Go to groundnews.com/skinny for a better way to stay informed. Subscribe through our link for 40% off unlimited access to world-wide coverage. 0:00 Why Enron matters for the AI conversation right now 5:00 Origins: the gas bank, Lay, Skilling, and Fastow 9:00 Mark-to-market accounting and booking 20 years of profit on day one 13:00 Growth targets, exec comp, and regulated vs deregulated energy 15:00 SPEs, the 3% rule, and Fastow's LJM 19:00 Circular earnings: the dark fiber swap and disguised loans 24:00 Earnings games, leverage ratios, and why cash flow matters 28:00 Every deal was a stinker (and Netflix before Netflix) 29:00 The collapse: Chanos, debt puts, the commercial paper run, Dynegy 35:00 Broadband, Blockbuster, and the $8 business valued at $60 billion 38:00 The fake trading floor 40:00 Take-or-pay, leases, and off-balance-sheet debt in AI today 44:00 Meta's Project Hyperion and thinking about risk holistically 50:00 Accounting is imperfect: pensions, EPS maximizers, and the AI lesson 55:00 California, the Raptors, Arthur Andersen, and who called it Why is Enron suddenly back in the conversation about AI? This week, we revisit The "Smartest Guys in the Room" to unpack what actually happened in the Enron scandal. While there are some similarities, a lot is different. Enron hid debt, manufactured earnings, and convinced Wall Street to buy a story that even many professional investors couldn’t explain. From a fake trading floor to financial structures backed by its own stock, we break down what actually happened behind one of the most infamous corporate collapses in history. Then we turn to the financing behind today’s AI boom: off-balance-sheet obligations, circular financing, and the gap between reported earnings and actual cash flow. We dig into Meta’s Project Hyperion, the long-term commitments funding the data center build-out, and why a chipmaker booking revenue today while its customer spreads the cost over years matters when evaluating the boom. Along the way, we explain mark-to-market accounting, special purpose vehicles, and why “it’s not on the balance sheet” doesn’t mean “there’s no risk.” To be clear, we’re not accusing today’s AI companies of Enron-style fraud. The point is understanding where the comparisons hold up, where they fall apart, and what investors should actually scrutinize. A technology can change the world without every company funding it earning a return. Revenue isn’t profit, profit isn’t cash flow, and a rising stock price isn’t proof that a business model works. Before you call something “the next Enron,” you need to understand the first one. Also if you want to learn more about accounting, valuation, M&A etc. Kristen spent 13 years teaching this to analysts and associates at bulge bracket investment banks and business schools and then spend two years creating the most comprehensive course out there. https://www.thewallstreetskinny.com/investment-banking-private-equity-fundamentals Use code: VIPCUSTOMER for 20% off
