Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Intelligence is getting cheaper every quarter, and the frontier labs pay for that. The companies that win are the ones that own data no one else has: every cheaper model makes their data more valuable and their costs lower. Own the data, not the frontier.

Antonio Linares

163,848 subscribers

17,676 görüntüleme • 11 saat önce •via X (Twitter)

Bilim & Teknoloji Finans

Anya Rossi• Live Now

Private livecam show

6 Yorum

Macro Bombastic profil fotoğrafı

Macro Bombastic11 saat önce

yeah owning the data is the real moat tbh

Marc 🏴 profil fotoğrafı

Marc 🏴11 saat önce

$META is a sweetspot between frontier and owning the data

Rockefella Capital profil fotoğrafı

Rockefella Capital11 saat önce

Release the podcast already?

Gabriella Brasil profil fotoğrafı

Gabriella Brasil11 saat önce

Data moats compound while model costs fall, which is exactly why you and @MattSevenich are my go to finance accounts.

Pianaland🇪🇺🇺🇦🇹🇼 profil fotoğrafı

Pianaland🇪🇺🇺🇦🇹🇼11 saat önce

Their bet is that if they own the frontier they will own the data by extension. Not saying they are right, but there is no angle where their Capex makes sense without taking it all…

Rusete profil fotoğrafı

Rusete10 saat önce

@CeoOndas esto es muy interesante para comprimir una propiedad de datos unificando todas las partes adquiridas y gracias a RegnaAI

Benzer Videolar

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,108,497 görüntüleme • 1 ay önce

Today, we’re excited to launch the Datology Curation Studio, customizable frontier data curation for every team training its own models. Data quality is the ultimate compute multiplier, and Curation Studio makes it easy for everyone.

Today, we’re excited to launch the Datology Curation Studio, customizable frontier data curation for every team training its own models. Data quality is the ultimate compute multiplier, and Curation Studio makes it easy for everyone.

DatologyAI

4,294,423 görüntüleme • 25 gün önce

On The Information's TITV talking about where open weights actually land inside a big company Nobody serious is picking one model. They'll ride the frontier of intelligence(rent) and most will train OSS models(own) on their own data for the work nobody else can do. For many critical end to end agentification the expertise is in your people and your systems, not on the internet.

On The Information's TITV talking about where open weights actually land inside a big company Nobody serious is picking one model. They'll ride the frontier of intelligence(rent) and most will train OSS models(own) on their own data for the work nobody else can do. For many critical end to end agentification the expertise is in your people and your systems, not on the internet.

Garrett Lord

90,767 görüntüleme • 1 ay önce

Why the companies of the future will have specialized models trained on their own data “You are creating value for the model company versus capturing that value for yourself. We need to have more of that surplus that gets created by the use of intelligence remain at the firm. Every firm will have a private eval benchmark, which is their core IP, and every model comes in and tries to hill climb on that eval.” Satya Nadella Love to hear your thoughts Harrison Chase Shreya Shankar Jerry Liu Anjney Midha

Why the companies of the future will have specialized models trained on their own data “You are creating value for the model company versus capturing that value for yourself. We need to have more of that surplus that gets created by the use of intelligence remain at the firm. Every firm will have a private eval benchmark, which is their core IP, and every model comes in and tries to hill climb on that eval.” Satya Nadella Love to hear your thoughts Harrison Chase Shreya Shankar Jerry Liu Anjney Midha

Harry Stebbings

12,629 görüntüleme • 7 gün önce

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.

Adeniyi.sui

13,834 görüntüleme • 4 ay önce

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Ihtesham Ali

17,433 görüntüleme • 4 ay önce

$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 görüntüleme • 2 ay önce

Introducing Contra Labs. The first frontier data and evaluation lab for Creative AI.

Introducing Contra Labs. The first frontier data and evaluation lab for Creative AI.

ben

451,222 görüntüleme • 7 ay önce

Physicians can’t own hospitals. That’s right — the Affordable Care Act made it illegal, and even capped the size of the ones that already existed. The claim was higher costs from self-referrals, but data shows physician hospitals are cheaper and better. #healthcare #doctor #policy #AffordableCareAct

Physicians can’t own hospitals. That’s right — the Affordable Care Act made it illegal, and even capped the size of the ones that already existed. The claim was higher costs from self-referrals, but data shows physician hospitals are cheaper and better. #healthcare #doctor #policy #AffordableCareAct

Real Doc Speaks

31,226 görüntüleme • 11 ay önce

NOW: President Trump has arrived at the EPA to announce a new AI energy initiative, making sure data centers are the ones footing the bill for their energy usage — NOT American families They'll actually be producing MORE energy than they use, driving down rates for families. "The American Innovators and consumers strive to win together because we're insisting that AI data centers and big tech companies pay THEIR OWN WAY." "They're going to have a lot of electricity left over, and they'll put that into the grid so it'll actually end up with more electricity. The rates are going to go DOWN."

NOW: President Trump has arrived at the EPA to announce a new AI energy initiative, making sure data centers are the ones footing the bill for their energy usage — NOT American families They'll actually be producing MORE energy than they use, driving down rates for families. "The American Innovators and consumers strive to win together because we're insisting that AI data centers and big tech companies pay THEIR OWN WAY." "They're going to have a lot of electricity left over, and they'll put that into the grid so it'll actually end up with more electricity. The rates are going to go DOWN."

Nick Sortor

58,971 görüntüleme • 3 ay önce

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir

4,530,772 görüntüleme • 4 ay önce

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."

Big Brain AI

11,770 görüntüleme • 4 ay önce

When it comes to data centers, Michigan should show the country how to do it right. That means: Data center companies, not Michigan families, pay for their own energy and grid upgrades – so rates don't go up. Data centers can't harm our water, air, or environment. Data centers pay their fair share in taxes to fund our schools, roads, and communities. Data centers are built with local union labor.

When it comes to data centers, Michigan should show the country how to do it right. That means: Data center companies, not Michigan families, pay for their own energy and grid upgrades – so rates don't go up. Data centers can't harm our water, air, or environment. Data centers pay their fair share in taxes to fund our schools, roads, and communities. Data centers are built with local union labor.

Mallory McMorrow

272,485 görüntüleme • 9 ay önce

$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 görüntüleme • 3 ay önce

The stakes around data are changing. As artificial intelligence moves into everything, the demands on our data compound. So do the risks. We now share our ideas, intentions, and reasoning with these systems. The skills and experience that we’ve spent a lifetime building. For institutions and builders, it’s their competitive intelligence, their proprietary insights. The edge that separates winners from everyone else. Data is our most valuable asset. This is also a moment of massive opportunity. Agentic AI has the potential to create new abundance for humanity. But for that future to be possible, agents need data that makes them capable. Data they control, that moves with them across applications, and that they can independently verify. As owners, we’ll need to prove to regulators what our agents did. All things that today’s infrastructure cannot provide. Sui represents the first half of the solution. A coordination layer built for the machine economy. Walrus is the data platform that completes it. Walrus makes data a first-class asset. The terms of access and monetization stay with its owner, not a vendor. It empowers builders to ship agents and apps without locking themselves or their customers into any model or platform. Users carry their data, their memory, and their work across systems while cryptographic proof replaces vendor trust. Everything is anchored on Sui: a coordination layer and a data layer built to move as one. Welcome to the new era of Walrus. The data platform built for the demands of AI.

The stakes around data are changing. As artificial intelligence moves into everything, the demands on our data compound. So do the risks. We now share our ideas, intentions, and reasoning with these systems. The skills and experience that we’ve spent a lifetime building. For institutions and builders, it’s their competitive intelligence, their proprietary insights. The edge that separates winners from everyone else. Data is our most valuable asset. This is also a moment of massive opportunity. Agentic AI has the potential to create new abundance for humanity. But for that future to be possible, agents need data that makes them capable. Data they control, that moves with them across applications, and that they can independently verify. As owners, we’ll need to prove to regulators what our agents did. All things that today’s infrastructure cannot provide. Sui represents the first half of the solution. A coordination layer built for the machine economy. Walrus is the data platform that completes it. Walrus makes data a first-class asset. The terms of access and monetization stay with its owner, not a vendor. It empowers builders to ship agents and apps without locking themselves or their customers into any model or platform. Users carry their data, their memory, and their work across systems while cryptographic proof replaces vendor trust. Everything is anchored on Sui: a coordination layer and a data layer built to move as one. Welcome to the new era of Walrus. The data platform built for the demands of AI.

Walrus

20,335 görüntüleme • 27 gün önce

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence, frontier labs can train on RL environments across every subject matter all in one place.

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence, frontier labs can train on RL environments across every subject matter all in one place.

Ali Ansari

333,257 görüntüleme • 11 ay önce

President Trump is right about data centers: "If you pick the right location, what it means for a community is jobs and lots of money and lower taxes. The data centers now are mandated by me that they build their own electricity, so they're not taking it from the grid."

President Trump is right about data centers: "If you pick the right location, what it means for a community is jobs and lots of money and lower taxes. The data centers now are mandated by me that they build their own electricity, so they're not taking it from the grid."

Innovation Council

387,312 görüntüleme • 2 ay önce

Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.

Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.

Milk Road AI

282,224 görüntüleme • 4 ay önce

.President Donald J. Trump on data centers: "If you pick the right location, what it means for a community is jobs and lots of money and lower taxes. The data centers now are mandated by me that they build their own electricity, so they're not taking it from the grid."

.President Donald J. Trump on data centers: "If you pick the right location, what it means for a community is jobs and lots of money and lower taxes. The data centers now are mandated by me that they build their own electricity, so they're not taking it from the grid."

Rapid Response 47

764,879 görüntüleme • 2 ay önce