematicians, but as a partner. CBS’s Kamal Afzali explains.

Rapidly advancing AI models have sparked breakthroughs in long-standing math problems. From solving problems at the International Math Olympiad to disproving decades-old conjectures - some in the math world are now questioning whether they have a future in the field. But not Professor Shayan Oveis Gharan, who won the 2026 Abacus Medal for his work on the theory of algorithms. He believes the power of AI is harnessed not as replacement for human mathematicians, but as a partner. CBS’s Kamal Afzali explains.

CBS News

57,637 просмотров • 11 дней назад

Jacob Bank, former Google product lead: "I built up this team of 40 AI marketing agents to work with me. I'm the only marketing person." In a 15-minute talk, he shows what one person with the right setup now runs alone. Forty agents. One human. His AI bill is $500 a month, against the $50,000 a human team would cost. That's the math quietly minting the first solo fortunes of the AI era. Watch the talk, then read the piece below. Bookmark this one.

Jacob Bank, former Google product lead: "I built up this team of 40 AI marketing agents to work with me. I'm the only marketing person." In a 15-minute talk, he shows what one person with the right setup now runs alone. Forty agents. One human. His AI bill is $500 a month, against the $50,000 a human team would cost. That's the math quietly minting the first solo fortunes of the AI era. Watch the talk, then read the piece below. Bookmark this one.

Zephyr

430,170 просмотров • 1 месяц назад

THE GREATEST LIVING MATHEMATICIAN EXPLAINED HOW HE ACTUALLY USES AI AND HIS ANSWER QUIETLY KILLS BOTH THE HYPE AND THE COPE, IT DOES NOT REPLACE HIM, AND IT IS NOT A TOY EITHER 83 minutes with Terence Tao -- Fields Medal winner, often called the Mozart of math. -> His framing: today's AI is a brilliant but unreliable grad student. Great flashes, confident nonsense, and you must check every step. So it does not hand you answers. It removes friction -- the boring computations you used to avoid and lets you try crazier ideas. His bigger idea is "Big math": AI-checked proofs let hundreds of mathematicians collaborate without trusting each other's work. The machine verifies, so humans finally scale. For builders the signal is clear. The win is not "AI does it for me". It is AI clearing the grunt work so you aim higher. You thought the question was whether AI beats the experts. Tao shows it is how far one mind reaches once the friction is gone. Save this. It is the most grounded take on AI you'll hear ↓

THE GREATEST LIVING MATHEMATICIAN EXPLAINED HOW HE ACTUALLY USES AI AND HIS ANSWER QUIETLY KILLS BOTH THE HYPE AND THE COPE, IT DOES NOT REPLACE HIM, AND IT IS NOT A TOY EITHER 83 minutes with Terence Tao -- Fields Medal winner, often called the Mozart of math. -> His framing: today's AI is a brilliant but unreliable grad student. Great flashes, confident nonsense, and you must check every step. So it does not hand you answers. It removes friction -- the boring computations you used to avoid and lets you try crazier ideas. His bigger idea is "Big math": AI-checked proofs let hundreds of mathematicians collaborate without trusting each other's work. The machine verifies, so humans finally scale. For builders the signal is clear. The win is not "AI does it for me". It is AI clearing the grunt work so you aim higher. You thought the question was whether AI beats the experts. Tao shows it is how far one mind reaches once the friction is gone. Save this. It is the most grounded take on AI you'll hear ↓

slash1s

54,348 просмотров • 1 месяц назад

Demis Hassabis has a test for whether AI is actually creative. Give it only the data Einstein had in 1901 and see if it invents relativity. He thinks today's text models will never pass it. This is his bar for true creativity. Not extrapolating something already known. Generating a genuinely new hypothesis about reality that nobody has reached before. The thing Einstein did in 1905 with a handful of papers that rewrote physics. Hassabis wants to hand an AI the same starting data Einstein had and see if it gets there on its own. No peeking past the cutoff. Then he explained why he doubts text alone can do it. Einstein daydreamed his way to relativity. He was a patent clerk imagining what it would look like to ride alongside a beam of light. He ran the experiment in his head first, then proved it with math. Yann LeCun, who won the Turing Award and ran AI at Meta, says the same thing in blunter terms. He says today's models don't understand the physical world as well as a housecat, and that text alone is "complete BS" as a path to real intelligence. Two of the most decorated minds in AI agree the missing piece is the same. Hassabis thinks an AI needs to understand the world of atoms, not just the world of bits. Most of the industry is racing to scale text models. Hassabis just told them the most important kind of thinking might live somewhere text can't reach. --- Watch the full interview at Semafor on YouTube

Demis Hassabis has a test for whether AI is actually creative. Give it only the data Einstein had in 1901 and see if it invents relativity. He thinks today's text models will never pass it. This is his bar for true creativity. Not extrapolating something already known. Generating a genuinely new hypothesis about reality that nobody has reached before. The thing Einstein did in 1905 with a handful of papers that rewrote physics. Hassabis wants to hand an AI the same starting data Einstein had and see if it gets there on its own. No peeking past the cutoff. Then he explained why he doubts text alone can do it. Einstein daydreamed his way to relativity. He was a patent clerk imagining what it would look like to ride alongside a beam of light. He ran the experiment in his head first, then proved it with math. Yann LeCun, who won the Turing Award and ran AI at Meta, says the same thing in blunter terms. He says today's models don't understand the physical world as well as a housecat, and that text alone is "complete BS" as a path to real intelligence. Two of the most decorated minds in AI agree the missing piece is the same. Hassabis thinks an AI needs to understand the world of atoms, not just the world of bits. Most of the industry is racing to scale text models. Hassabis just told them the most important kind of thinking might live somewhere text can't reach. --- Watch the full interview at Semafor on YouTube

Ihtesham Ali

18,905 просмотров • 2 месяцев назад

He broke the Bank of England, made $1 billion in a single day, ran a $12 billion fund for 30 years, and never had one losing year this is him sitting down with Bloomberg to explain why he just put most of his portfolio into one AI stock and why a guy who shorted entire currencies just put most of his portfolio into one AI stock bookmark & watch it today ↓

He broke the Bank of England, made $1 billion in a single day, ran a $12 billion fund for 30 years, and never had one losing year this is him sitting down with Bloomberg to explain why he just put most of his portfolio into one AI stock and why a guy who shorted entire currencies just put most of his portfolio into one AI stock bookmark & watch it today ↓

slash1s

225,314 просмотров • 3 месяцев назад

JPMorgan CEO invited Elon Musk for a private talk with 3,500 of his top investors - everyone applauded at the end - he bet his last money on SpaceX in 2008 - now if the company goes public he will become the world's first trillionaire 18-min workshop from an absolute genius sitting next to the greatest banker he answered: • How does he use AI in a $350B company? • Why is SpaceX going public now? • How will SpaceX make cargo to space cheaper than a transatlantic flight? bookmark & watch - this is an exclusive video of two of the greatest people in their fields

JPMorgan CEO invited Elon Musk for a private talk with 3,500 of his top investors - everyone applauded at the end - he bet his last money on SpaceX in 2008 - now if the company goes public he will become the world's first trillionaire 18-min workshop from an absolute genius sitting next to the greatest banker he answered: • How does he use AI in a $350B company? • Why is SpaceX going public now? • How will SpaceX make cargo to space cheaper than a transatlantic flight? bookmark & watch - this is an exclusive video of two of the greatest people in their fields

bodila

876,177 просмотров • 2 месяцев назад

I sat down with Robby Stein (Robby Stein), Google’s VP of Product for Search, at Google I/O. Robby is one of the most interesting product leaders in tech: he helped build Instagram Stories, Reels and Close Friends, and now leads core Google Search products including AI Overviews, AI Mode, Lens and ranking. We talked about one of the biggest shifts in the history of the web: Google Search becoming AI-native. Topics we covered: • AI Mode and whether it is an evolution of Search or a reinvention of it • how Google breaks complex questions into multiple searches behind the scenes • why AI search is much more expensive to run than traditional search • whether Google’s TPUs and infrastructure give it an advantage no one else can match • why Search volume is growing instead of being cannibalized by AI • the tension between great AI answers and traffic for publishers • how Google decides which sources and links to show • what a better internet could look like if AI Search works as intended The big question behind the whole conversation: If Google gives you the answer directly, what happens to the link-based web? A small caveat: sadly the microphones didnt work properly. Therefore the audio quality in this episode isn't perfect due to a recording issue - we appreciate your understanding.

I sat down with Robby Stein (Robby Stein), Google’s VP of Product for Search, at Google I/O. Robby is one of the most interesting product leaders in tech: he helped build Instagram Stories, Reels and Close Friends, and now leads core Google Search products including AI Overviews, AI Mode, Lens and ranking. We talked about one of the biggest shifts in the history of the web: Google Search becoming AI-native. Topics we covered: • AI Mode and whether it is an evolution of Search or a reinvention of it • how Google breaks complex questions into multiple searches behind the scenes • why AI search is much more expensive to run than traditional search • whether Google’s TPUs and infrastructure give it an advantage no one else can match • why Search volume is growing instead of being cannibalized by AI • the tension between great AI answers and traffic for publishers • how Google decides which sources and links to show • what a better internet could look like if AI Search works as intended The big question behind the whole conversation: If Google gives you the answer directly, what happens to the link-based web? A small caveat: sadly the microphones didnt work properly. Therefore the audio quality in this episode isn't perfect due to a recording issue - we appreciate your understanding.

Chubby♨️

30,486 просмотров • 3 месяцев назад

Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations

Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations

Mario Gabriele 🦊

17,899 просмотров • 4 месяцев назад

The Chinese president stood on a stage in Shanghai and laid out China's entire AI playbook in one speech. 🤯 His first-ever in-person appearance at the World AI Conference. I went through the whole thing and pulled out everything that matters. Here's what he said: → He opened with his signature line: "great changes unseen in a century are unfolding across the world." He's been saying this for years. This time he attached it directly to AI. → AI development should not be a "solo performance" by a single country. It should be a "symphony of international cooperation." That's a direct shot at anyone trying to monopolize AI through export controls and closed models. → He warned against overstretching the concept of national security in AI where one country puts its own security above everyone else's. Another direct shot. No names needed. → China opposes "new historical injustices" emerging in AI. One of the strongest-worded lines in the speech. He's positioning AI access as an equity issue, not just a technology issue. → He reaffirmed China's commitment to open-source AI in the name of "openness and shared benefit." This came days after Kimi K3 launched as an open-weights frontier model. → He pledged 5,000 AI training opportunities for developing countries over five years naming ASEAN, the Arab League, the African Union, CELAC, the SCO, and BRICS. → He committed to giving 30 countries access to MAZU a Chinese AI-powered meteorological warning system for early disaster detection. Countries that lose thousands of lives to storms they never saw coming. → One day before the speech, 29 countries signed the agreement creating a new World AI Cooperation Organization. Headquartered in Shanghai. → He referenced the Dartmouth Workshop of 1956 where AI was first proposed by American scientists. Then spent the rest of the speech explaining why AI shouldn't stay in America's hands alone. → China's core AI industry has officially crossed 1 trillion yuan ($148 billion). He said this matter-of-factly. No celebration. Just a data point. Here's what stood out to me. Strip away the politics and one thing is clear. He didn't pitch benchmarks or chatbots. He pitched AI as infrastructure. Weather warnings for countries that lose thousands of lives to storms they never saw coming. Training programs for regions the entire AI boom has skipped. Meanwhile most of the Western AI conversation revolves around which lab ships the next frontier model. I don't care who wins the race. I care whether the computing power reaches the people who need it.

The Chinese president stood on a stage in Shanghai and laid out China's entire AI playbook in one speech. 🤯 His first-ever in-person appearance at the World AI Conference. I went through the whole thing and pulled out everything that matters. Here's what he said: → He opened with his signature line: "great changes unseen in a century are unfolding across the world." He's been saying this for years. This time he attached it directly to AI. → AI development should not be a "solo performance" by a single country. It should be a "symphony of international cooperation." That's a direct shot at anyone trying to monopolize AI through export controls and closed models. → He warned against overstretching the concept of national security in AI where one country puts its own security above everyone else's. Another direct shot. No names needed. → China opposes "new historical injustices" emerging in AI. One of the strongest-worded lines in the speech. He's positioning AI access as an equity issue, not just a technology issue. → He reaffirmed China's commitment to open-source AI in the name of "openness and shared benefit." This came days after Kimi K3 launched as an open-weights frontier model. → He pledged 5,000 AI training opportunities for developing countries over five years naming ASEAN, the Arab League, the African Union, CELAC, the SCO, and BRICS. → He committed to giving 30 countries access to MAZU a Chinese AI-powered meteorological warning system for early disaster detection. Countries that lose thousands of lives to storms they never saw coming. → One day before the speech, 29 countries signed the agreement creating a new World AI Cooperation Organization. Headquartered in Shanghai. → He referenced the Dartmouth Workshop of 1956 where AI was first proposed by American scientists. Then spent the rest of the speech explaining why AI shouldn't stay in America's hands alone. → China's core AI industry has officially crossed 1 trillion yuan ($148 billion). He said this matter-of-factly. No celebration. Just a data point. Here's what stood out to me. Strip away the politics and one thing is clear. He didn't pitch benchmarks or chatbots. He pitched AI as infrastructure. Weather warnings for countries that lose thousands of lives to storms they never saw coming. Training programs for regions the entire AI boom has skipped. Meanwhile most of the Western AI conversation revolves around which lab ships the next frontier model. I don't care who wins the race. I care whether the computing power reaches the people who need it.

Vaibhav Sisinty

20,898 просмотров • 1 месяц назад

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

236,758 просмотров • 2 месяцев назад

New conversation with Henry Shevlin and Robert Long on AI consciousness and welfare! Among many other topics, we discuss: - Why we should take AI consciousness and welfare seriously - What Rob found doing the first external welfare evaluation of a frontier model, Claude, and his experiments on Claude Mythos - The "willing servitude" problem: if AI loves being helpful, is that good or horrifying? - Why AI companies might have an incentive to downplay AI consciousness (1/2)

New conversation with Henry Shevlin and Robert Long on AI consciousness and welfare! Among many other topics, we discuss: - Why we should take AI consciousness and welfare seriously - What Rob found doing the first external welfare evaluation of a frontier model, Claude, and his experiments on Claude Mythos - The "willing servitude" problem: if AI loves being helpful, is that good or horrifying? - Why AI companies might have an incentive to downplay AI consciousness (1/2)

Dan Williams

36,679 просмотров • 4 месяцев назад

FULL INTERVIEW: Martin Casado says AI is the first technology where you can put in $10 and reliably get something back. Everything before it was engineer, wait two years, cross your fingers. martin_casado is the a16z GP behind the firm's investments in both Cursor and OpenRouter. Days after SpaceX closed the $60B Cursor deal and Stripe agreed to buy OpenRouter, he sat down with Theo Jaffee and sophia dew to cover whether the labs win everything, when the subsidies stop, and why RSI is the wrong term: 02:24 whether decades of experience still matter in AI 03:48 what you would have done with a billion dollars ten years ago 04:56 20 people, $2 billion, one model 06:48 why more private capital grows the TAM rather than inflating it 07:35 the a16z conversation about going all-in on AI, seven years before GPT 08:39 why balance-sheet investors keep misreading these companies 10:36 the full case for the labs winning everything 12:12 why he thinks RSI is the wrong term, and what autocatalytic means 13:42 the full case against the labs winning everything 15:25 supply constraints easing in 2028, and his 80/60 split 17:12 why models turned out to be much stickier than anyone assumed 18:18 why real model routing is an AI-complete problem 21:13 what happens when the subsidies stop 21:36 how AI broke marketing, and why you can now buy users with a dollar 23:30 the Chinese operations arbitraging $200 subscription plans 25:43 the CMO is turning into a CFO 29:46 why Cursor iterated faster than anything he's seen outside an Elon company 32:21 what both deals say about strategic value versus business quality 34:10 why he doesn't think a VC's job is to know where to build 38:35 "as long as there's a hill for me to climb"

FULL INTERVIEW: Martin Casado says AI is the first technology where you can put in $10 and reliably get something back. Everything before it was engineer, wait two years, cross your fingers. martin_casado is the a16z GP behind the firm's investments in both Cursor and OpenRouter. Days after SpaceX closed the $60B Cursor deal and Stripe agreed to buy OpenRouter, he sat down with Theo Jaffee and sophia dew to cover whether the labs win everything, when the subsidies stop, and why RSI is the wrong term: 02:24 whether decades of experience still matter in AI 03:48 what you would have done with a billion dollars ten years ago 04:56 20 people, $2 billion, one model 06:48 why more private capital grows the TAM rather than inflating it 07:35 the a16z conversation about going all-in on AI, seven years before GPT 08:39 why balance-sheet investors keep misreading these companies 10:36 the full case for the labs winning everything 12:12 why he thinks RSI is the wrong term, and what autocatalytic means 13:42 the full case against the labs winning everything 15:25 supply constraints easing in 2028, and his 80/60 split 17:12 why models turned out to be much stickier than anyone assumed 18:18 why real model routing is an AI-complete problem 21:13 what happens when the subsidies stop 21:36 how AI broke marketing, and why you can now buy users with a dollar 23:30 the Chinese operations arbitraging $200 subscription plans 25:43 the CMO is turning into a CFO 29:46 why Cursor iterated faster than anything he's seen outside an Elon company 32:21 what both deals say about strategic value versus business quality 34:10 why he doesn't think a VC's job is to know where to build 38:35 "as long as there's a hill for me to climb"

MTS

212,186 просмотров • 6 дней назад

A NASA fellow claims 5 equations explain 99 percent of the physics that actually runs your world. Understand them deeply and you are set for life. His words. And the way he does it is what makes the video worth it. He does not teach the five as a list. He tells them as one story, where each equation is born from the one before it. F equals ma leads into gravity. Gravity rhymes with the law of electric charge, the exact same shape. That connects to magnetism. Magnetism folds into the wave. And the wave quietly opens the door to relativity and quantum physics. By the end, all of physics feels like a single sentence written by one hand, not a pile of formulas you were forced to memorize. The move that stays with you comes first. He says F equals ma is written the wrong way around, and rewrites it. One tiny flip, and the most famous equation on Earth suddenly means something school never told you. His point for why this matters now: in the age of AI, the people who understand reality itself stay in demand. Everyone else is just memorizing. Five equations. One thread. It is in the video.

A NASA fellow claims 5 equations explain 99 percent of the physics that actually runs your world. Understand them deeply and you are set for life. His words. And the way he does it is what makes the video worth it. He does not teach the five as a list. He tells them as one story, where each equation is born from the one before it. F equals ma leads into gravity. Gravity rhymes with the law of electric charge, the exact same shape. That connects to magnetism. Magnetism folds into the wave. And the wave quietly opens the door to relativity and quantum physics. By the end, all of physics feels like a single sentence written by one hand, not a pile of formulas you were forced to memorize. The move that stays with you comes first. He says F equals ma is written the wrong way around, and rewrites it. One tiny flip, and the most famous equation on Earth suddenly means something school never told you. His point for why this matters now: in the age of AI, the people who understand reality itself stay in demand. Everyone else is just memorizing. Five equations. One thread. It is in the video.

ooaes

435,003 просмотров • 1 месяц назад

Elon Musk explains why AI and robots are the future of the economy? “You can think of the economy as digital and physical intelligence. AI is advancing very rapidly, but it is still somewhat confined to the digital realm. You need end effectors in the form of humanoid robots. Then you can go from intelligence manifesting itself digitally to shaping atoms. An economy is the production of goods and the provision of services. If you have vast numbers of robots with vast amounts of digital intelligence, you have a quasi-infinite economy.”

Elon Musk explains why AI and robots are the future of the economy? “You can think of the economy as digital and physical intelligence. AI is advancing very rapidly, but it is still somewhat confined to the digital realm. You need end effectors in the form of humanoid robots. Then you can go from intelligence manifesting itself digitally to shaping atoms. An economy is the production of goods and the provision of services. If you have vast numbers of robots with vast amounts of digital intelligence, you have a quasi-infinite economy.”

DogeDesigner

44,728 просмотров • 1 месяц назад

Parker Conrad (Parker Conrad) is co-founder & CEO of Rippling, an all-in-one HR, Finance, and IT software company valued at $13.5B. A founder of two unicorns, he's had one of the more dramatic startup journeys in recent years. For How to Build the Future, Parker sat down with Garry Tan to share his origin story, how AI is changing the game, and why he thinks the future will be defined by "compound" software startups.

Parker Conrad (Parker Conrad) is co-founder & CEO of Rippling, an all-in-one HR, Finance, and IT software company valued at $13.5B. A founder of two unicorns, he's had one of the more dramatic startup journeys in recent years. For How to Build the Future, Parker sat down with Garry Tan to share his origin story, how AI is changing the game, and why he thinks the future will be defined by "compound" software startups.

Y Combinator

47,270 просмотров • 1 год назад

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

Neurochain.AI

105,899 просмотров • 2 лет назад

this is f*cking insane. Matthew Gallagher is the first one-man billion-dollar company. He built it SOLO with AI. $20,000 and two months to start. $401 million in year one, on track for $1.8 billion. Hims & Hers does the same business with 2,000 employees. Not a smaller team. No team. The AI writes the code. It answers the patients. It takes his calls in a clone of his voice. That is the whole org chart. Bookmark this before your feed buries it. Read the full guide below.

this is f*cking insane. Matthew Gallagher is the first one-man billion-dollar company. He built it SOLO with AI. $20,000 and two months to start. $401 million in year one, on track for $1.8 billion. Hims & Hers does the same business with 2,000 employees. Not a smaller team. No team. The AI writes the code. It answers the patients. It takes his calls in a clone of his voice. That is the whole org chart. Bookmark this before your feed buries it. Read the full guide below.

Jey

145,133 просмотров • 14 дней назад