No one can prove AI made them money...

AI was supposed to change everything. Trillions are being spent, companies are buying tools, and people are getting through parts of their work faster every day. So why is it still so hard to prove that AI is making those businesses more money? This video digs into the gap between the hype and the financial results. We look at findings from MIT, the NBER, McKinsey, and PwC, plus examples like Klarna, to see where AI is helping and where the returns get murky. Handling more support tickets or writing code faster sounds great, but it doesn’t automatically mean more sales or a healthier bottom line. There are still customers to win, systems to connect, and plenty of other bottlenecks. We also look at what happened when factories first got electricity, and why adding a new tool to an old way of working might only get you so far. The productivity J-curve offers one explanation for the wait, while David J. Teece’s theory of complementary assets raises another question: who actually gets the money? It might be the companies selling chips, cloud capacity, and AI services that capture the biggest gains. AI can save time and be useful, but turning that into more revenue is a different story. LinkedIn: https://www.linkedin.com/in/hariharan-jayakumar-silo Instagram: https://www.instagram.com/hariharan.jayakumar/ TimeStamps: 0:00 - AI Money Introduction 1:01 - Where’s The Money? 3:32 - The Productivity Trap 10:02 - Capturing Value Sources: https://pastebin.com/hP9Quvmd

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