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Every Pokémon Go player unknowingly trained future robots. Over the years, players photographed 30 billion images from cities around the world. And now that data is teaching delivery robots to navigate streets. Niantic (company behind Pokémon Go), spun out an AI company called Niantic Spatial that's turning years of...

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Rowan Cheung

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POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS. Source: NewsForce
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POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS. Source: NewsForce

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They just pulled off the most successful trick of the last decade. And you were probably fooled. 500 million people downloaded Pokemon Go and never knew the company was collecting 30 billion images of every space they walked through. That data was just sold off to train delivery robots. Meta has been using footage from their Ray-Ban smart glasses to train AI. That footage included people on the toilet, couples in bed, and bank card details accidentally filmed. They are now being sued in federal court. DoorDash just launched an app that pays 8 million dashers pennies to film themselves doing chores in their own homes. The footage trains the robots being built to replace them. The future they are building is one we are paying for. With our jobs. With our homes. With our most private moments.
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They just pulled off the most successful trick of the last decade. And you were probably fooled. 500 million people downloaded Pokemon Go and never knew the company was collecting 30 billion images of every space they walked through. That data was just sold off to train delivery robots. Meta has been using footage from their Ray-Ban smart glasses to train AI. That footage included people on the toilet, couples in bed, and bank card details accidentally filmed. They are now being sued in federal court. DoorDash just launched an app that pays 8 million dashers pennies to film themselves doing chores in their own homes. The footage trains the robots being built to replace them. The future they are building is one we are paying for. With our jobs. With our homes. With our most private moments.

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The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.

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Robots that climb skyscrapers to clean and inspect them! 🧼 Verobotics has built autonomous robots that clean and scan building exteriors, allowing building owners to proactively maintain and upkeep their buildings faster, cheaper, and smarter. The problem is decades old. The only way to clean or inspect a building's exterior has been to lower a person from a BMU (Building Maintenance Unit) at the top of the building. BMUs are heavy, can be out of service, complicated to operate, and limit cleaning to a couple of times per year. Their robots are lightweight, portable, and need no equipment on the roof. One person can deploy multiple robots to the building's facades and let the robots do the rest. Once they're done, the building owner not only has a clean building but also a digital twin of the building exterior for documenting health and identifying issues before they become risks. Robot spots a crack? Noted! They're equipped with onboard cameras that scan the surface, detect window frames to crawl over, and ensure not a spot is missed. That means a consistent result and thorough inspection every time. Onboard sensors continuously detect motion, location, and the surface beneath and around it. The data feeds to a hub that analyzes the surface in real time, building datasets that become the backbone of a digital twin for predictive maintenance. Love seeing the specialized robots bringing value from day 0! :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Fireside Alpha

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Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics
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MTS

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Teslaconomics

42,563 Aufrufe • vor 7 Monaten

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.
1:01

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

44,534 Aufrufe • vor 2 Monaten

"The day Pokémon Go came out, I always say it's the closest we've come to world peace." Cyan Banister explains why she invested in Niantic, the company that created Pokémon GO, before it became one of the biggest mobile games in history. Cyan's conviction came from watching people play Niantic’s first game: Ingress. "My friends were playing it. I started noticing other people playing it, but it was a fringe game that not a lot of people were playing." "But the behaviors they were displaying—they were renting helicopters. They were chartering boats. They were ditching their friends at DEF CON in Las Vegas to go out into the desert to claim these invisible things that didn't exist other than in this app." "Then I found out about Pokémon, and I was like, 'Oh my gosh, if you put Pokemon in this world? That's insane. It's going to blow up.'" "Trying to convince people after I invested to co-invest with me was impossible. They were like, 'No one's going to hold their phone up and look at invisible Pokémon.'" "Sure enough, I don't know if you were there the day that Pokémon Go came out. I always say it's the closest we've come to world peace." "It was one of the most magical few weeks of my life, and probably many people's lives when that game came out."
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"The day Pokémon Go came out, I always say it's the closest we've come to world peace." Cyan Banister explains why she invested in Niantic, the company that created Pokémon GO, before it became one of the biggest mobile games in history. Cyan's conviction came from watching people play Niantic’s first game: Ingress. "My friends were playing it. I started noticing other people playing it, but it was a fringe game that not a lot of people were playing." "But the behaviors they were displaying—they were renting helicopters. They were chartering boats. They were ditching their friends at DEF CON in Las Vegas to go out into the desert to claim these invisible things that didn't exist other than in this app." "Then I found out about Pokémon, and I was like, 'Oh my gosh, if you put Pokemon in this world? That's insane. It's going to blow up.'" "Trying to convince people after I invested to co-invest with me was impossible. They were like, 'No one's going to hold their phone up and look at invisible Pokémon.'" "Sure enough, I don't know if you were there the day that Pokémon Go came out. I always say it's the closest we've come to world peace." "It was one of the most magical few weeks of my life, and probably many people's lives when that game came out."

sourcery

14,837 Aufrufe • vor 2 Monaten

One of the things I’m most excited about in our recently announced partnership with Niantic Spatial 🌎, is how clearly it shows what becomes possible when world-class reconstruction technology is paired with a new kind of imagery infrastructure. At a high level: Spexi drone pilots capture imagery, and Niantic Spatial turns it into incredible city-scale reconstructions. But the real unlock is the infrastructure behind that capture. At Spexi, we’ve built what we believe is the world’s first fully standardized drone imagery infrastructure called LayerDrone. Anyone with a compatible drone and the right credentials can contribute. No building flight plans. No estimating overlap. No adjusting camera settings in the field. Pilots simply get within visual line of sight of a Spexigon, open the Spexi app, press “Fly,” and the drone autonomously captures the 25-acre area to our standard. That standardization means imagery can be collected consistently, affordably, and repeatedly across cities, one Spexigon at a time (we have now captured over 225,000 of them). That is what makes living digital twins possible, dynamic representations of the physical world that can be updated as the world changes. Niantic Spatial’s city-scale Gaussian splats show what becomes possible when the right pixels go into the system. As physical AI advances, those pixels matter even more. Robots, drones, vehicles, maps, and spatial intelligence systems will all need current, high-resolution data about the real world. And as you can see below.. the results are not just beautiful, but real, measurable reconstructions of the physical world, one Spexigon at a time!
1:54

One of the things I’m most excited about in our recently announced partnership with Niantic Spatial 🌎, is how clearly it shows what becomes possible when world-class reconstruction technology is paired with a new kind of imagery infrastructure. At a high level: Spexi drone pilots capture imagery, and Niantic Spatial turns it into incredible city-scale reconstructions. But the real unlock is the infrastructure behind that capture. At Spexi, we’ve built what we believe is the world’s first fully standardized drone imagery infrastructure called LayerDrone. Anyone with a compatible drone and the right credentials can contribute. No building flight plans. No estimating overlap. No adjusting camera settings in the field. Pilots simply get within visual line of sight of a Spexigon, open the Spexi app, press “Fly,” and the drone autonomously captures the 25-acre area to our standard. That standardization means imagery can be collected consistently, affordably, and repeatedly across cities, one Spexigon at a time (we have now captured over 225,000 of them). That is what makes living digital twins possible, dynamic representations of the physical world that can be updated as the world changes. Niantic Spatial’s city-scale Gaussian splats show what becomes possible when the right pixels go into the system. As physical AI advances, those pixels matter even more. Robots, drones, vehicles, maps, and spatial intelligence systems will all need current, high-resolution data about the real world. And as you can see below.. the results are not just beautiful, but real, measurable reconstructions of the physical world, one Spexigon at a time!

Alec Wilson

10,695 Aufrufe • vor 3 Monaten

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:
0:49

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

273,043 Aufrufe • vor 26 Tagen

Spirit Airlines stopped flying in May. Second bankruptcy in two years. Yet Google still wants to pay $10Million for the dead body. The planes are gone, the airport slots are sold. One asset left. At the bankruptcy auction, Google opened at 5 million dollars. An AI data company called Mercor countered at 7 and a half million. Then Google closed it at 10 Million. But the bids weren't for the planes or Airport slots. They were for 100 million internal emails. 500 million Teams messages. 30 million lines of code. Employee records that go back to 1986. And no, its not passenger data. This is purely internal: decades of how a real business thought, argued, and made decisions. Why pay that much for a dead company's inbox? Because it's the one thing AI can't fake. The most valuable data in the world right now is just real people thinking out loud… real decisions, real mistakes, real cause and effect. Which is why after the auction closed, another AI company came in with a 12 and a half million offer for it. Here's how you can leverage this kind of data for your business without a bankruptcy auction. People type their real, unfiltered questions into a search bar every single day… for free. And for business owners, those raw questions are a free roadmap: they tell you exactly what to create, what to fix on your site, and what to sell next. Tools like AnswerThePublic mine and present that exact data by looking at the different ways people search for a product. The AI data gold rush is just getting started, follow to not miss out!
1:31

Spirit Airlines stopped flying in May. Second bankruptcy in two years. Yet Google still wants to pay $10Million for the dead body. The planes are gone, the airport slots are sold. One asset left. At the bankruptcy auction, Google opened at 5 million dollars. An AI data company called Mercor countered at 7 and a half million. Then Google closed it at 10 Million. But the bids weren't for the planes or Airport slots. They were for 100 million internal emails. 500 million Teams messages. 30 million lines of code. Employee records that go back to 1986. And no, its not passenger data. This is purely internal: decades of how a real business thought, argued, and made decisions. Why pay that much for a dead company's inbox? Because it's the one thing AI can't fake. The most valuable data in the world right now is just real people thinking out loud… real decisions, real mistakes, real cause and effect. Which is why after the auction closed, another AI company came in with a 12 and a half million offer for it. Here's how you can leverage this kind of data for your business without a bankruptcy auction. People type their real, unfiltered questions into a search bar every single day… for free. And for business owners, those raw questions are a free roadmap: they tell you exactly what to create, what to fix on your site, and what to sell next. Tools like AnswerThePublic mine and present that exact data by looking at the different ways people search for a product. The AI data gold rush is just getting started, follow to not miss out!

Neil Patel

12,729 Aufrufe • vor 21 Tagen

Humanoid robots are starting to build humanoid robots. UBTECH just put a 14,000㎡ humanoid robot factory into operation in Liuzhou, China, with a designed takt time of one humanoid every 10 minutes and planned annual capacity in the tens of thousands. What’s interesting is that humanoid robots are already part of the production process. Cruzr Y1 and Y2 handle depalletizing, palletizing, material feeding and transport, using 3D vision to adapt to shifted boxes and changing pallet patterns. On the assembly side, Walker S2 and Cruzr are produced on the same line alongside cobots, autonomous logistics vehicles and assistive manipulators. The factory also has a 65㎡ automated warehouse capable of storing 112 humanoids, with AGVs, industrial robots and stacker cranes coordinated as one system. Before production, the factory was modeled 1:1 in Siemens Plant Simulation, covering workstations, racks and AGVs. Each robot gets its own SN, with components, batches, assembly parameters and inspection data tracked all the way to delivery. Humanoid robots are now entering automated manufacturing at the 10,000-unit scale. The next milestone may be when they can take part in enough of the assembly, testing and production process to truly build robots themselves.
1:02

Humanoid robots are starting to build humanoid robots. UBTECH just put a 14,000㎡ humanoid robot factory into operation in Liuzhou, China, with a designed takt time of one humanoid every 10 minutes and planned annual capacity in the tens of thousands. What’s interesting is that humanoid robots are already part of the production process. Cruzr Y1 and Y2 handle depalletizing, palletizing, material feeding and transport, using 3D vision to adapt to shifted boxes and changing pallet patterns. On the assembly side, Walker S2 and Cruzr are produced on the same line alongside cobots, autonomous logistics vehicles and assistive manipulators. The factory also has a 65㎡ automated warehouse capable of storing 112 humanoids, with AGVs, industrial robots and stacker cranes coordinated as one system. Before production, the factory was modeled 1:1 in Siemens Plant Simulation, covering workstations, racks and AGVs. Each robot gets its own SN, with components, batches, assembly parameters and inspection data tracked all the way to delivery. Humanoid robots are now entering automated manufacturing at the 10,000-unit scale. The next milestone may be when they can take part in enough of the assembly, testing and production process to truly build robots themselves.

CyberRobo

17,883 Aufrufe • vor 15 Tagen

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA
1:21

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

72,869 Aufrufe • vor 2 Monaten

Elon just confirmed how long every working human has left before the robots take over. "AI will probably increase the global economy by 20 to 30%. That's my rough estimate. Meaning, on the order of 20 to 30 trillion per year." So that's a second United States economy appearing out of thin air, EVERY single year. And Elon's whole point is that the machines produce it, not the people. He even gave us the exact date for the digital half: "AI will be able to do anything digital, anything that does not require the shaping of atoms by hand, by the end of next year." By the end of next year... And on software specifically: "It's going to be impossible for a human to compete in writing software with AI." So every coder, every analyst, every job that lives on a screen is on a 12 to 18 month timer, straight from the richest man alive. But that's only the part that runs on electricity. And this is where it gets crazy... "There will be at least a billion robots in 10 years, and each will produce at least five times the output of a human." "The billion humanoid robots will be more productive than all humans combined." A billion machines, out-working ALL 8 billion of us. And he put a deadline on it: 10 years. And he even called that estimate small. So the version where robots out-produce the entire human race is the number he treats as safe. He said he'd bet serious money on it. But how does a billion of anything get built that fast? Robots building robots building robots. It starts slow and then it goes vertical. That’s how a few thousand becomes a billion. Elon also explained his own formula for how useful a robot actually is. He said it's the quality of the AI software, times the quality of the AI chip, times the dexterity of the hands. Software and chips are the exact things Elon and Nvidia have spent a decade making exponential. The third one is the hand. And the hand is the thing robotics has been stuck on for 40 years. A machine can crush the best human alive at chess and still can't pick a strange object off a messy table the way a toddler can. Grip, pressure, touch, knowing how hard to squeeze. That problem has barely moved while everything digital went vertical. The thing he named as the hard part is the exact thing his whole 10 year number depends on. So it all comes down to one variable, and it's the slowest-moving one in the entire machine: The robot hand. The brain is basically solved. The fingers have to catch up to it this decade, at planetary scale. Now to be fair, this is also where the most money on Earth is pointed right now. Tesla and a dozen others are throwing everything at exactly this problem. Elon's software calls tend to land but his atoms calls tend to fail. Because anything physical always takes longer than anything digital. He's also made this exact robot call before with wildly different numbers, 10 billion of them by 2040 in one speech, five per human in another. So here's the real question: Do you believe robot hands get solved inside 10 years? Because that one bet is the core of Elon‘s prediction. Everything else already came true.
1:39

Elon just confirmed how long every working human has left before the robots take over. "AI will probably increase the global economy by 20 to 30%. That's my rough estimate. Meaning, on the order of 20 to 30 trillion per year." So that's a second United States economy appearing out of thin air, EVERY single year. And Elon's whole point is that the machines produce it, not the people. He even gave us the exact date for the digital half: "AI will be able to do anything digital, anything that does not require the shaping of atoms by hand, by the end of next year." By the end of next year... And on software specifically: "It's going to be impossible for a human to compete in writing software with AI." So every coder, every analyst, every job that lives on a screen is on a 12 to 18 month timer, straight from the richest man alive. But that's only the part that runs on electricity. And this is where it gets crazy... "There will be at least a billion robots in 10 years, and each will produce at least five times the output of a human." "The billion humanoid robots will be more productive than all humans combined." A billion machines, out-working ALL 8 billion of us. And he put a deadline on it: 10 years. And he even called that estimate small. So the version where robots out-produce the entire human race is the number he treats as safe. He said he'd bet serious money on it. But how does a billion of anything get built that fast? Robots building robots building robots. It starts slow and then it goes vertical. That’s how a few thousand becomes a billion. Elon also explained his own formula for how useful a robot actually is. He said it's the quality of the AI software, times the quality of the AI chip, times the dexterity of the hands. Software and chips are the exact things Elon and Nvidia have spent a decade making exponential. The third one is the hand. And the hand is the thing robotics has been stuck on for 40 years. A machine can crush the best human alive at chess and still can't pick a strange object off a messy table the way a toddler can. Grip, pressure, touch, knowing how hard to squeeze. That problem has barely moved while everything digital went vertical. The thing he named as the hard part is the exact thing his whole 10 year number depends on. So it all comes down to one variable, and it's the slowest-moving one in the entire machine: The robot hand. The brain is basically solved. The fingers have to catch up to it this decade, at planetary scale. Now to be fair, this is also where the most money on Earth is pointed right now. Tesla and a dozen others are throwing everything at exactly this problem. Elon's software calls tend to land but his atoms calls tend to fail. Because anything physical always takes longer than anything digital. He's also made this exact robot call before with wildly different numbers, 10 billion of them by 2040 in one speech, five per human in another. So here's the real question: Do you believe robot hands get solved inside 10 years? Because that one bet is the core of Elon‘s prediction. Everything else already came true.

Ricardo

449,882 Aufrufe • vor 26 Tagen

Erik Voorhees on the crypto UX realisation that took 15 years "Crypto has always had this UX challenge with humans. Every time you go to a crypto conference, whether it was back in 2011 or tomorrow, people talk about how do you make this stuff more usable to people" "It seems like perhaps it's just much more usable for robots and the machines and the AI, and that crypto is actually built for the machines and we didn't know it. The machines are now going to start running on the stuff and utilizing it" "They have no trouble with public key pairs, that's easy for them" "I don't see a future where the robots are using bank accounts at Wells Fargo. I do see a future where they're using crypto assets on decentralized rails"
0:48

Erik Voorhees on the crypto UX realisation that took 15 years "Crypto has always had this UX challenge with humans. Every time you go to a crypto conference, whether it was back in 2011 or tomorrow, people talk about how do you make this stuff more usable to people" "It seems like perhaps it's just much more usable for robots and the machines and the AI, and that crypto is actually built for the machines and we didn't know it. The machines are now going to start running on the stuff and utilizing it" "They have no trouble with public key pairs, that's easy for them" "I don't see a future where the robots are using bank accounts at Wells Fargo. I do see a future where they're using crypto assets on decentralized rails"

Laura Shin

49,876 Aufrufe • vor 1 Monat