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Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and...

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The All-In Podcast

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60,603 次观看 • 8 天前 •via X (Twitter)

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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 次观看 • 1 个月前

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)
0:38

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

Rohan Paul

26,597 次观看 • 12 天前

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.
0:37

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Fireside Alpha

162,832 次观看 • 28 天前

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 次观看 • 4 个月前

$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

11,921 次观看 • 11 天前

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

180,421 次观看 • 29 天前

.Decagon co-founder and CEO Jesse Zhang says enterprise open-source adoption is falling right now, and that's a sign of adoption rather than rejection: "Even though there's a lot of hype for open-source, the share of open-source inference is actually going down right now." "People are spinning up all these new use cases, and if you're spinning up new use cases, of course you're gonna use the frontier models until they're working." "Once it's at that point, they're heavily incentivized to use open-source because it's way cheaper and faster." "Enterprises have a lot of desire to move, but they can only do so many use cases at once. There is inertia there, and they have to go through all the model risk governance and all the security things." "I think it'll take time, but it will get there." Jesse Zhang
0:49

.Decagon co-founder and CEO Jesse Zhang says enterprise open-source adoption is falling right now, and that's a sign of adoption rather than rejection: "Even though there's a lot of hype for open-source, the share of open-source inference is actually going down right now." "People are spinning up all these new use cases, and if you're spinning up new use cases, of course you're gonna use the frontier models until they're working." "Once it's at that point, they're heavily incentivized to use open-source because it's way cheaper and faster." "Enterprises have a lot of desire to move, but they can only do so many use cases at once. There is inertia there, and they have to go through all the model risk governance and all the security things." "I think it'll take time, but it will get there." Jesse Zhang

a16z

35,742 次观看 • 16 天前

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source Brad Gerstner: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”
1:50

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source Brad Gerstner: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”

The All-In Podcast

52,289 次观看 • 1 个月前

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.
2:51

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 次观看 • 26 天前

Demis says he wants to see a Western open source AI stack and that we’re losing to China. He also says Google doesn’t have enough compute to build two frontier (open and closed) models, which is why Gemma is a smaller family of models. Watch this incredible clip. Shout out Y Combinator and Garry Tan for the fantastic interview.
1:23

Demis says he wants to see a Western open source AI stack and that we’re losing to China. He also says Google doesn’t have enough compute to build two frontier (open and closed) models, which is why Gemma is a smaller family of models. Watch this incredible clip. Shout out Y Combinator and Garry Tan for the fantastic interview.

Matthew Berman

296,678 次观看 • 3 个月前

Gavin Baker says open source AI actually makes closed models more valuable "Open source makes frontier tokens more valuable. If you have a 250 IQ intellect that can orchestrate these open source 150 IQ intellects, that makes the 250 IQ intellect more valuable because it can farm out tasks." "If we're going to use the Manhattan Project analogy, everybody talks about Richard Feynman. But they had Oppenheimer and effectively 30 Nobel laureates. But they needed 2,000 physicists who were top 10,000 in the world." "Having more affordable intelligence actually makes the frontier even more valuable. A very plausible outcome is one in which frontier tokens are 65 to 85% of the economic value. Open source tokens are like 80% of the volume. And that's good for everyone. And everyone wins."
1:29

Gavin Baker says open source AI actually makes closed models more valuable "Open source makes frontier tokens more valuable. If you have a 250 IQ intellect that can orchestrate these open source 150 IQ intellects, that makes the 250 IQ intellect more valuable because it can farm out tasks." "If we're going to use the Manhattan Project analogy, everybody talks about Richard Feynman. But they had Oppenheimer and effectively 30 Nobel laureates. But they needed 2,000 physicists who were top 10,000 in the world." "Having more affordable intelligence actually makes the frontier even more valuable. A very plausible outcome is one in which frontier tokens are 65 to 85% of the economic value. Open source tokens are like 80% of the volume. And that's good for everyone. And everyone wins."

dnap

27,408 次观看 • 1 天前

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 次观看 • 2 个月前

Chamath: Frontier AI Leaders “Created a Total F*cking Mess” Short-sighted fearmongering and immaturity from frontier AI leaders has created deep mistrust, threatening AI’s potential as an open engine of economic mobility. That mistrust gives hyperscalers the chance to position themselves as trusted gatekeepers, using KYC, audit trails, and compliance infrastructure to turn AI into an oligopoly. Chamath Palihapitiya on the All-In Pod: “I think the leaders of the frontier labs leave a lot to be desired. I think what we're seeing is a consistent pattern of evasiveness and immaturity, and I think that does a huge disservice to the entire movement of AI. The key to a vibrant life is rooted in economic mobility, and I think AI is the grand leveler. It is the thing that can enable everyone to have unique amounts of economic mobility because they are unencumbered to figure out what their upper bound is. And against that backdrop, we have to live in this constant doomerism, hype cycle, naivety, and I think it holds us back. How does it hold us back? Tactically, number one, it creates mistrust. I think that Silicon Valley was already decaying in the prestige that it held in American society. We built important things. Then we veered away from that, and we started building less important things. And now we're at a point where we've potentially started to rebuild important things again, but we have this veneer of negativity and mistrust that are created in large part because we just cannot get our sh*t together. And the leaders of the frontier labs are public enemy number one. Number two, I think what it creates, which I think is bad, but what it creates is an incredible opportunity for the hyperscalers. And the very simple opportunity is to convince governments all around the world, not just America, that they should be the gatekeeper. A: You can't trust these guys. B: These models are all over the place. C: Let us be the ones that provision them to the world. We will wrap it in KYC. I've been now talking about KYC for a while, right? Who are these customers? Do they have identification? Why are they allowed to run these models? What are they prompting? Let's keep them so that there's an audit trail. All of these things are going to become issues. The Frontier Lab folks made it an issue because of how they've handled all of this up until now. And what does that create? Now that creates an oligopoly for AI, the most powerful economically leveling instrument we've ever seen in the hands of maybe a handful of hyperscalers, who by the way, would make an incredibly compelling argument, and they would be right. And the only counterfactual to it would be, ‘Well, trust us, guys, it should actually be much more open and in a far more distributed environment.’ Can you imagine the cost and the complexity if you ask the neoscaler to build the same robust KYC or the same VPC infrastructure that Amazon and Microsoft and Google have spent decades investing trillions of dollars in? It's an impossibility, Jason. So you can take all of those datacenters off the map. You can take all of the neoscaler market off the map. All of this was preventable. So instead of a diverse, robust, open ecosystem giving a tool that is the fundamental unlock for humans, we are now going to debate gatekeeping and duopoly versus oligopoly. They have created a total f*cking mess, and it's a shame.”
3:26

Chamath: Frontier AI Leaders “Created a Total F*cking Mess” Short-sighted fearmongering and immaturity from frontier AI leaders has created deep mistrust, threatening AI’s potential as an open engine of economic mobility. That mistrust gives hyperscalers the chance to position themselves as trusted gatekeepers, using KYC, audit trails, and compliance infrastructure to turn AI into an oligopoly. Chamath Palihapitiya on the All-In Pod: “I think the leaders of the frontier labs leave a lot to be desired. I think what we're seeing is a consistent pattern of evasiveness and immaturity, and I think that does a huge disservice to the entire movement of AI. The key to a vibrant life is rooted in economic mobility, and I think AI is the grand leveler. It is the thing that can enable everyone to have unique amounts of economic mobility because they are unencumbered to figure out what their upper bound is. And against that backdrop, we have to live in this constant doomerism, hype cycle, naivety, and I think it holds us back. How does it hold us back? Tactically, number one, it creates mistrust. I think that Silicon Valley was already decaying in the prestige that it held in American society. We built important things. Then we veered away from that, and we started building less important things. And now we're at a point where we've potentially started to rebuild important things again, but we have this veneer of negativity and mistrust that are created in large part because we just cannot get our sh*t together. And the leaders of the frontier labs are public enemy number one. Number two, I think what it creates, which I think is bad, but what it creates is an incredible opportunity for the hyperscalers. And the very simple opportunity is to convince governments all around the world, not just America, that they should be the gatekeeper. A: You can't trust these guys. B: These models are all over the place. C: Let us be the ones that provision them to the world. We will wrap it in KYC. I've been now talking about KYC for a while, right? Who are these customers? Do they have identification? Why are they allowed to run these models? What are they prompting? Let's keep them so that there's an audit trail. All of these things are going to become issues. The Frontier Lab folks made it an issue because of how they've handled all of this up until now. And what does that create? Now that creates an oligopoly for AI, the most powerful economically leveling instrument we've ever seen in the hands of maybe a handful of hyperscalers, who by the way, would make an incredibly compelling argument, and they would be right. And the only counterfactual to it would be, ‘Well, trust us, guys, it should actually be much more open and in a far more distributed environment.’ Can you imagine the cost and the complexity if you ask the neoscaler to build the same robust KYC or the same VPC infrastructure that Amazon and Microsoft and Google have spent decades investing trillions of dollars in? It's an impossibility, Jason. So you can take all of those datacenters off the map. You can take all of the neoscaler market off the map. All of this was preventable. So instead of a diverse, robust, open ecosystem giving a tool that is the fundamental unlock for humans, we are now going to debate gatekeeping and duopoly versus oligopoly. They have created a total f*cking mess, and it's a shame.”

The All-In Podcast

141,660 次观看 • 1 个月前

Factory (Factory) CEO Matan Grinberg (Matan Grinberg) says "In the next 12 months, 90% of tokens will be going to open models." Enterprises are already shifting rapidly.. "At the beginning of the year, <1% of their tokens were going to open models." "Around March, it crossed 1%... & in May it crossed 10%." "These open models are incredibly performant, incredibly cheap, incredibly fast." While frontier closed models aren't going away: "They'll still have a place, but I think that place will be shrinking—at least in token share." And by the end of 2026? "At least in the enterprise, we'll probably cross the 50% threshold."
2:40

Factory (Factory) CEO Matan Grinberg (Matan Grinberg) says "In the next 12 months, 90% of tokens will be going to open models." Enterprises are already shifting rapidly.. "At the beginning of the year, <1% of their tokens were going to open models." "Around March, it crossed 1%... & in May it crossed 10%." "These open models are incredibly performant, incredibly cheap, incredibly fast." While frontier closed models aren't going away: "They'll still have a place, but I think that place will be shrinking—at least in token share." And by the end of 2026? "At least in the enterprise, we'll probably cross the 50% threshold."

Molly O’Shea

120,949 次观看 • 1 个月前

David Sacks says the data kills the open-source-is-winning narrative, closed models just went from 81% to 89% of enterprise spend. "This is why the share of wallet of closed models 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... Anyone who's saying these closed models are somehow losing, you're just not seeing it in the data." "Nikesh Arora said enterprises would like what he called model fungibility. They would love to commoditize these models and just hot swap them, headless. But then what do you do about memory, context, history? No one's really figured out a way to abstract that stuff away from the model yet."
0:58

David Sacks says the data kills the open-source-is-winning narrative, closed models just went from 81% to 89% of enterprise spend. "This is why the share of wallet of closed models 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... Anyone who's saying these closed models are somehow losing, you're just not seeing it in the data." "Nikesh Arora said enterprises would like what he called model fungibility. They would love to commoditize these models and just hot swap them, headless. But then what do you do about memory, context, history? No one's really figured out a way to abstract that stuff away from the model yet."

Fireside Alpha

54,383 次观看 • 1 个月前

Elon exposes OpenAI -- OpenAI execs betrayed the founding mission as soon as there was revenue and profits to be had -- If it started as a for-profit, Elon would own 50% Elon Musk: “There's a mountain of evidence that shows that OpenAI was created as an open source nonprofit.” “That's the exact description in the incorporation documents. They have completely violated that.” “And they tried to change the definition of OpenAI to mean open to everyone instead of open source, even though it always meant open source.” “ I came up with the name. That’s how I know.” “ I mean, essentially, since I came up with the idea for the company, named it, provided the A, B, and C rounds of funding, recruited the critical personnel, and told them everything I know, if that had been a commercial corporation, I'd probably own half the company.” “It was totally at my discretion. I could have done that.” “But I created it as an open source nonprofit for the world.” Chamath: “ Do you think the right thing to do is to take those models and just open source them today?” Elon: “ Yeah, I think that is what it was created to do, so it should.” “Try using any of the recent so-called OpenAI open source models, they don't work.” “They open sourced a broken, non-working version of their models as a fig leaf.” “I mean, do you know anyone who's running OpenAI's open source models? Jason: “No.” Elon: “Exactly.”
1:10

Elon exposes OpenAI -- OpenAI execs betrayed the founding mission as soon as there was revenue and profits to be had -- If it started as a for-profit, Elon would own 50% Elon Musk: “There's a mountain of evidence that shows that OpenAI was created as an open source nonprofit.” “That's the exact description in the incorporation documents. They have completely violated that.” “And they tried to change the definition of OpenAI to mean open to everyone instead of open source, even though it always meant open source.” “ I came up with the name. That’s how I know.” “ I mean, essentially, since I came up with the idea for the company, named it, provided the A, B, and C rounds of funding, recruited the critical personnel, and told them everything I know, if that had been a commercial corporation, I'd probably own half the company.” “It was totally at my discretion. I could have done that.” “But I created it as an open source nonprofit for the world.” Chamath: “ Do you think the right thing to do is to take those models and just open source them today?” Elon: “ Yeah, I think that is what it was created to do, so it should.” “Try using any of the recent so-called OpenAI open source models, they don't work.” “They open sourced a broken, non-working version of their models as a fig leaf.” “I mean, do you know anyone who's running OpenAI's open source models? Jason: “No.” Elon: “Exactly.”

The All-In Podcast

387,223 次观看 • 9 个月前

🇨🇳 CHINA IS ALMOST READY TO UNDERCUT THE WORLD'S BEST AI MODELS. Six months ago, that sounded impossible. Today GLM-5.2 is open source and costs a fraction of frontier models. Zhipu's co-founder says Fable-class Chinese AI comes even sooner than people think. This is the same playbook China ran on solar, on steel, on EVs. Build it cheaper. Give it away. Take the market. If China delivers frontier AI at a fraction of the price, the economics holding up the U.S. AI market start to crack.
0:20

🇨🇳 CHINA IS ALMOST READY TO UNDERCUT THE WORLD'S BEST AI MODELS. Six months ago, that sounded impossible. Today GLM-5.2 is open source and costs a fraction of frontier models. Zhipu's co-founder says Fable-class Chinese AI comes even sooner than people think. This is the same playbook China ran on solar, on steel, on EVs. Build it cheaper. Give it away. Take the market. If China delivers frontier AI at a fraction of the price, the economics holding up the U.S. AI market start to crack.

CryptoGoos

46,766 次观看 • 1 个月前

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

269,240 次观看 • 19 天前

“We have a crisis of open source models in the Western world. Outside of China, there are no good open source models. We don't even have any in the US now. The talent, the capital and the focus to be best-in-class at pre-training, mid-training, post-training is an extremely scarce skillset. The answer for a lot of countries may just be to take an open weights model from China, post-train or fine-tune their own version, and have that be what they start from." Everett Randle Why does the West have such poor open source models and is it a national security threat to rely on open-source Chinese models Demis Hassabis Yann LeCun Aidan Gomez Lilian Weng Jan Leike
1:20

“We have a crisis of open source models in the Western world. Outside of China, there are no good open source models. We don't even have any in the US now. The talent, the capital and the focus to be best-in-class at pre-training, mid-training, post-training is an extremely scarce skillset. The answer for a lot of countries may just be to take an open weights model from China, post-train or fine-tune their own version, and have that be what they start from." Everett Randle Why does the West have such poor open source models and is it a national security threat to rely on open-source Chinese models Demis Hassabis Yann LeCun Aidan Gomez Lilian Weng Jan Leike

Harry Stebbings

63,672 次观看 • 1 个月前

Aligned with Matan Grinberg's intuition that in the next 12 months, 90% of tokens could be going to open models. We're seeing more and more companies using frontier APIs for experimenting and open-source or private models for production and larger workloads!
1:08

Aligned with Matan Grinberg's intuition that in the next 12 months, 90% of tokens could be going to open models. We're seeing more and more companies using frontier APIs for experimenting and open-source or private models for production and larger workloads!

clem 🤗

24,138 次观看 • 1 个月前