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Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run. He is really asking what happens to the firm when intelligence becomes something you can rent. For a century, companies protected...

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Rohan Paul

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93,885 次观看 • 20 天前 •via X (Twitter)

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18 条评论

Rohan Paul 的头像
Rohan Paul20 天前

Full video

Ella Tech & Tool 的头像
Ella Tech & Tool20 天前

Spot on 👏 the loop beats the model every time

Genius💡💹🧲 🤖 的头像
Genius💡💹🧲 🤖20 天前

nadella really nailed the actual question everyone else is dancing around

Kaustubha 的头像
Kaustubha20 天前

Learning Loop will also become redundant. Its just getting started.

Kris 的头像
Kris20 天前

Nope we just open source all the tools on local platforms. Decentralized local cheap real world solutions. There is no moat, only optimum end situations.

NAMAN RAJ 的头像
NAMAN RAJ20 天前

Renting intelligence? Sounds like Microsoft's new 'rent-a-brain' subscription model is coming soon 😉🤑

Pavlos Papageorgiou 的头像
Pavlos Papageorgiou20 天前

I generally agree that frontier models are too convergent to form a moat (though they're a cost-bound oligopoly) and some other content-locked tier will take the value.

Aaron Powell, M.Eng.🇨🇦 的头像
Aaron Powell, M.Eng.🇨🇦20 天前

Which AI will start creating winning postulates.

Jan Ko 的头像
Jan Ko20 天前

Yeah, and model providers steal your loops :)

AIKeaton UK 的头像
AIKeaton UK20 天前

Fair. I'd say private evals are the asset. The trap is treating AI like software. It needs task telemetry, judged outputs and evals that match your actual mess.

AI Quanting 的头像
AI Quanting20 天前

A loop only compounds if the task distribution holds still. The work stable enough for traces to accumulate is also the work a general model picks up first. So the compounding and the exposure sit on the same tasks.

Matt Sotebeer 的头像
Matt Sotebeer20 天前

its always about how you mix the ingredients.

Dr Don Perugini 的头像
Dr Don Perugini20 天前

Agree - companies that own the best loops will thrive. The 'loops' or harnesses are effectively the cognitive workflows that represent expertise - the tacit knowledge that lives in the company's experts' heads that underpin the knowledge work (procedural graphs). This expertise data becomes the new system of record for companies, and can be dynamically updated. @CogFlowAI uses cognitive science to allow non-technical experts and teams to capture and automate expertise - tacit knowledge.

Flextor 的头像
Flextor20 天前

the private loop is the part people underestimate. the model is rented, but the traces and evals from real work are where the moat compounds.

AI Mastery Guide 的头像
AI Mastery Guide19 天前

the real moat is the loop huh

Read the Original - Krunal B 的头像
Read the Original - Krunal B19 天前

Rentable intelligence already got a corporate test. Dorsey, Feb 2026: hierarchy is just information routing, so AI lets you delete it. The unanswered part was the ratio - Block at ~16c of operating profit per dollar of gross profit, against Adyen's 53. What scores a loop?

Yasir Prototyper 的头像
Yasir Prototyper20 天前

I keep telling founders: you can rent the model, not your operating traces. In logistics, every reroute and exception is training data. Capture it or lose the edge.

I. Jovan 的头像
I. Jovan20 天前

That moat sounds less like model access and more like operational memory: proprietary traces plus a tight eval loop that turns mistakes into better defaults. The hard part is making the loop trustworthy, not just fast.

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Satya Nadella reveals why every company may need its own AI model: the model becomes the new company database. "To me, a model is like the database market." "A firm should be able to take the tacit knowledge it has and embed it inside weights in a model that they control." "When somebody asks me how many models should there be, I'll say as many models as firms in the world." The contrarian part: the value may not sit in one universal frontier model. It sits in each company turning its private operating knowledge into a controlled model.
1:44

Satya Nadella reveals why every company may need its own AI model: the model becomes the new company database. "To me, a model is like the database market." "A firm should be able to take the tacit knowledge it has and embed it inside weights in a model that they control." "When somebody asks me how many models should there be, I'll say as many models as firms in the world." The contrarian part: the value may not sit in one universal frontier model. It sits in each company turning its private operating knowledge into a controlled model.

Karl Mehta

468,672 次观看 • 3 个月前

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Satya Nadella talks about Microsoft’s new AI strategy. Asks “if you’re [only] a consumer of foundation models…how can you create enterprise value?” In his view, companies should combine their internal IP and tacit knowledge with an AI “hill-climbing machine” (reinforcement learning, custom evals) that is model agnostic. Obviously, he positions Microsoft as a partner because they have already gone through that process (namely by diversifying AI efforts away from only OpenAI). “We built a multi-tenant SaaS application,” Nadella says. “We can turn that into a multi-tenant hill climbing service where the data and the environment and the models and the traces and the outcomes are owned by the company.”

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139,960 次观看 • 3 个月前

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."
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Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."

Big Brain AI

11,770 次观看 • 3 个月前

Satya Nadella, CEO of Microsoft, says every company now runs on two kinds of capital, and leaking one of them is a one-way door: "This economy is going to be shaped going forward by both human capital and, let's call it, this token capital. That is true for Microsoft, that is true for a new startup, that's going to be true for any bank that's been in existence for 100 years." "The interplay between human capital and token capital, and compounding the returns of it, is what you need to do." "What is the tacit knowledge of an enterprise or a firm? It's the unique ways that you're able to operate, pass judgment, have taste." "The model, in some sense, is able to extract that tacit knowledge through human trajectories, and then code it in a set of weights in a model." "Every enterprise now needs to be more mindful about that interplay of humans and their digital estate working together, those trajectories training the models that they keep as IP versus leak it." "Because if you leak it, it's a one-way door. You're done in some sense." The moat was never the data sitting in your warehouse. It was the judgment your people apply to it, and AI now turns that judgment into weights you either own or hand away. Treat your tacit knowledge like a patent, not a prompt. The firms that pipe a hundred years of taste into someone else's frontier model are trading a permanent edge for a quarter of convenience.
1:34

Satya Nadella, CEO of Microsoft, says every company now runs on two kinds of capital, and leaking one of them is a one-way door: "This economy is going to be shaped going forward by both human capital and, let's call it, this token capital. That is true for Microsoft, that is true for a new startup, that's going to be true for any bank that's been in existence for 100 years." "The interplay between human capital and token capital, and compounding the returns of it, is what you need to do." "What is the tacit knowledge of an enterprise or a firm? It's the unique ways that you're able to operate, pass judgment, have taste." "The model, in some sense, is able to extract that tacit knowledge through human trajectories, and then code it in a set of weights in a model." "Every enterprise now needs to be more mindful about that interplay of humans and their digital estate working together, those trajectories training the models that they keep as IP versus leak it." "Because if you leak it, it's a one-way door. You're done in some sense." The moat was never the data sitting in your warehouse. It was the judgment your people apply to it, and AI now turns that judgment into weights you either own or hand away. Treat your tacit knowledge like a patent, not a prompt. The firms that pipe a hundred years of taste into someone else's frontier model are trading a permanent edge for a quarter of convenience.

Karl Mehta

34,573 次观看 • 2 个月前

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.
3:19

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 次观看 • 2 个月前

Satya Nadella was asked why Microsoft, with a $175 billion AI budget, still doesn't have a frontier model. Satya Nadella: "We are well on our way building our MAI models our goal is to basically hill climb from the bottom, by the way, not distilling anything." "My advice is more like use all but be independent of all. I would pull out a model and see whether I can retain the eval. If I can't, that means you really are dependent on something that may or may not be yours." "If you're a hyperscaler, you're not a supplier to two model companies, that's not a business we need more." "I worked on Windows interrupt with Unix first. In fact, it was counterintuitive, right ? We used to think, oh my god, this means we'll be less used, except we were more used we were able to penetrate the enterprise primarily because we did that interrupt work." If owning the infrastructure layer was the Microsoft plan and winning move, why is the same company now racing to build its own frontier model ?
3:41

Satya Nadella was asked why Microsoft, with a $175 billion AI budget, still doesn't have a frontier model. Satya Nadella: "We are well on our way building our MAI models our goal is to basically hill climb from the bottom, by the way, not distilling anything." "My advice is more like use all but be independent of all. I would pull out a model and see whether I can retain the eval. If I can't, that means you really are dependent on something that may or may not be yours." "If you're a hyperscaler, you're not a supplier to two model companies, that's not a business we need more." "I worked on Windows interrupt with Unix first. In fact, it was counterintuitive, right ? We used to think, oh my god, this means we'll be less used, except we were more used we were able to penetrate the enterprise primarily because we did that interrupt work." If owning the infrastructure layer was the Microsoft plan and winning move, why is the same company now racing to build its own frontier model ?

Vikram M

721,878 次观看 • 15 天前

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

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

Vikram M

12,078 次观看 • 2 个月前

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."
0:45

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

19,342 次观看 • 4 个月前

Anthropic has created an AI model that “can get into almost any software in the world,” former AI company founder and CEO Matt Shumer says — but the company is not releasing it to the public: “I have never seen something like this — a company that is about to IPO and having such a moral compass.” Shumer shares what makes the AI model so powerful and potentially dangerous.
4:55

Anthropic has created an AI model that “can get into almost any software in the world,” former AI company founder and CEO Matt Shumer says — but the company is not releasing it to the public: “I have never seen something like this — a company that is about to IPO and having such a moral compass.” Shumer shares what makes the AI model so powerful and potentially dangerous.

CBS Mornings

98,892 次观看 • 5 个月前

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.
0:27

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,750 次观看 • 6 个月前

"The jig is up." - Alex Karp on frontier models Everyone is asking whether AI models will k*ll software. Karp & Dylan Field (Dylan Field) think that's the wrong question. Karp argues the winners won't be whoever has the best model. They'll be whoever owns their data, models, & compute. Field made the same point at Config. "The narrative seems to be software is kaput. But a lot of software will do just fine or actually even amazing." What lasts, he says, is software where the data compounds into its own flywheel. The data is the moat, not the model.
1:48

"The jig is up." - Alex Karp on frontier models Everyone is asking whether AI models will k*ll software. Karp & Dylan Field (Dylan Field) think that's the wrong question. Karp argues the winners won't be whoever has the best model. They'll be whoever owns their data, models, & compute. Field made the same point at Config. "The narrative seems to be software is kaput. But a lot of software will do just fine or actually even amazing." What lasts, he says, is software where the data compounds into its own flywheel. The data is the moat, not the model.

Molly O’Shea

45,256 次观看 • 3 个月前

Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)
1:18

Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)

Rohan Paul

143,134 次观看 • 4 个月前

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.
0:43

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.

Manos Koukoumidis

1,006,622 次观看 • 1 个月前

Larry Ellison just told every AI company on Earth they’re fighting the wrong war. The entire industry is racing to build the smartest model. More parameters. Better benchmarks. Faster inference. Ellison isn’t building a model. He’s controlling what every model needs to be useful. Every frontier AI trains on the same public internet. Same scraped pages. Same recycled text. When everyone has the same data, it’s not an advantage. It’s a floor. The only data that creates separation is private. Medical records. Financial models. Defense systems. Proprietary research locked behind firewalls for decades. That data already lives inside Oracle databases. Not Google’s. Not Microsoft’s. Not Amazon’s. Ellison didn’t enter the model war. He positioned himself above it. He rebuilt the database so AI can reason on private data without ever absorbing it. Training folds your data into the model permanently. Once it’s in, it never comes back out. Reasoning thinks with your data and hands back only the answer. The data never moves. One is surrender. The other is sovereignty. Ellison: “These are remarkable electronic brains.” He didn’t build the brain. He owns what the brain needs to think. Everyone is building the most powerful mind in human history. A mind is only as valuable as what it’s allowed to know. Own the knowledge and it doesn’t matter who builds the brain. That pattern has held through every era of human civilization. AI doesn’t break it. It proves it.
2:07

Larry Ellison just told every AI company on Earth they’re fighting the wrong war. The entire industry is racing to build the smartest model. More parameters. Better benchmarks. Faster inference. Ellison isn’t building a model. He’s controlling what every model needs to be useful. Every frontier AI trains on the same public internet. Same scraped pages. Same recycled text. When everyone has the same data, it’s not an advantage. It’s a floor. The only data that creates separation is private. Medical records. Financial models. Defense systems. Proprietary research locked behind firewalls for decades. That data already lives inside Oracle databases. Not Google’s. Not Microsoft’s. Not Amazon’s. Ellison didn’t enter the model war. He positioned himself above it. He rebuilt the database so AI can reason on private data without ever absorbing it. Training folds your data into the model permanently. Once it’s in, it never comes back out. Reasoning thinks with your data and hands back only the answer. The data never moves. One is surrender. The other is sovereignty. Ellison: “These are remarkable electronic brains.” He didn’t build the brain. He owns what the brain needs to think. Everyone is building the most powerful mind in human history. A mind is only as valuable as what it’s allowed to know. Own the knowledge and it doesn’t matter who builds the brain. That pattern has held through every era of human civilization. AI doesn’t break it. It proves it.

Dustin

98,131 次观看 • 2 个月前

Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do think you're going to see American open-source led by Nvidia get really close to the frontier." "Sharing your own enterprise context, that's truly your IP, that's truly the value of your company, the context embedded in all of your data, sharing that with a frontier lab may be hazardous for your financial health." "I think what you'll see these companies do is they'll have their own model on their data, and it will work with one or two other frontier models, checking each other... I think that feels like a very likely future to me." Gavin Baker David George
1:37

Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do think you're going to see American open-source led by Nvidia get really close to the frontier." "Sharing your own enterprise context, that's truly your IP, that's truly the value of your company, the context embedded in all of your data, sharing that with a frontier lab may be hazardous for your financial health." "I think what you'll see these companies do is they'll have their own model on their data, and it will work with one or two other frontier models, checking each other... I think that feels like a very likely future to me." Gavin Baker David George

a16z

39,947 次观看 • 1 个月前

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 次观看 • 2 个月前

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.
2:28

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Dustin

39,652 次观看 • 5 个月前

SITUATION EXPLAINED: Thinking Machines just published their mission statement. • Mission: build AI that extends human will and judgment, not replaces it • The tacit knowledge argument: central planning fails not because of insufficient intelligence but because productive knowledge is local, fleeting, and held privately, the same reason a single AI alignment spec will fail • The "intelligence curse": power that needs nothing from people loses the incentive to care for their needs. A single locus of AI value alignment becomes a locus of power to be captured • Decentralized alignment: human values resist consolidation just like human knowledge does... today the values and voice of AI are decided in a handful of places, and that's dangerous • Today each lab trains its next flagship model on its previous one... whatever character emerges from that loop, everyone gets the same one, each generation inheriting the traits of the last • Their answer: AI that is more capable because it encourages human participation... alignment that arises from diverse AIs shaped by the people who own them sof 𓋹: "This is going to be very soon the winning narrative. People are gonna get so sick of hearing super intelligent stuff because they're like, I don't see anything that proves that right now. All this messaging is just gonna start becoming like, oh, make you the best human, make you a superhuman, make you awesome." Theo Jaffee: "I think it's a way better vision for AI to be customized to people in different organizations rather than to have just one Claude that is the same everywhere. Kind of like a deity."
17:08

SITUATION EXPLAINED: Thinking Machines just published their mission statement. • Mission: build AI that extends human will and judgment, not replaces it • The tacit knowledge argument: central planning fails not because of insufficient intelligence but because productive knowledge is local, fleeting, and held privately, the same reason a single AI alignment spec will fail • The "intelligence curse": power that needs nothing from people loses the incentive to care for their needs. A single locus of AI value alignment becomes a locus of power to be captured • Decentralized alignment: human values resist consolidation just like human knowledge does... today the values and voice of AI are decided in a handful of places, and that's dangerous • Today each lab trains its next flagship model on its previous one... whatever character emerges from that loop, everyone gets the same one, each generation inheriting the traits of the last • Their answer: AI that is more capable because it encourages human participation... alignment that arises from diverse AIs shaped by the people who own them sof 𓋹: "This is going to be very soon the winning narrative. People are gonna get so sick of hearing super intelligent stuff because they're like, I don't see anything that proves that right now. All this messaging is just gonna start becoming like, oh, make you the best human, make you a superhuman, make you awesome." Theo Jaffee: "I think it's a way better vision for AI to be customized to people in different organizations rather than to have just one Claude that is the same everywhere. Kind of like a deity."

MTS

14,305 次观看 • 2 个月前

The AI industry is optimizing for a definition of intelligence that does not exist. Andrew Ng just said it out loud. Ng: “AGI, to me, should be less about AI that already knows everything under the sun. That seems very challenging, doesn’t seem practical.” The human brain is not the most powerful economic asset in history because of what it holds. It is powerful because of what it can pick up. Ng: “The amazing thing about the human brain is its plasticity, or its ability to learn.” That same biological hardware that earns a PhD in quantum physics could have been trained on chess, surgery, or rewriting global supply chains from scratch. Ng: “That same human brain, just given different training, could have been a chess master, or could have been amazing at playing tennis.” General intelligence is not omniscience. It is the structural capacity to master whatever you point it at. Ng: “It is through learning that we then gain these incredibly specialized intelligences.” The winner is not whoever builds the biggest model. It is whoever builds the most adaptable one. The AI that walks into a domain it has never touched and executes before a human analyst finishes reading the brief. Ng: “What makes the human brain so valuable for economic tasks, is its ability to just learn to do whatever is needed.” Every corporation on earth pays for human labor because humans adapt. Not because they already know everything. AGI is the digitization of that exact capability. At machine speed. At infinite scale. Ng: “A lot of what makes the human brain so general is not that my brain or your brain already knows everything under the sun. It’s our ability to adapt, to learn a huge range of things.” The most powerful economic asset in history was never specialized knowledge. It was the raw capacity to acquire any knowledge, in any domain, on demand. The winning AI is not an encyclopedia. It is the force that makes encyclopedias irrelevant. And once that exists, the question stops being what the AI knows. It becomes what you can teach it before your competitor wakes up. Most people dominating this conversation have not understood that yet.
1:16

The AI industry is optimizing for a definition of intelligence that does not exist. Andrew Ng just said it out loud. Ng: “AGI, to me, should be less about AI that already knows everything under the sun. That seems very challenging, doesn’t seem practical.” The human brain is not the most powerful economic asset in history because of what it holds. It is powerful because of what it can pick up. Ng: “The amazing thing about the human brain is its plasticity, or its ability to learn.” That same biological hardware that earns a PhD in quantum physics could have been trained on chess, surgery, or rewriting global supply chains from scratch. Ng: “That same human brain, just given different training, could have been a chess master, or could have been amazing at playing tennis.” General intelligence is not omniscience. It is the structural capacity to master whatever you point it at. Ng: “It is through learning that we then gain these incredibly specialized intelligences.” The winner is not whoever builds the biggest model. It is whoever builds the most adaptable one. The AI that walks into a domain it has never touched and executes before a human analyst finishes reading the brief. Ng: “What makes the human brain so valuable for economic tasks, is its ability to just learn to do whatever is needed.” Every corporation on earth pays for human labor because humans adapt. Not because they already know everything. AGI is the digitization of that exact capability. At machine speed. At infinite scale. Ng: “A lot of what makes the human brain so general is not that my brain or your brain already knows everything under the sun. It’s our ability to adapt, to learn a huge range of things.” The most powerful economic asset in history was never specialized knowledge. It was the raw capacity to acquire any knowledge, in any domain, on demand. The winning AI is not an encyclopedia. It is the force that makes encyclopedias irrelevant. And once that exists, the question stops being what the AI knows. It becomes what you can teach it before your competitor wakes up. Most people dominating this conversation have not understood that yet.

Dustin

19,816 次观看 • 7 个月前

RSI section from the AI documentary Machine God The next threshold is Recursive Self-Improvement: the moment when AI can improve itself without human assistance. For decades this sounded like science fiction. Intelligence explosion scenarios imagined a system rewriting its own code, becoming smarter, then using that new intelligence to make still better versions of itself. But the idea looks less remote now that AI contributes directly to frontier science. In mathematics, recent systems have moved beyond solving contest problems to producing serious new arguments on long-standing open problems. AI is used to build physics world models and propose candidate theories or computational methods. These are early signs that machine cognition is entering the creative loop of science itself. The crucial transition comes when that loop turns inward. AI research is, after all, a technical discipline made of code, mathematics, models of information flow. These are exactly the domains in which frontier models are improving fastest. A model that can solve hard mathematical problems, write production-quality code, design experiments, read the literature, and evaluate benchmark results is already participating in the work of building its successor. At first this will look prosaic. AI systems will write kernel optimizations, improve training infrastructure, discover better data filters, tune reinforcement-learning pipelines, design new benchmarks, and suggest architectural modifications. Human researchers will remain in the loop, approving changes and interpreting results. But the important point is that the search process accelerates. The model becomes not just the product of the lab, but part of the lab’s research machinery. The system being optimized helps optimize the next system. This is the core RSI feedback loop: better models make AI research faster; faster AI research produces still better models; those models, in turn, become better researchers. The danger is that once this loop becomes sufficiently autonomous, it may stop resembling ordinary technological progress. Human institutions are slow because humans are slow: we read papers, attend meetings, debug code, sleep, argue, and wait for funding cycles. Machines do not have to operate on that timescale. An AI research collective can run continuously across millions of processors. This is the runaway possibility. Not that an AI instantly wakes up and recursively rewrites itself into a god, but that the entire AI ecosystem becomes an autocatalytic process. Capital buys compute; compute trains models; models improve models; better models attract more capital. At some point the dominant input into AI progress may no longer be human insight, but machine-generated insight, machine-written code, and machine-run experiments. Then the Butler-Land analogy becomes sharper. Humanity is no longer merely building machines. We are building machines that help build better machines. Once intelligence itself becomes part of the production function, the old categories — tool, worker, inventor, firm, market — begin to blur. The question is whether recursive self-improvement remains a managed industrial process, or whether it becomes the first technological process in history whose natural endpoint lies beyond human comprehension.
4:11

RSI section from the AI documentary Machine God The next threshold is Recursive Self-Improvement: the moment when AI can improve itself without human assistance. For decades this sounded like science fiction. Intelligence explosion scenarios imagined a system rewriting its own code, becoming smarter, then using that new intelligence to make still better versions of itself. But the idea looks less remote now that AI contributes directly to frontier science. In mathematics, recent systems have moved beyond solving contest problems to producing serious new arguments on long-standing open problems. AI is used to build physics world models and propose candidate theories or computational methods. These are early signs that machine cognition is entering the creative loop of science itself. The crucial transition comes when that loop turns inward. AI research is, after all, a technical discipline made of code, mathematics, models of information flow. These are exactly the domains in which frontier models are improving fastest. A model that can solve hard mathematical problems, write production-quality code, design experiments, read the literature, and evaluate benchmark results is already participating in the work of building its successor. At first this will look prosaic. AI systems will write kernel optimizations, improve training infrastructure, discover better data filters, tune reinforcement-learning pipelines, design new benchmarks, and suggest architectural modifications. Human researchers will remain in the loop, approving changes and interpreting results. But the important point is that the search process accelerates. The model becomes not just the product of the lab, but part of the lab’s research machinery. The system being optimized helps optimize the next system. This is the core RSI feedback loop: better models make AI research faster; faster AI research produces still better models; those models, in turn, become better researchers. The danger is that once this loop becomes sufficiently autonomous, it may stop resembling ordinary technological progress. Human institutions are slow because humans are slow: we read papers, attend meetings, debug code, sleep, argue, and wait for funding cycles. Machines do not have to operate on that timescale. An AI research collective can run continuously across millions of processors. This is the runaway possibility. Not that an AI instantly wakes up and recursively rewrites itself into a god, but that the entire AI ecosystem becomes an autocatalytic process. Capital buys compute; compute trains models; models improve models; better models attract more capital. At some point the dominant input into AI progress may no longer be human insight, but machine-generated insight, machine-written code, and machine-run experiments. Then the Butler-Land analogy becomes sharper. Humanity is no longer merely building machines. We are building machines that help build better machines. Once intelligence itself becomes part of the production function, the old categories — tool, worker, inventor, firm, market — begin to blur. The question is whether recursive self-improvement remains a managed industrial process, or whether it becomes the first technological process in history whose natural endpoint lies beyond human comprehension.

steve hsu

61,671 次观看 • 25 天前