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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.

Vivek Sen

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3,303,948 просмотров • 1 месяц назад •via X (Twitter)

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Larry Ellison $ORCL highlighted something critical: models like ChatGPT, Gemini, Grok, and Llama are all trained on largely the same public internet data. When everyone trains on the same information, models inevitably converge. That’s why AI is moving toward commoditization. The real moat isn’t the model itself. It’s the proprietary data behind it. Companies that can train on exclusive datasets gain an advantage competitors can’t replicate. Having data that no one else has will allow you to dominate your market.

Larry Ellison $ORCL highlighted something critical: models like ChatGPT, Gemini, Grok, and Llama are all trained on largely the same public internet data. When everyone trains on the same information, models inevitably converge. That’s why AI is moving toward commoditization. The real moat isn’t the model itself. It’s the proprietary data behind it. Companies that can train on exclusive datasets gain an advantage competitors can’t replicate. Having data that no one else has will allow you to dominate your market.

Daniel

1,134,039 просмотров • 4 месяцев назад

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 That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

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 moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

VraserX e/acc

167,043 просмотров • 6 месяцев назад

Larry Ellison just told every AI company on Earth they’re fighting the wrong war. The entire industry is racing to build the smartest model. More parameters. Better benchmarks. Faster inference. Ellison isn’t building a model. He’s controlling what every model needs to be useful. Every frontier AI trains on the same public internet. Same scraped pages. Same recycled text. When everyone has the same data, it’s not an advantage. It’s a floor. The only data that creates separation is private. Medical records. Financial models. Defense systems. Proprietary research locked behind firewalls for decades. That data already lives inside Oracle databases. Not Google’s. Not Microsoft’s. Not Amazon’s. Ellison didn’t enter the model war. He positioned himself above it. He rebuilt the database so AI can reason on private data without ever absorbing it. Training folds your data into the model permanently. Once it’s in, it never comes back out. Reasoning thinks with your data and hands back only the answer. The data never moves. One is surrender. The other is sovereignty. Ellison: “These are remarkable electronic brains.” He didn’t build the brain. He owns what the brain needs to think. Everyone is building the most powerful mind in human history. A mind is only as valuable as what it’s allowed to know. Own the knowledge and it doesn’t matter who builds the brain. That pattern has held through every era of human civilization. AI doesn’t break it. It proves it.

Larry Ellison just told every AI company on Earth they’re fighting the wrong war. The entire industry is racing to build the smartest model. More parameters. Better benchmarks. Faster inference. Ellison isn’t building a model. He’s controlling what every model needs to be useful. Every frontier AI trains on the same public internet. Same scraped pages. Same recycled text. When everyone has the same data, it’s not an advantage. It’s a floor. The only data that creates separation is private. Medical records. Financial models. Defense systems. Proprietary research locked behind firewalls for decades. That data already lives inside Oracle databases. Not Google’s. Not Microsoft’s. Not Amazon’s. Ellison didn’t enter the model war. He positioned himself above it. He rebuilt the database so AI can reason on private data without ever absorbing it. Training folds your data into the model permanently. Once it’s in, it never comes back out. Reasoning thinks with your data and hands back only the answer. The data never moves. One is surrender. The other is sovereignty. Ellison: “These are remarkable electronic brains.” He didn’t build the brain. He owns what the brain needs to think. Everyone is building the most powerful mind in human history. A mind is only as valuable as what it’s allowed to know. Own the knowledge and it doesn’t matter who builds the brain. That pattern has held through every era of human civilization. AI doesn’t break it. It proves it.

Dustin

56,568 просмотров • 1 день назад

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 просмотров • 7 месяцев назад

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.

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 просмотров • 2 месяцев назад

The next bottleneck in AI isn’t model performance. It’s data. From scraped internet content → real-world, consented, high-context data.

The next bottleneck in AI isn’t model performance. It’s data. From scraped internet content → real-world, consented, high-context data.

The DATA Foundation

12,642 просмотров • 28 дней назад

🚨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

🚨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 просмотров • 1 год назад

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Big Brain AI

19,195 просмотров • 1 месяц назад

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

Shay Boloor

262,509 просмотров • 1 месяц назад

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 ↓

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 ↓

Damir Akaza

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AI may be the most transformational technology since the railroads. Jim Bianco says today’s AI market concentration is beyond the internet and PC booms and the real issue may be that AI is still underbuilt, not overbuilt. Are markets pricing this correctly? #AI #Markets

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ALLINCRYPTO

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Molly O’Shea

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The future of robotics isn’t being built in factories, it’s being trained in simulation MasterBOT’s proprietary AI model is learning in real-time, powered by reinforcement and continuous feedback loops This is how machines learn to think $BOT

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MasterBOT

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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

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1/n We are excited to announce EchoPrime – the first echocardiography AI model capable of evaluating a full transthoracic echocardiogram study, identify the most relevant videos, and produce a comprehensive interpretation! Great work lead by Milos Vukadinovic, EchoPrime is the largest and most complex #echofirst AI model yet, trained on 10x the data of existing models.

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every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

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Years before AI infrastructure became the most competitive market in tech, we were proving that behind-the-meter computing works at scale. Bitcoin gave us that proof. Real assets. Real operations. Real performance through real market cycles. Now that same model — the same power strategy, the same operational discipline — is expanding to serve AI and high-performance compute demand. The workload evolves. The infrastructure holds. $SLNH

Years before AI infrastructure became the most competitive market in tech, we were proving that behind-the-meter computing works at scale. Bitcoin gave us that proof. Real assets. Real operations. Real performance through real market cycles. Now that same model — the same power strategy, the same operational discipline — is expanding to serve AI and high-performance compute demand. The workload evolves. The infrastructure holds. $SLNH

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