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Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is...

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167,043 次观看 • 6 个月前 •via X (Twitter)

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LARRY ELLISON: AI IS RAPIDLY COMMODITIZING BECAUSE MOST MODELS ARE TRAINED ON THE SAME PUBLIC INTERNET DATA. THE REAL COMPETITIVE EDGE ISN’T THE MODEL ANYMORE — IT’S ACCESS TO EXCLUSIVE, PROPRIETARY DATASETS. THAT MAY BE THE ONLY MOAT LEFT.
0:36

LARRY ELLISON: AI IS RAPIDLY COMMODITIZING BECAUSE MOST MODELS ARE TRAINED ON THE SAME PUBLIC INTERNET DATA. THE REAL COMPETITIVE EDGE ISN’T THE MODEL ANYMORE — IT’S ACCESS TO EXCLUSIVE, PROPRIETARY DATASETS. THAT MAY BE THE ONLY MOAT LEFT.

Vivek Sen

3,302,977 次观看 • 1 个月前

Larry Ellison just told the world what AI is coming for next. Not better chips. Not smarter architecture. Your private data. Ellison: “These AI models are trained on publicly available data. All the data on the internet.” Every major model right now is trained on the same thing. The entire public internet. Every article, every forum, every blog, every comment you left under a video in 2014. All of it scraped. All of it digested. All of it already inside the system. Ellison: “For these models to reach their peak value, you need to make privately owned data available to those models as well.” Billions of web pages. Trillions of words. The sum total of everything humanity ever made searchable. Not enough. “Peak value” doesn’t live on the open internet. It lives in your medical records. Your financial history. Your company’s internal files. The data you handed over every time you tapped “I agree” without reading a word. We spent three decades uploading ourselves to the internet willingly. Photos, opinions, locations, relationships, habits. We traded it all for convenience and never thought twice. That was phase one. Phase two doesn’t ask. Phase two is the hospital system. The insurance database. The enterprise backend holding decades of behavioral data that was never meant to leave the building. Ellison doesn’t run the flashiest AI lab on earth. He runs Oracle. The company sitting on more private data infrastructure than almost anyone. He’s not making a prediction. He’s describing a transfer of access that’s already underway. The companies that own the next decade of AI won’t have the best models. They’ll have the keys to the most private rooms. Privacy was never a right. It was a technical limitation. The data always existed. It was just too expensive to reach. AI made it worth reaching. The models already consumed everything you shared. Now they need everything you didn’t.
0:45

Larry Ellison just told the world what AI is coming for next. Not better chips. Not smarter architecture. Your private data. Ellison: “These AI models are trained on publicly available data. All the data on the internet.” Every major model right now is trained on the same thing. The entire public internet. Every article, every forum, every blog, every comment you left under a video in 2014. All of it scraped. All of it digested. All of it already inside the system. Ellison: “For these models to reach their peak value, you need to make privately owned data available to those models as well.” Billions of web pages. Trillions of words. The sum total of everything humanity ever made searchable. Not enough. “Peak value” doesn’t live on the open internet. It lives in your medical records. Your financial history. Your company’s internal files. The data you handed over every time you tapped “I agree” without reading a word. We spent three decades uploading ourselves to the internet willingly. Photos, opinions, locations, relationships, habits. We traded it all for convenience and never thought twice. That was phase one. Phase two doesn’t ask. Phase two is the hospital system. The insurance database. The enterprise backend holding decades of behavioral data that was never meant to leave the building. Ellison doesn’t run the flashiest AI lab on earth. He runs Oracle. The company sitting on more private data infrastructure than almost anyone. He’s not making a prediction. He’s describing a transfer of access that’s already underway. The companies that own the next decade of AI won’t have the best models. They’ll have the keys to the most private rooms. Privacy was never a right. It was a technical limitation. The data always existed. It was just too expensive to reach. AI made it worth reaching. The models already consumed everything you shared. Now they need everything you didn’t.

Dustin

18,851 次观看 • 1 个月前

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI
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BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

Molly O’Shea

69,876 次观看 • 9 个月前

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓
49:52

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

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AI only exists today because it is trained on real data. The better the data input, The better the AI output. We capture incredibly high-quality raw data of real humans, in 3D, from all angles. You do the Math ✍🏻 Our data can vastly improve and expand the capabilities of AI

Metahero.io

24,527 次观看 • 2 年前

Larry Ellison just told you where the real AI war is being fought. Every major AI model trains on the same public data. Same internet. Same scraped pages. Same recycled text. ChatGPT. Claude. Grok. Llama. They all learned from the same pile. Public data isn’t a competitive edge anymore. It’s the floor. The real separation is private data. Medical records. Financial models. Defense infrastructure. Proprietary research locked behind firewalls for decades. That’s where the highest-value information on Earth lives. And most of it already sits inside Oracle databases. Not Google’s servers. Not Microsoft’s cloud. Not Amazon’s. Oracle’s. Ellison didn’t try to build another AI model. He rebuilt the database so AI could reason directly on private data. Not train on it. Reason on it. Training means your data gets absorbed into the model. It leaves your hands. Reasoning means AI thinks with your data. Returns the insight. The data never moves. That’s not an upgrade. That’s the architecture enterprise AI actually needs. Ellison called these systems “remarkable electronic brains.” He wasn’t reaching for a metaphor. He was being literal. We built synthetic cognition. Not faster software. Not better algorithms. A tool that reasons. And he said what almost no one in tech will say out loud. It won’t replace us. It’ll make us something we’ve never been. Better scientists. Better surgeons. Better engineers. Better teachers. Every tool humanity ever built followed one pattern. It made the person holding it more powerful than the person without it. Fire. The printing press. Electricity. The internet. AI follows the same arc. With one difference. This tool thinks with you. The people who figure that out first won’t just have an advantage. They’ll solve problems the rest of the world didn’t know were solvable. We didn’t build our replacement. We built our upgrade. And history won’t remember who feared it. It’ll remember who used it.
2:07

Larry Ellison just told you where the real AI war is being fought. Every major AI model trains on the same public data. Same internet. Same scraped pages. Same recycled text. ChatGPT. Claude. Grok. Llama. They all learned from the same pile. Public data isn’t a competitive edge anymore. It’s the floor. The real separation is private data. Medical records. Financial models. Defense infrastructure. Proprietary research locked behind firewalls for decades. That’s where the highest-value information on Earth lives. And most of it already sits inside Oracle databases. Not Google’s servers. Not Microsoft’s cloud. Not Amazon’s. Oracle’s. Ellison didn’t try to build another AI model. He rebuilt the database so AI could reason directly on private data. Not train on it. Reason on it. Training means your data gets absorbed into the model. It leaves your hands. Reasoning means AI thinks with your data. Returns the insight. The data never moves. That’s not an upgrade. That’s the architecture enterprise AI actually needs. Ellison called these systems “remarkable electronic brains.” He wasn’t reaching for a metaphor. He was being literal. We built synthetic cognition. Not faster software. Not better algorithms. A tool that reasons. And he said what almost no one in tech will say out loud. It won’t replace us. It’ll make us something we’ve never been. Better scientists. Better surgeons. Better engineers. Better teachers. Every tool humanity ever built followed one pattern. It made the person holding it more powerful than the person without it. Fire. The printing press. Electricity. The internet. AI follows the same arc. With one difference. This tool thinks with you. The people who figure that out first won’t just have an advantage. They’ll solve problems the rest of the world didn’t know were solvable. We didn’t build our replacement. We built our upgrade. And history won’t remember who feared it. It’ll remember who used it.

Dustin

48,416 次观看 • 2 个月前

We've hit the data wall- all public internet data has been used to train AI. explains why the future of AI training lies in unlocking user-owned data from walled gardens 🔓
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We've hit the data wall- all public internet data has been used to train AI. explains why the future of AI training lies in unlocking user-owned data from walled gardens 🔓

vana

16,484 次观看 • 1 年前

Edge Computing 🤝 Federated Learning The next AI breakthrough lies in unlocking private data. By expanding training from public to private data, FLock enables AI models to specialise, delivering deeper insights and continuous innovation. Introducing the FLock FL Alliance.
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FLock.io

102,531 次观看 • 1 年前

$SNOW doesn't need to know whether usage is coming from a human analyst or an AI agent but just needs more workloads running through the platform. Thats why Snowflake is trying to become the governed data layer where enterprise data, AI models, applications & workflows all come together.
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$SNOW doesn't need to know whether usage is coming from a human analyst or an AI agent but just needs more workloads running through the platform. Thats why Snowflake is trying to become the governed data layer where enterprise data, AI models, applications & workflows all come together.

Shay Boloor

60,337 次观看 • 1 个月前

Today, Box announced new AI Agents to work with enterprise content, powering Deep Research, Search, and enhanced Data Extraction. There’s a tremendous amount of value that’s trapped in unstructured data, from contracts to research data, that we can finally unlock with AI.
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Today, Box announced new AI Agents to work with enterprise content, powering Deep Research, Search, and enhanced Data Extraction. There’s a tremendous amount of value that’s trapped in unstructured data, from contracts to research data, that we can finally unlock with AI.

Aaron Levie

60,262 次观看 • 1 年前

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg
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FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg

Mario Nawfal

15,855 次观看 • 7 个月前

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🚨LINDA YACCARINO: GROK GROWING FASTER THAN ANY AI MODEL BECAUSE OF REAL-TIME X DATA “What’s really important is that Grok is the only AI trained on real-time data from X. That’s why Grok is growing faster than any other competitor. Grok 3.5 is about to drop and will be the smartest model in the world.” Source: Axios Linda Yaccarino Elon Musk

Mario Nawfal

377,487 次观看 • 1 年前

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."
0:33

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

Select Committee on China

41,507 次观看 • 2 个月前

THE ALPHA YOU WERE LOOKING FOR! Perle Labs a crypto project built on Solana with the aim to help and train AI models using real human knowledge and data. Perle Labs creates a system where experts do AI tasks, all on chain & transparent! Join here :
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18,536 次观看 • 4 个月前

The lack of understanding of how current AI/ large language models work makes us susceptible to thinking that AI is more powerful and all-knowing than it actually is. That’s what concerns me the most.
2:09

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Sinead Bovell

143,642 次观看 • 3 年前

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.
1:24

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Aish

240,611 次观看 • 1 年前

AI’s real bottleneck is data. Control of that data is concentrating in private silos. At the recent Project Glitch, Art | Zoro ⟬⊻⟭ explained how Vana is decentralizing ownership to keep AI truly open and give value back to people.
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17,066 次观看 • 9 个月前