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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.

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3,313,691 просмотров • 3 месяцев 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

1,164,636 просмотров • 6 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 That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI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

167,094 просмотров • 9 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

98,113 просмотров • 2 месяцев 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 • 9 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

48,524 просмотров • 4 месяцев назад

Four American firms, Surge AI, Mercor, AfterQuery and Turing are turning the expertise of U.S. coders, physicians, lawyers, engineers and scientists into the training data that gives leading AI models their edge. If any of these companies are supplying the CCP or the Chinese military with the SAME sensitive data while also bidding for U.S. Army or Pentagon contracts, they should be disqualified. Why should China get access to the same American expertise and datasets being used to train our own AI? Washington’s inaction is allowing the CCP to gain access to the American expertise that gives our AI systems their strategic edge. I hope the Trump administration is keeping an eye on this.

Four American firms, Surge AI, Mercor, AfterQuery and Turing are turning the expertise of U.S. coders, physicians, lawyers, engineers and scientists into the training data that gives leading AI models their edge. If any of these companies are supplying the CCP or the Chinese military with the SAME sensitive data while also bidding for U.S. Army or Pentagon contracts, they should be disqualified. Why should China get access to the same American expertise and datasets being used to train our own AI? Washington’s inaction is allowing the CCP to gain access to the American expertise that gives our AI systems their strategic edge. I hope the Trump administration is keeping an eye on this.

Laura Loomer

155,407 просмотров • 1 месяц назад

“Until you actually see things operationally, it’s going to be hard to build DoorDash from scratch.” Our CEO Yash Patil sat down with Andy Fang on why cheaper software doesn’t erase years of operating advantage. DoorDash’s moat is its proprietary data, edge cases, and hard-won knowledge, and increasingly, the models trained on top of it.

“Until you actually see things operationally, it’s going to be hard to build DoorDash from scratch.” Our CEO Yash Patil sat down with Andy Fang on why cheaper software doesn’t erase years of operating advantage. DoorDash’s moat is its proprietary data, edge cases, and hard-won knowledge, and increasingly, the models trained on top of it.

Applied Compute

12,083 просмотров • 21 дней 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

19,342 просмотров • 3 месяцев 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

265,610 просмотров • 3 месяцев назад

The biggest danger isn’t AI. It’s getting left behind. “You need access to compute. You need access to your own data.” “The stack is compute, data, ability to manage data, ability to control the models, ability to train the models, and ability to compete against your competitors and adversaries.” “You are gonna need a lot of electricity. How are you gonna get it?” “I don’t think the AI revolution is a hundred percent good. There are huge dangers. But being left behind is probably the biggest danger now.”

The biggest danger isn’t AI. It’s getting left behind. “You need access to compute. You need access to your own data.” “The stack is compute, data, ability to manage data, ability to control the models, ability to train the models, and ability to compete against your competitors and adversaries.” “You are gonna need a lot of electricity. How are you gonna get it?” “I don’t think the AI revolution is a hundred percent good. There are huge dangers. But being left behind is probably the biggest danger now.”

Chad Wahlquist

30,401 просмотров • 19 дней 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

164,950 просмотров • 3 месяцев назад

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

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

ALLINCRYPTO

21,033 просмотров • 3 месяцев назад

I agree with Congressman Michael McCaul Michael McCaul on this issue. As I recently discussed on Loomer Unleashed a couple weeks ago, four American firms, Surge AI Surge AI, Mercor Mercor, AfterQuery AfterQuery, and Turing Turing are turning the expertise of U.S. coders, physicians, lawyers, engineers and scientists into the training data that gives leading AI models their edge. If any of these companies are supplying the CCP or the Chinese military with the SAME sensitive data while also bidding for U.S. Army or Pentagon contracts, they should be disqualified. Why should China get access to the same American expertise and datasets being used to train our own AI? Washington’s inaction is allowing the CCP to gain access to the American expertise that gives our AI systems their strategic edge. I hope the Trump administration is keeping an eye on this.

I agree with Congressman Michael McCaul Michael McCaul on this issue. As I recently discussed on Loomer Unleashed a couple weeks ago, four American firms, Surge AI Surge AI, Mercor Mercor, AfterQuery AfterQuery, and Turing Turing are turning the expertise of U.S. coders, physicians, lawyers, engineers and scientists into the training data that gives leading AI models their edge. If any of these companies are supplying the CCP or the Chinese military with the SAME sensitive data while also bidding for U.S. Army or Pentagon contracts, they should be disqualified. Why should China get access to the same American expertise and datasets being used to train our own AI? Washington’s inaction is allowing the CCP to gain access to the American expertise that gives our AI systems their strategic edge. I hope the Trump administration is keeping an eye on this.

Laura Loomer

67,935 просмотров • 21 дней назад

💡Why Vana? “Ultimately, AI models are only as good as their training data. So if you want to build the best AI, you need the best data.” shares the vision behind Vana’s mission, centered on DataDAOs that aggregate specific datasets, reward data owners for their contributions and level up AI models with high-quality data🏆

💡Why Vana? “Ultimately, AI models are only as good as their training data. So if you want to build the best AI, you need the best data.” shares the vision behind Vana’s mission, centered on DataDAOs that aggregate specific datasets, reward data owners for their contributions and level up AI models with high-quality data🏆

vana

108,377 просмотров • 1 год назад

$Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.$

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.

Milk Road AI

16,317 просмотров • 2 месяцев назад

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

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

MasterBOT

51,189 просмотров • 11 месяцев назад

$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.

$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.

Shay Boloor

62,547 просмотров • 18 дней назад

"The jig is up." - Alex Karp on frontier models Everyone is asking whether AI models will k*ll software. Karp & Dylan Field (Dylan Field) think that's the wrong question. Karp argues the winners won't be whoever has the best model. They'll be whoever owns their data, models, & compute. Field made the same point at Config. "The narrative seems to be software is kaput. But a lot of software will do just fine or actually even amazing." What lasts, he says, is software where the data compounds into its own flywheel. The data is the moat, not the model.

"The jig is up." - Alex Karp on frontier models Everyone is asking whether AI models will k*ll software. Karp & Dylan Field (Dylan Field) think that's the wrong question. Karp argues the winners won't be whoever has the best model. They'll be whoever owns their data, models, & compute. Field made the same point at Config. "The narrative seems to be software is kaput. But a lot of software will do just fine or actually even amazing." What lasts, he says, is software where the data compounds into its own flywheel. The data is the moat, not the model.

Molly O’Shea

45,256 просмотров • 2 месяцев назад