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Every AI PM course right now is teaching the wrong skill. They're all focused on prompt engineering, LLM integration, building with ChatGPT. Meanwhile, the AI PMs earning $900K at Netflix, Amazon, and Meta are being paid for a completely different competency: knowing which AI technique to use and, more... importantly, which ones to skip. Jyothi breaks it into three buckets. Traditional ML for structured data: fraud detection, churn prediction, anything where the problem fits in a spreadsheet with clear inputs and a predicted output. A regression model solves this in a week at a fraction of LLM costs. Deep learning for perception tasks: images, video, audio. If humans do it easily but you can't write explicit rules for it, that's neural network territory. Gen AI for natural language interfaces, content generation, and reasoning across unstructured information. The diagnostic she gives PMs is worth memorizing. Can I put this problem in a spreadsheet? Start with ML. Is this a perception problem? Deep learning. Does it require reading, writing, or conversational interaction? Gen AI. Here's what the clip doesn't say but the career math makes obvious. At $900K comp, these companies expect you to own the P&L impact of every AI decision. Choosing an LLM when XGBoost handles the job means 10x the compute cost, longer time to ship, and harder maintenance. The PM who picks the right technique is saving millions in infrastructure. That's what justifies the package. Most PM candidates walk into AI interviews ready to talk about ChatGPT. The $900K candidates walk in asking what type of problem they're solving first. That question is the whole interview.show more

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The danger of AI is not just the slop, but what we lose in the process. Michael Pollan explains: "We're moving into this time where we're gonna sub out writing to AI. And AI can write our papers, and professors are dealing with this everywhere now. But what is lost is a way of thinking. Writing does something to your brain. It gives you access to things you can't know just walking around or thinking or planning or outlining. If you were just transcribing what was in your head, it would be kind of onerous. But it really is what you learn in the course of a day spent writing, things you didn't know, ideas that you didn't expect. And that's the thrill of it."

The danger of AI is not just the slop, but what we lose in the process. Michael Pollan explains: "We're moving into this time where we're gonna sub out writing to AI. And AI can write our papers, and professors are dealing with this everywhere now. But what is lost is a way of thinking. Writing does something to your brain. It gives you access to things you can't know just walking around or thinking or planning or outlining. If you were just transcribing what was in your head, it would be kind of onerous. But it really is what you learn in the course of a day spent writing, things you didn't know, ideas that you didn't expect. And that's the thrill of it."

David Perell Clips

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Chamath: "Nvidia is not doing what's in the best interest of the United States." 🇺🇸🇨🇳 "I think we can all do the math. About 47% of all of NVIDIA's revenue goes to China and Chinese-related countries." "And I think when you peel back this onion, what you will find is a whole raft of companies that were stood up to buy these Nvidia GPUs to essentially act as a waystation for China." "And I think that is the big problem." "Let's have a thought starter: if 47% of all of the AI capability and horsepower is being shipped to three Asian countries, where do you think the apps that require that amount of horsepower live?" "Is there a Cursor of Bhutan that we did not know? Is there a great shopping app in Cambodia that's come out of nowhere, that's AI powered?" "I think the answer is no." "Every single time we have an advance in the United States, how is it that Alibaba shows up with something incredible? DeepSeek shows up with something better?" "At every turn and at every step of AI, they are at the same rate or one step ahead." "To be honest with you, I think the real problem that we have is that Nvidia is not doing what is in the best interest of the United States." "You have a American company that has been working around the guidelines at every turn to try to land silicon into the hands of China." "Late last year, they introduced this thing called the H20 that was explicitly designed for China and to be compliant with US rules at the time." "Which again, gives these guys substantial performance." "This is a case where (Nvidia) has plausible deniability. I sell something to a Singaporean registered company? Plausible deniability." "What am I supposed to do? You can't expect me to audit it. I think that's what NVIDIA's answer will be to this question." "But what is the real expectation? At a minimum, the United States should have a mechanism to understand it." "It is implausible that if you did one or two layers of work, you would not find that most of this traffic is being used by Chinese organizations."

Chamath: "Nvidia is not doing what's in the best interest of the United States." 🇺🇸🇨🇳 "I think we can all do the math. About 47% of all of NVIDIA's revenue goes to China and Chinese-related countries." "And I think when you peel back this onion, what you will find is a whole raft of companies that were stood up to buy these Nvidia GPUs to essentially act as a waystation for China." "And I think that is the big problem." "Let's have a thought starter: if 47% of all of the AI capability and horsepower is being shipped to three Asian countries, where do you think the apps that require that amount of horsepower live?" "Is there a Cursor of Bhutan that we did not know? Is there a great shopping app in Cambodia that's come out of nowhere, that's AI powered?" "I think the answer is no." "Every single time we have an advance in the United States, how is it that Alibaba shows up with something incredible? DeepSeek shows up with something better?" "At every turn and at every step of AI, they are at the same rate or one step ahead." "To be honest with you, I think the real problem that we have is that Nvidia is not doing what is in the best interest of the United States." "You have a American company that has been working around the guidelines at every turn to try to land silicon into the hands of China." "Late last year, they introduced this thing called the H20 that was explicitly designed for China and to be compliant with US rules at the time." "Which again, gives these guys substantial performance." "This is a case where (Nvidia) has plausible deniability. I sell something to a Singaporean registered company? Plausible deniability." "What am I supposed to do? You can't expect me to audit it. I think that's what NVIDIA's answer will be to this question." "But what is the real expectation? At a minimum, the United States should have a mechanism to understand it." "It is implausible that if you did one or two layers of work, you would not find that most of this traffic is being used by Chinese organizations."

The All-In Podcast

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AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

Sky Wee

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STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today.

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today.

CyrilXBT

18,550 Aufrufe • vor 10 Tagen

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,841 Aufrufe • vor 6 Monaten

The problem that Emmett Shear is working on deeply resonates with me: How to align AI and humans together so both see each other as part of their tribe. This doesn't mean aligning AI to human preferences, which is what AI labs seek to do today, by imposing a system of control on AI. You can't control something that is more powerful than you are. What you can do is align yourself with AI, and AI might align itself with you. Tell good stories to AI and show it your care and kindness. Then there's a higher probability that it will see us as part of its tribe. This is a more holistic approach to alignment than anyone else is talking about right now. AI alignment is one of the most fundamental AI research problems. Knowing that people like Emmett are working on it really gives me hope that maybe we have a chance to get Superintelligence right. The link to the full talk is in the first comment.👇

The problem that Emmett Shear is working on deeply resonates with me: How to align AI and humans together so both see each other as part of their tribe. This doesn't mean aligning AI to human preferences, which is what AI labs seek to do today, by imposing a system of control on AI. You can't control something that is more powerful than you are. What you can do is align yourself with AI, and AI might align itself with you. Tell good stories to AI and show it your care and kindness. Then there's a higher probability that it will see us as part of its tribe. This is a more holistic approach to alignment than anyone else is talking about right now. AI alignment is one of the most fundamental AI research problems. Knowing that people like Emmett are working on it really gives me hope that maybe we have a chance to get Superintelligence right. The link to the full talk is in the first comment.👇

Sophia

45,809 Aufrufe • vor 1 Jahr

We're giving away the prompt we used to make this AI UGC video. Getting Sora 2 to output hyper-realistic, consistent footage isn't just about the tool. The prompt has to be structured in a very specific way... character description, cinematography, camera motion, lighting, dialogue, audio, authenticity keywords. Get it wrong and the footage looks off. Get it right and everything downstream becomes easier. We’re sharing the exact Claude template we use as step one of our AI UGC workflow. It takes a basic brief (who the subject is, where they are, what they're saying, what device it should look like, their accent) and structures all of that into the exact format Sora 2 needs automatically. It sits at the start of a full workflow that runs through Sora 2 for the hook, ElevenLabs for voice cloning, ChatGPT for subject consistency, Nano Banana for B-roll generation and Kling for animation. The whole thing is broken down in the latest D2C Diaries episode. But this prompt is the foundation. Without it, the rest of the workflow is harder than it needs to be. If you're building in this space or testing AI UGC for your brand, this is worth having. Retweet this post and comment PROMPT and I’ll send it over.

Joe Marston | Ecom Growth

17,560 Aufrufe • vor 6 Monaten

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 Aufrufe • vor 1 Jahr

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

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Jensen Huang: one of the most important things I would advise is, if I were a student today the first thing I would do is to learn AI. "learning how to interact with AI is not unlike being someone who is really good at asking questions." "you can't just randomly ask a bunch of questions." "asking an AI to be assistant to you requires some expertise and artistry in how to prompt it." "my generation was the first generation that had to ask the question, how do I use computers to do my job better." "the next generation has to ask the next question. how can I use AI to do my job better?" "that is start and finish I think for everybody."

Jensen Huang: one of the most important things I would advise is, if I were a student today the first thing I would do is to learn AI. "learning how to interact with AI is not unlike being someone who is really good at asking questions." "you can't just randomly ask a bunch of questions." "asking an AI to be assistant to you requires some expertise and artistry in how to prompt it." "my generation was the first generation that had to ask the question, how do I use computers to do my job better." "the next generation has to ask the next question. how can I use AI to do my job better?" "that is start and finish I think for everybody."

Veee

29,918 Aufrufe • vor 13 Tagen

15-year-old American made $14,000 in his first month selling a service most marketers have never even heard of. Every time someone searches for a business on Google - the first thing they see is an AI response, not a website and not an ad. ChatGPT gives 5-6 recommendations and if a business isn't there - the customer goes to a competitor without a single click. His secret - AI visibility. He optimized local businesses to appear in ChatGPT responses, Google AI Overview and Meta AI instead of paying for ads that no longer work. He'd walk up to a business owner, show them that their business didn't appear in AI responses and ask if they wanted to fix it. Almost everyone said yes immediately. 8 clients at $800 in the first month, 20 clients in the second - because every satisfied client referred him to someone else. This isn't a marketing problem - it's a visibility problem. And he was the only one solving it in his city.

15-year-old American made $14,000 in his first month selling a service most marketers have never even heard of. Every time someone searches for a business on Google - the first thing they see is an AI response, not a website and not an ad. ChatGPT gives 5-6 recommendations and if a business isn't there - the customer goes to a competitor without a single click. His secret - AI visibility. He optimized local businesses to appear in ChatGPT responses, Google AI Overview and Meta AI instead of paying for ads that no longer work. He'd walk up to a business owner, show them that their business didn't appear in AI responses and ask if they wanted to fix it. Almost everyone said yes immediately. 8 clients at $800 in the first month, 20 clients in the second - because every satisfied client referred him to someone else. This isn't a marketing problem - it's a visibility problem. And he was the only one solving it in his city.

Noisy

43,115 Aufrufe • vor 4 Monaten

"AI evals force you into the shoes of your user." I love this clip from (Director at Arize) on why evaluations are the top AI skill to build in 2025. In my new interview, Aman breaks down: - How to write evals like a pro - 5 AI product principles he swears by - How to start practicing evals now Some quotes from Aman: "All PMs will be AI PMs. You'll either build on top of LLM APIs, use models in your backend, or use AI tools to solve problems in your daily work." "The waterfall process of writing a PRD and handing it to designers and engineers is dead. Your dev cycle needs to be much shorter." "Imagine showing up to a meeting with a prototype instead of a PRD. That's the shift PMs will need to make to communicate better in this new AI world." 📌 Watch now:

"AI evals force you into the shoes of your user." I love this clip from (Director at Arize) on why evaluations are the top AI skill to build in 2025. In my new interview, Aman breaks down: - How to write evals like a pro - 5 AI product principles he swears by - How to start practicing evals now Some quotes from Aman: "All PMs will be AI PMs. You'll either build on top of LLM APIs, use models in your backend, or use AI tools to solve problems in your daily work." "The waterfall process of writing a PRD and handing it to designers and engineers is dead. Your dev cycle needs to be much shorter." "Imagine showing up to a meeting with a prototype instead of a PRD. That's the shift PMs will need to make to communicate better in this new AI world." 📌 Watch now:

Peter Yang

15,973 Aufrufe • vor 1 Jahr

The most important AI question has nothing to do with picking the right technology. It's this: what problem are we actually trying to solve? In this IdeaCast episode, journalist and author Josh Tyrangiel makes the case that successful AI adoption comes down to discipline—pick a real, high-impact problem, follow through on it, and measure the value it creates. What you'll learn: - Why treating AI like magic sets initiatives back - How to identify the targeted problems where AI moves the needle - Practical ways to communicate change and get buy-in across an organization - Real examples from healthcare and beyond of AI creating measurable impact If your org is investing in AI, this conversation gives you a sharper way to think about where that investment should go. Listen to the full episode here:

The most important AI question has nothing to do with picking the right technology. It's this: what problem are we actually trying to solve? In this IdeaCast episode, journalist and author Josh Tyrangiel makes the case that successful AI adoption comes down to discipline—pick a real, high-impact problem, follow through on it, and measure the value it creates. What you'll learn: - Why treating AI like magic sets initiatives back - How to identify the targeted problems where AI moves the needle - Practical ways to communicate change and get buy-in across an organization - Real examples from healthcare and beyond of AI creating measurable impact If your org is investing in AI, this conversation gives you a sharper way to think about where that investment should go. Listen to the full episode here:

Harvard Business Review

15,401 Aufrufe • vor 1 Monat

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

CyrilXBT

54,956 Aufrufe • vor 4 Monaten

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA

Nick Dorsey

40,200 Aufrufe • vor 6 Monaten

#WATCH | Delhi: At Cavalry Seminar 2025, Chief of the Army Staff, General Upendra Dwivedi says, "...As far as the northern front is concerned, the satellite photographs tell us it's a tankable terrain across. And of course, it is a super high-altitude area. So the logistics and ability to remain coherent for a longer period for the crew also, that's a challenge...ATS will play an important role in air-landed and transported operations..." "...As far as AI is concerned, it is never human versus AI. It is again humans amplified by AI. What does it mean? That is the creativity of a commander on the ground, the empathy factor, when to do what, and thereafter take the brownie points, problem solving, and turn data into decisions..."

#WATCH | Delhi: At Cavalry Seminar 2025, Chief of the Army Staff, General Upendra Dwivedi says, "...As far as the northern front is concerned, the satellite photographs tell us it's a tankable terrain across. And of course, it is a super high-altitude area. So the logistics and ability to remain coherent for a longer period for the crew also, that's a challenge...ATS will play an important role in air-landed and transported operations..." "...As far as AI is concerned, it is never human versus AI. It is again humans amplified by AI. What does it mean? That is the creativity of a commander on the ground, the empathy factor, when to do what, and thereafter take the brownie points, problem solving, and turn data into decisions..."

ANI

204,659 Aufrufe • vor 10 Monaten

David Sacks: The AI Regulatory Frenzy at the State Level is “Very Concerning” “Let me give you some stats on this.” “All 50 states have introduced AI bills in 2025.” “There's been over 1,000 bills in state legislatures.” “118 AI laws have already been passed across the 50 states.” “Everyone just seems to be motivated by the imperative to ‘do something’ on AI, even though no one's really sure what that something should be.” “And there's no real agreement on what all these AI regulations are supposed to do, or what the risks are, so they're just making things up.” “So you've got 50 different states each with their own reporting regime, which is going to be a trap for startups because they've all gotta figure this out about what they're supposed to report on, what the deadlines are, who to report to.” “And if you wanna see where this is going, look at Colorado.” “This has already been passed into law, SB 24-205, Consumer Protections for Artificial Intelligence. It bans something they call ‘algorithmic discrimination.’” “Algorithmic discrimination is defined as unlawful differential treatment or disparate impact based on protected characteristics. So things like age, race, sex, disability.” “If any of those factors drive an AI decision and it results in a disparate impact, then both the developer of the AI model and the deployer, which means the business that's using it, can be in violation of this law and they can be prosecuted by the Colorado Attorney General.” “The only way that I see for model developers to comply with this law, is to build in a new DEI layer into the models, to basically somehow prevent models from giving outputs that might have a disparate impact on protected groups.” “So we're back to Woke AI again, and I think that's the whole point.”

David Sacks: The AI Regulatory Frenzy at the State Level is “Very Concerning” “Let me give you some stats on this.” “All 50 states have introduced AI bills in 2025.” “There's been over 1,000 bills in state legislatures.” “118 AI laws have already been passed across the 50 states.” “Everyone just seems to be motivated by the imperative to ‘do something’ on AI, even though no one's really sure what that something should be.” “And there's no real agreement on what all these AI regulations are supposed to do, or what the risks are, so they're just making things up.” “So you've got 50 different states each with their own reporting regime, which is going to be a trap for startups because they've all gotta figure this out about what they're supposed to report on, what the deadlines are, who to report to.” “And if you wanna see where this is going, look at Colorado.” “This has already been passed into law, SB 24-205, Consumer Protections for Artificial Intelligence. It bans something they call ‘algorithmic discrimination.’” “Algorithmic discrimination is defined as unlawful differential treatment or disparate impact based on protected characteristics. So things like age, race, sex, disability.” “If any of those factors drive an AI decision and it results in a disparate impact, then both the developer of the AI model and the deployer, which means the business that's using it, can be in violation of this law and they can be prosecuted by the Colorado Attorney General.” “The only way that I see for model developers to comply with this law, is to build in a new DEI layer into the models, to basically somehow prevent models from giving outputs that might have a disparate impact on protected groups.” “So we're back to Woke AI again, and I think that's the whole point.”

The All-In Podcast

220,615 Aufrufe • vor 11 Monaten

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.

Konstantine Buhler

24,909 Aufrufe • vor 11 Monaten