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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)show more

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Chamath Palihapitiya on why the trillion-dollar AI opportunity is being ignored: "The people and the person that invented refrigeration made some money, but most of the money was made by Coca-Cola who used refrigeration to build an empire." Most people are competing for the wrong prize entirely. "The Coca-Cola has yet to be built. And those are the companies that are really going to monetize it." But to understand why, you need to grasp one fundamental truth about how machine learning actually works: "If you take 1,000 of the same inputs and give it to Facebook and Microsoft and Google and Amazon, they'll all come up with the same machine learning model." Think about what that means. The most well-resourced companies on the planet, with billions in compute, armies of researchers, and decades of engineering talent, are all arriving at the same destination. The model race is a race to a tie. So where does the real value get created? One extra ingredient. "If you have one extra thing, one little ingredient that all of those other companies don't have, your output can be markedly different. It's like giving two great chefs the same ingredients, but one has an extra one. That person has the ability to do something very special." The trillion-dollar opportunity belongs to whoever finds the one ingredient nobody else has and builds the Coca-Cola on top of it. Everyone is staring at the refrigerator. Nobody is building the Coca-Cola. That's exactly where the opportunity is.

Chamath Palihapitiya on why the trillion-dollar AI opportunity is being ignored: "The people and the person that invented refrigeration made some money, but most of the money was made by Coca-Cola who used refrigeration to build an empire." Most people are competing for the wrong prize entirely. "The Coca-Cola has yet to be built. And those are the companies that are really going to monetize it." But to understand why, you need to grasp one fundamental truth about how machine learning actually works: "If you take 1,000 of the same inputs and give it to Facebook and Microsoft and Google and Amazon, they'll all come up with the same machine learning model." Think about what that means. The most well-resourced companies on the planet, with billions in compute, armies of researchers, and decades of engineering talent, are all arriving at the same destination. The model race is a race to a tie. So where does the real value get created? One extra ingredient. "If you have one extra thing, one little ingredient that all of those other companies don't have, your output can be markedly different. It's like giving two great chefs the same ingredients, but one has an extra one. That person has the ability to do something very special." The trillion-dollar opportunity belongs to whoever finds the one ingredient nobody else has and builds the Coca-Cola on top of it. Everyone is staring at the refrigerator. Nobody is building the Coca-Cola. That's exactly where the opportunity is.

Big Brain AI

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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 value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations. Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone. Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes. That means the real asset is not just the model. The asset is the environment that keeps improving the model in ways competitors cannot copy. Private evals become strategic memory. Workflow traces become training signal. Human judgment becomes a way to steer compounding, not just correct mistakes. This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge. A company that builds a disciplined learning loop can turn everyday work into accumulating IP. The future firm may therefore be measured by how well it converts its unique activity into durable model improvement. The frontier will not belong only to whoever owns the largest model. It will belong to whoever owns the best loop. ---- From "Stanford Online" YouTube channel, (link in comment)

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Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

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Google is releasing updates that will let their AI scan all your photos and it will be used to create highly personalized AI image generations or you, your family, pets and more The AI is going to have access to your Gmail, YouTube, search history, photos, calendar and more. The concerning part is Google has admitted to using user data to improve its models People have screenshots of passwords, credit cards, photos of themselves doing intimate things. And all of that is gonna get uploaded to an AI system that is one of many that is consistently shown to be unreliable and not safe at all when it comes to data privacy Let’s not forget that have data leaks, one happened not too long ago Do we really want AI to have access to all this information and being sent back to these massive companies? You’ll be able to opt out of some of it but for your phone to have access to certain features you’ll need to opt in It always starts out optional, then becomes mandatory

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Wall Street Apes

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.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

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Deutsch Explains

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Sam Altman's new interview: AI should not be designed to pursue goals that are disconnected from human needs. People must remain at the center of AI development. “I have no interest in building a super-smart AI that accomplishes some non-human goals. People should react. People should say, ‘Hey, this is what I want, and this is what I do not want.’ I do not think the issue is that we have failed to explain the benefits. We say, ‘AI is going to cure a bunch of diseases,’ and people say, ‘Okay, that is great, but that is not really my question. My question is: What is my role in the future? What is my economic future? What is my agency? How do I know that my kids and my family will still be able to have fulfilling, creative expression, struggle, drive the world forward, grow, and do this thing together in a way that has worked for a long time?’ When people in AI say, ‘Sure, there are going to be no jobs,’ or ‘50% of jobs are going to go away,’ or ‘90% of jobs are going to go away,’ and ‘AI is going to be smarter than you at everything,’ and ‘We will give you some basic income, but you are not really going to have a role,’ that is horrible. And by the way, if an AI company says, ‘Maybe we are going to destroy all the jobs, and we will be the most valuable company in the world,’ people should look at you like, ‘Yeah, that is a terrible message.’ I do not think the problem is that we have not articulated the upsides. I think people actually believe us. They hear, ‘AI may cure your cancer,’ and they think, ‘That sounds great.’ I think we, as an industry, have failed to explain how people stay in control of determining the future at every step, and how people can still have a meaningful life in all the ways we care about.” ---- From "CNBC Television" YouTube channel, (link in comment)

Sam Altman's new interview: AI should not be designed to pursue goals that are disconnected from human needs. People must remain at the center of AI development. “I have no interest in building a super-smart AI that accomplishes some non-human goals. People should react. People should say, ‘Hey, this is what I want, and this is what I do not want.’ I do not think the issue is that we have failed to explain the benefits. We say, ‘AI is going to cure a bunch of diseases,’ and people say, ‘Okay, that is great, but that is not really my question. My question is: What is my role in the future? What is my economic future? What is my agency? How do I know that my kids and my family will still be able to have fulfilling, creative expression, struggle, drive the world forward, grow, and do this thing together in a way that has worked for a long time?’ When people in AI say, ‘Sure, there are going to be no jobs,’ or ‘50% of jobs are going to go away,’ or ‘90% of jobs are going to go away,’ and ‘AI is going to be smarter than you at everything,’ and ‘We will give you some basic income, but you are not really going to have a role,’ that is horrible. And by the way, if an AI company says, ‘Maybe we are going to destroy all the jobs, and we will be the most valuable company in the world,’ people should look at you like, ‘Yeah, that is a terrible message.’ I do not think the problem is that we have not articulated the upsides. I think people actually believe us. They hear, ‘AI may cure your cancer,’ and they think, ‘That sounds great.’ I think we, as an industry, have failed to explain how people stay in control of determining the future at every step, and how people can still have a meaningful life in all the ways we care about.” ---- From "CNBC Television" YouTube channel, (link in comment)

Rohan Paul

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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 görüntüleme • 1 ay önce

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

ANI

18,753 görüntüleme • 4 ay önce

Marc Andreessen says AI is hyper democratizing. “Is the future of the world going to be one or a small number of companies, or for that matter, governments or super AIs that kind of own and control everything?” “Or is this a technology that's going to diffuse out and be in everybody's hands?” “This is the time in which AI is actually hyper democratizing. It has spread to more individuals, both in the country and around the world in the shortest period of time of any new technology I think in history.” “The best AIs in the world are in the consumer products.” “This technology is going to be in everybody's hands. And everybody is going to be able to use it to optimize the things that they do, have it be a thought partner, assistant for building companies.” Marc Andreessen 🇺🇸

Marc Andreessen says AI is hyper democratizing. “Is the future of the world going to be one or a small number of companies, or for that matter, governments or super AIs that kind of own and control everything?” “Or is this a technology that's going to diffuse out and be in everybody's hands?” “This is the time in which AI is actually hyper democratizing. It has spread to more individuals, both in the country and around the world in the shortest period of time of any new technology I think in history.” “The best AIs in the world are in the consumer products.” “This technology is going to be in everybody's hands. And everybody is going to be able to use it to optimize the things that they do, have it be a thought partner, assistant for building companies.” Marc Andreessen 🇺🇸

a16z

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Dan has a distinctive lens for understanding companies and his view on LLMs as a blend of Netflix and Spotify is particularly interesting: "You are spending a ton of money upfront to train these models. You go sell them at extremely high incremental margins. You don't know what the revenues are going to be, but you want to sell as much as possible so that you can get the cash flow to build the next model. That's very similar to Netflix. When you're an early mover in this fixed asset business, you invest heavily, you get the revenues, and you spread it out over an increasing number of people. The difference is Netflix's content was differentiated. The models are more similar than they are different at any given time. That's where the Spotify analogy comes in. The differentiating factor will not necessarily be that Google gives you a better answer. However, the personalization matters. The first mover advantage is the more that these models know about you, you build up this data history and it becomes very sticky. What makes Spotify have pricing power? It's because they've tailored the service to take a product which is a commodity, and personalize it to the point where you're willing to pay a premium for that commodity."

Dan has a distinctive lens for understanding companies and his view on LLMs as a blend of Netflix and Spotify is particularly interesting: "You are spending a ton of money upfront to train these models. You go sell them at extremely high incremental margins. You don't know what the revenues are going to be, but you want to sell as much as possible so that you can get the cash flow to build the next model. That's very similar to Netflix. When you're an early mover in this fixed asset business, you invest heavily, you get the revenues, and you spread it out over an increasing number of people. The difference is Netflix's content was differentiated. The models are more similar than they are different at any given time. That's where the Spotify analogy comes in. The differentiating factor will not necessarily be that Google gives you a better answer. However, the personalization matters. The first mover advantage is the more that these models know about you, you build up this data history and it becomes very sticky. What makes Spotify have pricing power? It's because they've tailored the service to take a product which is a commodity, and personalize it to the point where you're willing to pay a premium for that commodity."

Patrick OShaughnessy

58,893 görüntüleme • 4 ay önce

"Yes, it’s too soon to tell, but it is interesting that large language models, as opposed to social media, have tended to be much more aligned with what we would consider objective reality and truth. That is, I think it’s not so easy to get a large language model to endorse a ridiculous conspiracy theory, such as that the 2020 U.S. election was stolen or that COVID vaccines are actually microchips that Bill Gates is implanting to surveil us. All of those ideas are very easy to find on social media. And it may indeed be, as you say, that large language models tend to gravitate toward common priors—namely, all the knowledge that’s out there on the web. But, in particular, during pre-training and post-training—the fine-tuning process—they are engineered to be aligned with truth and reality. And that is because the AI companies have a different business model than the social media companies." From an episode of Scaling Theory with Thibault Schrepel: Steven Pinker on Common Knowledge, From Eye Contact to the Super Bowl

"Yes, it’s too soon to tell, but it is interesting that large language models, as opposed to social media, have tended to be much more aligned with what we would consider objective reality and truth. That is, I think it’s not so easy to get a large language model to endorse a ridiculous conspiracy theory, such as that the 2020 U.S. election was stolen or that COVID vaccines are actually microchips that Bill Gates is implanting to surveil us. All of those ideas are very easy to find on social media. And it may indeed be, as you say, that large language models tend to gravitate toward common priors—namely, all the knowledge that’s out there on the web. But, in particular, during pre-training and post-training—the fine-tuning process—they are engineered to be aligned with truth and reality. And that is because the AI companies have a different business model than the social media companies." From an episode of Scaling Theory with Thibault Schrepel: Steven Pinker on Common Knowledge, From Eye Contact to the Super Bowl

Steven Pinker

56,851 görüntüleme • 3 gün önce

Naval Ravikant’s checklist for starting a company “The most important thing is there are no formulas. At the end of the day, you have to do what you love, and you have to do it even though people tell you it’ll never work. But that being said, if there was a formula [for starting a company], I would put it something like this.” Naval started seven companies before AngelList and this is the checklist he recommends running through before starting a startup: 1. Pick a great cofounder. This is most important: “You can do a company on your own, but it’s like you can raise a child on your own, but you probably shouldn’t. You need someone who’s going to be there with you.” This has it’s own checklist. Your cofounder should be: a. Very high intelligence (”hopefully they make you feel dumb, or they’re not smart enough”) b. Very high energy (”They should be extremely hardworking. A founder is someone who never has to be motivated. You should not have to be telling them to do their job.”) c. Very high integrity. (”a smart, hardworking crook who’s going to cheat you is the worst kind of person to be paired up with.”) 2. Pick a very large market. “Notice I don’t talk about the idea. I think ideas are almost irrelevant… The more important thing is that you pick a large space that you’re knowledgeable and passionate about. And then you will figure out what the right thing to do within that space is.” You want to be able to say to investors: “This is a space where there’s a huge market. I’m really knowledgeable and passionate about it. Here’s the great person that I have doing it with me. And here’s the minimum viable product that we have built. That will show that we can test in the marketplace… You iterate until you get to product/market fit… And then you go and you raise money from people you trust. And you use that money to scale.”

Naval Ravikant’s checklist for starting a company “The most important thing is there are no formulas. At the end of the day, you have to do what you love, and you have to do it even though people tell you it’ll never work. But that being said, if there was a formula [for starting a company], I would put it something like this.” Naval started seven companies before AngelList and this is the checklist he recommends running through before starting a startup: 1. Pick a great cofounder. This is most important: “You can do a company on your own, but it’s like you can raise a child on your own, but you probably shouldn’t. You need someone who’s going to be there with you.” This has it’s own checklist. Your cofounder should be: a. Very high intelligence (”hopefully they make you feel dumb, or they’re not smart enough”) b. Very high energy (”They should be extremely hardworking. A founder is someone who never has to be motivated. You should not have to be telling them to do their job.”) c. Very high integrity. (”a smart, hardworking crook who’s going to cheat you is the worst kind of person to be paired up with.”) 2. Pick a very large market. “Notice I don’t talk about the idea. I think ideas are almost irrelevant… The more important thing is that you pick a large space that you’re knowledgeable and passionate about. And then you will figure out what the right thing to do within that space is.” You want to be able to say to investors: “This is a space where there’s a huge market. I’m really knowledgeable and passionate about it. Here’s the great person that I have doing it with me. And here’s the minimum viable product that we have built. That will show that we can test in the marketplace… You iterate until you get to product/market fit… And then you go and you raise money from people you trust. And you use that money to scale.”

Startup Archive

36,050 görüntüleme • 1 yıl önce

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 görüntüleme • 7 ay önce

Gavin's takes on Microsoft, Google, Meta, & Amazon: Microsoft ($MSFT): "I like Satya, I admire him. He's an exceptional CEO, and I give him a lot of credit for the decisions he's made. But he did go from, "We're going to make Google dance," to being the product manager of Copilot in 3 years. The decision Satya is making now, which the market has punished him for, but I think is the right decision — who knows how fast Azure could be growing if they were willing to just sell GPUs to OpenAI. 'We're going to use our compute internally to make our own products better.' One reason Copilot was so bad, or has been so bad, is that there wasn't enough compute available. They're fixing that. He's making good decisions that are risky decisions, to position Microsoft for this world where frontier models are no longer API-accessible. It's a really courageous decision that I give him a lot of credit for. Microsoft probably would be an $800 stock today if they were using their GPUs to serve solely OpenAI and Anthropic's capacity instead of using them for their own products." Google ($GOOG): "Google was incredible last year because they had that TPU advantage, which is now gone. The reason I think they're still in a great position is they have the most compute of everyone. We talked about the value of installed bases being higher as a result of shortages — they have the biggest installed base of compute. Google I/O is this week. If they don't release something that even slightly leapfrogs OpenAI and/or Claude, that's interesting. It's not a disaster for Google, it's just interesting. Between the amount of data they have, the YouTube data, the amount of compute, the search business — Google's never not going to be in a good position. You see that with GCP going crazy." Meta ($META): "You've got to give Zuckerberg immense credit, for what he's done in terms of making Meta an AI-first company internally. He is the only one of those true internet giants to have done that. I give him a lot of credit for paying up when he did for contracts, that talent. And Muse was a really big upside surprise. It was the first model from MSL, and it's not on the Pareto frontier with xAI, Google's one entrant, OpenAI and Claude, but it's pretty close. That was very impressive to me. So Meta is in a better position — still not as strong of an absolute position as Google, but a better position." Amazon ($AMZN): "Amazon is in a really strong position because of Trainium. You're going to see real P&L efficiencies from robotics over the next 18 months in their retail business. I actually think Nova — their internal models are not where Muse is, but they're better than they get credit for. The two companies who are the most deeply engaged with startups are Amazon and Nvidia by a mile. It's going to end up being a pretty big advantage for Nvidia and Amazon — with Google right behind them — to have this engagement that you just don't see from these other hyperscalers."

Gavin's takes on Microsoft, Google, Meta, & Amazon: Microsoft ($MSFT): "I like Satya, I admire him. He's an exceptional CEO, and I give him a lot of credit for the decisions he's made. But he did go from, "We're going to make Google dance," to being the product manager of Copilot in 3 years. The decision Satya is making now, which the market has punished him for, but I think is the right decision — who knows how fast Azure could be growing if they were willing to just sell GPUs to OpenAI. 'We're going to use our compute internally to make our own products better.' One reason Copilot was so bad, or has been so bad, is that there wasn't enough compute available. They're fixing that. He's making good decisions that are risky decisions, to position Microsoft for this world where frontier models are no longer API-accessible. It's a really courageous decision that I give him a lot of credit for. Microsoft probably would be an $800 stock today if they were using their GPUs to serve solely OpenAI and Anthropic's capacity instead of using them for their own products." Google ($GOOG): "Google was incredible last year because they had that TPU advantage, which is now gone. The reason I think they're still in a great position is they have the most compute of everyone. We talked about the value of installed bases being higher as a result of shortages — they have the biggest installed base of compute. Google I/O is this week. If they don't release something that even slightly leapfrogs OpenAI and/or Claude, that's interesting. It's not a disaster for Google, it's just interesting. Between the amount of data they have, the YouTube data, the amount of compute, the search business — Google's never not going to be in a good position. You see that with GCP going crazy." Meta ($META): "You've got to give Zuckerberg immense credit, for what he's done in terms of making Meta an AI-first company internally. He is the only one of those true internet giants to have done that. I give him a lot of credit for paying up when he did for contracts, that talent. And Muse was a really big upside surprise. It was the first model from MSL, and it's not on the Pareto frontier with xAI, Google's one entrant, OpenAI and Claude, but it's pretty close. That was very impressive to me. So Meta is in a better position — still not as strong of an absolute position as Google, but a better position." Amazon ($AMZN): "Amazon is in a really strong position because of Trainium. You're going to see real P&L efficiencies from robotics over the next 18 months in their retail business. I actually think Nova — their internal models are not where Muse is, but they're better than they get credit for. The two companies who are the most deeply engaged with startups are Amazon and Nvidia by a mile. It's going to end up being a pretty big advantage for Nvidia and Amazon — with Google right behind them — to have this engagement that you just don't see from these other hyperscalers."

Invest Like the Best

52,492 görüntüleme • 1 ay önce

AI Isn’t the Threat, Refusing to Adapt Is "If you have a job, you can either sit there and say, 'well, this company, all they want to do is be able to replace me with AI.' What a negative place to come from." "I think whatever is going on, companies have to get better. Individual performers have to get better. Managers have to get better, and leaders have to get better. If we don't do that, Vinny, you are right. Everybody, everybody could be replaced if we don't collectively get better."

AI Isn’t the Threat, Refusing to Adapt Is "If you have a job, you can either sit there and say, 'well, this company, all they want to do is be able to replace me with AI.' What a negative place to come from." "I think whatever is going on, companies have to get better. Individual performers have to get better. Managers have to get better, and leaders have to get better. If we don't do that, Vinny, you are right. Everybody, everybody could be replaced if we don't collectively get better."

PBD Podcast

11,195 görüntüleme • 1 ay önce

Is Traditional Software Engineering Dead? “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes. It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own… And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.” That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.” And I think that still applies in this age of AI.”

Naval

847,727 görüntüleme • 4 ay önce

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

393,378 görüntüleme • 5 ay önce

.Anish Acharya says the future of AI isn’t one model to rule them all—and explains why platforms that integrate multiple models will benefit the most: "I think we're going to need and rely on all of the models." "It's sort of like if you have a team of people... if you have five people, they could all do a basic set of things pretty capably." "But then they all have their specializations. Maybe one of them is really good at closing a customer who doesn't want to sign the deal, and one of them is really good at culture and getting the best out of the team." "There are some areas in which they are going to build apps, and that will be a threat to app companies. But there are many areas in which app companies are advantaged. Cursor and Krea are great examples of this—products where you benefit from being multi-model." "When you actually use a creative tool, you don't want to just use Nano Banana, you want to have access to OpenAI, Nano Banana, Kling—all of them—Qwen, you name it. So using a single interface to access all the models is powerful." Anish Acharya on BILLIONS with Guillaume Moubeche

.Anish Acharya says the future of AI isn’t one model to rule them all—and explains why platforms that integrate multiple models will benefit the most: "I think we're going to need and rely on all of the models." "It's sort of like if you have a team of people... if you have five people, they could all do a basic set of things pretty capably." "But then they all have their specializations. Maybe one of them is really good at closing a customer who doesn't want to sign the deal, and one of them is really good at culture and getting the best out of the team." "There are some areas in which they are going to build apps, and that will be a threat to app companies. But there are many areas in which app companies are advantaged. Cursor and Krea are great examples of this—products where you benefit from being multi-model." "When you actually use a creative tool, you don't want to just use Nano Banana, you want to have access to OpenAI, Nano Banana, Kling—all of them—Qwen, you name it. So using a single interface to access all the models is powerful." Anish Acharya on BILLIONS with Guillaume Moubeche

a16z

33,042 görüntüleme • 3 ay önce

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

MTS

14,406 görüntüleme • 12 gün önce

Rick Rubin tells Andrew Huberman how he deals with creative or writer’s block. He treats his work like a diary entry (and doesn’t worry about internal or external judgment): ➡️ “What's the cause of the block? The block is usually something that's either personal ("I'm not good enough") or it can be a confidence issue ("I don't have anything to say") or it could be...thinking about someone else ("nobody's going to like what I make"). Do you know what I'm saying? So, it's either fear of self-judgment or external judgment. If you're making something with a freedom of "this is something I'm making for myself for now", that is all [you have to do]. It is a diary entry. Everything I make is a diary entry. The beauty of a diary entry is that I can write my diary entry and you can't tell me that my diary entry wasn't good enough. Or that [the diary entry] is not what I experienced. Of course it's what I experienced: I'm writing a personal diary for myself and no one else can judge if it is my experience of my life. Everything we make can be that: a personal reflection of who we are in that moment of time. It doesn't have to be the greatest you could ever do. It doesn't have to have any expectation that it's going to change the world. It doesn't have to sell a certain number of copies for any reason. It doesn't have any of those things at all. It is "I'm making this thing for me and I want to do it to the best of my ability and to where I feel good about it". [The work] is honest of where I'm at and if you're living in this world of just being honest to where you're at, there's nothing blocking you. There are no blocks. The blocks are all based on dealing with a different force or a different perception that is made up.” ⬅️

Rick Rubin tells Andrew Huberman how he deals with creative or writer’s block. He treats his work like a diary entry (and doesn’t worry about internal or external judgment): ➡️ “What's the cause of the block? The block is usually something that's either personal ("I'm not good enough") or it can be a confidence issue ("I don't have anything to say") or it could be...thinking about someone else ("nobody's going to like what I make"). Do you know what I'm saying? So, it's either fear of self-judgment or external judgment. If you're making something with a freedom of "this is something I'm making for myself for now", that is all [you have to do]. It is a diary entry. Everything I make is a diary entry. The beauty of a diary entry is that I can write my diary entry and you can't tell me that my diary entry wasn't good enough. Or that [the diary entry] is not what I experienced. Of course it's what I experienced: I'm writing a personal diary for myself and no one else can judge if it is my experience of my life. Everything we make can be that: a personal reflection of who we are in that moment of time. It doesn't have to be the greatest you could ever do. It doesn't have to have any expectation that it's going to change the world. It doesn't have to sell a certain number of copies for any reason. It doesn't have any of those things at all. It is "I'm making this thing for me and I want to do it to the best of my ability and to where I feel good about it". [The work] is honest of where I'm at and if you're living in this world of just being honest to where you're at, there's nothing blocking you. There are no blocks. The blocks are all based on dealing with a different force or a different perception that is made up.” ⬅️

Trung Phan

1,619,350 görüntüleme • 2 yıl önce