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Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep...

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Fireside Alpha

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10,037 просмотров • 24 дней назад •via X (Twitter)

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David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”
0:59

David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”

The All-In Podcast

274,299 просмотров • 4 дней назад

🇺🇸 DAVID SACKS: MODELS ARE LEAPFROGGING EACH OTHER AND BECOMING SPECIALIZED "The model companies are still leapfrogging each other to some degree with their latest versions. And again, that should not be possible if one model achieved rapid takeoff. The other thing you're seeing is that it's not like one model is dominating all the other models. You're seeing that models are developing areas of competitive advantage. They're becoming increasingly specialized." Source: The All-In Podcast David Sacks
0:47

🇺🇸 DAVID SACKS: MODELS ARE LEAPFROGGING EACH OTHER AND BECOMING SPECIALIZED "The model companies are still leapfrogging each other to some degree with their latest versions. And again, that should not be possible if one model achieved rapid takeoff. The other thing you're seeing is that it's not like one model is dominating all the other models. You're seeing that models are developing areas of competitive advantage. They're becoming increasingly specialized." Source: The All-In Podcast David Sacks

Mario Nawfal

45,461 просмотров • 11 месяцев назад

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.
0:24

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

Karl Mehta

31,891 просмотров • 24 дней назад

Palantir CEO Alex Karp on the Wealth Tax: "It just punishes." "It starts with the billionaires." "Every single person at this table is going to be paying a wealth tax only to punish us." "And the reason for it is—these models have been completely, irresponsibly oversold." " And the sell is, 'It's dangerous for everyone—which is why I can give it to all your adversaries, but I can't give it to the Department of War." "Or, I can't safely give it to an enterprise in this country without being certain that the alpha of that business could transfer to this model tomorrow." "At every single enterprise I deal with, these people are livid." "They're like, 'I'm paying for tokens that create no value. These people are stealing the weights and alpha of my business, and they're creating a wealth tax that does not help the poor." Via CNBC
1:32

Palantir CEO Alex Karp on the Wealth Tax: "It just punishes." "It starts with the billionaires." "Every single person at this table is going to be paying a wealth tax only to punish us." "And the reason for it is—these models have been completely, irresponsibly oversold." " And the sell is, 'It's dangerous for everyone—which is why I can give it to all your adversaries, but I can't give it to the Department of War." "Or, I can't safely give it to an enterprise in this country without being certain that the alpha of that business could transfer to this model tomorrow." "At every single enterprise I deal with, these people are livid." "They're like, 'I'm paying for tokens that create no value. These people are stealing the weights and alpha of my business, and they're creating a wealth tax that does not help the poor." Via CNBC

Jawwwn

308,798 просмотров • 28 дней назад

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.
0:45

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Ihtesham Ali

17,433 просмотров • 26 дней назад

David Sacks laid out the cleanest theory about why Anthropic keeps calling for government regulation of AI. The answer has nothing to do with safety and everything to do with market structure. Anthropic spent months writing blog posts warning that AI was dangerous. Dario gave interviews about existential risk. He published a piece calling for an FAA-style agency to approve all AI models before release. He primed government officials to treat frontier AI as a threat requiring oversight. Then one of Anthropic's own most trusted partners reported a credible jailbreak from Fable 5. And the government did exactly what Dario had spent months conditioning them to do. They rolled it back. Sacks called it on the All-In podcast. Dario got exactly what he wanted. The FAA for AI is not a safety mechanism. It is a moat. A government approval process for new model releases does not hurt Anthropic. They already have the models. It hurts every competitor who does not. It hurts open source models that cannot be regulated because there is no company to regulate. It hurts the Chinese labs only insofar as they care about the American market at all. The only companies that benefit from a labyrinthine government approval process are the ones already at the frontier who can afford to wait out the review cycle. That is Anthropic. That is OpenAI. Nobody else. The proof is in what they did not do. Chimath pointed it out directly. If you are genuinely worried about misuse, you implement know-your-customer verification. You make people identify themselves before accessing the most powerful models. Anthropic could have done that tomorrow. They did not. They do not want KYC. KYC is transparent. KYC can be audited. KYC gives users due process. What they built instead was an invisible surveillance system that profiles you, degrades your access without telling you, and asks the government to make sure no one else can offer you an alternative. If you thought this was safety then you are wrong. That is capture. Sacks said the response should be simple. Fix the jailbreak, come back to market, and do not reward Dario with the regulatory architecture he has been engineering for years. We will see if anyone is listening. WATCH THE FULL PODCAST ON The All-In Podcast
1:22

David Sacks laid out the cleanest theory about why Anthropic keeps calling for government regulation of AI. The answer has nothing to do with safety and everything to do with market structure. Anthropic spent months writing blog posts warning that AI was dangerous. Dario gave interviews about existential risk. He published a piece calling for an FAA-style agency to approve all AI models before release. He primed government officials to treat frontier AI as a threat requiring oversight. Then one of Anthropic's own most trusted partners reported a credible jailbreak from Fable 5. And the government did exactly what Dario had spent months conditioning them to do. They rolled it back. Sacks called it on the All-In podcast. Dario got exactly what he wanted. The FAA for AI is not a safety mechanism. It is a moat. A government approval process for new model releases does not hurt Anthropic. They already have the models. It hurts every competitor who does not. It hurts open source models that cannot be regulated because there is no company to regulate. It hurts the Chinese labs only insofar as they care about the American market at all. The only companies that benefit from a labyrinthine government approval process are the ones already at the frontier who can afford to wait out the review cycle. That is Anthropic. That is OpenAI. Nobody else. The proof is in what they did not do. Chimath pointed it out directly. If you are genuinely worried about misuse, you implement know-your-customer verification. You make people identify themselves before accessing the most powerful models. Anthropic could have done that tomorrow. They did not. They do not want KYC. KYC is transparent. KYC can be audited. KYC gives users due process. What they built instead was an invisible surveillance system that profiles you, degrades your access without telling you, and asks the government to make sure no one else can offer you an alternative. If you thought this was safety then you are wrong. That is capture. Sacks said the response should be simple. Fix the jailbreak, come back to market, and do not reward Dario with the regulatory architecture he has been engineering for years. We will see if anyone is listening. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

25,284 просмотров • 1 месяц назад

Chamath fed Dario Amodei's own essays into Claude and asked for a psychological profile. What came back should be required reading for every investor in frontier AI. The model identified a pattern. Dario distrusts other labs. He distrusts authoritarian states. He distrusts markets to distribute the gains fairly. He distrusts institutions to move fast enough. And after Mythos, he distrusts the government to wield power transparently. That is a very long list of untrustworthy actors. The list of trustworthy ones is conspicuously short. And it has a suspicious tendency to resolve toward people who reason the way he does, operating under rules he helped design. Claude named it precisely. Not megalomania. Epistemic exceptionalism. The quiet, defensible conviction that disagreement is always downstream of error. That when your safety framework requires someone to hold the keys and your analysis keeps concluding every other key holder cannot be trusted, you have built a machine that outputs the same answer no matter what you feed it. The tell was a single word. When the Mythos situation collapsed, Anthropic called it a misunderstanding. That word choice under pressure assumes that if everyone simply understood correctly, they would agree with him. Sacks put it simply on the pod. They believe AI is super dangerous and only they are virtuous enough to control it. That is not a safety framework. That is a monopoly with a philosophy attached. WATCH THE FULL PODCAST ON The All-In Podcast
3:15

Chamath fed Dario Amodei's own essays into Claude and asked for a psychological profile. What came back should be required reading for every investor in frontier AI. The model identified a pattern. Dario distrusts other labs. He distrusts authoritarian states. He distrusts markets to distribute the gains fairly. He distrusts institutions to move fast enough. And after Mythos, he distrusts the government to wield power transparently. That is a very long list of untrustworthy actors. The list of trustworthy ones is conspicuously short. And it has a suspicious tendency to resolve toward people who reason the way he does, operating under rules he helped design. Claude named it precisely. Not megalomania. Epistemic exceptionalism. The quiet, defensible conviction that disagreement is always downstream of error. That when your safety framework requires someone to hold the keys and your analysis keeps concluding every other key holder cannot be trusted, you have built a machine that outputs the same answer no matter what you feed it. The tell was a single word. When the Mythos situation collapsed, Anthropic called it a misunderstanding. That word choice under pressure assumes that if everyone simply understood correctly, they would agree with him. Sacks put it simply on the pod. They believe AI is super dangerous and only they are virtuous enough to control it. That is not a safety framework. That is a monopoly with a philosophy attached. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

300,712 просмотров • 1 месяц назад

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.
1:40

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dustin

686,754 просмотров • 5 месяцев назад

Dario told the Senate in 2023 that “the scaling of open-source models” was going down “a very dangerous path.” in other words: open weight models are fine until they get good enough to threaten the closed-model business, then they become a matter of national security.
2:14

Dario told the Senate in 2023 that “the scaling of open-source models” was going down “a very dangerous path.” in other words: open weight models are fine until they get good enough to threaten the closed-model business, then they become a matter of national security.

ℏεsam

523,182 просмотров • 1 месяц назад

David Sacks: Anthropic Wants to Ban Open Source AI in America Listen to All-In to stay a few weeks (or months) ahead of everyone else David Sacks back in May: “I think where it's all leading to is an effort to ban open source models. There's a lot of breadcrumbs leading here. You look at a lot of the rhetoric around how models need to have guardrails, and that with open source models, the guardrails can be removed and therefore they're dangerous. You see this rhetoric already in Anthropic's blog posts. Any threat that they describe, they kind of go out of their way to take that shot at open source models. They're trying to create ideas or put predicate facts in the public record to justify an action later on. I think it's just a matter of time before they feel like they're at a position where maybe they can push for that type of ban directly.”
0:40

David Sacks: Anthropic Wants to Ban Open Source AI in America Listen to All-In to stay a few weeks (or months) ahead of everyone else David Sacks back in May: “I think where it's all leading to is an effort to ban open source models. There's a lot of breadcrumbs leading here. You look at a lot of the rhetoric around how models need to have guardrails, and that with open source models, the guardrails can be removed and therefore they're dangerous. You see this rhetoric already in Anthropic's blog posts. Any threat that they describe, they kind of go out of their way to take that shot at open source models. They're trying to create ideas or put predicate facts in the public record to justify an action later on. I think it's just a matter of time before they feel like they're at a position where maybe they can push for that type of ban directly.”

The All-In Podcast

85,735 просмотров • 7 дней назад

Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:
2:07

Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:

Uncover AI

36,393 просмотров • 17 дней 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 • 17 дней назад

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.
4:38

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

Milk Road AI

34,298 просмотров • 24 дней назад

🚨 David Sacks on How Blue State AI Laws Are Encouraging “Woke AI” “The only way that I see for model developers to comply with this law is to build in a new DEI layer into the models.”
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🚨 David Sacks on How Blue State AI Laws Are Encouraging “Woke AI” “The only way that I see for model developers to comply with this law is to build in a new DEI layer into the models.”

Chief Nerd

97,714 просмотров • 9 месяцев назад

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

54,302 просмотров • 18 дней назад

The tape scored Alex Karp’s CNBC hit as a crash out Mediaite clipped “you sound pretty angry” Futurism called it a nervous breakdown David Sacks called it the best enterprise AI safety argument of the year I think it was simpler It was a purchase order Karp said what technical customers want is control over their compute, models, data stack and alpha That’s the whole AI stack in four line items Call it The Alpha Transfer When you rent intelligence by the token, your alpha transfers to the landlord The lab sees where value is created on top of its model, then ships that vertical itself Figma was the warning shot Claude Design shipped three days after Mike Krieger left Figma’s board Figma got hit Anthropic’s valuation kept ripping This is why control matters Owned compute Open weights Private data Neutral orchestration layer Palantir is expensive because the market already believes this ServiceNow is cheap because the market still doubts it Karp wasn’t ranting He was reading the enterprise AI bill of materials Rent tokens, lose alpha $PLTR $NOW $NVDA
2:49

The tape scored Alex Karp’s CNBC hit as a crash out Mediaite clipped “you sound pretty angry” Futurism called it a nervous breakdown David Sacks called it the best enterprise AI safety argument of the year I think it was simpler It was a purchase order Karp said what technical customers want is control over their compute, models, data stack and alpha That’s the whole AI stack in four line items Call it The Alpha Transfer When you rent intelligence by the token, your alpha transfers to the landlord The lab sees where value is created on top of its model, then ships that vertical itself Figma was the warning shot Claude Design shipped three days after Mike Krieger left Figma’s board Figma got hit Anthropic’s valuation kept ripping This is why control matters Owned compute Open weights Private data Neutral orchestration layer Palantir is expensive because the market already believes this ServiceNow is cheap because the market still doubts it Karp wasn’t ranting He was reading the enterprise AI bill of materials Rent tokens, lose alpha $PLTR $NOW $NVDA

Ben Pouladian

66,777 просмотров • 27 дней назад

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.
0:48

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

Adeniyi.sui

13,419 просмотров • 26 дней назад

Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.
0:47

Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.

Adam Cohen

23,040 просмотров • 2 месяцев назад

Open models are already being used in the enterprise. Over 85% of the Fortune 500 companies already use Ollama to fulfill specific tasks. Jeffrey Morgan Why open models and Ollama? Ownership. Open models are yours to keep, customize, and optimize. Affordable. Run it the way you like, in your own environment. Private. Your data belongs to you.
1:37

Open models are already being used in the enterprise. Over 85% of the Fortune 500 companies already use Ollama to fulfill specific tasks. Jeffrey Morgan Why open models and Ollama? Ownership. Open models are yours to keep, customize, and optimize. Affordable. Run it the way you like, in your own environment. Private. Your data belongs to you.

ollama

42,136 просмотров • 13 дней назад

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.
3:21

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.

Milk Road AI

16,317 просмотров • 21 дней назад