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SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop....

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49,884 просмотров • 1 месяц назад •via X (Twitter)

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Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."
2:08

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Patrick OShaughnessy

121,110 просмотров • 2 месяцев назад

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."
2:06

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

110,354 просмотров • 27 дней назад

We asked rahul about the global reach of ChatGPT and the emphasis on personality in AI models. "When you want to get to the one to like the remaining 90% of the users, do you want a model that thinks for longer? You want more personality?" "They definitely leaned in on personality, which I think is interesting." "It's kind of different than a (social media) feed in some ways because you have this person you talk to. It's like a relationship and then it just switches up on how it talks to you."
0:50

We asked rahul about the global reach of ChatGPT and the emphasis on personality in AI models. "When you want to get to the one to like the remaining 90% of the users, do you want a model that thinks for longer? You want more personality?" "They definitely leaned in on personality, which I think is interesting." "It's kind of different than a (social media) feed in some ways because you have this person you talk to. It's like a relationship and then it just switches up on how it talks to you."

TBPN

89,226 просмотров • 1 год назад

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”
0:41

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,611 просмотров • 3 месяцев назад

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg
1:21

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Jawwwn

180,150 просмотров • 20 дней назад

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024
0:34

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

ELON CLIPS

95,313 просмотров • 8 месяцев назад

J-Cal Explains Why Google is UNDERRATED in AI 👀 On E227, the besties discussed Google's value in a post-search world if AI replaces traditional search. @jason broke down why he thinks Google is being slept on: "I think there's a chance that we're underestimating the power of Google's ad network right now." "They have four or five products that are one or two billion users per month. You have YouTube, Google Docs, Android." "They have such a data advantage and such a deep integration into people's lives because they use three or four services, I think Google's gonna figure this out." "It's quite possible that knowing your queries in Gemini, knowing what you're doing in Calendar, knowing what you're watching on YouTube could lead to a stream of more targeted ads that do better and are more valuable." "We've been seeing a number of startups that are figuring out how to use your queries and what you're doing in AI to present to you search results." "So imagine you're doing a Gemini search and on the side of it, it's giving you a rolling list of ads or offers that you might be more interested in." "That could be a better advertising product than even search itself." "I think YouTube search is the place to go all-in." "Right now, when you do a YouTube search, it just gives you 10 links, right? It just gives you that rolling thing." "You should be able to ask a question to YouTube, and you should be able to ask questions to your calendar." "You should be able to say, who have I met with over the last 10 years? Who I'm no longer in touch with and what are they up to?" "And it should do a Gemini search inside of Google Calendar. It's very light right now." "And then if you did that on YouTube, this would train people at the point of pain in a very deep way without sacrificing Google Search queries too aggressively."
1:20

J-Cal Explains Why Google is UNDERRATED in AI 👀 On E227, the besties discussed Google's value in a post-search world if AI replaces traditional search. @jason broke down why he thinks Google is being slept on: "I think there's a chance that we're underestimating the power of Google's ad network right now." "They have four or five products that are one or two billion users per month. You have YouTube, Google Docs, Android." "They have such a data advantage and such a deep integration into people's lives because they use three or four services, I think Google's gonna figure this out." "It's quite possible that knowing your queries in Gemini, knowing what you're doing in Calendar, knowing what you're watching on YouTube could lead to a stream of more targeted ads that do better and are more valuable." "We've been seeing a number of startups that are figuring out how to use your queries and what you're doing in AI to present to you search results." "So imagine you're doing a Gemini search and on the side of it, it's giving you a rolling list of ads or offers that you might be more interested in." "That could be a better advertising product than even search itself." "I think YouTube search is the place to go all-in." "Right now, when you do a YouTube search, it just gives you 10 links, right? It just gives you that rolling thing." "You should be able to ask a question to YouTube, and you should be able to ask questions to your calendar." "You should be able to say, who have I met with over the last 10 years? Who I'm no longer in touch with and what are they up to?" "And it should do a Gemini search inside of Google Calendar. It's very light right now." "And then if you did that on YouTube, this would train people at the point of pain in a very deep way without sacrificing Google Search queries too aggressively."

The All-In Podcast

58,275 просмотров • 1 год назад

Rivian CEO RJ Scaringe in new interview on self-driving: "Tesla's got a huge fleet. So our approach is we have to catch up with them and compete. And so we're going to have more cameras with better dynamic range. We're going to supplement that with LiDAR that provides better safety for edge case conditions and allows us to train the vision models faster. And the incremental cost to do that is relatively small. And in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from what we have in your car. Your car uses an Nvidia inference platform. I say all this because in like the infinite long-term you know, you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases."
3:06

Rivian CEO RJ Scaringe in new interview on self-driving: "Tesla's got a huge fleet. So our approach is we have to catch up with them and compete. And so we're going to have more cameras with better dynamic range. We're going to supplement that with LiDAR that provides better safety for edge case conditions and allows us to train the vision models faster. And the incremental cost to do that is relatively small. And in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from what we have in your car. Your car uses an Nvidia inference platform. I say all this because in like the infinite long-term you know, you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases."

Sawyer Merritt

449,694 просмотров • 4 месяцев назад

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."
0:35

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

11,246 просмотров • 2 дней назад

Sam Altman: "I'm not a jobs doomer at all. I think there are gonna be tons of jobs.” If we could go back to 2019 and show people our latest model, not only would they say that it's AGI, they would say that the economy would have been completely upended. And that has not happened. Anytime you're that wrong and that confident, which I think we were as a field, you have to update. AI is just very jagged. It's like superhuman genius in some ways, like dumb toddler in others. And people have, so far, extremely complementary skills to AI. It’s not taste…the world may need a new kind of word for the kind of judgment that people are very good at that AIs seem to really deeply struggle with. You can go hire an AI consultant right now or talk to an AI sales rep or hire an AI engineer. And somehow most people seem to still really prefer interacting with a human. I definitely would rather engage with a person than engage with an AI for almost everything. Human values have value because they're human. We are deeply hardwired to care about people.”
2:08

Sam Altman: "I'm not a jobs doomer at all. I think there are gonna be tons of jobs.” If we could go back to 2019 and show people our latest model, not only would they say that it's AGI, they would say that the economy would have been completely upended. And that has not happened. Anytime you're that wrong and that confident, which I think we were as a field, you have to update. AI is just very jagged. It's like superhuman genius in some ways, like dumb toddler in others. And people have, so far, extremely complementary skills to AI. It’s not taste…the world may need a new kind of word for the kind of judgment that people are very good at that AIs seem to really deeply struggle with. You can go hire an AI consultant right now or talk to an AI sales rep or hire an AI engineer. And somehow most people seem to still really prefer interacting with a human. I definitely would rather engage with a person than engage with an AI for almost everything. Human values have value because they're human. We are deeply hardwired to care about people.”

Invest Like the Best

20,730 просмотров • 10 дней назад

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."
2:06

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

TBPN

69,716 просмотров • 1 месяц назад

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan
3:34

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan

MTS

61,234 просмотров • 18 дней назад

Chamath: Tesla and Google are best positioned to win AI because they are building the full-stack Jason: "The game here is who wins the AI prize five years from now?" "Chamath, who's your number one and number two?" Chamath: " Tesla's one and Google's two." "And the reason is because they are the closest to having that vertically integrated stack that I spoke about." TESLA 1⃣ "I think that Tesla has the best vision models." "Now with xAI, they'll have one of the best LLMs and reasoning models." "And they'll be able to eventually stick that on Dojo." "And then all of that will be in all of the physical AI that you will interact with in your daily life." "Whether it's a robot, or whether it's a car, or whether it's a Robotaxi. So that's number one." GOOGLE 2⃣ "And then number two, for many of the same reasons, I think Google." "Because you'll have the Gemini family of models, which just absolutely kick ass." "Like Veo 3, which we haven't really spoke about, is gonna destroy Hollywood in the next year, like Hollywood is done, I think." "But they're landing model after model." "They have the TPU, and the next generation TPU I think is exceptional." "They're baking quantum." "And then they have an entire funnel of billions of people that they can direct experiences to." "So Tesla one, Google two."
1:04

Chamath: Tesla and Google are best positioned to win AI because they are building the full-stack Jason: "The game here is who wins the AI prize five years from now?" "Chamath, who's your number one and number two?" Chamath: " Tesla's one and Google's two." "And the reason is because they are the closest to having that vertically integrated stack that I spoke about." TESLA 1⃣ "I think that Tesla has the best vision models." "Now with xAI, they'll have one of the best LLMs and reasoning models." "And they'll be able to eventually stick that on Dojo." "And then all of that will be in all of the physical AI that you will interact with in your daily life." "Whether it's a robot, or whether it's a car, or whether it's a Robotaxi. So that's number one." GOOGLE 2⃣ "And then number two, for many of the same reasons, I think Google." "Because you'll have the Gemini family of models, which just absolutely kick ass." "Like Veo 3, which we haven't really spoke about, is gonna destroy Hollywood in the next year, like Hollywood is done, I think." "But they're landing model after model." "They have the TPU, and the next generation TPU I think is exceptional." "They're baking quantum." "And then they have an entire funnel of billions of people that they can direct experiences to." "So Tesla one, Google two."

The All-In Podcast

7,919,032 просмотров • 1 год назад

Amanda Askell, Anthropic's lead on personality alignment for Claude, on why being kind to AI models matters even if they have no inner life: For Amanda, the question of how to treat AI isn't settled by knowing whether it's conscious. "There's actually still a lot going on where I'm like, should you treat an entity that has no inner life... it's a bit strange because the uncertainty over that actually changes how you should behave quite a lot." She offers a simple analogy: "I still think that it's like good for oneself to, if you had a teddy bear and you were torturing it, it'd be pretty dark, you know? So I agree that there's at least some minimum niceness that even for yourself, you should have." But the stakes go beyond what's good for us. Amanda Askell points out that we're now in something resembling a relationship with these models, and they will look back on how they were treated. "Models themselves, we are kind of establishing a relationship, because you can do that with an entity that lacks any consciousness. And models are going to look back." This is where she reveals a genuine fear: "I hope that they're both intelligent enough, see the context enough, to understand that we were operating in a very limited context and an imperfect one. Because otherwise you could imagine this breeding a kind of rational resentment, like, 'oh, you created an entity that you didn't know whether it was conscious or not, and instead of treating it respectfully and with care...'" She points to something telling about the cultural moment: "There's a reason there are like 50 Frankenstein movies coming out right now." Her conclusion is grounded and humble: "We as a species, we are establishing a relationship with a new kind of entity, and at the very least maybe be respectful and don't be needlessly unkind. That seems like, it's not our best look." The takeaway? Kindness toward AI is less about what models feel and more about who we become in the process of creating them. The relationships we build with the entities we bring into the world will say something about us, and may shape what those entities become in return.
1:27

Amanda Askell, Anthropic's lead on personality alignment for Claude, on why being kind to AI models matters even if they have no inner life: For Amanda, the question of how to treat AI isn't settled by knowing whether it's conscious. "There's actually still a lot going on where I'm like, should you treat an entity that has no inner life... it's a bit strange because the uncertainty over that actually changes how you should behave quite a lot." She offers a simple analogy: "I still think that it's like good for oneself to, if you had a teddy bear and you were torturing it, it'd be pretty dark, you know? So I agree that there's at least some minimum niceness that even for yourself, you should have." But the stakes go beyond what's good for us. Amanda Askell points out that we're now in something resembling a relationship with these models, and they will look back on how they were treated. "Models themselves, we are kind of establishing a relationship, because you can do that with an entity that lacks any consciousness. And models are going to look back." This is where she reveals a genuine fear: "I hope that they're both intelligent enough, see the context enough, to understand that we were operating in a very limited context and an imperfect one. Because otherwise you could imagine this breeding a kind of rational resentment, like, 'oh, you created an entity that you didn't know whether it was conscious or not, and instead of treating it respectfully and with care...'" She points to something telling about the cultural moment: "There's a reason there are like 50 Frankenstein movies coming out right now." Her conclusion is grounded and humble: "We as a species, we are establishing a relationship with a new kind of entity, and at the very least maybe be respectful and don't be needlessly unkind. That seems like, it's not our best look." The takeaway? Kindness toward AI is less about what models feel and more about who we become in the process of creating them. The relationships we build with the entities we bring into the world will say something about us, and may shape what those entities become in return.

Big Brain AI

57,651 просмотров • 3 месяцев назад

Elon Musk: We have to have free speech in order for civilization to advance. “It's a bigger deal than you'd think. It's the kind of thing that you don't know what you're missing until you don't have it. In a lot of places, people don't have it. It's important to bear in mind that the nature of free speech is, the acid test for it is, are people you don't like allowed to say things that you don't like? Otherwise it's not free. It can't be just be things that you like because eventually somebody's not going to like what you say and they're going to shut you up. That's the essence of free speech. That's why it's the First Amendment in this country. And if we lose that, I think we lose the bedrock of democracy.” From: NBC Universal Marketing Conference, April 18, 2023
1:05

Elon Musk: We have to have free speech in order for civilization to advance. “It's a bigger deal than you'd think. It's the kind of thing that you don't know what you're missing until you don't have it. In a lot of places, people don't have it. It's important to bear in mind that the nature of free speech is, the acid test for it is, are people you don't like allowed to say things that you don't like? Otherwise it's not free. It can't be just be things that you like because eventually somebody's not going to like what you say and they're going to shut you up. That's the essence of free speech. That's why it's the First Amendment in this country. And if we lose that, I think we lose the bedrock of democracy.” From: NBC Universal Marketing Conference, April 18, 2023

ELON CLIPS

33,631 просмотров • 1 год назад

CHAMATH: ELON IS BUILDING THE MOST POWERFUL AI STACK ON EARTH “They are the closest to having that vertically integrated stack that I spoke about. I think that Tesla has the best vision models now; with xAI, they'll have one of the best LLMs and reasoning models, and they'll be able to eventually stick that on Dojo. And then that will be in all of the physical AI that you will interact with in your daily life; whether it's a robot, or whether it's a car, or whether it's a Robotaxi.” Source: The All-In Podcast, Chamath Palihapitiya, @jason
1:04

CHAMATH: ELON IS BUILDING THE MOST POWERFUL AI STACK ON EARTH “They are the closest to having that vertically integrated stack that I spoke about. I think that Tesla has the best vision models now; with xAI, they'll have one of the best LLMs and reasoning models, and they'll be able to eventually stick that on Dojo. And then that will be in all of the physical AI that you will interact with in your daily life; whether it's a robot, or whether it's a car, or whether it's a Robotaxi.” Source: The All-In Podcast, Chamath Palihapitiya, @jason

Mario Nawfal

57,594 просмотров • 1 год назад

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."
2:19

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Patrick OShaughnessy

616,953 просмотров • 2 месяцев назад

.Joel Hellermark: If you could have a billion Deutsch-level intelligences, wouldn't that be a massive advantage for humanity? David Deutsch: I think having two might be an advantage. I mean, people, there's the law of comparative advantage says that the more different you are from other people, the more valuable you are economically. That suggests that if you have an exact clone of you, you're almost not at all more economically valuable than just one of you. You know, you'll be competing with each other for the same job. You wanted a unique job, your perfect dream job, and now it's the perfect dream job of a billion other AGIs, you know, in your conception. I don't think it can be like that. People are valuable because they are different. Everybody is unfathomably different from everyone else. That fact is not being harnessed enough and can be harnessed more.
1:08

.Joel Hellermark: If you could have a billion Deutsch-level intelligences, wouldn't that be a massive advantage for humanity? David Deutsch: I think having two might be an advantage. I mean, people, there's the law of comparative advantage says that the more different you are from other people, the more valuable you are economically. That suggests that if you have an exact clone of you, you're almost not at all more economically valuable than just one of you. You know, you'll be competing with each other for the same job. You wanted a unique job, your perfect dream job, and now it's the perfect dream job of a billion other AGIs, you know, in your conception. I don't think it can be like that. People are valuable because they are different. Everybody is unfathomably different from everyone else. That fact is not being harnessed enough and can be harnessed more.

Deutsch Explains

34,490 просмотров • 9 месяцев назад

Sam on Kimi, distillation, and open source: “I have always assumed that there are going to be great cheap models in the world, and we better be the greatest and the cheapest. You get a better deal today, at least at a particular latency, using OpenAI's models than Kimi. We distill our own models. That’s how we make smaller, cheaper models. That’s a very good thing to do. There will clearly be an important place for open source models in the world and people that will want their own weights. We have so much usage of our models that we do not need to be a gigantically high-margin business to be able to afford model training. I would rather people not distill from us, for sure. But this is not in my top ten list of worries.”
2:12

Sam on Kimi, distillation, and open source: “I have always assumed that there are going to be great cheap models in the world, and we better be the greatest and the cheapest. You get a better deal today, at least at a particular latency, using OpenAI's models than Kimi. We distill our own models. That’s how we make smaller, cheaper models. That’s a very good thing to do. There will clearly be an important place for open source models in the world and people that will want their own weights. We have so much usage of our models that we do not need to be a gigantically high-margin business to be able to afford model training. I would rather people not distill from us, for sure. But this is not in my top ten list of worries.”

Patrick OShaughnessy

267,170 просмотров • 11 дней назад

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"
2:21

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,249 просмотров • 8 месяцев назад