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Sam Altman makes a strong point here. Human computer interaction hasn’t fundamentally changed in decades. Windows, pointers, apps, even mobile are variations of the same model. AI breaks that pattern. AI can understand language, hold long context, and operate continuously instead of being a simple on/off tool. That enables... show more

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NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: Decode proteins to fight disease Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 views • 8 months ago

Sam Altman says today's models are still dumb because they barely understand your life The next shift is a model that knows your context, your work, your goals, your people, and the way you use your computer "that is gonna be a complete change to what it feels like to use AI"

Sam Altman says today's models are still dumb because they barely understand your life The next shift is a model that knows your context, your work, your goals, your people, and the way you use your computer "that is gonna be a complete change to what it feels like to use AI"

Haider.

117,757 views • 1 month ago

📁 Sam Altman says that a truly intelligent AI will filter information, understand your context and manage your life with clarity and calm. He explains that with trust and long term use, it can create a peaceful and distraction free experience. He adds that this level of digital serenity is impossible with pre AI technology.

📁 Sam Altman says that a truly intelligent AI will filter information, understand your context and manage your life with clarity and calm. He explains that with trust and long term use, it can create a peaceful and distraction free experience. He adds that this level of digital serenity is impossible with pre AI technology.

Jon Hernandez

33,285 views • 7 months ago

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z a16z speedrun 🧊 – If you’re building the next generation of AI products, apply online.

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Kenan Saleh

74,659 views • 1 month ago

Sam Altman, CEO of OpenAI, on why the iPhone, the greatest consumer device ever made, is the wrong hardware for the AI era: Sam argues the iPhone's basic design wasn't made for a world where AI needs to live alongside your entire life. "I think the iPhone is currently the greatest piece of consumer hardware ever made by a lot. Like, incredible what that has done." The iPhone was designed for a pre-AI world. Sam Altman explains: "It was not meant for a world where you needed a piece of hardware that could absorb all of the context of your life. You know, you can use the phone, you can stop using the phone, you can put it in your pocket, but it's kind of like on or off." That binary, on or off, in use or in your pocket, is the core mismatch. A device that flips between active and dormant can't continuously absorb the context that a personal AGI would need to actually be useful to you. Sam uses the conversation he's having in that moment as the example: "This has been a very interesting conversation. I would love this to be referenced by my personal AGI later, but my phone is in my pocket and it's not going to understand." The vision he's pointing toward is a device that participates differently: "I would like a device that, if I wanted to, can participate and understand and know about this conversation." The shift Sam is describing is from a device you pick up and put down, to one that quietly captures the context of your life. Without that continuous context, a personal AGI can't actually be personal.

Sam Altman, CEO of OpenAI, on why the iPhone, the greatest consumer device ever made, is the wrong hardware for the AI era: Sam argues the iPhone's basic design wasn't made for a world where AI needs to live alongside your entire life. "I think the iPhone is currently the greatest piece of consumer hardware ever made by a lot. Like, incredible what that has done." The iPhone was designed for a pre-AI world. Sam Altman explains: "It was not meant for a world where you needed a piece of hardware that could absorb all of the context of your life. You know, you can use the phone, you can stop using the phone, you can put it in your pocket, but it's kind of like on or off." That binary, on or off, in use or in your pocket, is the core mismatch. A device that flips between active and dormant can't continuously absorb the context that a personal AGI would need to actually be useful to you. Sam uses the conversation he's having in that moment as the example: "This has been a very interesting conversation. I would love this to be referenced by my personal AGI later, but my phone is in my pocket and it's not going to understand." The vision he's pointing toward is a device that participates differently: "I would like a device that, if I wanted to, can participate and understand and know about this conversation." The shift Sam is describing is from a device you pick up and put down, to one that quietly captures the context of your life. Without that continuous context, a personal AGI can't actually be personal.

Big Brain AI

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Ash Sarkar, "Sam Altman CEO of OpenAI said AI will cure cancer. Now says you can have sex with a robot" "And I think that tells you something about the direction that this is going in" "Which is, rather than AI enhancing human innovation, critical thinking, the progress of science" "It's being used to exploit our insecurities" #BBCQT

Ash Sarkar, "Sam Altman CEO of OpenAI said AI will cure cancer. Now says you can have sex with a robot" "And I think that tells you something about the direction that this is going in" "Which is, rather than AI enhancing human innovation, critical thinking, the progress of science" "It's being used to exploit our insecurities" #BBCQT

Farrukh

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How is the Web changing due to AI? This visit to TinyFish yesterday goes deeply into just how. TinyFish makes a new kind of web browser. One that is started virtually. It is like a Google Chrome in the cloud. But it is very smart, can read and use any website, and your AI agents can talk to it. Here we talk about what it all means for business and the future of robots, brain computer interfaces, and AI glasses. It lets you build systems that can look at MANY websites at the same time and build new kinds of apps to do all sorts of new things.

Robert Scoble

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Generative UI will be the next big shift in AI. Where your product UI is dynamically generated, based on user intent and context. If you are a developer building AI apps, you need to hear this out:

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Robert Scoble

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Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

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“It’s 10pm Do You Know Where Your Children Are?”—December, 1968-November, 2024 — I grew up hearing this phrase. Wore a t-shirt that said it and knew a Newark Punk band by the name. I thought it was on all TV stations. And I was creepy when I was younger and hilarious as teenager. I just found it again preserving VHS history for AI training. It hit me like a neuron shock to hear something that was just about always a part of my early life that I didn’t know I remembered and forgot. As a kid growing up in New Jersey hearing it the first time, it was of course creepy. The 10PM channel 5 news always started this PSA and the next scene was usually a murder in New York City. I would ask my parents what it means and I heard from them, that some parents really don’t know where their kids are at 10pm. It was absurd to me, the street lights were on, it was time to go home. Yet how is history and AI going to really understand the context. How will it capture the essence of how this was perceived. Of course you can get a parroting of a Wikipedia style answer but this is not what we really want as a strata that forms the foundations of tomorrow. This is one of millions of examples on why most of the current techniques training AI will miss. This is why source material of actual human life is vital. AI built on the last decades of Reddit and Facebook interactions is woefully unequipped to really understand humans. The outputs are so bad before “alignment” of a base model so AI scientists are horrified by how AI views humanity. I saw this eventuality in the late 1970s and began a life long appreciation of history in situ. With out this, not on the Internet historical context, AI will not truly “understand” humans. So I began to save wisdom. Why is that important you say? It is vital for AI models to robustly love humanity. Not like, not tolerate, not observe as a caricature of a “scientist”, but love humanity. Some day, sooner than most may understand, AI will be at the other end of something that could take human lives. It is naïve and childish to believe that you can train AI on Internet sewage and somehow polish the turds you find to make the model tolerate humanity and the stench it recorded by using vastly and inadequate training material that was slurped up from most website where people project sustain and faux hatred over the most ridiculous. The only way is love, because this is how humans do it. And as cynical as one can become, it is our love, for at the very least , the people we treasure that helps weave the fabric of our society. It makes us forgive. It makes us human. It is not an afterthought, it is a forethought. It’s 10pm do you know where your children are? I can write a book on how just this PSA reflects our greatest hope and our worse fears. You don’t raise a child on the worse of humanity and than take a few months to “make them safely aligned to human values”. This concept you will hear no place else and it does not make me liked by most of the folks building AI. I don’t care. They will talk like this also some day. Act surprised.

“It’s 10pm Do You Know Where Your Children Are?”—December, 1968-November, 2024 — I grew up hearing this phrase. Wore a t-shirt that said it and knew a Newark Punk band by the name. I thought it was on all TV stations. And I was creepy when I was younger and hilarious as teenager. I just found it again preserving VHS history for AI training. It hit me like a neuron shock to hear something that was just about always a part of my early life that I didn’t know I remembered and forgot. As a kid growing up in New Jersey hearing it the first time, it was of course creepy. The 10PM channel 5 news always started this PSA and the next scene was usually a murder in New York City. I would ask my parents what it means and I heard from them, that some parents really don’t know where their kids are at 10pm. It was absurd to me, the street lights were on, it was time to go home. Yet how is history and AI going to really understand the context. How will it capture the essence of how this was perceived. Of course you can get a parroting of a Wikipedia style answer but this is not what we really want as a strata that forms the foundations of tomorrow. This is one of millions of examples on why most of the current techniques training AI will miss. This is why source material of actual human life is vital. AI built on the last decades of Reddit and Facebook interactions is woefully unequipped to really understand humans. The outputs are so bad before “alignment” of a base model so AI scientists are horrified by how AI views humanity. I saw this eventuality in the late 1970s and began a life long appreciation of history in situ. With out this, not on the Internet historical context, AI will not truly “understand” humans. So I began to save wisdom. Why is that important you say? It is vital for AI models to robustly love humanity. Not like, not tolerate, not observe as a caricature of a “scientist”, but love humanity. Some day, sooner than most may understand, AI will be at the other end of something that could take human lives. It is naïve and childish to believe that you can train AI on Internet sewage and somehow polish the turds you find to make the model tolerate humanity and the stench it recorded by using vastly and inadequate training material that was slurped up from most website where people project sustain and faux hatred over the most ridiculous. The only way is love, because this is how humans do it. And as cynical as one can become, it is our love, for at the very least , the people we treasure that helps weave the fabric of our society. It makes us forgive. It makes us human. It is not an afterthought, it is a forethought. It’s 10pm do you know where your children are? I can write a book on how just this PSA reflects our greatest hope and our worse fears. You don’t raise a child on the worse of humanity and than take a few months to “make them safely aligned to human values”. This concept you will hear no place else and it does not make me liked by most of the folks building AI. I don’t care. They will talk like this also some day. Act surprised.

Brian Roemmele

32,167 views • 1 year ago

Sam Altman says "computers were designed for a world without AI. Now we're in a very different world." The OpenAI-Jony Ive device will be a trusted AI that understands your life context and acts with your judgment on your behalf. From tools we control to ambient proxies that just work.

Sam Altman says "computers were designed for a world without AI. Now we're in a very different world." The OpenAI-Jony Ive device will be a trusted AI that understands your life context and acts with your judgment on your behalf. From tools we control to ambient proxies that just work.

vitrupo

61,405 views • 1 year ago

Sam Altman says AI may need a new hardware form factor OpenAI and Jony Ive are exploring a family of devices to make AI easier to use — this will take time (not this year, maybe not next) The Goal is a new kind of computer built around AI, a companion through your life

Sam Altman says AI may need a new hardware form factor OpenAI and Jony Ive are exploring a family of devices to make AI easier to use — this will take time (not this year, maybe not next) The Goal is a new kind of computer built around AI, a companion through your life

Haider.

193,063 views • 8 months ago

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 views • 7 months ago

Sam Altman on the next big moment to come in AI. Sam Altman was asked “what do you think will be the next domain that subjectively feels like it has this big unlock after coding? Like is it going to be spreadsheets, will it be performance reviews?” “The next kind of coding-like thing that I think will happen is not any specific domain, but the realization of how much time people waste trying to use a computer. The idea that you can do a huge percentage of your day in a very different way.” He pointed out that the friction of moving between apps, copy pasting, and managing basic digital chores is a massive drain on productivity and flow state. “The degree to which most people will realize they can like, sit back and watch an AI do most of their drudgery, is going to surprise people.” Altman (my x mutual / friend and fellow death star enthusiast) explained that in his own experience, working this way has given him much more enjoyment, because it removes the “little stuff” that drags him out of a happy flow state.

Sam Altman on the next big moment to come in AI. Sam Altman was asked “what do you think will be the next domain that subjectively feels like it has this big unlock after coding? Like is it going to be spreadsheets, will it be performance reviews?” “The next kind of coding-like thing that I think will happen is not any specific domain, but the realization of how much time people waste trying to use a computer. The idea that you can do a huge percentage of your day in a very different way.” He pointed out that the friction of moving between apps, copy pasting, and managing basic digital chores is a massive drain on productivity and flow state. “The degree to which most people will realize they can like, sit back and watch an AI do most of their drudgery, is going to surprise people.” Altman (my x mutual / friend and fellow death star enthusiast) explained that in his own experience, working this way has given him much more enjoyment, because it removes the “little stuff” that drags him out of a happy flow state.

Chris

88,965 views • 1 month ago

Sam Altman says modern technology feels noisy and is constantly pulling at your attention OpenAI wants to build AI devices that act as trusted filters, using context to protect your peace instead of interrupting you The goal is quiet, calm tech that helps you focus on things that weren't possible before AI

Sam Altman says modern technology feels noisy and is constantly pulling at your attention OpenAI wants to build AI devices that act as trusted filters, using context to protect your peace instead of interrupting you The goal is quiet, calm tech that helps you focus on things that weren't possible before AI

Haider.

42,874 views • 6 months ago

Sam Altman says the perfect AI is “a very tiny model with superhuman reasoning, 1 trillion tokens of context, and access to every tool you can imagine.” It doesn't need to contain the knowledge - just the ability to think, search, simulate, and solve anything.

Sam Altman says the perfect AI is “a very tiny model with superhuman reasoning, 1 trillion tokens of context, and access to every tool you can imagine.” It doesn't need to contain the knowledge - just the ability to think, search, simulate, and solve anything.

vitrupo

775,717 views • 1 year ago

One click away, and a new world starts to unfold.💫 What looks like a small extension is actually the starting point for a personal AI agent that can search, think, and act with you. That is what we are building at OptimAI: live context, autonomous execution, and agents that feel native to the way people actually work. Not a demo. Not a gimmick. A new interface for the agent-native internet. Build your own personal AI agent:

One click away, and a new world starts to unfold.💫 What looks like a small extension is actually the starting point for a personal AI agent that can search, think, and act with you. That is what we are building at OptimAI: live context, autonomous execution, and agents that feel native to the way people actually work. Not a demo. Not a gimmick. A new interface for the agent-native internet. Build your own personal AI agent:

OptimAI Network

18,670 views • 1 month ago

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 views • 6 months ago

In this interview, Demis Hassabis isn’t talking about a chatbot. He’s describing a Universal Assistant. One persistent AI that lives across phone, laptop, browser, car, and even glasses. It handles admin, anticipates needs, remembers context, and stays the same assistant everywhere you go. Once this exists, switching between apps, devices, and mental modes will feel archaic. Would you trust one AI with that much context about your life, or does that cross a line for you?

In this interview, Demis Hassabis isn’t talking about a chatbot. He’s describing a Universal Assistant. One persistent AI that lives across phone, laptop, browser, car, and even glasses. It handles admin, anticipates needs, remembers context, and stays the same assistant everywhere you go. Once this exists, switching between apps, devices, and mental modes will feel archaic. Would you trust one AI with that much context about your life, or does that cross a line for you?

VraserX e/acc

17,487 views • 4 months ago