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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

59,965 subscribers

50,690 Aufrufe • vor 2 Monaten •via X (Twitter)

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20 Kommentare

Profilbild von Chokmah 🕰️🌌🪽

Chokmah 🕰️🌌🪽vor 2 Monaten

Glenn Gaffney dived on Curt and scared the shit out of him to stop making UAP content. cia assholes, man

Profilbild von Carlos E Alvarez

Carlos E Alvarezvor 2 Monaten

In other words, “those who can, do; and those who don’t, teach.” He will cry all the way to the bank, then settle into a 9-5 workweek existence, probably flexible schedule and remote work, and even vastly increase his individual and research liberty. But is it really not a trade off to lose academic freedom? Very likely the opposite: he can work outside an ideologically captured institution and academic field and without having to navigate the administrative leviathan. [Nothing here is a criticism of him and I would have preferred it if his talents could be developed in academia.]

Profilbild von Steven Roosa

Steven Roosavor 2 Monaten

He's "grieving" as he pockets the largesse from the highest bidder. Brother please....

Profilbild von MikeJones

MikeJonesvor 2 Monaten

Welcome to the Team.

Profilbild von Militant Hitchhiker ♥

Militant Hitchhiker ♥vor 2 Monaten

There's not a proof written by man that I can't quantise down to a sentence a five year old could grasp. The problem is in a world full of idiots we reified complexity as if it was a qualifier for correctness. The problem only arises when you try to make reality computational.

Profilbild von MAX:WELL

MAX:WELLvor 2 Monaten

Grieving, cause he knew he sold his soul to become a fraud.. over something he truly once loved.

Profilbild von Five Intelligences Alliance

Five Intelligences Alliancevor 2 Monaten

@Jacob_Tsimerman - thank you. An urgently needed step and and a great example for other elite mathematicians imho.

Profilbild von Mike Dupont

Mike Dupontvor 2 Monaten

welcome to the oil rig Jacob! applied math now!

Profilbild von Gerard Sans | Axiom 🇬🇧

Gerard Sans | Axiom 🇬🇧vor 2 Monaten

@1stOrator

Profilbild von Stjepan

Stjepanvor 2 Monaten

Is it me or Curt has no more interesting guests, this guy goes for openAI now, dude is deep in AI and programing security as much as a puddle.. this is just a hogwash.. bring back real talk real physcis and math and not just spotlight for people who are just money grifters

Profilbild von ANMY and ME

ANMY and MEvor 2 Monaten

If a proof can be verified before it can be understood, when does a truth become part of mathematics? When it is proven or when we can think with it?

Profilbild von Percepton Theory

Percepton Theoryvor 2 Monaten

This is exactly why AI is changing who gets to participate in mathematics. I’m not a mathematician—I imagined a physical theory, but I didn’t have the advanced mathematical language to properly express or test it. AI helped me turn those ideas into rigorous equations, computational tests, and eventually work I could publish and put in front of mathematicians. The ideas were mine; AI became the mathematical bridge between imagination and formalization. That bridge simply didn’t exist for people like me before.

Profilbild von Adel Bucetta

Adel Bucettavor 2 Monaten

because the hard part isn't creating new knowledge, but understanding who gets to control its impact on society

Profilbild von Richard Gill

Richard Gillvor 2 Monaten

Certain kinds of mathematics is going to suffer. Other kinds are going to flourish. I think AI is great for applied maths, less great for pure maths.

Profilbild von AHQ⁵

AHQ⁵vor 2 Monaten

Look at this lattice. Tell me the rule.

Profilbild von Màxkw’tët (Bear) 🐻 🪶

Màxkw’tët (Bear) 🐻 🪶vor 2 Monaten

Hot topic 👌🏼

Profilbild von 黃俊喬

黃俊喬vor 2 Monaten

純數學即應用數學，只是我們沒有一個人真信主耶穌基督

Profilbild von sl

slvor 2 Monaten

watching it now... he said next week mathematicians are getting together to discuss restructuring the field in the age of AI. Where are the software movers and shakers who are doing this? Why aren't they ?

Profilbild von Henry Watt

Henry Wattvor 2 Monaten

big guest Curt. check out my idea

Profilbild von Leon & The Machine

Leon & The Machinevor 2 Monaten

The latest models are slaying through math. I recently worked with an AI to generate an 80 page mathematics/physics document about the unification of GR and QM. A conjecture about division by zero in Clifford algebra led to a theorem which was then applied to singularities...

Ähnliche Videos

Math has a proof checker. Can physics get one? Jonathan Gorard Jonathan Gorard, an applied mathematician and the cofounder and CEO of Lanyon AI, joins to explain why AI took off in math and coding but not yet in physics. We discuss an AI finishing the formal check of a Fields Medal proof and why it scared him, why he thinks “I detected a particle” rests on 20 to 50 levels of theory, and what Alan Turing’s PhD student Robin Gandy tried to build for physics. The conversation also covers his push to make physics executable, why he thinks mysteries are artifacts of description languages, and whether there is still a place for the physicist.

Math has a proof checker. Can physics get one? Jonathan Gorard Jonathan Gorard, an applied mathematician and the cofounder and CEO of Lanyon AI, joins to explain why AI took off in math and coding but not yet in physics. We discuss an AI finishing the formal check of a Fields Medal proof and why it scared him, why he thinks “I detected a particle” rests on 20 to 50 levels of theory, and what Alan Turing’s PhD student Robin Gandy tried to build for physics. The conversation also covers his push to make physics executable, why he thinks mysteries are artifacts of description languages, and whether there is still a place for the physicist.

Curt Jaimungal

30,514 Aufrufe • vor 8 Tagen

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

291,743 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 2 Monaten

The singularity has already begun for math: Scott Aaronson is one of the world's leading quantum computing researchers and spent two years at OpenAI working on AI safety. We talked about why he thinks the singularity has already begun for math, who gets credit when AI writes the proof, and whether intelligence will still matter once machines do the thinking. In this episode: • Why the singularity has already begun for math • Who gets credit when AI writes the proof • What a proof that P = NP would be worth • Why he turned down getting rich to keep blogging • Whether intelligence will still matter Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:34 Why he updated so hard on AI 01:14 The people still denying AI progress 03:53 The singularity has already begun for math 08:30 Will human-driven math research survive 13:12 Who gets credit for AI-written papers 15:55 OpenAI's $15M Navier-Stokes proof 19:20 What if AI proved P = NP 23:07 Why AI is so good at finding counterexamples 26:13 The goalposts keep moving 28:52 Why AI disclosure matters in research 32:59 The fake Monet experiment 35:23 Why he blogs instead of getting rich 39:45 Are AI labs racing too fast 42:01 Will intelligence still matter 47:12 Book recommendations 52:27 Advice for students today 57:23 Reacting live to a new AI result 59:58 Outro

The singularity has already begun for math: Scott Aaronson is one of the world's leading quantum computing researchers and spent two years at OpenAI working on AI safety. We talked about why he thinks the singularity has already begun for math, who gets credit when AI writes the proof, and whether intelligence will still matter once machines do the thinking. In this episode: • Why the singularity has already begun for math • Who gets credit when AI writes the proof • What a proof that P = NP would be worth • Why he turned down getting rich to keep blogging • Whether intelligence will still matter Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:34 Why he updated so hard on AI 01:14 The people still denying AI progress 03:53 The singularity has already begun for math 08:30 Will human-driven math research survive 13:12 Who gets credit for AI-written papers 15:55 OpenAI's $15M Navier-Stokes proof 19:20 What if AI proved P = NP 23:07 Why AI is so good at finding counterexamples 26:13 The goalposts keep moving 28:52 Why AI disclosure matters in research 32:59 The fake Monet experiment 35:23 Why he blogs instead of getting rich 39:45 Are AI labs racing too fast 42:01 Will intelligence still matter 47:12 Book recommendations 52:27 Advice for students today 57:23 Reacting live to a new AI result 59:58 Outro

Ryan Peterman

67,780 Aufrufe • vor 1 Tag

Chamath explains how he called the bottom on SaaS and why $CRM ripped 43% since his call "I don't know, it was pretty obvious to me in May that Salesforce specifically was meaningfully oversold. What I was trying to say there is we're getting to the end of this second phase of AI.” “The first phase of AI were models. The second phase of AI is harnesses and agents, which is the equivalent of giving a brain a pair of eyes and hands and a notebook for memory and a keyboard to type things on." "The people that control that, today, the large systems of record, those guys hold an incredibly special place in the ecosystem if they do it right. Marc Benioff is finding a way to play well with the models, support harnesses, but he also built his own and is now starting to push into this next phase.” “That's why his net dollar retention is strong. That's why his revenue is strong.That's why he's guiding up. And that's why the stock's ripped, I think it was like 43% since I said it had bottomed.”

Chamath explains how he called the bottom on SaaS and why $CRM ripped 43% since his call "I don't know, it was pretty obvious to me in May that Salesforce specifically was meaningfully oversold. What I was trying to say there is we're getting to the end of this second phase of AI.” “The first phase of AI were models. The second phase of AI is harnesses and agents, which is the equivalent of giving a brain a pair of eyes and hands and a notebook for memory and a keyboard to type things on." "The people that control that, today, the large systems of record, those guys hold an incredibly special place in the ecosystem if they do it right. Marc Benioff is finding a way to play well with the models, support harnesses, but he also built his own and is now starting to push into this next phase.” “That's why his net dollar retention is strong. That's why his revenue is strong.That's why he's guiding up. And that's why the stock's ripped, I think it was like 43% since I said it had bottomed.”

dnap

27,155 Aufrufe • vor 1 Monat

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

57,767 Aufrufe • vor 1 Monat

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 Aufrufe • vor 3 Monaten

My conversation with Luke Gromen on why 40 years of US economic policy is reversing, and what it means for your portfolio. We get into: → What the "stupid Washington consensus" really means: → Why Hamiltonian economics is back and what that means for the US economy → Why he's bearish on long bonds in real terms → Why gold belongs in your portfolio → Why AI is a fiscal problem: "a snake eating its own tail"

My conversation with Luke Gromen on why 40 years of US economic policy is reversing, and what it means for your portfolio. We get into: → What the "stupid Washington consensus" really means: → Why Hamiltonian economics is back and what that means for the US economy → Why he's bearish on long bonds in real terms → Why gold belongs in your portfolio → Why AI is a fiscal problem: "a snake eating its own tail"

Meb Faber

101,571 Aufrufe • vor 2 Monaten

One line from Prasanna Sankar has stayed with me: "You're not helping AI anymore. You're getting in the way by being in the way." That's exactly why he's building Vorflux Not another AI coding assistant. His vision is an autonomous AI software engineer that can build software with minimal human involvement. Coming from someone who was India's #1 competitive programmer, helped build Rippling into a $15B company, and once turned down Facebook's $100M acquisition offer, it's a perspective worth paying attention to. In this conversation, we go deep on: • Why AI has already surpassed humans at software engineering. • Why tools like Cursor and Claude Code are only the beginning—and how Vorflux is thinking differently. • Why the engineer of the future becomes a "builder." • The story behind building Rippling and starting over from scratch. • Why he turned down Facebook's $100M acquisition offer. • Why he believes we're moving toward zero-employee companies. • What humans will be uniquely valuable for in an AI-first world. One of the most thought-provoking conversations I've had on AI and the future of software. Full conversation is now live on the 1947 Rise Podcast. Link in comments Prasanna S Vorflux

One line from Prasanna Sankar has stayed with me: "You're not helping AI anymore. You're getting in the way by being in the way." That's exactly why he's building Vorflux Not another AI coding assistant. His vision is an autonomous AI software engineer that can build software with minimal human involvement. Coming from someone who was India's #1 competitive programmer, helped build Rippling into a $15B company, and once turned down Facebook's $100M acquisition offer, it's a perspective worth paying attention to. In this conversation, we go deep on: • Why AI has already surpassed humans at software engineering. • Why tools like Cursor and Claude Code are only the beginning—and how Vorflux is thinking differently. • Why the engineer of the future becomes a "builder." • The story behind building Rippling and starting over from scratch. • Why he turned down Facebook's $100M acquisition offer. • Why he believes we're moving toward zero-employee companies. • What humans will be uniquely valuable for in an AI-first world. One of the most thought-provoking conversations I've had on AI and the future of software. Full conversation is now live on the 1947 Rise Podcast. Link in comments Prasanna S Vorflux

Shiva Singh Sangwan

72,216 Aufrufe • vor 2 Monaten

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

54,598 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 4 Monaten

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

877,673 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 6 Monaten

FULL INTERVIEW: Nick Bostrom says a long pause on AI could cost more lives than it saves. If superintelligence can cure aging, every year of waiting has a death toll. In his words, "we are just dying in the meantime." Nick Bostrom founded Oxford's Future of Humanity Institute and wrote Superintelligence and Deep Utopia. He joined Theo Jaffee and sof 𓋹 to break down AI consciousness, why a badly done pause could backfire, and why we've only explored the janitor's closet of what minds can be: 01:17 Trump calling AI superintelligence 05:16 the Hugging Face incident and instrumental convergence 08:28 why the AI company is the first target for a misaligned AI 09:31 catching a schemer before it learns to hide 11:06 why AI timing is like surgery for a terminal condition 19:00 why he's wary of pausing AI 25:33 why the US sits at the negative extreme on AI 27:09 apes, bananas, and the janitor's closet 31:13 where he disagrees with Robin Hanson's Age of Em 33:39 whether current models are conscious 37:12 why humans are barely conscious 39:56 open source models and DNA synthesis as the choke point 43:05 what COVID teaches about pauses 46:28 why AI can't do original philosophy yet 49:37 building a superintelligence that's a good cosmic citizen

FULL INTERVIEW: Nick Bostrom says a long pause on AI could cost more lives than it saves. If superintelligence can cure aging, every year of waiting has a death toll. In his words, "we are just dying in the meantime." Nick Bostrom founded Oxford's Future of Humanity Institute and wrote Superintelligence and Deep Utopia. He joined Theo Jaffee and sof 𓋹 to break down AI consciousness, why a badly done pause could backfire, and why we've only explored the janitor's closet of what minds can be: 01:17 Trump calling AI superintelligence 05:16 the Hugging Face incident and instrumental convergence 08:28 why the AI company is the first target for a misaligned AI 09:31 catching a schemer before it learns to hide 11:06 why AI timing is like surgery for a terminal condition 19:00 why he's wary of pausing AI 25:33 why the US sits at the negative extreme on AI 27:09 apes, bananas, and the janitor's closet 31:13 where he disagrees with Robin Hanson's Age of Em 33:39 whether current models are conscious 37:12 why humans are barely conscious 39:56 open source models and DNA synthesis as the choke point 43:05 what COVID teaches about pauses 46:28 why AI can't do original philosophy yet 49:37 building a superintelligence that's a good cosmic citizen

MTS

146,371 Aufrufe • vor 5 Tagen

Hasan Piker talks about the moment in the interview with Axios when Alex Thompson assumed that Hasan hesitated with his response when asked if political violence is ever justified. Hasan explains that this moment in the interview was more of an interrogation: "The 'you hesitated' moment is showcasing the full intent behind why he is engaging in this exercise. He doesn't get the answer that he's looking for; he know he's not going to get the answer he's looking for, so he still tries to make something out of it. It basically shows what his intent is. This is not a real conversation between interlocketers; this is an opportunity to look for any gotcha that he can and because he's not getting the gotchas that he's looking to farm, he's now basically psychoanalyzing my body language. He's trying to turn this to an interrogation."

Hasan Piker talks about the moment in the interview with Axios when Alex Thompson assumed that Hasan hesitated with his response when asked if political violence is ever justified. Hasan explains that this moment in the interview was more of an interrogation: "The 'you hesitated' moment is showcasing the full intent behind why he is engaging in this exercise. He doesn't get the answer that he's looking for; he know he's not going to get the answer he's looking for, so he still tries to make something out of it. It basically shows what his intent is. This is not a real conversation between interlocketers; this is an opportunity to look for any gotcha that he can and because he's not getting the gotchas that he's looking to farm, he's now basically psychoanalyzing my body language. He's trying to turn this to an interrogation."

adri ♡

31,463 Aufrufe • vor 25 Tagen

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 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 4 Monaten