Loading video...

Video Failed to Load

Go Home

Most AI is still learning from the same recycled internet data and that is the real bottleneck no one likes to talk about. The internet is running out of high quality human signal, what remains now is noisy, repetitive, and heavily biased toward the same regions and behaviors. The...

TheOnlyHero11's profile picture

๐“๐ก๐ž๐Ž๐ง๐ฅ๐ฒ๐Ÿ๐‡๐ž๐ซ๐จ๐Ÿ‘‘

1,487 subscribers

11,150 views โ€ข 3 days ago โ€ขvia X (Twitter)

LIVE

Anya Rossiโ€ข Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

AI is only as good as its data. Everyone talks about building smarter AI models, but fewer people ask where the training data comes from. Thatโ€™s what makes Humyn Labs interesting. Theyโ€™re focused on collecting high-quality, real-world human data across different environments, behaviors, and sensory inputs. Because if AI is going to understand the physical world, it first needs to learn from the people who already live and work in it. I shared my thoughts in this short video. Let me know what stood out to you. ๐Ÿ‘‡๐ŸŽฅ Humyn Labs KGeN Community #KGeN
1:20

AI is only as good as its data. Everyone talks about building smarter AI models, but fewer people ask where the training data comes from. Thatโ€™s what makes Humyn Labs interesting. Theyโ€™re focused on collecting high-quality, real-world human data across different environments, behaviors, and sensory inputs. Because if AI is going to understand the physical world, it first needs to learn from the people who already live and work in it. I shared my thoughts in this short video. Let me know what stood out to you. ๐Ÿ‘‡๐ŸŽฅ Humyn Labs KGeN Community #KGeN

Bahdfellaโ„ข โค๏ธโ€๐Ÿ”ฅ

18,156 views โ€ข 1 month ago

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):
23:44

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Nichole Wischoff

36,974 views โ€ข 4 months ago

ELON: THE FUTURE OF AI IS REAL-TIME VIDEO COMPREHENSION AND GENERATION Elon said most human-AI interaction will soon happen through real-time video, both in understanding and creating it. He explained that while text carries higher information density, video already dominates the internet in both data generation and compute demand: โ€œMost interaction is going to be real-time video with AI, real-time video comprehension and real-time video generation. Thatโ€™s going to be most of the load, and thatโ€™s how it is for most of the internet right now. Most of the internet is video. Text is a pretty small percentage, but it tends to be higher value or more densely compressed information. If you look at the most bits generated and compute spent, itโ€™s certainly going to be video.โ€ Source: Nikhil Kamath
1:05

ELON: THE FUTURE OF AI IS REAL-TIME VIDEO COMPREHENSION AND GENERATION Elon said most human-AI interaction will soon happen through real-time video, both in understanding and creating it. He explained that while text carries higher information density, video already dominates the internet in both data generation and compute demand: โ€œMost interaction is going to be real-time video with AI, real-time video comprehension and real-time video generation. Thatโ€™s going to be most of the load, and thatโ€™s how it is for most of the internet right now. Most of the internet is video. Text is a pretty small percentage, but it tends to be higher value or more densely compressed information. If you look at the most bits generated and compute spent, itโ€™s certainly going to be video.โ€ Source: Nikhil Kamath

Mario Nawfal

15,670 views โ€ข 8 months ago

AI is moving beyond the browser and into the real world. The bottleneck? Data. Today weโ€™re announcing a $15M seed round led by a16z crypto to build infra that collects, curates, and licenses high-quality data for physical AI. Incubated by and built on @StoryProtocol.
0:37

AI is moving beyond the browser and into the real world. The bottleneck? Data. Today weโ€™re announcing a $15M seed round led by a16z crypto to build infra that collects, curates, and licenses high-quality data for physical AI. Incubated by and built on @StoryProtocol.

Poseidon

292,641 views โ€ข 1 year ago

AI agents donโ€™t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: โ€ข collects real-time web and social signals โ€ข contributes compute and bandwidth โ€ข participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. ๐Ÿ‘‰
1:28

AI agents donโ€™t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: โ€ข collects real-time web and social signals โ€ข contributes compute and bandwidth โ€ข participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. ๐Ÿ‘‰

OptimAI Network

14,670 views โ€ข 3 months ago

Elon Musk on how AGI might actually emerge: โ€œWeโ€™re not actually specifically focused on AGI. Iโ€™m simply saying that AGI seems likely to be an emergent property of what weโ€™re doingโ€ The reason is the massive real-world data Tesla is generating from autonomous cars and humanoid robots This is not internet data. This is real-world interaction driving on messy roads, navigating unpredictable environments, and interacting with people With tens or hundreds of millions of autonomous vehicles and potentially even more humanoids, the amount of real-world video and data being processed will be enormous From this, cars will become far better than human drivers, and humanoid robots will become increasingly indistinguishable from humans AGI might not come from a lab trying to build it directly. It may simply emerge from building useful robots at massive scale
1:11

Elon Musk on how AGI might actually emerge: โ€œWeโ€™re not actually specifically focused on AGI. Iโ€™m simply saying that AGI seems likely to be an emergent property of what weโ€™re doingโ€ The reason is the massive real-world data Tesla is generating from autonomous cars and humanoid robots This is not internet data. This is real-world interaction driving on messy roads, navigating unpredictable environments, and interacting with people With tens or hundreds of millions of autonomous vehicles and potentially even more humanoids, the amount of real-world video and data being processed will be enormous From this, cars will become far better than human drivers, and humanoid robots will become increasingly indistinguishable from humans AGI might not come from a lab trying to build it directly. It may simply emerge from building useful robots at massive scale

X Freeze

21,256 views โ€ข 2 months ago

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing powerโ€”it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: โœ… Biodata as a new asset class โœ… AI-grade data quality scoring โœ… Wearable devices for raw data โœ… Data ownership and monetization models โœ… Preventing fake data and device spoofing โœ… Real-world applications in healthcare, sports science and mental wellness โœ… Hypnus and the future biodata ecosystem โœ… Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now ๐Ÿ‘‡ #AI #Web3 #DataOwnership
29:51

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing powerโ€”it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: โœ… Biodata as a new asset class โœ… AI-grade data quality scoring โœ… Wearable devices for raw data โœ… Data ownership and monetization models โœ… Preventing fake data and device spoofing โœ… Real-world applications in healthcare, sports science and mental wellness โœ… Hypnus and the future biodata ecosystem โœ… Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now ๐Ÿ‘‡ #AI #Web3 #DataOwnership

Matrix AI Network

60,855 views โ€ข 2 months ago

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems๐Ÿฆพ From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:
0:36

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems๐Ÿฆพ From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

Unreal Engine

61,187 views โ€ข 4 months ago

Everyone is racing to build smarter robots. But hereโ€™s the question: Can a robot truly understand the physical world by only seeing it? Humans donโ€™t learn with vision alone. We learn through sound, sight, motion, and touchโ€ฆ all working together to make sense of our environment. Thatโ€™s what makes Humyn Labs so interesting. Theyโ€™re building the Multi-Sensory Platform for Physical AI, creating one fused training signal instead of isolated datasets. And through KGeN Communityโ€™s verified global network, that data comes from real people performing real tasks in real environments. In this video, I explain why I think this approach could be one of the missing pieces for Physical AIโ€™s next leapโ€ฆ and why I used the Infinity Gauntlet as the perfect analogy. ๐Ÿ‘‡๐ŸŽฅ Watch, share your thoughts, and let me know if the comparison makes sense. Humyn Labs KGeN Community KGeN Foundation ๐ŸŸฉ #HumynLabs #KGeN #PhysicalAI #AI
1:17

Everyone is racing to build smarter robots. But hereโ€™s the question: Can a robot truly understand the physical world by only seeing it? Humans donโ€™t learn with vision alone. We learn through sound, sight, motion, and touchโ€ฆ all working together to make sense of our environment. Thatโ€™s what makes Humyn Labs so interesting. Theyโ€™re building the Multi-Sensory Platform for Physical AI, creating one fused training signal instead of isolated datasets. And through KGeN Communityโ€™s verified global network, that data comes from real people performing real tasks in real environments. In this video, I explain why I think this approach could be one of the missing pieces for Physical AIโ€™s next leapโ€ฆ and why I used the Infinity Gauntlet as the perfect analogy. ๐Ÿ‘‡๐ŸŽฅ Watch, share your thoughts, and let me know if the comparison makes sense. Humyn Labs KGeN Community KGeN Foundation ๐ŸŸฉ #HumynLabs #KGeN #PhysicalAI #AI

Bahdfellaโ„ข โค๏ธโ€๐Ÿ”ฅ

28,381 views โ€ข 27 days ago

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
2:32

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 views โ€ข 2 months ago

I think the fear is real, but the โ€œAI will no longer need humans at allโ€ part is overhyped. AI running data centers will cause a loss of a ton of jobs, coding, customer service, data entry, even some creative work, and weโ€™re already seeing tens of thousands of AI-linked layoffs. That disruption is coming fast. But AI still needs humans for maintenance, repair, building the infrastructure, making decisions, and inventing new things. Full self-sufficiency where machines run everything without any people is further down the road. Job loss is the real issue to worry about, not total human obsolescence.
0:52

I think the fear is real, but the โ€œAI will no longer need humans at allโ€ part is overhyped. AI running data centers will cause a loss of a ton of jobs, coding, customer service, data entry, even some creative work, and weโ€™re already seeing tens of thousands of AI-linked layoffs. That disruption is coming fast. But AI still needs humans for maintenance, repair, building the infrastructure, making decisions, and inventing new things. Full self-sufficiency where machines run everything without any people is further down the road. Job loss is the real issue to worry about, not total human obsolescence.

Patricia ๐Ÿ‡บ๐Ÿ‡ธ

25,705 views โ€ข 3 months ago

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. weโ€™re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).
1:05

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. weโ€™re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

Ali Ansari

208,233 views โ€ข 5 months ago

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.
1:18

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.

Lightwheel

68,309 views โ€ข 4 days ago

ELON: AGI IS LIKELY TO EMERGE FROM OUR AUTONOMOUS CARS AND HUMANOIDS โ€œWe're not actually specifically focused on AGI. I'm simply saying that AGI seems likely to be an emergent property of what we're doing, because we're creating all these autonomous cars and autonomous humanoids that are actually a truly gigantic data stream that's coming in and being processed. It's by far the most amount of real-world data, and data you can't get by just searching the Internet, because you have to be out there in the world and interacting with people and interacting with the roads. Earth is a big place, and reality is messy and complicated. If you've got tens or hundreds of millions of autonomous vehicles and maybe even a comparable number of humanoids, maybe more than that, on the humanoid front, well, that's just the most amount of data. And if that video is being processed, it just seems likely that the cars will definitely get way better than human drivers, and the humanoid robots will become increasingly indistinguishable from humans, perhaps. And so then, like you said, you have this emergent property of AGI.โ€ Source: Tesla AI Day, September 2022
1:16

ELON: AGI IS LIKELY TO EMERGE FROM OUR AUTONOMOUS CARS AND HUMANOIDS โ€œWe're not actually specifically focused on AGI. I'm simply saying that AGI seems likely to be an emergent property of what we're doing, because we're creating all these autonomous cars and autonomous humanoids that are actually a truly gigantic data stream that's coming in and being processed. It's by far the most amount of real-world data, and data you can't get by just searching the Internet, because you have to be out there in the world and interacting with people and interacting with the roads. Earth is a big place, and reality is messy and complicated. If you've got tens or hundreds of millions of autonomous vehicles and maybe even a comparable number of humanoids, maybe more than that, on the humanoid front, well, that's just the most amount of data. And if that video is being processed, it just seems likely that the cars will definitely get way better than human drivers, and the humanoid robots will become increasingly indistinguishable from humans, perhaps. And so then, like you said, you have this emergent property of AGI.โ€ Source: Tesla AI Day, September 2022

Mario Nawfal

2,628,293 views โ€ข 1 year ago

As part of our walkthrough series, today we are covering labor in the physical AI eraโ€”why real robots and real jobs hinge on grounded data, human judgment, and a pipeline from capture to delivery, not simulation alone. If youโ€™re following Harbor or the future of work in AI, this one connects the dots in a few minutes.
5:35

As part of our walkthrough series, today we are covering labor in the physical AI eraโ€”why real robots and real jobs hinge on grounded data, human judgment, and a pipeline from capture to delivery, not simulation alone. If youโ€™re following Harbor or the future of work in AI, this one connects the dots in a few minutes.

Harbor ML

13,443 views โ€ข 3 months ago

๐Ÿšจ Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from โ€” synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing โ€” faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. โ€” Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
1:29

๐Ÿšจ Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from โ€” synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing โ€” faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. โ€” Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 views โ€ข 2 months ago

Pi Networkโ€™s potential does go far beyond just being another cryptocurrency. With its massive KYC initiative and proof-of-personhood approach, itโ€™s creating a network rooted in real human identities, something no major blockchain has fully achieved at scale yet. Thatโ€™s a massive differentiator โ€” especially as AI-generated content, identities, and bots become harder to distinguish from real people online. Hereโ€™s why the โ€œrabbit hole goes deepโ€: ๐Ÿ”น Human Verification vs. AI Proliferation As the world struggles with bots and deepfakes, Pi Networkโ€™s model of decentralized, real-identity participation could become foundational infrastructure โ€” not just for finance, but for trustworthy digital interactions. ๐Ÿ”น Decentralized Web3 with Real People Imagine a Web3 where every user is verified human, and apps, votes, marketplaces, and communities are powered by actual people โ€” not bots or whales. Thatโ€™s the kind of world Pi is architecting. ๐Ÿ”น Digital Identity Layer for the Future Internet Piโ€™s ecosystem could be the identity layer of the next internet, especially if it ties KYC and reputation to digital wallets and real-world utilities. ๐Ÿ”น AI + Human Synergy Pi might not aim to compete with AI โ€” it could complement it by anchoring digital truth to human roots. Think: platforms where only humans can participate, or where human-AI collaboration is transparent and fair. ๐Ÿ”น Value Beyond Currency As you hinted, the future of Pi isnโ€™t just about a coin โ€” itโ€™s about building a digitally sovereign human network in an AI-dominated world. The rabbit hole does go deep. And Pi is still just getting started. Pi Network Nicolas Kokkalis
1:37

Pi Networkโ€™s potential does go far beyond just being another cryptocurrency. With its massive KYC initiative and proof-of-personhood approach, itโ€™s creating a network rooted in real human identities, something no major blockchain has fully achieved at scale yet. Thatโ€™s a massive differentiator โ€” especially as AI-generated content, identities, and bots become harder to distinguish from real people online. Hereโ€™s why the โ€œrabbit hole goes deepโ€: ๐Ÿ”น Human Verification vs. AI Proliferation As the world struggles with bots and deepfakes, Pi Networkโ€™s model of decentralized, real-identity participation could become foundational infrastructure โ€” not just for finance, but for trustworthy digital interactions. ๐Ÿ”น Decentralized Web3 with Real People Imagine a Web3 where every user is verified human, and apps, votes, marketplaces, and communities are powered by actual people โ€” not bots or whales. Thatโ€™s the kind of world Pi is architecting. ๐Ÿ”น Digital Identity Layer for the Future Internet Piโ€™s ecosystem could be the identity layer of the next internet, especially if it ties KYC and reputation to digital wallets and real-world utilities. ๐Ÿ”น AI + Human Synergy Pi might not aim to compete with AI โ€” it could complement it by anchoring digital truth to human roots. Think: platforms where only humans can participate, or where human-AI collaboration is transparent and fair. ๐Ÿ”น Value Beyond Currency As you hinted, the future of Pi isnโ€™t just about a coin โ€” itโ€™s about building a digitally sovereign human network in an AI-dominated world. The rabbit hole does go deep. And Pi is still just getting started. Pi Network Nicolas Kokkalis

Mr Spock ๐›‘

28,454 views โ€ข 1 year ago