Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

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

261,302 subscribers

3,294,198 Aufrufe • vor 18 Tagen •via X (Twitter)

Nachrichten & Politik Wissenschaft & Technologie Finanzen

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

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

1,128,258 Aufrufe • vor 3 Monaten

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 That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI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

167,036 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 6 Monaten

The AI Trillion-Dollar Moat 💰🤖 On the last The All-In Podcast, david friedberg dropped a truth bomb: Data is the REAL moat in AI. This came right after DeepSeek—China’s latest AI model—shook the industry, forcing a hard look at OpenAI’s valuation and the billion-dollar model race. 🚨 AI investors are waking up: Is scaling with NVIDIA chips the endgame? Or is it DATA? 🤔 david friedberg says what we at Masa have always believed: Data is the moat. travis kalanick (Uber co-founder) doubled down—stating data is the tentpole emerging in AI’s future. DeepSeek proved open-source models are winning. Even OpenAI CEO Sam Altman admitted it. But what about open-source data? The AI arms race isn’t just about bigger models—it’s about scalable, high-quality, AI-ready data. That’s why we built Masa: a decentralized, open-source real-time data network on Bittensor SN42. And with dTAO coming soon… The real moat is about to be exposed. $MASA is inevitable. 🚀

The AI Trillion-Dollar Moat 💰🤖 On the last The All-In Podcast, david friedberg dropped a truth bomb: Data is the REAL moat in AI. This came right after DeepSeek—China’s latest AI model—shook the industry, forcing a hard look at OpenAI’s valuation and the billion-dollar model race. 🚨 AI investors are waking up: Is scaling with NVIDIA chips the endgame? Or is it DATA? 🤔 david friedberg says what we at Masa have always believed: Data is the moat. travis kalanick (Uber co-founder) doubled down—stating data is the tentpole emerging in AI’s future. DeepSeek proved open-source models are winning. Even OpenAI CEO Sam Altman admitted it. But what about open-source data? The AI arms race isn’t just about bigger models—it’s about scalable, high-quality, AI-ready data. That’s why we built Masa: a decentralized, open-source real-time data network on Bittensor SN42. And with dTAO coming soon… The real moat is about to be exposed. $MASA is inevitable. 🚀

Masa

22,701 Aufrufe • vor 1 Jahr

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

48,334 Aufrufe • vor 1 Monat

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.

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

17,896 Aufrufe • vor 23 Tagen

🚨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 Aufrufe • vor 1 Jahr

$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

258,373 Aufrufe • vor 16 Tagen

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 Aufrufe • vor 13 Tagen

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

162,298 Aufrufe • vor 4 Tagen

💡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

108,354 Aufrufe • vor 1 Jahr

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

51,156 Aufrufe • vor 8 Monaten

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.

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.

David Ouyang, MD

153,028 Aufrufe • vor 1 Jahr

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

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 Aufrufe • vor 2 Jahren

What we’re seeing: frontier robotics teams are rebuilding data pipelines from scratch because generic licensed datasets aren’t moving their models forward. The next wave won’t be about more data. It’ll be about precise, domain-specific data that actually improves performance. The bottleneck isn’t compute. It’s the right data. 🎥 Our CCO Arthur at Wrapped’s Multichain Day (ETHDenver 🏔🦬🦄)

What we’re seeing: frontier robotics teams are rebuilding data pipelines from scratch because generic licensed datasets aren’t moving their models forward. The next wave won’t be about more data. It’ll be about precise, domain-specific data that actually improves performance. The bottleneck isn’t compute. It’s the right data. 🎥 Our CCO Arthur at Wrapped’s Multichain Day (ETHDenver 🏔🦬🦄)

Perle Labs

30,488 Aufrufe • vor 3 Monaten

Ready to experience the future of #AI? #Inferium's MVP is coming. Be among the first to: 🧠 Evaluate cutting-edge AI models ⚡ Run #inference on AI models 💰 Earn rewards & top the leaderboard Join waitlist now to secure early access and exclusive perks: #Inferium #MVP #AI #Inference

Ready to experience the future of #AI? #Inferium's MVP is coming. Be among the first to: 🧠 Evaluate cutting-edge AI models ⚡ Run #inference on AI models 💰 Earn rewards & top the leaderboard Join waitlist now to secure early access and exclusive perks: #Inferium #MVP #AI #Inference

Inferium

37,307 Aufrufe • vor 1 Jahr

Larry Ellison says today's AI models are no longer just inferencing; they reason like we do These systems handle deduction, calculation, and strategy far faster than people "at Oracle, most code is now AI-generated" We declare our intent; the model writes the procedure

Larry Ellison says today's AI models are no longer just inferencing; they reason like we do These systems handle deduction, calculation, and strategy far faster than people "at Oracle, most code is now AI-generated" We declare our intent; the model writes the procedure

Haider.

172,446 Aufrufe • vor 8 Monaten

Google's best model is actually hidden Gemini 2.5 Pro with Web Grounding is incredible and only available in AI Studio You get free access to the most powerful model with real-time data: - Access AI Studio - Select Gemini 2.5 Pro Preview - Activate Grounding with Search Gemini will search the data, think, then give you the answers with the sources.

Google's best model is actually hidden Gemini 2.5 Pro with Web Grounding is incredible and only available in AI Studio You get free access to the most powerful model with real-time data: - Access AI Studio - Select Gemini 2.5 Pro Preview - Activate Grounding with Search Gemini will search the data, think, then give you the answers with the sources.

Paul Couvert

149,669 Aufrufe • vor 1 Jahr

LARRY ELLISON PUSHES AI MRNA CANCER INJECTIONS IN TRUMP'S PROJECT STARGATE PRESENTATION Oracle CEO Larry Ellison took the stage to announce that AI technology will be linked to mRNA cancer "vaccines". This is of course science fiction with no basis in reality. MRNA is the most dangerous technology in the field of medicine today.

LARRY ELLISON PUSHES AI MRNA CANCER INJECTIONS IN TRUMP'S PROJECT STARGATE PRESENTATION Oracle CEO Larry Ellison took the stage to announce that AI technology will be linked to mRNA cancer "vaccines". This is of course science fiction with no basis in reality. MRNA is the most dangerous technology in the field of medicine today.

Robin Monotti

86,527 Aufrufe • vor 1 Jahr

1/3 Zesh AI Layer is an advanced AI solution on SUI, powered by a library of data models that act as its engine. Zesh layer is designed for building cutting-edge AI products, giving every project on Zesh access to its expansive and ever-evolving library of data models. As more products, users, and activity takes place in the Zesh ecosystem, the data model library continually learns and grows. This is AI that evolves with you.

1/3 Zesh AI Layer is an advanced AI solution on SUI, powered by a library of data models that act as its engine. Zesh layer is designed for building cutting-edge AI products, giving every project on Zesh access to its expansive and ever-evolving library of data models. As more products, users, and activity takes place in the Zesh ecosystem, the data model library continually learns and grows. This is AI that evolves with you.

Zesh AI Layer

1,044,803 Aufrufe • vor 1 Jahr