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Why data is a trillion-dollar market "The bigger models scale, the more data you need. Data is a very durable need. If you believe in the scaling of models, then absolutely you should believe in the data market. I believe it's going to be at least a $100BN by... show more

Harry Stebbings

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20,227 次观看 • 4 天前 •via X (Twitter)

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We believe the future of prediction markets is decentralised and permissionless. Anyone being able to make a market on anything. In order to do this you need AI and data you can verify and trust. This will all run on the Somnia blockchain.

We believe the future of prediction markets is decentralised and permissionless. Anyone being able to make a market on anything. In order to do this you need AI and data you can verify and trust. This will all run on the Somnia blockchain.

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$Get the data you need at a fraction of the cost. All you have to do is switch to Noble.$

Get the data you need at a fraction of the cost. All you have to do is switch to Noble.

Noble Mobile

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Sam Altman in a new interview says Orbital Data Centers are not going to matter at scale this decade: “I honestly think the idea, with the current landscape of putting data centers in space is ridiculous. If you do the very rough math of launch costs relative to the cost of power on earth and to say nothing how you are going to fix a broken GPU in space— they do break a lot unfortunately— we are not there yet. There will be a time, space is great for a lot of things, Orbital Data Centers are not something that is going to matter at scale this decade.” This clip isn’t going to age well for Sam.

Sam Altman in a new interview says Orbital Data Centers are not going to matter at scale this decade: “I honestly think the idea, with the current landscape of putting data centers in space is ridiculous. If you do the very rough math of launch costs relative to the cost of power on earth and to say nothing how you are going to fix a broken GPU in space— they do break a lot unfortunately— we are not there yet. There will be a time, space is great for a lot of things, Orbital Data Centers are not something that is going to matter at scale this decade.” This clip isn’t going to age well for Sam.

Nic Cruz Patane

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"The enterprise's ability to leverage this depends on its ability to create training data as fast as it can. Not enough people are focused on training data. I need to get into the brains of the people who solve these problems, extract that knowledge, and codify it so I can write my own playbooks and rules. Every new phone call, every new case is a learning opportunity. You have to give the organisational knowledge to some learning system that you have to build." Nikesh Arora What does no one know that everyone should know about training data Brendan (can/do) Garrett Lord Jeff Richards Lin Qiao Yann LeCun Anastasios Nikolas Angelopoulos

"The enterprise's ability to leverage this depends on its ability to create training data as fast as it can. Not enough people are focused on training data. I need to get into the brains of the people who solve these problems, extract that knowledge, and codify it so I can write my own playbooks and rules. Every new phone call, every new case is a learning opportunity. You have to give the organisational knowledge to some learning system that you have to build." Nikesh Arora What does no one know that everyone should know about training data Brendan (can/do) Garrett Lord Jeff Richards Lin Qiao Yann LeCun Anastasios Nikolas Angelopoulos

Harry Stebbings

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As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

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Are humanoids the largest addressable market in history? Andrew Kang says "to understand that you have to look at what is the total market for human labor. It's something like 50% of GDP, some people put it at $40 trillion or $60 trillion." "The way that I would think about it is a humanoid can do everything that a human can do in the future, but it doesn't need to rest, it doesn't need to take breaks, it doesn't need to go on vacation. It's not going to quit on you, so you don't need to hire and retrain somebody else."

Are humanoids the largest addressable market in history? Andrew Kang says "to understand that you have to look at what is the total market for human labor. It's something like 50% of GDP, some people put it at $40 trillion or $60 trillion." "The way that I would think about it is a humanoid can do everything that a human can do in the future, but it doesn't need to rest, it doesn't need to take breaks, it doesn't need to go on vacation. It's not going to quit on you, so you don't need to hire and retrain somebody else."

Anthony Pompliano 🌪

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$MU is going to $2000 and therefore be a 2 trillion market capitalization! You have to believe in yourself. You have to have self confidence, faith, and do the work. Sell 10 $MU at $2,000 and put the computer in your closet.

$MU is going to $2000 and therefore be a 2 trillion market capitalization! You have to believe in yourself. You have to have self confidence, faith, and do the work. Sell 10 $MU at $2,000 and put the computer in your closet.

Chris and the Markets

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Mark Cuban: OpenAI is shitting away their money. They'll never get a return on that trillion dollar spend. "There's a lot of FUD being put out about the spending. We're going to spend a trillion dollars because we need all this data center capacity. Well, the data center, the ability to process is going to get faster, cheaper" "I think a lot of the numbers that they're throwing out there aren't going to come to fruition, which is why I say they're full of shit" "We don't know if the business of foundational models is going to be like the streaming industry where there's one leader and a bunch of players that make money, or some that make money at least, or search where there's effectively one company" "So they need to raise all the money, go all in and spend everything they can, kiss all the rings around the world in hopes of being the one" "It's going to be very difficult, but I don't think it's going to pay off the way they expect" "They're pissing away the money at scale. It's hard to do, right? But if you don't go all in like that, you can't keep on raising money."

Mark Cuban: OpenAI is shitting away their money. They'll never get a return on that trillion dollar spend. "There's a lot of FUD being put out about the spending. We're going to spend a trillion dollars because we need all this data center capacity. Well, the data center, the ability to process is going to get faster, cheaper" "I think a lot of the numbers that they're throwing out there aren't going to come to fruition, which is why I say they're full of shit" "We don't know if the business of foundational models is going to be like the streaming industry where there's one leader and a bunch of players that make money, or some that make money at least, or search where there's effectively one company" "So they need to raise all the money, go all in and spend everything they can, kiss all the rings around the world in hopes of being the one" "It's going to be very difficult, but I don't think it's going to pay off the way they expect" "They're pissing away the money at scale. It's hard to do, right? But if you don't go all in like that, you can't keep on raising money."

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Kaitlan Collins: “Do you think undocumented immigrants should have access to federally funded healthcare?” Rep. Alexandria Ocasio-Cortez (D-NY): “I believe personally that healthcare is a human right. I believe that every person should be able to go to the doctor.” Kaitlan Collins: “But if you could choose, do you believe [the law should be changed, allowing illegal aliens to have free healthcare]?” Rep. Alexandria Ocasio-Cortez (D-NY): “Pardon?… I believe in a single-payer healthcare system. Where if you go in and need a doctor, you can get the medical attention you need.”

Kaitlan Collins: “Do you think undocumented immigrants should have access to federally funded healthcare?” Rep. Alexandria Ocasio-Cortez (D-NY): “I believe personally that healthcare is a human right. I believe that every person should be able to go to the doctor.” Kaitlan Collins: “But if you could choose, do you believe [the law should be changed, allowing illegal aliens to have free healthcare]?” Rep. Alexandria Ocasio-Cortez (D-NY): “Pardon?… I believe in a single-payer healthcare system. Where if you go in and need a doctor, you can get the medical attention you need.”

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Palantir CEO Alex Karp on why Europe has no tech scene: "You basically have different parts of the stack: you have the GPU part—chips." "In an enterprise, you're going to have a data integration part of the stack. You can do that by hand." "If you want civil liberties—we do that." "You have an application part of the stack—ontology." "And you have the LLMs, both closed and open models." "To have a tech scene in Europe, you need world-leading components in all those parts. That's not realistic." "So you're going to have to figure out which components are actually the ones that could advance European industry, European sovereignty." "What's really happening in Europe is, you have bureaucrats whose primary idea is, 'How do I protect the market against American companies?'" "Everything else is downstream." Via Mathias Döpfner

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"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)

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Kevin Hassett: "The data can't be propaganda. The data has to be something you can trust, because decision-makers throughout the economy trust that these are the data that they can build a factory because they believe, or cut interest rates because they believe. And if the data aren't that good, then it's a real problem for the US."

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#WATCH | Delhi: On the #CasteCensus, author and Advaita teacher Acharya Prashant says, "Any action depends on the intention. Caste census means collecting more data, authentic data and data in itself are neither forward nor backwards, good nor bad. It depends on what you want to what you intend to do with the data. If you want to identify the marginalised groups in a better way, so that more accurate and more efficient policy making can happen, then it's a good step, a forward step... On the other hand, if you use this data to cement cost identification further, then obviously it's a step backwards. If you use this data for electoral gains by targeting particular caste groups concentrated in particular electorates or geographies, then obviously it's a backwards thing..."

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82,457 次观看 • 1 年前

LLMs had the internet. Robotics doesn’t, which is why we’re bringing the Scale AI data engine into the physical world. Robotics won’t be solved by blindly collecting 100x more demos. Raw trajectories need structure (intent, subgoals, failures, recoveries, edge cases, quality signals) or you just average mistakes. At Scale Labs, we’re building that data flywheel, mapping the right data to the right capabilities and scaling what works globally !

LLMs had the internet. Robotics doesn’t, which is why we’re bringing the Scale AI data engine into the physical world. Robotics won’t be solved by blindly collecting 100x more demos. Raw trajectories need structure (intent, subgoals, failures, recoveries, edge cases, quality signals) or you just average mistakes. At Scale Labs, we’re building that data flywheel, mapping the right data to the right capabilities and scaling what works globally !

jade

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Dario Amodei reveals the two convictions that made him leave OpenAI to start Anthropic "The first conviction was the scaling laws" "If you scale up models, give them more data and more compute, you find incredible increases in performance. I was finding that in 2019 with GPT-2" "There were a lot of folks inside and outside who didn't believe it at all. We made the case to leadership, this is going to be a big deal" "The second was, if these models are going to match the capability of the human brain, we better get this right" "Despite a lot of language about doing it in the right way, I was just not convinced there was a real and serious conviction to do it the right way"

Dario Amodei reveals the two convictions that made him leave OpenAI to start Anthropic "The first conviction was the scaling laws" "If you scale up models, give them more data and more compute, you find incredible increases in performance. I was finding that in 2019 with GPT-2" "There were a lot of folks inside and outside who didn't believe it at all. We made the case to leadership, this is going to be a big deal" "The second was, if these models are going to match the capability of the human brain, we better get this right" "Despite a lot of language about doing it in the right way, I was just not convinced there was a real and serious conviction to do it the right way"

Jaynit Makwana

248,140 次观看 • 2 个月前

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Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

TBPN

91,955 次观看 • 6 个月前

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

144,618 次观看 • 6 个月前

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🚨 Sam Altman Throws Shade at Elon’s Vision of Putting Data Centers in Space “Putting data centers in space is ridiculous … Do the very rough math of launch costs relative to the cost of power we can do on Earth … Orbital data centers are not something that's going to matter at scale this decade.”

Chief Nerd

261,663 次观看 • 5 个月前

"If you're betting against space data centers, you're betting against compute growing." - Pranav Myana, author of the recently viral model on space compute "Our minds haven't evolved to understand just how much space there is in space." "If you don't believe there's going to be an AI revolution, if you don't believe compute is going to grow exponentially, you don't believe in [data centers in space]."

"If you're betting against space data centers, you're betting against compute growing." - Pranav Myana, author of the recently viral model on space compute "Our minds haven't evolved to understand just how much space there is in space." "If you don't believe there's going to be an AI revolution, if you don't believe compute is going to grow exponentially, you don't believe in [data centers in space]."

TBPN

31,939 次观看 • 7 个月前