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Every time you accepted a salary, chose a price, or walked into a negotiation, the other person was running game theory in their head. You were guessing. This 1-hour Yale lecture by Professor Ben Polak will change how you read people and make decisions forever. MBAs pay $150K to learn this. Yale posted it on YouTube for free. Save this post. Watch it this tonight. Follow @codewithimanshu for more high-signal content that actually changes the trajectory of your career. ↓ Here's why most people lose every negotiation they enter. You walked into your last salary discussion hoping for the best. They walked in with frameworks. Payoff matrices. Dominant strategies. Backward induction. Nash equilibrium. You said "I was thinking $85K." They already knew the number you'd accept. Because they ran the game before you sat down. That's not a skill gap. That's a universe gap. And it's costing you $20K, $50K, $100K every single year. ↓ Game theory isn't math for MBAs. It's the operating system of every human interaction. Job negotiations. Pricing decisions. Business deals. Relationships. The person who understands it wins by default. Not because they're smarter. Because they're playing a different game. You're playing checkers thinking it's chess. They're playing chess thinking it's 4D chess. Professor Ben Polak teaches Yale's most famous game theory course. Students pay $80,000/year for access to him. His full lecture is now on YouTube. Free. ↓ What 1 hour with Polak teaches you. How to predict what the other side will do before they do it. When to hold your position and when to fold. Why "winning" a negotiation sometimes costs more than losing. How to structure offers the other side can't refuse. The exact math behind every pricing decision in your life. This is what investment bankers use. What hedge fund managers use. What startup founders use to raise money. What CEOs use to run companies. You can have it for free. In 1 hour. Tonight. Or keep walking into negotiations unarmed. ↓ 1 hour of Netflix tonight: you forget by Tuesday. 1 hour of Polak tonight: you negotiate differently for the next 40 years. Same time. One is a distraction. The other is a compounding asset. Save this post. Watch the lecture. Follow @codewithimanshu for more high-signal content that actually changes the trajectory of your career.

“𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝗮𝗽𝗽𝘀… 𝗮𝗻𝗱 𝗶𝘁’𝘀 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗲𝗹𝘆 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝘀.” This MIT lecture quietly does something most AI content never does. It forces you to stop thinking about tools for a minute and ask a much harder question: what is computation, really? It starts like a normal lecture. Then, before you know it, it is dismantling the way we talk about intelligence, learning, abstraction, and even what we think machines are doing when they “think.” 🎩 And just when you think MIT cannot get any more MIT… the professor puts on a wizard hat and turns eval and apply into something that feels half computer science, half spell-casting. Strange. Brilliant. Oddly unforgettable. 💡 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Because too many people are building AI careers on surface-level fluency. They know the tools. They know the demos. They know the buzzwords. But the foundations? That is often where the silence begins. And that is risky. We keep using labels that sound far more advanced than they really are: → Artificial intelligence is not truly intelligent → AI agents do not really have agency → Machines do not “learn” the way people imagine they do That is why lectures like this matter so much. They take you beneath the hype and back to the layer that actually lasts: → abstraction → evaluation → computation To me, that is the real divide in AI now. Some people are learning how to use the latest tools. Others are learning how to understand what those tools are really doing. The second group will build the future. The first group will keep reposting it. What do you think matters more in AI right now: mastering the tools, or understanding the foundations underneath them? #AI #ArtificialIntelligence #ComputerScience #MIT #MachineLearning #Innovation #Technology #FutureOfWork #Learning