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Google is doing some great AI work in India that is actually helping farmers on the ground and honestly, almost nobody is talking about it. It’s surprising how much we focus on every new model drop, while some of the most meaningful AI work is happening quietly in the...

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AshutoshShrivastava

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37,066 次观看 • 3 天前 •via X (Twitter)

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Google just took a big step towards building ChatGPT for Earth. AlphaEarth Foundations does something clever -- instead of drowning in petabytes of Earth observation data, it creates compact summaries of every 10x10m square on Earth by fusing optical, radar, LiDAR, and climate data. The kicker is it can see through clouds in Ecuador and reveal hidden agricultural patterns in Canada. MapBiomas and Global Ecosystems Atlas already using it for conservation work.
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Google just took a big step towards building ChatGPT for Earth. AlphaEarth Foundations does something clever -- instead of drowning in petabytes of Earth observation data, it creates compact summaries of every 10x10m square on Earth by fusing optical, radar, LiDAR, and climate data. The kicker is it can see through clouds in Ecuador and reveal hidden agricultural patterns in Canada. MapBiomas and Global Ecosystems Atlas already using it for conservation work.

Bilawal Sidhu

165,867 次观看 • 1 年前

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.
0:11

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.

Yohan

45,187 次观看 • 5 个月前

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.
0:14

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

Sundar Pichai

864,602 次观看 • 1 年前

Wait for it! Solid shift eastward - out to sea - on many of our models and even the new NHC forecast. These are the Google AI ensembles. What’s happening here - partly - is #Humberto is now forecast to be a Cat 5 monster #Hurricane and is exerting an even bigger influence on #Imelda, helping to pull it away like a magnet. Also the upper low in SE is now forecast to be a bit weaker exerting less influence. This is not set in stone, and it may shift back, but it’s a good sign. It’s worth noting the Google AI has been pretty consistent for days with most members showing a stall, then turn east out to sea. Fingers crossed!
0:11

Wait for it! Solid shift eastward - out to sea - on many of our models and even the new NHC forecast. These are the Google AI ensembles. What’s happening here - partly - is #Humberto is now forecast to be a Cat 5 monster #Hurricane and is exerting an even bigger influence on #Imelda, helping to pull it away like a magnet. Also the upper low in SE is now forecast to be a bit weaker exerting less influence. This is not set in stone, and it may shift back, but it’s a good sign. It’s worth noting the Google AI has been pretty consistent for days with most members showing a stall, then turn east out to sea. Fingers crossed!

Jeff Berardelli

85,444 次观看 • 11 个月前

Silencio has surpassed 1 million on-chain devices on peaq This is the live count of real contributors using their smartphones to capture and stream the physical world into digital infrastructure. Each device is opt-in, privacy-preserving, and individually rewarded in $SLC. Together, these contributors have recorded more than 10.6 million hours of real-world acoustic signal. The network spans 190 countries and has generated over 38.8 Billion environmental datapoints. All of it is live. All of it is on-chain. Silencio represents a deployed DePIN system operating at global scale. Its data is already integrated into major enterprise marketplaces. Its token economy is active, deflationary, and fully aligned with participation. This milestone is not just about numbers. It is about trust, coordination, and measurable impact. It proves that decentralized networks can scale without compromising on quality, compliance, or utility. Built on peaq. Secured by $SLC and the Blocksound Foundation. Powered by people. This is not the future of data infrastructure. It is already in motion.
0:16

Silencio has surpassed 1 million on-chain devices on peaq This is the live count of real contributors using their smartphones to capture and stream the physical world into digital infrastructure. Each device is opt-in, privacy-preserving, and individually rewarded in $SLC. Together, these contributors have recorded more than 10.6 million hours of real-world acoustic signal. The network spans 190 countries and has generated over 38.8 Billion environmental datapoints. All of it is live. All of it is on-chain. Silencio represents a deployed DePIN system operating at global scale. Its data is already integrated into major enterprise marketplaces. Its token economy is active, deflationary, and fully aligned with participation. This milestone is not just about numbers. It is about trust, coordination, and measurable impact. It proves that decentralized networks can scale without compromising on quality, compliance, or utility. Built on peaq. Secured by $SLC and the Blocksound Foundation. Powered by people. This is not the future of data infrastructure. It is already in motion.

Silencio | Voice Data for AI

22,863 次观看 • 1 年前

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.
0:19

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,454 次观看 • 9 个月前

The companies racing Elon Musk to build AI are paying him more than 2 billion dollars a month to do it. Anthropic pays SpaceX 1.25 billion dollars a month. Google pays 920 million. They are not buying rockets. They are renting compute, the scarce Nvidia chips that train frontier models, from a data center in Memphis called Colossus that Musk's rocket company now owns. SpaceX folded xAI into itself in February, and with it the 220,000 chips built to train Grok. Then it rented them to Grok's rivals. Anthropic took the entire first building. Google leased 110,000 more. The contracts signed so far run past 80 billion dollars. SpaceX is no longer a rocket company that dabbles in AI. It is one of the largest AI compute landlords on Earth, and its biggest tenants are the rivals it is trying to beat. Musk is not choosing between building AI on the ground and building it in space. He is using one to fund the other. The 80 billion in ground contracts is the cash engine. Starmind, the million-satellite constellation built to leave the ground behind, is what the cash builds. So the rivals are financing his exit from the planet. Every dollar Anthropic and Google pay for compute in Memphis helps fund the orbital network designed to strand them on the surface. They are paying the toll on the road they are trying to win, and the toll is building Musk a road no one else can reach. The piece works out who is really renting from whom.
0:10

The companies racing Elon Musk to build AI are paying him more than 2 billion dollars a month to do it. Anthropic pays SpaceX 1.25 billion dollars a month. Google pays 920 million. They are not buying rockets. They are renting compute, the scarce Nvidia chips that train frontier models, from a data center in Memphis called Colossus that Musk's rocket company now owns. SpaceX folded xAI into itself in February, and with it the 220,000 chips built to train Grok. Then it rented them to Grok's rivals. Anthropic took the entire first building. Google leased 110,000 more. The contracts signed so far run past 80 billion dollars. SpaceX is no longer a rocket company that dabbles in AI. It is one of the largest AI compute landlords on Earth, and its biggest tenants are the rivals it is trying to beat. Musk is not choosing between building AI on the ground and building it in space. He is using one to fund the other. The 80 billion in ground contracts is the cash engine. Starmind, the million-satellite constellation built to leave the ground behind, is what the cash builds. So the rivals are financing his exit from the planet. Every dollar Anthropic and Google pay for compute in Memphis helps fund the orbital network designed to strand them on the surface. They are paying the toll on the road they are trying to win, and the toll is building Musk a road no one else can reach. The piece works out who is really renting from whom.

Shanaka Anslem Perera ⚡

97,906 次观看 • 2 个月前

Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ
0:16

Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ

SandboxAQ

129,416 次观看 • 1 年前

💰$3.5 billion Bill Gates-funded Breakthrough Energy Ventures (BEV, 2015) is a climate-tech platform covering policy and research projects. Its contrails team, builds forecasting models and helps define criteria for a ‘warming contrail.’ Contrails org is a science-led nonprofit focused on ➡️policy⬅️, research, open tools, and adoption by airlines and air-traffic systems. It produces models, materials, and publications on ‘aviation-induced cloudiness’ and ‘persistent contrails,’ and ➡️publicizes information⬅️ on formation and altitude adjustments used in current applied work. It partners with the United Kingdom on Operation Blue Skies, contributing forecast assessment, trial design, and data analysis for testing flight-level changes over the North Atlantic. Marc Shapiro, who leads the work, reviewed the National Academies research agenda requested by NASA on the same topic. Contrails org collaborates with Google Research on AI models that use satellite imagery and weather data to predict contrail-forming regions. Separately, Google/Alphabet entities (via Verily’s Debug project) have run and sought EPA permits for large-scale releases of Wolbachia-infected male mosquitoes in California and Florida. ~Babooschka
0:13

💰$3.5 billion Bill Gates-funded Breakthrough Energy Ventures (BEV, 2015) is a climate-tech platform covering policy and research projects. Its contrails team, builds forecasting models and helps define criteria for a ‘warming contrail.’ Contrails org is a science-led nonprofit focused on ➡️policy⬅️, research, open tools, and adoption by airlines and air-traffic systems. It produces models, materials, and publications on ‘aviation-induced cloudiness’ and ‘persistent contrails,’ and ➡️publicizes information⬅️ on formation and altitude adjustments used in current applied work. It partners with the United Kingdom on Operation Blue Skies, contributing forecast assessment, trial design, and data analysis for testing flight-level changes over the North Atlantic. Marc Shapiro, who leads the work, reviewed the National Academies research agenda requested by NASA on the same topic. Contrails org collaborates with Google Research on AI models that use satellite imagery and weather data to predict contrail-forming regions. Separately, Google/Alphabet entities (via Verily’s Debug project) have run and sought EPA permits for large-scale releases of Wolbachia-infected male mosquitoes in California and Florida. ~Babooschka

Reclaim Our Skies

13,343 次观看 • 6 天前

AI will kill Polymarket. $2.2M in 2 months using probability models. This news is going to blow up the internet. Polymarket trader made $2.2M in just 2 months using AI. His account is traded entirely by a bot. I’ve heard plenty of stories about AI trading bots before, and almost all of them turned out to be scams or didn’t work properly. But this case is different and honestly I’m shocked. He uses AI probability models, training machine learning to estimate real odds based on news and social media data. If his model says an outcome has a 60% chance, while the market prices it at 50% (50¢), he buys because the market is mispricing it. According to his profile: > His prediction accuracy is 74%. That’s insane. He runs an ensemble of 10 AI models that retrain themselves every week to stay up to date. What do you think about this? This feels like the new reality.
0:15

AI will kill Polymarket. $2.2M in 2 months using probability models. This news is going to blow up the internet. Polymarket trader made $2.2M in just 2 months using AI. His account is traded entirely by a bot. I’ve heard plenty of stories about AI trading bots before, and almost all of them turned out to be scams or didn’t work properly. But this case is different and honestly I’m shocked. He uses AI probability models, training machine learning to estimate real odds based on news and social media data. If his model says an outcome has a 60% chance, while the market prices it at 50% (50¢), he buys because the market is mispricing it. According to his profile: > His prediction accuracy is 74%. That’s insane. He runs an ensemble of 10 AI models that retrain themselves every week to stay up to date. What do you think about this? This feels like the new reality.

igorizuchaetcrypty

509,887 次观看 • 8 个月前

First ever color Kindle Scribe is here and think customers are going to really like it. We completely redesigned it from the ground up, and it’s now ultra-thin, writing and sketching feels like it’s happening on paper, and moving between pages is almost instantaneous. The AI we’ve baked in (featuring our Nova models) is especially useful, helping people find their notes, create note summaries, and ask questions of their notes. Think this is gonna change how folks work.
0:15

First ever color Kindle Scribe is here and think customers are going to really like it. We completely redesigned it from the ground up, and it’s now ultra-thin, writing and sketching feels like it’s happening on paper, and moving between pages is almost instantaneous. The AI we’ve baked in (featuring our Nova models) is especially useful, helping people find their notes, create note summaries, and ask questions of their notes. Think this is gonna change how folks work.

Andy Jassy

66,261 次观看 • 8 个月前

"CAN I EXCHANGE MY FULLY PAID FLAT FOR YOUR RURAL LAND?" A white guy in Manchester, England had a frank discussion with an African immigrant about economic development and empowerment in Africa. The English guy asked my fellow African: 'Do you by any chance own land in Africa?' The brother said yes he owns about 10 hectares in his rural village. "So, why come all the way to England when you have the means of production that can make you very rich?' The African brother started giving excuses: the land is undeveloped; the land has no borehole; that he don't have agricultural equipment to till the land; the government is not supporting rural farmers and many other excuses. The Englishman stood there listening quietly. After 20 minutes of giving all sorts of excuses, my African brother looked up at the quiet Englishman, hoping he understood why he travelled thousands of kilometres to come to England to work in menial jobs! The Englishman ask him "Can I exchange my fully paid flat and give you the title deeds and my 2 cars for your 10 hectares of land in Africa?" The African brother was stunned. He did not understand why a white man with a fully furnished flat and everything, would want to go and farm on a rural undeveloped land. The white guy sat him down and explained: ask any white man, he would gladly develop that land into a highly successful farm. He would grow high value crops eg all types of vegetables, strawberries, start cattle fattening programmes and develop a highly successful farm. The Englishman told him all the wealth is in that piece of land. He can make millions of dollars from that fertile land. The African brother spent days pondering about what his white friend said. After several months of planning and selling all he had in England, my brother came back home and started farming on his 10 hectares of land: putting up a borehole, installing solar energy, started poultry, piggery and cattle fattening and a horticulture project. We should never under-value our rural farms and travel to go work in distant lands. Our future is in developing our rural land and turn it into highly productive farms. Here is Wenceslous Nyamupfukudza, a youth farmer, consultant and agribusiness expert helping all farmers including those developing in rural communities. He developed his rural farm and grows all types of vegetables and keeps cattle. He teaches other rural farmers how to effectively and efficiently use the land for maximum agricultural productivity. We need to change our mindset about the land in our rural areas! It is our wealth!!! Knowledge is potential power, understand and grow your specialised knowledge - Think and Grow rich!
1:08

"CAN I EXCHANGE MY FULLY PAID FLAT FOR YOUR RURAL LAND?" A white guy in Manchester, England had a frank discussion with an African immigrant about economic development and empowerment in Africa. The English guy asked my fellow African: 'Do you by any chance own land in Africa?' The brother said yes he owns about 10 hectares in his rural village. "So, why come all the way to England when you have the means of production that can make you very rich?' The African brother started giving excuses: the land is undeveloped; the land has no borehole; that he don't have agricultural equipment to till the land; the government is not supporting rural farmers and many other excuses. The Englishman stood there listening quietly. After 20 minutes of giving all sorts of excuses, my African brother looked up at the quiet Englishman, hoping he understood why he travelled thousands of kilometres to come to England to work in menial jobs! The Englishman ask him "Can I exchange my fully paid flat and give you the title deeds and my 2 cars for your 10 hectares of land in Africa?" The African brother was stunned. He did not understand why a white man with a fully furnished flat and everything, would want to go and farm on a rural undeveloped land. The white guy sat him down and explained: ask any white man, he would gladly develop that land into a highly successful farm. He would grow high value crops eg all types of vegetables, strawberries, start cattle fattening programmes and develop a highly successful farm. The Englishman told him all the wealth is in that piece of land. He can make millions of dollars from that fertile land. The African brother spent days pondering about what his white friend said. After several months of planning and selling all he had in England, my brother came back home and started farming on his 10 hectares of land: putting up a borehole, installing solar energy, started poultry, piggery and cattle fattening and a horticulture project. We should never under-value our rural farms and travel to go work in distant lands. Our future is in developing our rural land and turn it into highly productive farms. Here is Wenceslous Nyamupfukudza, a youth farmer, consultant and agribusiness expert helping all farmers including those developing in rural communities. He developed his rural farm and grows all types of vegetables and keeps cattle. He teaches other rural farmers how to effectively and efficiently use the land for maximum agricultural productivity. We need to change our mindset about the land in our rural areas! It is our wealth!!! Knowledge is potential power, understand and grow your specialised knowledge - Think and Grow rich!

Wenceslous Nyamupfukudza

71,295 次观看 • 2 年前

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.
0:15

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

280,790 次观看 • 3 个月前

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
0:16

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 次观看 • 1 年前

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
0:10

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

54,776 次观看 • 8 个月前

The "Neoclouds are too scared of Jensen to offer TPUs!" tweet reminded me of this quote from the Odd Lots episode with Hudson River Trading's Head of AI: "If you're using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock in.. It's a complicated thing if you go down that path. But if you're compute hungry like the big labs, you'll take what you can get. I think Anthropic takes TPUs, Trainium, and GPUs, they need them all." You can also just look at the hoops and hurdles Google is going through to find external buyers of TPUs: - Invested $10B+ in Anthropic (and committed to invest $40B+) - Back-stopped Anthropic's data center leases so that Anthropic's data center developers can obtain financing to house the TPU's Anthropic is buying from Google - Created a new neocloud via a JV with Blackstone to offer TPUs (e.g. buy them from Google to rent) CoreWeave and Nebius are already sold out on the GPUs for which they have data center capacity to bring online.. Both want to diversify their customer bases away from hyperscalers and large AI labs. Doesn't seem to make any sense for them to offer TPUs, when Anthropic is the only company actually buying TPUs from Google, and only after being heavily incentivized to do so. $GOOG $CRWV $NBIS
0:18

The "Neoclouds are too scared of Jensen to offer TPUs!" tweet reminded me of this quote from the Odd Lots episode with Hudson River Trading's Head of AI: "If you're using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock in.. It's a complicated thing if you go down that path. But if you're compute hungry like the big labs, you'll take what you can get. I think Anthropic takes TPUs, Trainium, and GPUs, they need them all." You can also just look at the hoops and hurdles Google is going through to find external buyers of TPUs: - Invested $10B+ in Anthropic (and committed to invest $40B+) - Back-stopped Anthropic's data center leases so that Anthropic's data center developers can obtain financing to house the TPU's Anthropic is buying from Google - Created a new neocloud via a JV with Blackstone to offer TPUs (e.g. buy them from Google to rent) CoreWeave and Nebius are already sold out on the GPUs for which they have data center capacity to bring online.. Both want to diversify their customer bases away from hyperscalers and large AI labs. Doesn't seem to make any sense for them to offer TPUs, when Anthropic is the only company actually buying TPUs from Google, and only after being heavily incentivized to do so. $GOOG $CRWV $NBIS

Rittenhouse Research

13,919 次观看 • 2 个月前

this AI Agent finds stressed crops on $500k-2M/year farms, on autopilot. here's how farmers can use it to save entire harvests: - watches every Sentinel-2 satellite pass in real time - spots which field zones are struggling before anyone sets foot outside - pulls real imagery straight from ESA's satellites, no site visits required - maps every field zone with exact actions - irrigate, fertilize, monitor - calculates water deficit + expected yield impact - drops a personalized PDF report in the farmer's inbox by 6am - every step from satellite to inbox is automated. a real system running on real satellite data for real farms. reply "FARM" and I'll send you the full guide so you can build it too
0:13

this AI Agent finds stressed crops on $500k-2M/year farms, on autopilot. here's how farmers can use it to save entire harvests: - watches every Sentinel-2 satellite pass in real time - spots which field zones are struggling before anyone sets foot outside - pulls real imagery straight from ESA's satellites, no site visits required - maps every field zone with exact actions - irrigate, fertilize, monitor - calculates water deficit + expected yield impact - drops a personalized PDF report in the farmer's inbox by 6am - every step from satellite to inbox is automated. a real system running on real satellite data for real farms. reply "FARM" and I'll send you the full guide so you can build it too

Chris

109,580 次观看 • 4 个月前

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.
0:11

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Melvin

180,198 次观看 • 1 个月前

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.
0:10

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,244 次观看 • 2 个月前

FX1 is proud to announce our latest strategic partnership with Muhdo, the pioneers of DNA and epigenetics-based performance tracking, to redefine how combat sports athletes optimize their training, recovery, and career longevity. At the heart of this partnership is MuhdoFightHub, an innovative platform that empowers fighters with real-time insights into their bodies, using genetic, biometric, and lifestyle data to enhance performance, reduce injury risk, and maximize potential. By integrating FX1’s AI-powered combat sports intelligence into Muhdo’s Fight Hub, we are building the ultimate fighter performance ecosystem—an all-in-one app that merges AI-driven fight analytics with cutting-edge health science. Beyond empowering athletes, Muhdo will also enhance FX1’s fighter profiles by providing athlete lifestyle data, adding a new and innovative dimension to FX1’s sports intelligence ecosystem. This level of deep, personalized data does not exist in mainstream sports data platforms, giving FX1 an exclusive advantage in how fight analytics are captured, analyzed, and commercialized. Together, FX1 and MuhdoFightHub are pioneering the future of combat sports intelligence, giving fighters the tools they need to compete at the highest level, backed by science and AI-driven insights. We’re excited to bring this game-changing innovation to the world of combat sports—this is just the beginning!
0:14

FX1 is proud to announce our latest strategic partnership with Muhdo, the pioneers of DNA and epigenetics-based performance tracking, to redefine how combat sports athletes optimize their training, recovery, and career longevity. At the heart of this partnership is MuhdoFightHub, an innovative platform that empowers fighters with real-time insights into their bodies, using genetic, biometric, and lifestyle data to enhance performance, reduce injury risk, and maximize potential. By integrating FX1’s AI-powered combat sports intelligence into Muhdo’s Fight Hub, we are building the ultimate fighter performance ecosystem—an all-in-one app that merges AI-driven fight analytics with cutting-edge health science. Beyond empowering athletes, Muhdo will also enhance FX1’s fighter profiles by providing athlete lifestyle data, adding a new and innovative dimension to FX1’s sports intelligence ecosystem. This level of deep, personalized data does not exist in mainstream sports data platforms, giving FX1 an exclusive advantage in how fight analytics are captured, analyzed, and commercialized. Together, FX1 and MuhdoFightHub are pioneering the future of combat sports intelligence, giving fighters the tools they need to compete at the highest level, backed by science and AI-driven insights. We’re excited to bring this game-changing innovation to the world of combat sports—this is just the beginning!

FX1

15,838 次观看 • 1 年前