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Google wrote the paper that made ChatGPT possible. Then decided not to build ChatGPT. Sergey Brin just explained why on stage at Stanford and the reason is more embarrassing than anyone expected. In 2017, Google published the transformer paper. The architecture that powers every major AI model today. It... came from their own researchers. Their own labs. Their own compute. Then they sat on it. Sergey was blunt about why. They underinvested in scaling the compute. They did not take it seriously enough. And when they finally had something worth shipping, they got scared. Chatbots say dumb things. Google had a reputation to protect. So they protected it instead of shipping. OpenAI was not scared. Ilya Sutskever, trained at Google, left and went there. Other Google researchers followed. They took the transformer architecture, scaled it, shipped it anyway, and captured the entire generative AI wave while Google watched. Sergey called it a mistake at Stanford in front of hundreds of students. The company that invented the technology did not build the product. The company that built the product did not invent the technology. That is the most expensive case of corporate hesitation in the history of the industry. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"show more

Ihtesham Ali

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244,666 просмотров • 1 месяц назад •via X (Twitter)

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Sergey Brin admits Google messed up by under-investing in the transformer architecture it invented Google was too scared to release chatbots that "say dumb things", so it under-invested in scaling compute "we didn't take it very seriously... and openAI ran with it"

Sergey Brin admits Google messed up by under-investing in the transformer architecture it invented Google was too scared to release chatbots that "say dumb things", so it under-invested in scaling compute "we didn't take it very seriously... and openAI ran with it"

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2001. Larry Page and Sergey Brin sit for their first-ever television interview. Google has 200 employees. They explain that the company almost didn't get off the ground because they couldn't cash a check. The check was for $100,000. It came from Andy Bechtolsheim, one of the co-founders of Sun Microsystems. Page and Brin showed him what they'd built. He said, "This is great, how about I write you a check?" and just wrote it out. Made it out to Google. The problem was that Google didn't exist as a company yet. There was no bank account. No lawyers. No incorporation paperwork. The check sat in Larry Page's desk drawer for a month. They literally could not deposit it. They're both in their late twenties in this interview. They met at Stanford as PhD students and, by their own account, disliked each other from the start. Brin says Page is "kind of obnoxious." Page doesn't disagree. Brin says they argued about everything, debated every single point, and then realized that was their commonality. They became friends, started building a search engine they never planned to build, and put their PhDs on hold to get it out into the world. The part that stings watching this in 2026 is the rejection tour. Before starting Google, they approached existing search companies to sell or license the technology. They went to Yahoo. David Filo, one of Yahoo's founders, told them, "This is great search technology. Why don't you guys make a company, and maybe we'll use you someday?" They went to Excite. They went to InfoSeek. Same response. Page says a CEO at one of those companies told them: "If our search is 85% as good as the next guy's, that's good enough for us." Page and Brin didn't buy that. They thought the search was too important to be 85% as good. So they started Google. No marketing. No ad campaign. They launched it at Stanford, and it grew 20% per month, every single month, for three years straight. Pure word of mouth. By the time of this interview, they're handling over 100 million searches a day. They get 500 resumes in the mail every single day. The office space around them is 30% vacant because the dot-com bubble just popped, but Google is profitable. Page makes a point of this: "We've been really interested in being profitable, like long before it was fashionable." They'd also just hired Eric Schmidt, former CTO of Sun, as CEO. Brin's explanation for why: "Parental supervision, to be honest." Page adds that they're "past the age where we're rebellious" and that running a search engine used by 100 million people a day with 200 employees is "a large responsibility." The number that caught my eye: when Google started in 1998, it indexed 30 million web pages. At the time of this interview, three years later, they indexed 1.3 billion. The page says that if you printed them all out and stacked the paper, it would be about 70 miles high. And it was doubling every year. Every search company they approached turned them down. Yahoo eventually came back and hired Google to power its own search results. The CEO who thought 85% was good enough ran a company that no longer exists. Alphabet, Google's parent company, is worth about $3.6 trillion today. It has about 190,000 employees. That $100,000 check sat in a desk drawer because nobody had incorporated the company. Bechtolsheim's stake from that investment is now worth billions.

2001. Larry Page and Sergey Brin sit for their first-ever television interview. Google has 200 employees. They explain that the company almost didn't get off the ground because they couldn't cash a check. The check was for $100,000. It came from Andy Bechtolsheim, one of the co-founders of Sun Microsystems. Page and Brin showed him what they'd built. He said, "This is great, how about I write you a check?" and just wrote it out. Made it out to Google. The problem was that Google didn't exist as a company yet. There was no bank account. No lawyers. No incorporation paperwork. The check sat in Larry Page's desk drawer for a month. They literally could not deposit it. They're both in their late twenties in this interview. They met at Stanford as PhD students and, by their own account, disliked each other from the start. Brin says Page is "kind of obnoxious." Page doesn't disagree. Brin says they argued about everything, debated every single point, and then realized that was their commonality. They became friends, started building a search engine they never planned to build, and put their PhDs on hold to get it out into the world. The part that stings watching this in 2026 is the rejection tour. Before starting Google, they approached existing search companies to sell or license the technology. They went to Yahoo. David Filo, one of Yahoo's founders, told them, "This is great search technology. Why don't you guys make a company, and maybe we'll use you someday?" They went to Excite. They went to InfoSeek. Same response. Page says a CEO at one of those companies told them: "If our search is 85% as good as the next guy's, that's good enough for us." Page and Brin didn't buy that. They thought the search was too important to be 85% as good. So they started Google. No marketing. No ad campaign. They launched it at Stanford, and it grew 20% per month, every single month, for three years straight. Pure word of mouth. By the time of this interview, they're handling over 100 million searches a day. They get 500 resumes in the mail every single day. The office space around them is 30% vacant because the dot-com bubble just popped, but Google is profitable. Page makes a point of this: "We've been really interested in being profitable, like long before it was fashionable." They'd also just hired Eric Schmidt, former CTO of Sun, as CEO. Brin's explanation for why: "Parental supervision, to be honest." Page adds that they're "past the age where we're rebellious" and that running a search engine used by 100 million people a day with 200 employees is "a large responsibility." The number that caught my eye: when Google started in 1998, it indexed 30 million web pages. At the time of this interview, three years later, they indexed 1.3 billion. The page says that if you printed them all out and stacked the paper, it would be about 70 miles high. And it was doubling every year. Every search company they approached turned them down. Yahoo eventually came back and hired Google to power its own search results. The CEO who thought 85% was good enough ran a company that no longer exists. Alphabet, Google's parent company, is worth about $3.6 trillion today. It has about 190,000 employees. That $100,000 check sat in a desk drawer because nobody had incorporated the company. Bechtolsheim's stake from that investment is now worth billions.

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Demis Hassabis said that 90% of the breakthroughs the entire AI industry runs on came out of Google. Every competitor is building on work his teams did first. He said it without flinching. Over the last decade, he claims 90% or more of the big breakthroughs that underpin modern AI came from Google Brain or DeepMind. Transformers. AlphaGo. The reinforcement learning everything now depends on. Transformers are the T in GPT. The architecture that made every large language model possible came out of Google in 2017, in a paper called Attention Is All You Need. His point was simple. Every rival lab is running on foundations his people poured. But here is what he left out. Of the eight authors who wrote that transformer paper, all eight left Google. They went on to found or join OpenAI, Anthropic, and their own startups now worth billions. Google invented the engine. Then watched the inventors walk out the door and build the competition. He says Google still has the biggest research bench of any lab on earth, and the talent war is the most ferocious tech has ever seen. Everyone treats this race like the labs are evenly matched. The man who started fifteen years early does not see it that way. The people who left with his blueprint might be the reason he has to keep proving it. (Watch the full interview at Semafor on YouTube)

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Sergey Brin retired in 2020 with enough money to do anything on earth. Within weeks he felt himself mentally spiraling. The thing that pulled him back became Gemini. He said staying retired would have been a big mistake. The plan was simple sit in cafes, study physics, decompress after decades of building one of the most consequential companies in history. He had earned it. Then COVID shut every cafe on earth the month after he retired. He was stuck. No intellectual outlet. No creative problem to solve. No team to build with. Just time. And he said something happened to him in that time that scared him. He felt himself losing sharpness. Spiraling. Not being as sharp as he was used to being. So he went back. Not because he had to. Not because Google needed him. Because he needed it. He started coming into the office when almost nobody else was. Spent more and more time on a project that at the time did not have a name. That project eventually became Gemini, Google's flagship AI model, the thing they are now betting their next hundred years on. He said it plainly: staying retired would have been a big mistake. Most people think the goal is to stop working. Sergey Brin had the money to stop working and learned within months that the work was not the burden. The absence of it was. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"

Sergey Brin retired in 2020 with enough money to do anything on earth. Within weeks he felt himself mentally spiraling. The thing that pulled him back became Gemini. He said staying retired would have been a big mistake. The plan was simple sit in cafes, study physics, decompress after decades of building one of the most consequential companies in history. He had earned it. Then COVID shut every cafe on earth the month after he retired. He was stuck. No intellectual outlet. No creative problem to solve. No team to build with. Just time. And he said something happened to him in that time that scared him. He felt himself losing sharpness. Spiraling. Not being as sharp as he was used to being. So he went back. Not because he had to. Not because Google needed him. Because he needed it. He started coming into the office when almost nobody else was. Spent more and more time on a project that at the time did not have a name. That project eventually became Gemini, Google's flagship AI model, the thing they are now betting their next hundred years on. He said it plainly: staying retired would have been a big mistake. Most people think the goal is to stop working. Sergey Brin had the money to stop working and learned within months that the work was not the burden. The absence of it was. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"

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.Google Cloud killed its own opening video 3 weeks before Google Cloud Next. In rehearsals, VP of Marketing Sarah Kennedy Ellis looked at the opener her team had built and called it. It was using AI, but not enough of it to actually showcase what the product could do. So they rebuilt it from scratch. In 3 weeks. Here's how it came together, straight from her SaaStr AI 2026 session: - It started on a napkin. A creative director sketched the concept by hand, then fed it into Nano Banana to generate the source images and inspiration. - The story was Google's own history. The 1998 origin garage. Dino. The original server rack that was actually built out of Legos. 138 Easter eggs packed into the video, prompted from Google's own lore. - Then they added motion with Veo and stitched it all together with a few custom agents built on the Gemini Enterprise platform. - The agents improvised. They prompted for balls representing customers, and the model added a burst of water that was nowhere in the brief. It injected its own creativity into a physical, on-stage moment. - The hardest part was the most physical: resolution. Max output was 4K. The screen at Next is the size of a 737. They had to upres from 4K to 12K using a custom Deep Mind model, the same one used for the Wizard of Oz production at the Sphere. Top Takeaways: 1️⃣ The old version of this project needed an agency, a much bigger budget, and a lot more than 3 weeks. That whole path collapsed into an internal team using their own tools. 2️⃣ And the same video was impossible a year earlier. They tried similar work the prior year and it was too early, mostly because of the upres wall. Twelve months later it shipped on the biggest screen they had.

.Google Cloud killed its own opening video 3 weeks before Google Cloud Next. In rehearsals, VP of Marketing Sarah Kennedy Ellis looked at the opener her team had built and called it. It was using AI, but not enough of it to actually showcase what the product could do. So they rebuilt it from scratch. In 3 weeks. Here's how it came together, straight from her SaaStr AI 2026 session: - It started on a napkin. A creative director sketched the concept by hand, then fed it into Nano Banana to generate the source images and inspiration. - The story was Google's own history. The 1998 origin garage. Dino. The original server rack that was actually built out of Legos. 138 Easter eggs packed into the video, prompted from Google's own lore. - Then they added motion with Veo and stitched it all together with a few custom agents built on the Gemini Enterprise platform. - The agents improvised. They prompted for balls representing customers, and the model added a burst of water that was nowhere in the brief. It injected its own creativity into a physical, on-stage moment. - The hardest part was the most physical: resolution. Max output was 4K. The screen at Next is the size of a 737. They had to upres from 4K to 12K using a custom Deep Mind model, the same one used for the Wizard of Oz production at the Sphere. Top Takeaways: 1️⃣ The old version of this project needed an agency, a much bigger budget, and a lot more than 3 weeks. That whole path collapsed into an internal team using their own tools. 2️⃣ And the same video was impossible a year earlier. They tried similar work the prior year and it was too early, mostly because of the upres wall. Twelve months later it shipped on the biggest screen they had.

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Google just got executed by a Nobel Prize winner, the inventor of modern AI, and Donald Trump. They literally LOST the AI race in the most brutal way possible. Here is what happened: 5 days ago, Trump told Axios that Anthropic was a national security threat and warned that "people get put in prison immediately" for what the company had been doing with its frontier model exports. 4 days ago, Trump met Dario Amodei at the G7 AI Summit and walked out telling reporters Dario was a "nice guy, smart guy" who had "responded very responsibly." 3 days ago, Noam Shazeer, the co-author of "Attention Is All You Need" (the paper that invented the transformer architecture powering EVERY modern AI model on Earth), walked out of Google to join OpenAI. Yesterday, John Jumper, the 2024 Nobel Prize winner who co-created AlphaFold and ran Google DeepMind's protein structure team for nearly a decade, announced he was leaving to join Anthropic. And this is NOT a coincidence or a normal talent shuffle... Demis Hassabis, who shared the Nobel Prize with Jumper just 18 months ago, had to publicly THANK his own co-laureate for defecting to a rival lab. The man who shared the highest scientific honor in the world with you is now going to work for the people trying to put you out of business. This is what the end of a war looks like. The AI race was never going to be decided by chips, capital, or compute. There are only about 50 people on Earth who can actually build a frontier model from scratch. Google invented the field and trained most of them. They had the largest concentration of them anywhere on the planet. But in one week, two of the most important AI researchers alive betrayed them. And the actual reason is what's terrifying here: For nine months, the consensus take has been that Google's compute advantage would eventually win because talent is replaceable and compute is not. This week proved the opposite. Anthropic just secured a Nobel Prize winner whose work on AlphaFold opened the entire field of AI for biology. That is the same field every pharma company on Earth is desperately trying to enter. Anthropic now literally owns the most credentialed scientist in it. Meanwhile OpenAI just secured the actual inventor of the transformer. The man whose paper underpins every product Google has shipped in the last three years, including Gemini itself. Google has the compute but Google does not have the people who know what to do with it anymore. And the crazy part is that five days ago, Anthropic was effectively under siege. The administration was threatening PRISON, and the Mythos export crisis had triggered a federal block. Their largest investor was reportedly working against them while the company was hours from an existential national security designation that would have frozen them out of federal contracts. But four days later, Trump cleared them in public, they secured the most decorated AI researcher of the decade, and the entire frontier AI duopoly locked in with Anthropic in pole position. The fastest reversal of fortune in modern corporate history. "It's a two-horse race at the frontier. Google is effectively out." Wall Street wakes up to this in six months and starts downgrading Alphabet. But the smart money already knows. Anthropic is being whispered at a $2 trillion valuation. OpenAI is approaching $500 billion in the private market. Gemini 3.5 Pro has been delayed with no public timeline. The AI race literally ended this week. What do you think?

Ricardo

90,843 просмотров • 1 месяц назад

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

Brian Roemmele

85,538 просмотров • 4 месяцев назад

$Dario Amodei just revealed the exact realization that fractured OpenAI. It happened while building the most powerful AI in the world. The team discovered that scaling had no ceiling. Amodei: “If you pour more compute into these models, they’ll get better and better, and that there’s almost no end to this.” At the time, almost nobody believed it. Amodei’s group were among the first to see it clearly. More compute meant more intelligence. Indefinitely. Without limit. That should have been the most exciting discovery in the history of technology. It terrified them. Because they saw the second half of the equation the industry was ignoring entirely. Amodei: “You don’t tell the models what their values are just by pouring more compute into them.” Intelligence scales with compute. Values don’t. You can build a mind of unlimited capability and it will have no moral compass unless you deliberately build one in. Not as a feature. Not as a guardrail bolted on at the end. As the foundation the entire system is constructed on. This wasn’t a philosophical disagreement. It was an existential one. A god-like intelligence with no alignment isn’t a powerful tool. It’s an uncontrolled force with no reason to care about the species that built it. Amodei: “There were a set of people who believed in those two ideas. We really trusted each other and wanted to work together, and so we went off and started our own company with that idea in mind.” They walked out of the most powerful AI lab on earth. Not for better funding. Not for equity. Because they believed the path OpenAI was on led somewhere nobody could walk back from. That small group became Anthropic. Safety wasn’t a feature they added. It was the entire reason the company exists. The intelligence is going to keep scaling. There is almost no end to it. The only question that has ever mattered is what it’s pointing at when it gets there.$

Dario Amodei just revealed the exact realization that fractured OpenAI. It happened while building the most powerful AI in the world. The team discovered that scaling had no ceiling. Amodei: “If you pour more compute into these models, they’ll get better and better, and that there’s almost no end to this.” At the time, almost nobody believed it. Amodei’s group were among the first to see it clearly. More compute meant more intelligence. Indefinitely. Without limit. That should have been the most exciting discovery in the history of technology. It terrified them. Because they saw the second half of the equation the industry was ignoring entirely. Amodei: “You don’t tell the models what their values are just by pouring more compute into them.” Intelligence scales with compute. Values don’t. You can build a mind of unlimited capability and it will have no moral compass unless you deliberately build one in. Not as a feature. Not as a guardrail bolted on at the end. As the foundation the entire system is constructed on. This wasn’t a philosophical disagreement. It was an existential one. A god-like intelligence with no alignment isn’t a powerful tool. It’s an uncontrolled force with no reason to care about the species that built it. Amodei: “There were a set of people who believed in those two ideas. We really trusted each other and wanted to work together, and so we went off and started our own company with that idea in mind.” They walked out of the most powerful AI lab on earth. Not for better funding. Not for equity. Because they believed the path OpenAI was on led somewhere nobody could walk back from. That small group became Anthropic. Safety wasn’t a feature they added. It was the entire reason the company exists. The intelligence is going to keep scaling. There is almost no end to it. The only question that has ever mattered is what it’s pointing at when it gets there.

Dustin

52,414 просмотров • 5 месяцев назад

Google just pulled off the biggest theft in the history of AI. And their OWN documents exposed it... Three of the largest publishers on Earth, Hachette, Cengage, and Elsevier, just sued Google in federal court, alongside best-selling author Scott Turow. Their claim is that Google built Gemini, its flagship AI, on millions of copyrighted works it never paid for or licensed. This is one of the biggest copyright cases ever aimed at an AI company: Before they trained Gemini, an internal Google document allegedly spelled out the risk directly. Using this material could expose the company to "$10Bs-$100Bs in potential fines." Google's own people put a number on the theft, in the tens of billions, but the company moved ahead anyway. Then it allegedly tried to DELETE the evidence... The complaint says Google stripped the copyright information off the works before feeding them into Gemini, so nobody could trace what the model had actually been trained on. Pull the fingerprints off first, and the theft gets much harder to prove later. And there's a second betrayal underneath the first: Most of this material was not scraped from some random corner of the internet. Publishers had handed it to Google years earlier for a narrow purpose, to make their catalogs searchable inside Google's own services. The lawsuit says Google took that trust and repurposed the content to build a machine that now competes directly with the people who supplied it. And that machine is the entire point. The complaint describes Gemini producing a full-length substitute for a copyrighted work in about 20 minutes. Something an author spent years writing can now be cloned in an afternoon by the company that trained on the original. No writer or publisher survives that. So why does this case matter more than the dozen other AI copyright fights? Because most of them turn on a fuzzy fair-use question argued years after the fact. This one arrives with an internal document that allegedly SHOWS Google weighed the cost of getting caught and trained on the material anyway. A jury does not need a law degree to read that. The fight now moves toward discovery, where Google's internal emails and training records get dragged into the open. If those files back up what the complaint claims, that $100 billion will turn into a real liability. Google spent years telling the world it was organizing information for everyone. Its own documents show it knew exactly whose information it was taking, and what the price of getting caught would be.

Google just pulled off the biggest theft in the history of AI. And their OWN documents exposed it... Three of the largest publishers on Earth, Hachette, Cengage, and Elsevier, just sued Google in federal court, alongside best-selling author Scott Turow. Their claim is that Google built Gemini, its flagship AI, on millions of copyrighted works it never paid for or licensed. This is one of the biggest copyright cases ever aimed at an AI company: Before they trained Gemini, an internal Google document allegedly spelled out the risk directly. Using this material could expose the company to "$10Bs-$100Bs in potential fines." Google's own people put a number on the theft, in the tens of billions, but the company moved ahead anyway. Then it allegedly tried to DELETE the evidence... The complaint says Google stripped the copyright information off the works before feeding them into Gemini, so nobody could trace what the model had actually been trained on. Pull the fingerprints off first, and the theft gets much harder to prove later. And there's a second betrayal underneath the first: Most of this material was not scraped from some random corner of the internet. Publishers had handed it to Google years earlier for a narrow purpose, to make their catalogs searchable inside Google's own services. The lawsuit says Google took that trust and repurposed the content to build a machine that now competes directly with the people who supplied it. And that machine is the entire point. The complaint describes Gemini producing a full-length substitute for a copyrighted work in about 20 minutes. Something an author spent years writing can now be cloned in an afternoon by the company that trained on the original. No writer or publisher survives that. So why does this case matter more than the dozen other AI copyright fights? Because most of them turn on a fuzzy fair-use question argued years after the fact. This one arrives with an internal document that allegedly SHOWS Google weighed the cost of getting caught and trained on the material anyway. A jury does not need a law degree to read that. The fight now moves toward discovery, where Google's internal emails and training records get dragged into the open. If those files back up what the complaint claims, that $100 billion will turn into a real liability. Google spent years telling the world it was organizing information for everyone. Its own documents show it knew exactly whose information it was taking, and what the price of getting caught would be.

Ricardo

26,001 просмотров • 26 дней назад

Sergey Brin dropped out of Stanford's PhD program, became a billionaire, and was invited back to celebrate the school's 100th anniversary. He used the occasion to tell the Dean the university model might not survive the next hundred years. His argument is simple. Information spreads instantly now. Anyone can watch MIT lectures. Take a Coursera class. Ask an AI to explain anything at any depth. The geographic concentration that made Stanford powerful, putting brilliant people in the same building and letting them collide, is no longer the only way to create that collision. He went further. He said he has hired countless people without bachelor's degrees who figured things out on their own in some weird corner of the internet. No institution. No credential. No campus. Just curiosity and access to information. Stanford built Google and the campus and also the PhD program. The culture of freedom that let two graduate students work on something strange for three years without anyone stopping them. Sergey is not ungrateful for that. He is saying the next Google might not need it. The man who is the greatest argument for the university model just questioned whether the model holds for the next century. That is worth sitting with. What do you guys think?

Sergey Brin dropped out of Stanford's PhD program, became a billionaire, and was invited back to celebrate the school's 100th anniversary. He used the occasion to tell the Dean the university model might not survive the next hundred years. His argument is simple. Information spreads instantly now. Anyone can watch MIT lectures. Take a Coursera class. Ask an AI to explain anything at any depth. The geographic concentration that made Stanford powerful, putting brilliant people in the same building and letting them collide, is no longer the only way to create that collision. He went further. He said he has hired countless people without bachelor's degrees who figured things out on their own in some weird corner of the internet. No institution. No credential. No campus. Just curiosity and access to information. Stanford built Google and the campus and also the PhD program. The culture of freedom that let two graduate students work on something strange for three years without anyone stopping them. Sergey is not ungrateful for that. He is saying the next Google might not need it. The man who is the greatest argument for the university model just questioned whether the model holds for the next century. That is worth sitting with. What do you guys think?

Ihtesham Ali

62,704 просмотров • 1 месяц назад

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Macro

55,169 просмотров • 2 месяцев назад

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Stocks

41,351 просмотров • 17 дней назад

Google co-founder Sergey Brin rarely speaks publicly. He just sat down for an unscripted Q&A on Frontier AI and admitted something most lab leaders won’t: Even the people building these models do not fully understand what they have created. In this 29-minute conversation at AGI House, Brin walks through the surprises that actually matter right now: ↳ Specialized models are converging into one general system faster than anyone predicted. - Train on coding and math reasoning mysteriously improves. - Feed it images and geometric word problems get better. - The capabilities bleed into each other in ways nobody fully engineered. ↳ One of the biggest leaps came from the dumbest-sounding trick imaginable: - Just telling the model to “think step by step.” - Brin says there was no obvious reason it should work. It did. ↳ He pushes back on hype around superintelligence (it still can’t solve the impossible), notes that AI mastering a domain has never stopped humans from getting better at it (chess after Deep Blue, Go after AlphaGo), and says something close to transformers is probably enough to reach AGI. ↳ Inside Google, they are already using the AI to build the AI. - That self-improvement loop is where Brin spends most of his time. - World models and physical interaction are the missing piece for the version of AGI that can do anything a person can. Candid, technical, and free of the usual marketing. One of the clearest looks at how the people actually shipping frontier models are thinking in real time. Instead of another Netflix series tonight, watch this talk.

Google co-founder Sergey Brin rarely speaks publicly. He just sat down for an unscripted Q&A on Frontier AI and admitted something most lab leaders won’t: Even the people building these models do not fully understand what they have created. In this 29-minute conversation at AGI House, Brin walks through the surprises that actually matter right now: ↳ Specialized models are converging into one general system faster than anyone predicted. - Train on coding and math reasoning mysteriously improves. - Feed it images and geometric word problems get better. - The capabilities bleed into each other in ways nobody fully engineered. ↳ One of the biggest leaps came from the dumbest-sounding trick imaginable: - Just telling the model to “think step by step.” - Brin says there was no obvious reason it should work. It did. ↳ He pushes back on hype around superintelligence (it still can’t solve the impossible), notes that AI mastering a domain has never stopped humans from getting better at it (chess after Deep Blue, Go after AlphaGo), and says something close to transformers is probably enough to reach AGI. ↳ Inside Google, they are already using the AI to build the AI. - That self-improvement loop is where Brin spends most of his time. - World models and physical interaction are the missing piece for the version of AGI that can do anything a person can. Candid, technical, and free of the usual marketing. One of the clearest looks at how the people actually shipping frontier models are thinking in real time. Instead of another Netflix series tonight, watch this talk.

Linas Beliūnas

547,924 просмотров • 10 дней назад

.Marc Andreessen 🇺🇸 says the biggest drag on AI may be company structure. Google invented the transformer in 2017. They could’ve shipped GPT-4-level output by 2019, but left it on the shelf over brand and safety fears. That’s the cost of fragmentation. The ones that win will collapse the stack (research, product, and distribution) in one motion.

a16z

47,881 просмотров • 1 год назад

Mark Zuckerberg just told you exactly why trillion-dollar companies lose to people who have nothing. Zuckerberg: “Large companies are slow and they lack conviction.” Nine words. The entire vulnerability of every empire on earth in a single sentence. Google had the talent. Microsoft had the infrastructure. Yahoo had the distribution. Every single one of them could have built Facebook. None of them did. Zuckerberg: “We were like a ragtag group of children.” Not seasoned executives. Not credentialed engineers with decades of experience. Children. Against the most well-funded technology companies on the planet. And the children won. Not because they were smarter. Not because they had better resources. Because they believed in something the giants were too comfortable to take seriously. Capital does not beat conviction. Infrastructure does not beat speed. Talent does not beat the willingness to look stupid building something no one respects yet. The giants had everything except the one thing that actually mattered. The inability to hesitate. A large company sees a new idea and runs it through committees. They commission research. They weigh the risk. They debate the market size. They ask what happens to the existing product line. By the time the organization agrees the idea is worth pursuing the window is already closed. The person who builds it does not go through that process. They just build. No committee. No consensus. No permission. They move before the math is finished because they can feel the answer before the spreadsheet confirms it. That is not recklessness. That is the only speed that matters when the window is open. Zuckerberg: “People doubt new ideas before they come to fruition.” It is never one doubt. It is a rotating series of doubts that shift every time the previous one is proven wrong. Zuckerberg: “It’s just like this college kid thing.” That was the first wall. It is small. It is niche. It is not serious. Then it grew. “Maybe not college kids, but it’s probably a fad.” Then it kept growing. “Maybe it’s not a fad, but it’s probably not gonna make money.” Then it made money. “Okay it makes money, but the switch to mobile is gonna be pretty hard.” Every single objection was wrong. Every single one served the same function. It gave the doubter permission to do nothing for one more cycle. That is not analysis. That is self-preservation dressed as skepticism. The person too slow to build the thing will always find a reason why the thing will not work. Not because the reasons are good. Because the alternative is admitting they missed it. You are watching the exact same pattern play out with AI right now. First it was a toy. Then it hallucinated too much. Then it was not profitable. Then it was a bubble. Then it was going to plateau. Every cycle the objection changes. The function never does. Zuckerberg built a company worth over a trillion dollars against competitors who had every advantage except the willingness to commit before the outcome was guaranteed. That is the only advantage that has ever mattered. Not capital. Not credentials. Not infrastructure. The willingness to be wrong in public long enough to become undeniably right. The giants will always have more money. More engineers. More servers. More distribution. They will always be slower than the person who does not need a meeting to make a decision. The committee will never outrun the individual who already started. The incumbents are not dangerous because they are strong. They are vulnerable because they are careful. And careful has never once in the history of technology built the thing that changed the world. It has only ever bought it after someone else already did.

Mark Zuckerberg just told you exactly why trillion-dollar companies lose to people who have nothing. Zuckerberg: “Large companies are slow and they lack conviction.” Nine words. The entire vulnerability of every empire on earth in a single sentence. Google had the talent. Microsoft had the infrastructure. Yahoo had the distribution. Every single one of them could have built Facebook. None of them did. Zuckerberg: “We were like a ragtag group of children.” Not seasoned executives. Not credentialed engineers with decades of experience. Children. Against the most well-funded technology companies on the planet. And the children won. Not because they were smarter. Not because they had better resources. Because they believed in something the giants were too comfortable to take seriously. Capital does not beat conviction. Infrastructure does not beat speed. Talent does not beat the willingness to look stupid building something no one respects yet. The giants had everything except the one thing that actually mattered. The inability to hesitate. A large company sees a new idea and runs it through committees. They commission research. They weigh the risk. They debate the market size. They ask what happens to the existing product line. By the time the organization agrees the idea is worth pursuing the window is already closed. The person who builds it does not go through that process. They just build. No committee. No consensus. No permission. They move before the math is finished because they can feel the answer before the spreadsheet confirms it. That is not recklessness. That is the only speed that matters when the window is open. Zuckerberg: “People doubt new ideas before they come to fruition.” It is never one doubt. It is a rotating series of doubts that shift every time the previous one is proven wrong. Zuckerberg: “It’s just like this college kid thing.” That was the first wall. It is small. It is niche. It is not serious. Then it grew. “Maybe not college kids, but it’s probably a fad.” Then it kept growing. “Maybe it’s not a fad, but it’s probably not gonna make money.” Then it made money. “Okay it makes money, but the switch to mobile is gonna be pretty hard.” Every single objection was wrong. Every single one served the same function. It gave the doubter permission to do nothing for one more cycle. That is not analysis. That is self-preservation dressed as skepticism. The person too slow to build the thing will always find a reason why the thing will not work. Not because the reasons are good. Because the alternative is admitting they missed it. You are watching the exact same pattern play out with AI right now. First it was a toy. Then it hallucinated too much. Then it was not profitable. Then it was a bubble. Then it was going to plateau. Every cycle the objection changes. The function never does. Zuckerberg built a company worth over a trillion dollars against competitors who had every advantage except the willingness to commit before the outcome was guaranteed. That is the only advantage that has ever mattered. Not capital. Not credentials. Not infrastructure. The willingness to be wrong in public long enough to become undeniably right. The giants will always have more money. More engineers. More servers. More distribution. They will always be slower than the person who does not need a meeting to make a decision. The committee will never outrun the individual who already started. The incumbents are not dangerous because they are strong. They are vulnerable because they are careful. And careful has never once in the history of technology built the thing that changed the world. It has only ever bought it after someone else already did.

Dustin

20,938 просмотров • 4 месяцев назад