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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... control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.show more

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🇺🇸 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

🇺🇸 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 views • 10 months ago

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.

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

31,447 views • 4 days ago

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

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

303,274 views • 8 days ago

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.

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 views • 6 days ago

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

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

25,121 views • 12 days ago

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

300,537 views • 19 days ago

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.

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

685,335 views • 4 months ago

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.

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

518,489 views • 13 days ago

🚨 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.”

🚨 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

96,504 views • 9 months ago

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.

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

33,493 views • 4 days ago

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

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

66,376 views • 7 days ago

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.

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

19,522 views • 1 month ago

🇺🇸DAVID SACKS: WHEN THE VP DELIVERED A SPEECH ON AI OPPORTUNITY, IT WAS LIKE A SHOT ACROSS THE BOW! “I think that the vice president—you knew he was going to deliver an important speech as soon as he got up there and said, "I'm here to talk about not AI safety, but AI opportunity." To understand what a bracing statement that was, and really almost like a shot across the bow, you have to understand the history and context of these events. For the last couple of years, these events have been exclusively focused on AI safety.” Source: The All-In Podcast David Sacks

🇺🇸DAVID SACKS: WHEN THE VP DELIVERED A SPEECH ON AI OPPORTUNITY, IT WAS LIKE A SHOT ACROSS THE BOW! “I think that the vice president—you knew he was going to deliver an important speech as soon as he got up there and said, "I'm here to talk about not AI safety, but AI opportunity." To understand what a bracing statement that was, and really almost like a shot across the bow, you have to understand the history and context of these events. For the last couple of years, these events have been exclusively focused on AI safety.” Source: The All-In Podcast David Sacks

Mario Nawfal

273,952 views • 1 year ago

Ansem explains the thesis behind Dolphin AI “Dolphin is the provider of the uncensored model that Venice uses” “ChatGPT, Claude, all of the big labs have very strict rules on what you can ask the models, they’re censored very heavily” “We’re not in control of what that censorship is and what they’re telling the models not to say” “One of crypto’s core tenets is not having to rely on some centralized entity deciding what you can and cannot do” “They want the technology to be free and open to everyone. Uncensored models are one way crypto is looking at doing that”

Ansem explains the thesis behind Dolphin AI “Dolphin is the provider of the uncensored model that Venice uses” “ChatGPT, Claude, all of the big labs have very strict rules on what you can ask the models, they’re censored very heavily” “We’re not in control of what that censorship is and what they’re telling the models not to say” “One of crypto’s core tenets is not having to rely on some centralized entity deciding what you can and cannot do” “They want the technology to be free and open to everyone. Uncensored models are one way crypto is looking at doing that”

Market Bubble

20,688 views • 1 month ago

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Big Brain AI

19,195 views • 1 month ago

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir

4,376,266 views • 8 days ago

Anthropic CEO Dario Amodei: We don’t know if the models are conscious. We are not even sure what it would mean for a model to be conscious. But we’re open to the idea that it could be.

Anthropic CEO Dario Amodei: We don’t know if the models are conscious. We are not even sure what it would mean for a model to be conscious. But we’re open to the idea that it could be.

Clash Report

32,344 views • 4 months ago

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

85,503 views • 12 days ago

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 views • 4 days ago

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."

TBPN

46,746 views • 3 months ago